Download Customer Relationship Management (CRM) - E

Name : Mrs.Julie
Subject : Customer Relationship Mgt
Class: BBM
Unit: I to V
Relationship Marketing
Relationship marketing is not about having a "buddy-buddy" relationship with your
customers. Customers do not want that. Relationship Marketing uses the eventdriven tactics of customer retention marketing, but treats marketing as a process
over time rather than single unconnected events. By molding the marketing
message and tactics to the LifeCycle of the customer, the Relationship Marketing
approach achieves very high customer satisfaction and is highly profitable.
The relationship marketing process is usually defined as a series of stages, and
there are many different names given to these stages, depending on the marketing
perspective and the type of business. For example, working from the relationship
beginning to the end:Interaction > Communication > Valuation > Termination
Awareness > Comparison > Transaction > Reinforcement > Advocacy
Suspect > Prospect > Customer > Partner > Advocate > Former Customer
Using the relationship marketing approach, you customize programs for individual
consumer groups and the stage of the process they are going through as opposed to
some forms of database marketing where everybody would get virtually the same
promotions, with perhaps a change in offer. The stage in the customer LifeCycle
determines the marketing approach used with the customer.
A simple example of this would be sending new customers a "Welcome Kit,"
which might have an incentive to make a second purchase. If 60 days pass and the
customer has not made a second purchase, you would follow up with an e-mailed
discount. You are using customer behavior over time (the customer LifeCycle) to
trigger the marketing approach.
Relationship Marketing
Customer Relationship Management (CRM)
Customer Relationship Management continues to be the most vibrant,
critical and evolving technology category in today's market. CRM today is no
longer just about enterprise software. Rather, today's CRM is a flexible
solution where you can mix software, hosted services and other components to
meet your specific business needs.
Who are your best customers? What can you do to retain them? How can you
attract others like them? How can you increase your customers’ profits? The
truth is that most companies have difficulty understanding and managing
customer life cycles and profitability. This is due to problems designing and
executing effective marketing campaigns, and problems measuring their
effectiveness.
Figure1. Customer Relationship Management (CRM) Cube
A survey of more than 1,600 business and IT professionals conducted by The
Data Warehousing Institute found that close to 50% had CRM project budgets
of less than $500,000. Clearly, CRM doesn't have to be a budget-buster.
What’s more, Forester estimated that CRM revenues will grow from $42.8
billion in 2002 to $73.8 billion in 2007, a compound annual growth rate of
11.5%.
For CRM to be truly effective, an organization must decide what kind of
customer information it’s looking for and what it intends to do with it. Look at
how customer information comes into a business, where and how it’s stored,
and how it’s used. Then have company analysts comb through the data to get a
complete view of each customer and pinpoint areas where better services are
needed. One way to assess the need for a CRM project is to count the ways a
customer can access the company. The more there are, the greater the need for
the single centralized customer view a CRM system can provide.
Figure 2. Customer Relationship Management (CRM) Cycle
Customer Relationship Management enables real-time availability checks,
contract management, billing management, fulfillment visibility, and order
tracking, giving you the features and functions necessary for marketing
planning, campaign management, telemarketing, lead generation, and
customer segmentation. In addition, CRM allows you to offer ongoing
customer care across all channels – with a customer-interaction center, Webbased customer self-service capabilities, service and claims management, field
service and dispatch, and installed-base management.
CRM helps your business:
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Provide better customer service
Make call centers more efficient
Cross-sell products more effectively
Have sales staff close deals faster
Simplify marketing and sales processes
Discover new customers
Increase customer revenues
Customer Relationship Management goes beyond sales, marketing and
customer-service applications into business intelligence, analytics, hosted
applications, mobile capabilities and much more! By thinking more
insightfully about what your customers are worth, you can focus your
resources on attracting and keeping the right type of customers. This focus, in
turn, will make your CRM efforts more productive and position you better for
innovation and growth.
What is the goal of CRM?
The idea of CRM is that it helps businesses use technology and human
resources to gain insight into the behavior of customers and the value of those
customers. If it works as hoped, a business can:
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provide better customer service
make call centers more efficient
cross sell products more effectively
help sales staff close deals faster
simplify marketing and sales processes
discover new customers
increase customer revenues
Phases
The three phases in which CRM support the relationship between a business and its
customers are to:
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Acquire: CRM can help a business acquire new customers through contact
management, selling, and fulfillment.[3]
Enhance: web-enabled CRM combined with customer service tools offers
customers service from a team of sales and service specialists, which offers
customers the convenience of one-stop shopping.[3]
Retain: CRM software and databases enable a business to identify and
reward its loyal customers and further develop its targeted marketing and
relationship marketing initiatives.[4]
Challenges
Tools and workflows can be complex, especially for large businesses. Previously
these tools were generally limited to contact management: monitoring and
recording interactions and communications. Software solutions then expanded to
embrace deal tracking, territories, opportunities, and at the sales pipeline itself.
Next came the advent of tools for other client-interface business functions, as
described below. These tools have been, and still are, offered as on-premises
software that companies purchase and run on their own IT infrastructure.
Sales force automation
Sales force automation (SFA) involves using software to streamline all phases of
the sales process, minimizing the time that sales representatives need to spend on
each phase. This allows sales representatives to pursue more clients in a shorter
amount of time than would otherwise be possible. At the heart of SFA is a contact
management system for tracking and recording every stage in the sales process for
each prospective client, from initial contact to final disposition. Many SFA
applications also include insights into opportunities, territories, sales forecasts and
workflow automation, quote generation, and product knowledge. Modules for Web
2.0 e-commerce and pricing are new, emerging interests in SFA.
Marketing
CRM systems for marketing help the enterprise identify and target potential clients
and generate leads for the sales team. A key marketing capability is tracking and
measuring multichannel campaigns, including email, search, social media,
telephone and direct mail. Metrics monitored include clicks, responses, leads,
deals, and revenue. This has been superseded by marketing automation and
Prospect Relationship Management (PRM) solutions which track customer
behaviour and nurture them from first contact to sale, often cutting out the active
sales process altogether.
Customer service and support
Recognizing that service is an important factor in attracting and retaining
customers, organizations are increasingly turning to technology to help them
improve their clients’ experience while aiming to increase efficiency and minimize
costs. Even so, a 2009 study revealed that only 39% of corporate executives
believe their employees have the right tools and authority to solve client
problemsThe core for these applications has been and still is comprehensive call
center solutions, including such features as intelligent call routing, computer
telephone integration (CTI), and escalation capabilities.
Analytics
Relevant analytics capabilities are often interwoven into applications for sales,
marketing, and service. These features can be complemented and augmented with
links to separate, purpose-built applications for analytics and business intelligence.
Sales analytics let companies monitor and understand client actions and
preferences, through sales forecasting and data quality.
Integrated/Collaborative
Departments within enterprises — especially large enterprises — tend to function
with little collaboration.[8] More recently, the development and adoption of these
tools and services have fostered greater fluidity and cooperation among sales,
service, and marketing. This finds expression in the concept of collaborative
systems which uses technology to build bridges between departments. For
example, feedback from a technical support center can enlighten marketers about
specific services and product features clients are asking for. Reps, in their turn,
want to be able to pursue these opportunities without the burden of re-entering
records and contact data into a separate SFA system. Owing to these factors, many
of the top-rated and most popular products come as integrated suites.
Small business
For small business, basic client service can be accomplished by a contact manager
system: an integrated solution that lets organizations and individuals efficiently
track and record interactions, including emails, documents, jobs, faxes, scheduling,
and more. These tools usually focus on accounts rather than on individual contacts.
They also generally include opportunity insight for tracking sales pipelines plus
added functionality for marketing and service. As with larger enterprises, small
businesses are finding value in online solutions, especially for mobile and
telecommuting workers.
Social media
Social media sites like Twitter, LinkedIn and Facebook are amplifying the voice of
people in the marketplace and are having profound and far-reaching effects on the
ways in which people buy. Customers can now research companies online and then
ask for recommendations through social media channels, making their buying
decision without contacting the company.
People also use social media to share opinions and experiences on companies,
products and services. As social media is not as widely moderated or censored as
mainstream media, individuals can say anything they want about a company or
brand, positive or negative.
Non-profit and membership-based
Systems for non-profit and membership-based organizations help track
constituents and their involvement in the organization. Capabilities typically
include tracking the following: fund-raising, demographics, membership levels,
membership directories, volunteering and communications with individuals.
Strategy
For larger-scale enterprises, a complete and detailed plan is required to obtain the
funding, resources, and company-wide support that can make the initiative of
choosing and implementing a system successful. Benefits must be defined, risks
assessed, and cost quantified in three general areas:
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Processes: Though these systems have many technological components,
business processes lie at its core. It can be seen as a more client-centric way
of doing business, enabled by technology that consolidates and intelligently
distributes pertinent information about clients, sales, marketing
effectiveness, responsiveness, and market trends. Therefore, a company
must analyze its business workflows and processes before choosing a
technology platform; some will likely need re-engineering to better serve the
overall goal of winning and satisfying clients. Moreover, planners need to
determine the types of client information that are most relevant, and how
best to employ them.[
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People: For an initiative to be effective, an organization must convince its
staff that the new technology and workflows will benefit employees as well
as clients. Senior executives need to be strong and visible advocates who can
clearly state and support the case for change. Collaboration, teamwork, and
two-way communication should be encouraged across hierarchical
boundaries, especially with respect to process improvement.[
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Technology: In evaluating technology, key factors include alignment with
the company’s business process strategy and goals, including the ability to
deliver the right data to the right employees and sufficient ease of adoption
and use. Platform selection is best undertaken by a carefully chosen group of
executives who understand the business processes to be automated as well as
the software issues. Depending upon the size of the company and the breadth
of data, choosing an application can take anywhere from a few weeks to a
year or more.
Implementation
Implementation issues
Increases in revenue, higher rates of client satisfaction, and significant savings in
operating costs are some of the benefits to an enterprise. Proponents emphasize
that technology should be implemented only in the context of careful strategic and
operational planning.[12] Implementations almost invariably fall short when one or
more facets of this prescription are ignored:
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Poor planning: Initiatives can easily fail when efforts are limited to choosing
and deploying software, without an accompanying rationale, context, and
support for the workforce.[13] In other instances, enterprises simply automate
flawed client-facing processes rather than redesign them according to best
practices.
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Poor integration: For many companies, integrations are piecemeal initiatives
that address a glaring need: improving a particular client-facing process or
two or automating a favored sales or client support channelSuch “point
solutions” offer little or no integration or alignment with a company’s
overall strategy. They offer a less than complete client view and often lead
to unsatisfactory user experiences.
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Toward a solution: overcoming siloed thinking. Experts advise organizations
to recognize the immense value of integrating their client-facing operations.
In this view, internally-focused, department-centric views should be
discarded in favor of reorienting processes toward information-sharing
across marketing, sales, and service. For example, sales representatives need
to know about current issues and relevant marketing promotions before
attempting to cross-sell to a specific client. Marketing staff should be able to
leverage client information from sales and service to better target campaigns
and offers. And support agents require quick and complete access to a
client’s sales and service history
Adoption issues
Historically, the landscape is littered with instances of low adoption rates. In 2003,
a Gartner report estimated that more than $1 billion had been spent on software
that was not being used. More recent research indicates that the problem, while
perhaps less severe, is a long way from being solved. According to CSO Insights,
less than 40 percent of 1,275 participating companies had end-user adoption rates
above 90 percent.[15]
In a 2007 survey from the U.K., four-fifths of senior executives reported that their
biggest challenge is getting their staff to use the systems they had installed.
Further, 43 percent of respondents said they use less than half the functionality of
their existing system; 72 percent indicated they would trade functionality for ease
of use; 51 percent cited data synchronization as a major issue; and 67 percent said
that finding time to evaluate systems was a major problem.[16] With expenditures
expected to exceed $11 billion in 2010,[16] enterprises need to address and
overcome persistent adoption challenges. Specialists offer these
recommendations[15] for boosting adoptions rates and coaxing users to blend these
tools into their daily workflow:
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Choose a system that is easy to use: All solutions are not created equal.
Some vendors offer more user-friendly applications than others, and
simplicity should be as important a decision factor as functionality.
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Choose the right capabilities: Employees need to know that time invested in
learning and usage will yield personal advantages. If not, they will work
around or ignore the system.
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Provide training: Changing the way people work is no small task, and help is
usually a requirement. Even with today’s more usable systems, many
staffers still need assistance with learning and adoption
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Lead by example: Showing employees that upper management fully
supports the use of a new application by using the application themselves
may increase the likelihood that employees will adopt the application.[citation
needed]
Privacy and data security system
One of the primary functions of these tools is to collect information about clients,
thus a company must consider the desire for privacy and data security, as well as
the legislative and cultural norms. Some clients prefer assurances that their data
will not be shared with third parties without their prior consent and that safeguards
are in place to prevent illegal access by third parties.
Market structures
This market grew by 12.5 percent in 2008, from revenue of $8.13 billion in 2007 to
$9.15 billion in 2008.[17] The following table lists the top vendors in 2006-2008
(figures in millions of US dollars) published in Gartner studies.[18][19]
Vendor
SAP
2008
Revenue
2,055
Oracle
1,475
Salesforce.com 965
Microsoft
581
Amdocs
451
Others
3,620
Total
9,147
2008
Share
(%)
22.5 (2.8)
16.1
10.6
6.4
4.9
39.6
100
2007
Revenue
2007
Share
(%)
2006
Revenue
2006
Share
(%)
2,050.8
25.3
1,681.7
26.6
1,319.8
676.5
332.1
421.0
3,289.1
8,089.3
16.3
8.3
4.1
5.2
40.6
100
1,016.8
451.7
176.1
365.9
2,881.6
6,573.8
15.5
6.9
2.7
5.6
43.7
100
Related trends
Many CRM vendors offer Web-based tools (cloud computing) and software as a
service (SaaS), which are accessed via a secure Internet connection and displayed
in a Web browser. These applications are sold as subscriptions, with customers not
needing to invest in purchasing and maintaining IT hardware, and subscription fees
are a fraction of the cost of purchasing software outright.
The era of the "social customer"[20] refers to the use of social media (Twitter,
Facebook, LinkedIn, Yelp, customer reviews in Amazon etc) by customers in ways
that allow other potential customers to glimpse real world experience of current
customers with the seller's products and services. This shift increases the power of
customers to make purchase decisions that are informed by other parties sometimes
outside of the control of the seller or seller's network. In response, CRM
philosophy and strategy has shifted to encompass social networks and user
communities, podcasting, and personalization in addition to internally generated
marketing, advertising and webpage design. With the spread of self-initiated
customer reviews, the user experience of a product or service requires increased
attention to design and simplicity, as customer expectations have risen. CRM as a
philosophy and strategy is growing to encompass these broader components of the
customer relationship, so that businesses may anticipate and innovate to better
serve customers, referred to as "Social CRM".
Satisfied customers at are the core of any successful business. As such, effective
customer relationship management (CRM) is a vital consideration for each and
every company, large or small.
Companies in any industry can benefit greatly from the large variety of customer
relationship software and other CRM tools that are widely available today.
Advantages include:
• Increased profitability
• Increased market share
• Decreased expenses
• Improved customer service
• Improved client loyalty and retention
• Streamlined marketing and sales processes
• Higher productivity
• More efficient targeting and profiling
• More efficient call center operations
• Increased cross-selling and up-selling
There are a wide variety of options when it comes to choosing a client relationship
management program, but these are some of the more common choices:
Sugar CRM - When it comes to software specially designed for CRM, Sugar has
been a leader since the company debuted in 2004. The complete line of programs,
which includes various editions and versions, helps businesses in the key areas of
sales, marketing and support. It allows for easy organization and sharing of
customer information, as well as precise and useful measuring, analyzing and
reporting of critical metrics. Users can also develop platforms customized to their
specific needs, allowing them to communicate internally and externally in a
manner that works best for them.
Sage CRM - Sage CRM is perhaps best known for being very easy to use and
quick to deploy. With automated features that retrieve sales information and track
marketing data, as well as real-time mobile access to all vital customer
information, this client relationship management tool can suit just about any type
of business. It also allows for complete, two-way synchronization with Microsoft
Outlook email, calendar, tasks and contacts.
Sage ACT! CRM - For individual users or smaller organizations, Sage ACT! CRM
is one of the preferred CRM tools. It assists in creating and maintaining longlasting customer relationships, increasing profitability and streamlining operations.
The straightforward dashboard platform and intuitive interface allows for easy
organization of and access to contact data. Like Sage CRM, it works with
Microsoft Outlook to ensure all necessary information is always close at hand.
Sage ACT! customer relationship software is also available in a version crafted
specifically for accounting professionals.
Businesses select a customer relationship management tool based on individual
operational needs. While some may do well with one that provides general
functions, others may require specialized programs targeted for specific niche
markets. Advice and consultation from an experienced software consultant is
recommended to make the most effective choice.
Customer Relationship Management or CRM is a combination of enterprise
strategies, business processes and information technologies used to learn more
about customers' needs and behaviors in order to develop stronger relationships
with them. CRM software systems automate many customer-related business tasks.
CRM applications are traditionally developed as client-server software which
incurs higher initial cost of ownership. The proliferation of the Internet and the
Web has fueled the rapid growth of Web-based CRM or online CRM applications.
Web CRM systems are widely deployed for web based call center, contact
management, trouble ticket, personal information manager and scheduling.
The life cycle of CRM consists of three phases - customer acquisition, customer
relationship enhancements and customer retention. CRM software streamlines
CRM activities at each phase of customer relationship management.
Customer Acquisition
Contact management module and direct marketing module of CRM allow
companies to effectively promote and market their products and services to
prospects. Those modules help speed up the acquiring processes and reduce the
cost of acquiring new customers.
Customer Relationship Enhancements
CRM helps companies better understand existing customers' needs and behaviors
and enhance the profitability from existing customers by cross-selling. They can
customize their products and services to individual customers' needs and
preferences.
Customer Retention
Customer service module of CRM system gives the organizations the edge in
services. They can increase customer satisfaction while reducing the cost of
support. Customer retention is critical to the overall profitability of an
organization. A customer
CUSTOMER LIFE CYCLE
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Building Healthy Relationships
Building Healthy Relationships
1. Speak a little less, listen a little more
Most people get tremendous pleasure from speaking about themselves. But, here
we have to be careful; if we always speak about our achievements or tribulations,
people will get fed up with our egoism.
If we are willing and able to listen to others, we will find it much appreciated by
our friends. Some people are not aware of how much they dominate the
conversation. If you find you are always talking about yourself, consider the advice
of the Greek philosopher, Epictectus:
“Nature gave us one tongue and two ears so we could hear twice as much as we
speak.”
2. Which is more important being right or maintaining harmony?
A lot of problems in relationships occur because we want to maintain our personal
pride. Don’t insist on always having the last word. Healthy relationships are not
built through winning meaningless arguments. Be willing to back down; most
arguments are not of critical importance anyway.
3. Avoid Gossip
If we value someone’s friendship we will not take pleasure in commenting on their
frequent failings. They will eventually hear about it. But, whether we get found out
or not, we weaken our relationships when we dwell on negative qualities. Avoid
gossiping about anybody; subconsciously we don’t trust people who have a
reputation for gossip. We instinctively trust and value people who don’t feel the
need to criticise others.
4. Forgiveness
Forgiveness is not just a cliché, it’s a powerful and important factor in maintaining
healthy relationships. However, real forgiveness also means that we are willing to
forget the experience. If we forgive one day, but then a few weeks later bring up
the old misdeed, this is not real forgiveness. When we make mistakes, just consider
how much we would appreciate others forgiving and forgetting.
5. Know When to Keep Silent
If you think a friend has a bad or unworkable idea, don’t always argue against it;
just keep silent and let them work things out for themselves. It’s a mistake to
always feel responsible for their actions. You can offer support to friends, but you
can’t live their life for them.
6. Right Motive
If you view friendship from the perspective of “what can I get from this?” you are
making a big mistake. This kind of relationship proves very tentative. If you make
friendships with the hope of some benefit, you will find that people will have a
similar attitude to you. This kind of friendship leads to insecurity and jealousy.
Furthermore, these fair weather friends will most likely disappear just when you
need them most. Don’t look upon friends with the perspective “what can I get out
of this?”. True friendship should be based on mutual support and good will,
irrespective of any personal gain.
7. Oneness.
The real secret of healthy relationships is developing a feeling of oneness. This
means that you will consider the impact on others of your words and actions. If
you have a true feeling of oneness, you will find it difficult to do anything that
causes suffering to your friends. When there is a feeling of oneness, your
relationships will be free of jealousy and insecurity.
For example, it is a feeling of oneness which enables you to share in the success of
your friends. This is much better than harbouring feelings of jealousy. To develop
oneness we have to let go of feelings of superiority and inferiority; good
relationships should not be based on a judgemental approach. In essence,
successful friendship depends on the golden rule: “do unto others as you would
have done to yourself.” This is the basis of healthy relationships.
8. Humour
Don’t take yourself too seriously. Be willing to laugh at yourself and be selfdeprecating. This does not mean we have to humiliate ourselves, far from it — it
just means we let go of our ego. Humour is often the best antidote for relieving
tense situations.
9. Work at Relationships but don’t over analyze
Maintaining healthy relationships doesn’t mean we have to spend several hours in
the psychiatrist’s chair. It means we take a little time to consider others,
remembering birthdays and anniversaries etc. But, it is a mistake to spend several
hours ruminating and dissecting relationships. This makes the whole thing very
mental; it’s better to forget any negative experiences. Good friendships should be
built on spontaneity and newness, sharing a moment of humour can often do more
benefit than several hours of discussion.
10. Concern and Detachment
Healthy relationships should be built on a degree of detachment. Here, people
often make a mistake; they think that being detached means, “not caring”.
However, this is not the case. Often when we develop a very strong attachment we
expect the person to behave in a certain way. When they don’t we feel miserable
and try to change them. A good friendship based on detachment means we will
always offer good will, but we will not be upset if they wish to go a different way.
.
Unit II
Customer Relationship Management Overview
The customer-oriented business knows its customers and their needs.
Customer Relationship Management (CRM) refers to a collaborative philosophy or
system of business practices implemented across an enterprise to organize the
acquisition, aggregation, and analysis of customer profiles.
Customer information is already captured from numerous points, such as sales
information systems, call centers, and surveys. CRM Services allow this information
to be shared across the company in order to create a customer-centric organization.
The methods and software used for dissecting the collected customer data are called
business intelligence systems. Once the data is analyzed, the most profitable customer
demographics for the company can be targeted and catered to, and the long-term
retention of this group will result in increased customer satisfaction and therefore
increased revenue.
Customer-focused organizational intelligence is also a way to differentiate a
company. It can unify disparate departmental goals, and improve the customer buying
experience, effective lead generation, marketing campaign management, sales, order
fulfillment, and customer service.
Customer Interaction and Data Collection
The telephone continues to be the main point of customer contact for companies, and
call centers are generally created to handle a large volume of calls. Therefore, of the
various kinds of customer interaction services that should be integrated into a CRM
strategy, quality, user-friendly call center software, including any voice recognition
software, VoiceXML applications, or IVR systems that recognize and reply to
customer voice inquiries, are critical.
As customers demand the availability of goods and services via other media, many
companies refer to their call center as a contact center. Contact center management is
an IT challenge, as the contact center software may need to be a central input site for
contact management software, customer support software, and other customer contact.
The customer profile data gathered in these systems, from name and address capture
to the type of inquiry made, could be input from websites, emails, text chat, Voice
over IP (VoIP), or even social networking systems, wikis, or blogs.
This data may also need to be routed across the enterprise for CRM strategies, and is
typically also used for multi-channel marketing. The information from a contact
center may need to be merged with incoming customer information from other areas,
such as sales information systems, mobile technology-based field force automation,
partner relationship management (PRM) software, and campaign management in a
standardized data warehouse before being cleaned, shared, analyzed, and customized,
resulting in effective customer profiling.
The process generally requires an enterprise architecture composed of various
configurable Web Services that allow for the company-wide integration of customer
information input via the Internet.
The Evolution of CRM
Customer Relationship Management, or “CRM” as most people know it, is that
dirty little acronym that loosely brands a wide array of software packages under
the same, very large umbrella. The watered down “CRM” category is
comfortably applied to a wide range of business applications which can range
from call center management software, to e-commerce storefronts, and even
artificial intelligence packages that hone in on consumer buying habits.
“Front Office” CRM is the more classic depiction of CRM and one most
familiar to the majority of businesses as it’s primarily focused on addressing the
sales, marketing and service objectives required by many companies. When
“Front Office” CRM was unveiled to the business market it was an epiphany for
many businesses. After all, it’s predecessors were Contact Managers, which
subsequently evolved into Sales Force Automation (SFA) applications; both of
which were oftentimes not accessible from the internet and required some
mechanism of synchronization (that rarely worked) for offline use of CRM data
– aspects that were very counterproductive for salespeople.
The concept of “Front Office” CRM has served many businesses very well, but
in the same breath it has also failed many organizations resulting in costly
implementations and the all too common negative return on investment. Like
most new technologies, this market has expanded rapidly and evolved with the
age of the Internet, overall market maturity and lessons learned.
A new era of CRM has emerged; one where businesses define the relationships
that are most important to them and mold the application to their specific needs.
Gone are the days of being force fed the cookie cutter sales, marketing and
service aspects often found in “Front Office” CRM. Now businesses can
configure an application that works for them and without a degree in Computer
Science.
This new era of CRM is focused on relationship management. Some in the
industry call it “XRM” because the X factor can mean any relationship
important for a business to manage information around. After all, isn’t every
business characterized and defined as the interactions between people, products,
services and money? Yes, the customer is the pinnacle aspect for every business,
the ultimate brass ring; and no one is arguing this will ever change. But if a
business is unable to efficiently manage the information and interactions
between the people, products, services and money it will be much more difficult
to acquire and retain the ultimate goal; the customer.
This paradigm shift from CRM to XRM takes the focus away from the software
application and straight to the underlying foundation of the application; the
software platform. Businesses can define the people, money and things that
matter most to them and define the interactions between these relationships.
More importantly, they invest in a software platform and define the
underpinnings for an application that works the way their business works,
streamlines their operational workflow, and manages the specific relationships
pertinent to their business.
In this new era of relationship management, the CRM software vendors that
once reveled in the days of “Front Office” CRM applications are quickly
disappearing from the CRM landscape.
Only a very few software vendors will emerge as the leaders in the XRM or
relationship management space. Success will come to those who have always
been focused on the software platform but have also served as leaders in the
application space as well.
Microsoft is one of the few software vendors that re-focused their CRM
application strategy back toward the software platform, specifically for their line
of business management solutions for financial, supply chain and customer
relationship management, the latter referring to the Microsoft Dynamics CRM
product.
Microsoft Dynamics CRM embodies the concept of XRM since it’s ultimately a
development platform with shrink wrapped CRM functionality for Sales,
Service, Marketing and Analytics. But it’s the platform which allows businesses
to define their own X factor. For example, an Insurance agency focused on
providing employee benefit plans would need to manage the relationships
between clients, quotes, policies, carriers, policy rates, producers and
commissions. A Christmas tree broker would need to manage the relationships
between farms, trees, truck distributors, retail locations and orders. A Wealth
Management company might need to manage relationships between clients,
portfolio accounts, legal instruments and family relationships. These examples
are just a testament to the value of investing in a flexible XRM platform for any
business or organization. And now that businesses are able to configure a rich
application in line with their own specific needs, nomenclature, and workflow
the return on investment can be companywide and not just focused on specific
department silos.
In the case of Microsoft, its platform allows for the seamless integration with
Microsoft Outlook and the other Office applications, is accessible from the
internet, can work offline in the same way Outlook works offline and employs a
powerful workflow engine. Even though it comes with the traditional prepackaged CRM bells and whistles, it’s the platform it sits on top of that
businesses are investing in. It’s the platform that businesses use as the
foundation for configuring a relationship management solution with their X
factors in mind. This is a game changer in the world of traditional CRM.
Unit 4

Firm Level
A value chain is a chain of activities for a firm operating in a specific industry. The
business unit is the appropriate level for construction of a value chain, not the
divisional level or corporate level. Products pass through all activities of the chain
in order, and at each activity the product gains some value. The chain of activities
gives the products more added value than the sum of added values of all activities.
It is important not to mix the concept of the value chain with the costs occurring
throughout the activities. A diamond cutter can be used as an example of the
difference. The cutting activity may have a low cost, but the activity adds much of
the value to the end product, since a rough diamond is significantly less valuable
than a cut diamond. Typically, the described value chain and the documentation of
processes, assessment and auditing of adherence to the process routines are at the
core of the quality certification of the business, e.g. ISO 9001.
Activities
The value chain categorizes the generic value-adding activities of an organization.
The "primary activities" include: inbound logistics, operations (production),
outbound logistics, marketing and sales (demand), and services (maintenance). The
"support activities" include: administrative infrastructure management, human
resource management, technology (R&D), and procurement. The costs and value
drivers are identified for each value activity.
Industry Level
An industry value chain is a physical representation of the various processes that
are involved in producing goods (and services), starting with raw materials and
ending with the delivered product (also known as the supply chain). It is based on
the notion of value-added at the link (read: stage of production) level. The sum
total of link-level value-added yields total value. The French Physiocrat's Tableau
économique is one of the earliest examples of a value chain. Wasilly Leontief's
Input-Output tables, published in the 1950's, provide estimates of the relative
importance of each individual link in industry-level value-chains for the U.S.
economy.
Significance
The value chain framework quickly made its way to the forefront of management
thought as a powerful analysis tool for strategic planning. The simpler concept of
value streams, a cross-functional process which was developed over the next
decade, had some success in the early 1990s
The value-chain concept has been extended beyond individual firms. It can apply
to whole supply chains and distribution networks. The delivery of a mix of
products and services to the end customer will mobilize different economic factors,
each managing its own value chain. The industry wide synchronized interactions of
those local value chains create an extended value chain, sometimes global in
extent. Porter terms this larger interconnected system of value chains the "value
system." A value system includes the value chains of a firm's supplier (and their
suppliers all the way back), the firm itself, the firm distribution channels, and the
firm's buyers (and presumably extended to the buyers of their products, and so on).
Capturing the value generated along the chain is the new approach taken by many
management strategists. For example, a manufacturer might require its parts
suppliers to be located nearby its assembly plant to minimize the cost of
transportation. By exploiting the upstream and downstream information flowing
along the value chain, the firms may try to bypass the intermediaries creating new
business models, or in other ways create improvements in its value system.
Value chain analysis has also been successfully used in large Petrochemical Plant
Maintenance Organizations to show how Work Selection, Work Planning, Work
Scheduling and finally Work Execution can (when considered as elements of
chains) help drive Lean approaches to Maintenance. The Maintenance Value Chain
approach is particularly successful when used as a tool for helping Change
Management as it is seen as more user friendly than other business process tools.
Value chain analysis has also been employed in the development sector as a means
of identifying poverty reduction strategies by upgrading along the value chain [4].
Although commonly associated with export-oriented trade, development
practitioners have begun to highlight the importance of developing national and
intra-regional chains in addition to international ones [5].
SCOR
The Supply-Chain Council, a global trade consortium in operation with over 700
member companies, governmental, academic, and consulting groups participating
in the last 10 years, manages the Supply-Chain Operations Reference (SCOR), the
de facto universal reference model for Supply Chain including Planning,
Procurement, Manufacturing, Order Management, Logistics, Returns, and Retail;
Product and Service Design including Design Planning, Research, Prototyping,
Integration, Launch and Revision, and Sales including CRM, Service Support,
Sales, and Contract Management which are congruent to the Porter framework.
The SCOR framework has been adopted by hundreds of companies as well as
national entities as a standard for business excellence, and the US DOD has
adopted the newly-launched Design-Chain Operations Reference (DCOR)
framework for product design as a standard to use for managing their development
processes. In addition to process elements, these reference frameworks also
maintain a vast database of standard process metrics aligned to the Porter model, as
well as a large and constantly researched database of prescriptive universal best
practices for process execution.
Value Reference Model
A Value Reference Model (VRM) developed by the global not-for-profit Value
Chain Group offers an open source semantic dictionary for value chain
management encompassing one unified reference framework representing the
process domains of product development, customer relations and supply networks.
The integrated process framework guides the modeling, design, and measurement
of business performance by uniquely encompassing the plan, govern and execute
requirements for the design, product, and customer aspects of business.
The Value Chain Group claims VRM to be next generation Business Process
Management that enables value reference modeling of all business processes and
provides product excellence, operations excellence, and customer excellence.
Six business functions of the Value Chain:






Research and Development
Design of Products, Services, or Processes
Production
Marketing & Sales
Distribution
Customer Service
Customer ecosystem
DEFINITION: BizTalk is an industry initiative headed by Microsoft to promote
Extensible Markup Language (XML) as the common data exchange language for
e-commerce and application integration on the Internet. While not a standards
body per se, the group is fostering a common XML message-passing architecture
to tie systems together. BizTalk says that the growth of e-commerce requires
businesses using different
Market strategy blueprints focuses on three key pillars:



Market Position
Growth Strategy
Innovation Strategy
Our service lines are built to strengthen your Go to Market strategy – find out how.
For any company in the market today providing financial services directly to
customers—fromtraditional brick and mortar banks to brokerage companies to
insurance providers—success stilldepends on building and maintaining long-term,
profitable relationships with customers. To achievethis success, financial services
companies must truly understand what their customers want anddon’t want and
must react to customer lifestyle changes over time.As direct as these goals seem, a
vast number of Customer Relationship Management (or CRM)initiatives attempted
over the past ten years have yet to deliver any real business value. Instead of
providing full-level integration across an enterprise, traditional CRM offerings
focus on only one ofthe following categories of information: analytical or
transactional.CRM analytical applications, designed to work with customer
information, usually stop short ofturning that information into useful knowledge; in
other words, these applications tell analystswhere problems are, but do not provide
what is needed to act upon that knowledge.CRM transactional applications, which
service customer accounts and provide primary customertouch points, typically fail
to achieve their full potential because they do not provide immediateaccess to
critical customer insight. So, today’s technology marketplace consists of analytical
applications that provide insight without action, and transactional applications that
provide actionwithout insight.
For years, people in the financial services industry have talked about the need to
bridge the gapbetween insight and action by implementing a “customer ecosystem”
that turns data into insightand insight into action to achieve profitable and
productive relationships with their customers. Thiswhitepaper describes the
customer ecosystem, outlines the requirements, and shows how it can
benefit a financial services company.
WHAT IS A CUSTOMER ECOSYSTEM?
In scientific terms, an ecosystem is formed by the interaction of a community of
organisms with theirenvironment. The ecosystem has multiple, interdependent
components that function as a unit.Innovators in the financial services community
have long envisioned a similar environment wheredata, technology, people, and
business processes operate in synchronization to improve customer
satisfaction and business performance. The key to achieving this vision is having
the capacity todiscern the true nature of customer relationships in an intuitive
manner. This capacity is known as“customer insight.”
The concept of the customer ecosystem can be further broken down into two key
aspects: theCustomer Lifecycle, and the Information Lifecycle.
THE CUSTOMER LIFE CYCLE
The relationship that a financial services company has with a customer evolves
over time in a naturallife cycle. This “customer lifecycle” has a number of
interdependent processes.Financial services companies often start their relationship
with a customer through acquisitionefforts. In marketing terms, this is the process
of taking "suspects" and determining which ones are"prospects." These prospects
are then made offers and converted into customers. Many banks todaycast a very
wide net using print and broadcast media to attract new customers. While this is an
important step for establishing a brand, it tends to drive unqualified “suspects” into
bank marketingefforts. Larger companies use complicated, expensive databases
and sophisticated modelingtechniques to directly target the desired prospects to
reduce the number of potential customers.Even with these capabilities in place,
response rates and the value of the new relationship are usuallylow. Acquisition
costs for new retail banking customers average about $200 per customer. Since
manynew customers start with basic deposit products, new relationships tend to be
shallow and can beunprofitable.
Suspect
HighValue
Customer
Average
Customer
Low
Value
Customer
Prospect
3 Insight Ecosystems
THE CUSTOMER ECOSYSTEM
The annual contribution of customers varies widely from low to high with an
average of $300. In mostinstitutions, 20% of customers generate 150%-200% of
the profit. High value customers tend to havemultiple accounts, but the average
consumer in the US has relationships with 4 different institutions.Most banks do
not have the ability to understand what segments of their customer base generate
what contribution or what lower value customers have the potential to be highvalue customers.
Financial services companies also have to worry about customer attrition—
customers turning backinto suspects. Retail banks in the US, on average, lose 15%
of their customers per year with each pointof attrition representing up to 1-2% of
net income loss. Most do not have the capability to understandthe type of
customers that leave, or their reasons for doing so. Unfortunately, it is often the
customers with the more complicated but higher value relationships that leave.
Therefore, just tokeep revenue and profit even, banks have to acquire a larger
number of lower value relationships.
To further complicate the customer life cycle challenge, existing processes and
systems are usuallyisolated. Even the largest companies don't have the ability to
keep track of a customer as they movefrom one part of the life cycle to another.
The lack of well-defined business metrics means that these problems are often
invisible to bankexecutives. Specific key performance indicators for acquisition,
contribution, and attrition are oftenundefined and unknown. These busines and
technology challenges often limit any progress towardachieving the customer
ecosystem vision.
Improving Customer Life Cycle Management
To improve customer lifecycle management, financial services companies must
take a moreintegrated, holistic approach to the problem. Improving individual parts
of the life cycle means havinginsight into the previous and next stage.
In the acquisition stage, it is vital that companies understand what type of customer
they are trying toattract in order to target the right prospects. This requires an
understanding of how individualcustomer segments generate profit, what products
they buy, and what characteristics predictprofitability. With this information, a
financial services company can be much more specific about thetype of prospect
that they want and what combinations of products and services will make that
prospect profitable.This can not only decrease overall marketing expenses, it can
also increase the response rates ofcampaigns. The prospects that are converted will
also contribute more profit to the bottom line.It is also important to understand
which prospects, as customers, would be most likely to leave. Thisrequires that you
understand what customers leave and what causes them to leave. For example, if
you understand the characteristics of customers that leave because of more
competitive rates, youcan predict the life time value of these prospects before they
are customers.
Insight Ecosystems 4
THE CUSTOMER ECOSYSTEM
Cross-selling and up-selling processes can also be improved through integration of
the life cycle.Insight into your current customer base is important in these efforts.
However, it is also importantthat you understand what offers led to the consumer
or business becoming a customer. Did theyrespond to direct mail? Did they come
to the bank through personal relationships?Retention efforts can also benefit from
this more integrated approach. Understanding current andpredicted customer
contribution leads to insight into what customers you need to retain, and which
customers should leave. Understanding how customers respond to cross-sell
strategies and alsoindicate strategies for retaining the right customers.
Finally, it is very important to use customer data wisely. Treating a customer as a
prospect or sendingoffers for products that a customer already has leaves an
impression with a customer that you don’tknow them or their needs.
THE INFORMATION LIFE CYCLE
Financial service companies also use another type of cycle to interact with
customers and prospectsand to effectively manage business performance. Whether
the processes are manual or automatedthe Information Life Cycle deals with the
use of data to gather insight into customer behavior andinteractions with
customers. As with the Customer Life Cycle, financial institutions face a number
ofchallenges.Most companies have an enormous amount of data on their
customers. A bank with $2 billion inassets has over 1 billion pieces of information
on their customers. Even with the best core processingapplications, the quality of
customer data decays over time. The data often sits in isolated systems
used only for specific purposes. In retail banking, the Customer Information File or
Customer
Insight
Action
Interaction
DataFor any company in the market today providing financial services directly to
customers—fromtraditional brick and mortar banks to brokerage companies to
insurance providers—success still -term, profitable relationships with customers.
To achievethis success, financial services companies must truly understand what
their customers want anddon’t want and must react to customer lifestyle changes
over time.As direct as these goals seem, a vast number of Customer Relationship
Management (or CRM)initiatives attempted over the past ten years have yet to
deliver any real business value. Instead ofproviding full-level integration across an
enterprise, traditional CRM offerings focus on only one ofthe following categories
of information: analytical or transactional.
CRM analytical applications, designed to work with customer information, usually
stop short ofturning that information into useful knowledge; in other words, these
applications tell analystswhere problems are, but do not provide what is needed to
act upon that knowledge.CRM transactional applications, which service customer
accounts and provide primary customertouch points, typically fail to achieve their
full potential because they do not provide immediateaccess to critical customer
insight. So, today’s technology marketplace consists of analyticalapplications that
provide insight without action, and transactional applications that provide action
without insight.
For years, people in the financial services industry have talked about the need to
bridge the gapbetween insight and action by implementing a “customer ecosystem”
that turns data into insightand insight into action to achieve profitable and
productive relationships with their customers. Thiswhitepaper describes the
customer ecosystem, outlines the requirements, and shows how it canbenefit a
financial services company.
Vendor selection
The vendor selection process can be a very complicated and emotional
undertaking if you don't know how to approach it from the very start. Here
are five steps to help you select the right vendor for your business. This
guide will show you how to analyze your business requirements, search for
prospective vendors, lead the team in selecting the winning vendor and
provide you with insight on contract negotiations and avoiding negotiation
mistakes.
1. Analyze the Business Requirements
Before you begin to gather data or perform interviews, assemble a team of people
who have a vested interest in this particular vendor selection process. The first task
that the vendor selection team needs accomplish is to define, in writing, the
product, material or service that you are searching for a vendor. Next define the
technical and business requirements. Also, define the vendor requirements. Finally,
publish your document to the areas relevant to this vendor selection process and
seek their input. Have the team analyze the comments and create a final document.
In summary:
1.
2.
3.
4.
5.
Assemble an Evaluation Team
Define the Product, Material or Service
Define the Technical and Business Requirements
Define the Vendor Requirements
Publish a Requirements Document for Approval
2. Vendor Search
Now that you have agreement on the business and vendor requirements, the team
now must start to search for possible vendors that will be able to deliver the
material, product or service. The larger the scope of the vendor selection process
the more vendors you should put on the table. Of course, not all vendors will meet
your minimum requirements and the team will have to decide which vendors you
will seek more information from. Next write a Request for Information (RFI) and
send it to the selected vendors. Finally, evaluate their responses and select a small
number of vendors that will make the "Short List" and move on to the next round.
In summary:
1.
2.
3.
4.
Compile a List of Possible Vendors
Select Vendors to Request More Information From
Write a Request for Information (RFI)
Evaluate Responses and Create a "Short List" of Vendors
3. Request for Proposal (RFP) and Request for Quotation (RFQ)
The business requirements are defined and you have a short list of vendors that you
want to evaluate. It is now time to write a Request for Proposal or Request for
Quotation. Which ever format you decide, your RFP or RFQ should contain the
following sections:
1.
2.
3.
4.
5.
6.
7.
Submission Details
Introduction and Executive Summary
Business Overview & Background
Detailed Specifications
Assumptions & Constraints
Terms and Conditions
Selection Criteria
4. Proposal Evaluation and Vendor Selection
The main objective of this phase is to minimize human emotion and political
positioning in order to arrive at a decision that is in the best interest of the
company. Be thorough in your investigation, seek input from all stakeholders and
use the following methodology to lead the team to a unified vendor selection
decision:
1.
2.
3.
4.
5.
6.
Preliminary Review of All Vendor Proposals
Record Business Requirements and Vendor Requirements
Assign Importance Value for Each Requirement
Assign a Performance Value for Each Requirement
Calculate a Total Performance Score
Select a the Winning Vendor
5. Contract Negotiation Strategies
The final stage in the vendor selection process is developing a contract negotiation
strategy. Remember, you want to "partner" with your vendor and not "take them to
the cleaners." Review your objectives for your contract negotiation and plan for the
negotiations be covering the following items:
1.
2.
3.
4.
List Rank Your Priorities Along With Alternatives
Know the Difference Between What You Need and What You Want
Know Your Bottom Line So You Know When to Walk Away
Define Any Time Constraints and Benchmarks
5.
6.
7.
Assess Potential Liabilities and Risks
Confidentiality, non-compete, dispute resolution, changes in requirements
Do the Same for Your Vendor (i.e. Walk a Mile in Their Shoes)
6.Contract Negotiation Mistakes
The smallest mistake can kill an otherwise productive contract negotiation
process. Avoid these ten contract negotiation mistakes and avoid
jeopardizing an otherwise productive contract negotiation process.
Relationship Marketing was first defined as a form of marketing developed from
direct response marketing campaigns which emphasizes customer retention and
satisfaction, rather than a dominant focus on sales transactions.
As a practice, Relationship Marketing differs from other forms of marketing in that
it recognizes the long term value of customer relationships and extends
communication beyond intrusive advertising and sales promotional messages.
With the growth of the internet and mobile platforms, Relationship Marketing has
continued to evolve and move forward as technology opens more collaborative and
social communication channels. This includes tools for managing relationships
with customers that goes beyond simple demographic and customer service data.
Relationship Marketing extends to include Inbound Marketing efforts (a
combination of search optimization and Strategic Content), PR, Social Media and
Application Development.
Just like Customer relationship management(CRM), Relationship Marketing is a
broadly recognized, widely-implemented strategy for managing and nurturing a
company’s interactions with clients and sales prospects. It also involves using
technology to, organize, synchronize business processes (principally sales and
marketing activities) and most importantly, automate those marketing and
communication activities on concrete marketing sequences that could run in
autopilot (also known as marketing sequences). The overall goals are to find,
attract, and win new clients, nurture and retain those the company already has,
entice former clients back into the fold, and reduce the costs of marketing and
client service. [1] Once simply a label for a category of software tools, today, it
generally denotes a company-wide business strategy embracing all client-facing
departments and even beyond. When an implementation is effective, people,
processes, and technology work in synergy to increase profitability, and reduce
operational costs.

Development
Relationship Marketing refers to a long-term and mutually beneficial arrangement
where both the buyer and seller have an interest in providing a more satisfying
exchange. This approach attempts to transcend the simple purchase-exchange
process with a customer to make more meaningful and richer contact by providing
a more holistic, personalized purchase, and uses the experience to create stronger
ties.
According to Liam Alvey [1], relationship marketing can be applied when there are
competitive product alternatives for customers to choose from; and when there is
an ongoing and periodic desire for the product or service.
Fornell and Wernerfelt[2] used the term "defensive marketing" to describe attempts
to reduce customer turnover and increase customer loyalty. This customerretention approach was contrasted with "offensive marketing" which involved
obtaining new customers and increasing customers' purchase frequency. Defensive
marketing focused on reducing or managing the dissatisfaction of your customers,
while offensive marketing focused on "liberating" dissatisfied customers from your
competition and generating new customers. There are two components to defensive
marketing: increasing customer satisfaction and increasing switching barriers.
Modern consumer marketing originated in the 1950s and 1960s as companies
found it more profitable to sell relatively low-value products to masses of
customers. Over the decades, attempts have been made to broaden the scope of
marketing, relationship marketing being one of these attempts. Arguably, customer
value has been greatly enriched by these contributions.
The practice of relationship marketing has been facilitated by several generations
of customer relationship management software that allow tracking and analyzing
of each customer's preferences, activities, tastes, likes, dislikes, and complaints.
For example, an automobile manufacturer maintaining a database of when and how
repeat customers buy their products, the options they choose, the way they finance
the purchase etc., is in a powerful position to develop one-to-one marketing offers
and product benefits.
In web applications, the consumer shopping profile can be built as the person
shops on the website. This information is then used to compute what can be his or
her likely preferences in other categories. These predicted offerings can then be
shown to the customer through cross-sell, email recommendation and other
channels.
Relationship marketing has also migrated back into direct mail, allowing marketers
to take advantage of the technological capabilities of digital, toner-based printing
presses to produce unique, personalized pieces for each recipient. Marketers can
personalize documents by any information contained in their databases, including
name, address, demographics, purchase history, and dozens (or even hundreds) of
other variables. The result is a printed piece that (ideally) reflects the individual
needs and preferences of each recipient, increasing the relevance of the piece and
increasing the response rate.
Scope
Relationship marketing has also been strongly influenced by reengineering.
According to (process) reengineering theory, organizations should be structured
according to complete tasks and processes rather than functions. That is, crossfunctional teams should be responsible for a whole process, from beginning to end,
rather than having the work go from one functional department to another.
Traditional marketing is said to use the functional (or 'silo') department approach.
The legacy of this can still be seen in the traditional four P's of the marketing mix.
Pricing, product management, promotion, and placement. According to Gordon
(1999), the marketing mix approach is too limited to provide a usable framework
for assessing and developing customer relationships in many industries and should
be replaced by the relationship marketing alternative model where the focus is on
customers, relationships and interaction over time, rather than markets and
products.
In contrast, relationship marketing is cross-functional marketing. It is organized
around processes that involve all aspects of the organization. In fact, some
commentators prefer to call relationship marketing "relationship management" in
recognition of the fact that it involves much more than that which is normally
included in marketing.
Martin Christopher, Adrian Payne, and David Ballantyne[3] at the Cranfield School
of Management claim that relationship marketing has the potential to forge a new
synthesis between quality management, customer service management, and
marketing. They see marketing and customer service as inseparable.
Relationship marketing involves the application of the marketing philosophy to all
parts of the organization. Every employee is said to be a "part-time marketer". The
way Regis McKenna (1991) puts it:
"Marketing is not a function, it is a way of doing business . . . marketing has
to be all pervasive, part of everyone's job description, from the receptionist
to the board of directors.
Approaches
Satisfaction
Relationship marketing relies upon the communication and acquisition of
consumer requirements solely from existing customers in a mutually beneficial
exchange usually involving permission for contact by the customer through an
"opt-in" system.[ With particular relevance to customer satisfaction the relative
price and quality of goods and services produced or sold through a company
alongside customer service generally determine the amount of sales relative to that
of competing companies. Although groups targeted through relationship marketing
may be large, accuracy of communication and overall relevancy to the customer
remains higher than that of direct marketing, but has less potential for generating
new leads than direct marketing and is limited to Viral marketing for the
acquisition of further customers.
Retention
A key principle of relationship marketing is the retention of customers through
varying means and practices to ensure repeated trade from preexisting customers
by satisfying requirements above those of competing companies through a
mutually beneficial relationship This technique is now used as a means of
counterbalancing new customers and opportunities with current and existing
customers as a means of maximizing profit and counteracting the "leaky bucket
theory of business" in which new customers gained in older direct marketing
oriented businesses were at the expense of or coincided with the loss of older
customers. This process of "churning" is less economically viable than retaining all
or the majority of customers using both direct and relationship management as lead
generation via new customers requires more investment.
Many companies in competing markets will redirect or allocate large amounts of
resources or attention towards customer retention as in markets with increasing
competition it may cost 5 times more to attract new customers than it would to
retain current customers, as direct or "offensive" marketing requires much more
extensive resources to cause defection from competitors.[
However, it is suggested that because of the extensive classic marketing theories
center on means of attracting customers and creating transactions rather than
maintaining them, the majority usage of direct marketing used in the past is now
gradually being used more alongside relationship marketing as its importance
becomes more recognizable
It is claimed by Reichheld and Sasser that a 5% improvement in customer
retention can cause an increase in profitability of between 25 and 85 percent (in
terms of net present value) depending on the industry. However Carrol, P. and
Reichheld, F. dispute these calculations, claiming they result from faulty crosssectional analysis. Research by John Fleming and Jim Asplund indicates that
engaged customers generate 1.7 times more revenue than normal customers, while
having engaged employees and engaged customers returns a revenue gain of 3.4
times the norm.
According to Buchanan and Gilles [11], the increased profitability associated with
customer retention efforts occurs because of several factors that occur once a
relationship has been established with a customer.





The cost of acquisition occurs only at the beginning of a relationship, so the
longer the relationship, the lower the amortized cost.
Account maintenance costs decline as a percentage of total costs (or as a
percentage of revenue).
Long-term customers tend to be less inclined to switch, and also tend to be
less price sensitive. This can result in stable unit sales volume and increases
in dollar-sales volume.
Long-term customers may initiate free word of mouth promotions and
referrals.
Long-term customers are more likely to purchase ancillary products and
high margin supplemental products.



Customers that stay with you tend to be satisfied with the relationship and
are less likely to switch to competitors, making it difficult for competitors to
enter the market or gain market share.
Regular customers tend to be less expensive to service because they are
familiar with the process, require less "education", and are consistent in their
order placement.
Increased customer retention and loyalty makes the employees' jobs easier
and more satisfying. In turn, happy employees feed back into better
customer satisfaction in a virtuous circle.
Relationship marketers speak of the "relationship ladder of customer loyalty". It
groups types of customers according to their level of loyalty. The ladder's first rung
consists of "prospects", that is, people that have not purchased yet but are likely to
in the future. This is followed by the successive rungs of "customer", "client",
"supporter", "advocate", and "partner". The relationship marketer's objective is to
"help" customers get as high up the ladder as possible. This usually involves
providing more personalized service and providing service quality that exceeds
expectations at each step.
Customer retention efforts involve considerations such as the following:
1. Customer valuation - Gordon (1999) describes how to value customers and
categorize them according to their financial and strategic value so that
companies can decide where to invest for deeper relationships and which
relationships need to be served differently or even terminated.
2. Customer retention measurement - Dawkins and Reichheld (1990)
calculated a company's "customer retention rate". This is simply the
percentage of customers at the beginning of the year that are still customers
by the end of the year. In accordance with this statistic, an increase in
retention rate from 80% to 90% is associated with a doubling of the average
life of a customer relationship from 5 to 10 years. This ratio can be used to
make comparisons between products, between market segments, and over
time.
3. Determine reasons for defection - Look for the root causes, not mere
symptoms. This involves probing for details when talking to former
customers. Other techniques include the analysis of customers' complaints
and competitive benchmarking (see competitor analysis).
4. Develop and implement a corrective plan - This could involve actions to
improve employee practices, using benchmarking to determine best
corrective practices, visible endorsement of top management, adjustments to
the company's reward and recognition systems, and the use of "recovery
teams" to eliminate the causes of defections.
A technique to calculate the value to a firm of a sustained customer relationship
has been developed. This calculation is typically called customer lifecycle value.
Retention strategies also build barriers to customer switching. This can be done by
product bundling (combining several products or services into one "package" and
offering them at a single price), cross selling (selling related products to current
customers), cross promotions (giving discounts or other promotional incentives to
purchasers of related products), loyalty programs (giving incentives for frequent
purchases), increasing switching costs (adding termination costs, such as mortgage
termination fees), and integrating computer systems of multiple organizations
(primarily in industrial marketing).
Many relationship marketers use a team-based approach. The rationale is that the
more points of contact between the organization and customer, the stronger will be
the bond, and the more secure the relationship.
Application
Relationship marketing and traditional (or transactional) marketing are not
mutually exclusive and there is no need for a conflict between them. A relationship
oriented marketer still has choices at the level of practice, according to the
situation variables. Most firms blend the two approaches to match their portfolio of
products and services. Virtually all products have a service component to them and
this service component has been getting larger in recent decades.
Relationship Marketing or Experiential Marketing Agencies. Some of the most
well known brands and marketing campaigns include:







Coca Cola
Volkswagen
Nescafe
BT
Häagen-Dazs
eBay
Dell
Internal marketing
Relationship marketing also stresses what it calls internal marketing. This refers to
using a marketing orientation within the organization itself. It is claimed that many
of the relationship marketing attributes like collaboration, loyalty and trust
determine what "internal customers" say and do. According to this theory, every
employee, team, or department in the company is simultaneously a supplier and a
customer of services and products. An employee obtains a service at a point in the
value chain and then provides a service to another employee further along the
value chain. If internal marketing is effective, every employee will both provide
and receive exceptional service from and to other employees. It also helps
employees understand the significance of their roles and how their roles relate to
others'. If implemented well, it can also encourage every employee to see the
process in terms of the customer's perception of value added, and the organization's
strategic mission. Further it is claimed that an effective internal marketing program
is a prerequisite for effective external marketing efforts. (George, W. 1990)
The six markets model
Christopher, Payne and Ballantyne (1991) from Cranfield University goes further.
They identify six markets which they claim are central to relationship marketing.
They are: internal markets, supplier markets, recruitment markets, referral markets,
influence markets, and customer markets.
Referral marketing is developing and implementing a marketing plan to stimulate
referrals. Although it may take months before you see the effect of referral
marketing, this is often the most effective part of an overall marketing plan and the
best use of resources.
Marketing to suppliers is aimed at ensuring a long-term conflict-free relationship in
which all parties understand each others' needs and exceed each others'
expectations. Such a strategy can reduce costs and improve quality.
Influence markets involve a wide range of sub-markets including: government
regulators, standards bodies, lobbyists, stockholders, bankers, venture capitalists,
financial analysts, stockbrokers, consumer associations, environmental
associations, and labor associations. These activities are typically carried out by the
public relations department, but relationship marketers feel that marketing to all six
markets is the responsibility of everyone in the organization. Each market may
require its own explicit strategies and a separate marketing mix for each.
Unit III
Sales force automation
Sales force automation (SFA) software is a type of program that automates
business tasks such as inventory control, sales processing, and tracking of customer
interactions, as well as analyzing sales forecasts and performance. Businesses may
have a custom version developed specifically for their needs, or choose from
among the increasing number of sales automation software products, such as
Interact Commerce's ACT! and GoldMine Software's GoldMine. Sales automation
software is sometimes called sales automation software, and sometimes called
customer relations management ( CRM ) software.
SFA packages typically include a Web-ready database, an e-mail package, and
customizable template s. A three-tiered architecture is typically used to separate
the database, server, and application to reduce programming demands on clients. A
module-based design is generally used, to allow users to customize the package to
suit their needs.
Contact and client management comprise part of a much larger business activity:
customer relationship management (CRM). CRM applications have revolutionized
the way companies conduct business and provide customer support. They are
especially critical for sales, where relationships are key. However, conventional
client management software provides just a small portion of the capabilities needed
to automate and optimize a sales organization’s contact management activities and
resources.
That’s why so many companies have replaced limited on-premises contact
management software with salesforce.com’s Web-based CRM solution. Salesforce
CRM integrates an array of advanced contact management features into the larger
sphere of CRM. Because users access Salesforce CRM through the “cloud,”
(Internet), its capabilities and data are always available to everyone in the
organization. This approach results in more productive tracking, sharing, and
analysis of customer information than is possible with conventional contact
management software. With Salesforce CRM, you gain the power to centralize,
share, manage, and analyze all your contact management data, free from tedious
manual processes and administrative hassles.
Contact Management Software as a Service
With salesforce.com’s cloud-based CRM solution, workgroup contact management
and collaboration is much faster and more efficient.



Work from any Web browser. Unlike conventional contact management
software, Salesforce CRM doesn’t tie you to a single computer. All you need
is a Web browser and Internet connection. This flexibility makes it easier to
work from home and also on the road—where so much critical customer
contact takes place. With mobile Salesforce CRM access via popular devices
like the BlackBerry and iPhone, you can update and annotate contact
management information instantly, while customer interactions are fresh in
your mind. Once entered in Salesforce CRM, your business-related notes
and data become available to everyone. That’s a lot different than onpremises client management software, which requires your entire team to
wait until you get back to the office, boot up your desktop computer, and
find the time to make updates.
Contact management with a real-time, 360-degree view. Salesforce.com
lets your team automatically share and report on contact- and customerrelated activities, tasks, and calendar events in real time. Everyone gets a full
and current picture of all customer records, automatically—there’s no
painful and unreliable synchronization required. This capability puts
Salesforce CRM in a class by itself: client management software tools can’t
match its complete customer view or its easy team collaboration.
With information-rich contact management profiles that are always
available, accessible, and up to date, Salesforce CRM delivers benefits that
conventional software lacks: Complete management visibility into contact
and customer histories. Higher productivity. Better and faster collaboration.
Minimal chance of leads falling through the cracks. Fewer manual
processes. And less reliance on paper and spreadsheets.
Contact management that keeps private information private. With
Salesforce CRM, contacts that are not linked to an account remain invisible,
regardless of your organization’s sharing model. Only the owner of the
contact and administrators can view it. Contact management sharing and
workflow rules do not apply to information that is private.
Eliminating the Drawbacks of On-Premises Client Management Software

Contact management without IT headaches. Salesforce.com is delivered
as an online application that requires just an Internet connection and Web
browser. There’s no additional hardware or software to install. By contrast,
sharing on-premises client management software over a local area network
(LAN) entails costs and hassles such as IT consultants and staff plus server
hardware and maintenance. Such contact management software is generally
beyond the ability of non-technical business users to manage on their own.
Contact Management that Works the Way You Do
Over the years, many business professionals have grown accustomed to using
Microsoft Outlook for corporate email. You don’t have to give up this convenience
to take advantage of the greater scope of contact management features in
Salesforce CRM: the two applications are already integrated.


Contact management with seamless Outlook integration. You can
continue to use Outlook for email and calendaring with the assurance that all
your contact management data will be captured in Salesforce CRM for
organization-wide visibility. No cutting-and-pasting is required. You can
even embed Salesforce CRM directly into Outlook, and navigate to any
Salesforce CRM tab, using folders available right in the Outlook window.
Although some traditional client management software systems can co-exist
with Outlook, they don’t provide the Web-based ease of collaboration, ITfree affordability, and anytime/anywhere access of salesforce.com’s cloudcomputing solution. Equally important, no client management software
comes close to delivering the same robust, wide-ranging contact
management feature set.
Contact management with maximum ease and flexibility. You can log
incoming/outgoing Outlook messages in accounts, contacts, leads,
opportunities, or cases in Salesforce CRM. Contact, appointment book, and
task information are easily shared between the two applications. You also
have the flexibility to selectively log only necessary emails in Salesforce
CRM. That means you can minimize manual processes and realize a
corresponding
spike
in
productivity.
All this is invaluable for busy sales reps, who get a complete customer
picture at a glance—including contacts, assets, account history, the latest
news, and more.
Proactive Contact Management
Unlike typical contact management software systems, Salesforce CRM does a lot
more than merely capture leads. It moves you to the all-important next step of
pursuing them in the most productive, efficient, and effective way so you can
maximize their conversion to opportunities and ultimately, to successful sales.
The Lead Search and Merge features let reps easily identify prior interactions with
a lead before following up. Duplicate leads can easily be merged with existing
leads, contacts, and accounts. You can set up lead queues and lead assignment
rules to automatically route leads to the right sales organization or rep based on
customized business rules. For the ultimate in contact management, you can
monitor leads end-to-end—from creation to conversion—with capabilities that
include automatic date/time stamping, campaign or lead source tracking, lead
status changes, and lead activity management.
Ultimately, there’s no meaningful contest between the contact management power
of cloud-based Salesforce CRM and the limited features of conventional, onpremises contact management software.
Any organization, whether new or old, whether small or big need to run smoothly
and achieve the goals and objectives which it has set forth. For this they had
developed and implemented their own management concepts. There are basically
four management concepts that allow any organization to handle the tactical,
planned and set decisions. The four basic functions of the management are just to
have
a
controlled
plan
over
the
preventive
measure.
The
four
functions
of
management
are:
The
base
function
is
to:
Plan
It is the foundation area of management. It is the base upon which the all the areas
of management should be built. Planning requires administration to assess; where
the company is presently set, and where it would be in the upcoming. From there
an appropriate course of action is determined and implemented to attain the
company’s
goals
and
objectives
Planning is unending course of action. There may be sudden strategies where
companies have to face. Sometimes they are uncontrollable. You can say that they
are external factors that constantly affect a company both optimistically and
pessimistically. Depending on the conditions, a company may have to alter its
course of action in accomplishing certain goals. This kind of preparation,
arrangement is known as strategic planning. In strategic planning, management
analyzes inside and outside factors that may affect the company and so objectives
and goals. Here they should have a study of strengths and weaknesses,
opportunities and threats. For management to do this efficiently, it has to be very
practical
and
ample.
The
subsequent
function
is
to:
Organize
The second function of the management is getting prepared, getting organized.
Management must organize all its resources well before in hand to put into practice
the course of action to decide that has been planned in the base function. Through
this process, management will now determine the inside directorial configuration;
establish and maintain relationships, and also assign required resources.
While determining the inside directorial configuration, management ought to look
at the different divisions or departments. They also see to the harmonization of
staff, and try to find out the best way to handle the important tasks and expenditure
of information within the company. Management determines the division of work
according to its need. It also has to decide for suitable departments to hand over
authority
and
responsibilities.
The
third
function
is
to:
Direct
Directing is the third function of the management. Working under this function
helps the management to control and supervise the actions of the staff. This helps
them to assist the staff in achieving the company’s goals and also accomplishing
their personal or career goals which can be powered by motivation,
communication,
department
dynamics,
and
department
leadership.
Employees those which are highly provoked generally surpass in their job
performance and also play important role in achieving the company’s goal. And
here lies the reason why managers focus on motivating their employees. They
come about with prize and incentive programs based on job performance and
geared
in
the
direction
of
the
employees
requirements.
It is very important to maintain a productive working environment, building
positive interpersonal relationships, and problem solving. And this can be done
only with Effective communication. Understanding the communication process
and working on area that need improvement, help managers to become more
effective communicators. The finest technique of finding the areas that requires
improvement is to ask themselves and others at regular intervals, how well they are
doing. This leads to better relationship and helps the managers for better directing
plans.
The
final
function
is
to:
Control
Control, the last of four functions of management, includes establishing
performance standards which are of course based on the company’s objectives. It
also involves evaluating and reporting of actual job performance. When these
points are studied by the management then it is necessary to compare both the
things. This study on comparision of both decides further corrective and preventive
actions.
In an effort of solving performance problems, management should higher
standards. They should straightforwardly speak to the employee or department
having problem. On the contrary, if there are inadequate resources or disallow
other external factors standards from being attained, management had to lower
their standards as per requirement. The controlling processes as in comparison with
other three, is unending process or say continuous process. With this management
can make out any probable problems. It helps them in taking necessary preventive
measures against the consequences. Management can also recognize any further
developing
problems
that
need
corrective
actions.
Effective and efficient management leads to success, the success where it attains
the objectives and goals of the organizations. Of course for achieving the ultimate
goal and aim management need to work creatively in problem solving in all the
four functions. Management not only has to see the needs of accomplishing the
goals but also has to look in to the process that their way is feasible for the
company.
Enterprise Marketing Management defines a category of software used by
marketing operations to manage their end-to-end internal processes. EMM is
subset of Marketing Technologies which consists of a total of 3 key technology
types that allow for corporations and customers to participate in a holistic and realtime marketing campaign.
EMM consists of other marketing software categories such as Web Analytics,
Campaign Management, Digital Asset Management, Web Content Management,
Marketing Resource Management, Marketing Dashboards, Lead Management,
Event-driven Marketing, Predictive Modeling and more. The goal of deploying and
using EMM is to improve both the efficiency and effectiveness of marketing by
increasing operational efficiency, decreasing costs and waste, and standardizing
marketing processes for an accurate and predictable time to market. The benefit of
using an EMM suite rather than a variety of point solutions is improved
collaboration, efficiency and visibility across the entire marketing function, as well
as reduced total cost of ownership. Depending on the variable combinations of
solutions, EMM can mean several different things to specific brands and industries.
Enterprise Marketing Management allows for corporations to put in place a
baseline of their operations that will allow them to begin evolution towards a
holistic solution that incorporates customer experience, expectation and brand
value associated with Marketing Technologies.
Enterprise Marketing Management defines a category of software used by
marketing operations to manage their end-to-end internal processes. EMM is
subset of Marketing Technologies which consists of a total of 3 key technology
types that allow for corporations and customers to participate in a holistic and realtime marketing campaign.
EMM consists of other marketing software categories such as Web Analytics,
Campaign Management, Digital Asset Management, Web Content Management,
Marketing Resource Management, Marketing Dashboards, Lead Management,
Event-driven Marketing, Predictive Modeling and more. The goal of deploying and
using EMM is to improve both the efficiency and effectiveness of marketing by
increasing operational efficiency, decreasing costs and waste, and standardizing
marketing processes for an accurate and predictable time to market. The benefit of
using an EMM suite rather than a variety of point solutions is improved
collaboration, efficiency and visibility across the entire marketing function, as well
as reduced total cost of ownership. Depending on the variable combinations of
solutions, EMM can mean several different things to specific brands and industries.
Enterprise Marketing Management allows for corporations to put in place a
baseline of their operations that will allow them to begin evolution towards a
holistic solution that incorporates customer experience, expectation and brand
value associated with Marketing Technologies.
Status of Customer Relationship Management in India
Introduction
Relationship marketing is emerging as the core marketing activity for businesses
operating infiercely competitive environments. On average, businesses spend six
times more to acquirecustomers than they do to keep them (Gruen, 1997).
Therefore, many firms are now payingmore attention to their relationships with
existing customers to retain them and increase theirshare of customer’s purchases.
Worldwide service organizations have been pioneers in developing customer
retentionstrategies. Banks have relationship managers for select customers, airlines
have frequent flyerprograms to reward loyal customers, credit cards offer
redeemable bonus points for increasedcard usage, telecom service operators
provide customised services to their heavy users, andhotels have personalized
services for their regular guests.
Literature Review
Until recently, most marketers focused on attracting customers from its target
segments usingthe tools and techniques developed for mass marketing in the
industrial era. In theinformation era, this is proving to be highly ineffective in most
competitive markets.Slowing growth rates, intensifying competition and
technological developments madebusinesses look for ways to reduce costs and
improve their effectiveness. Business process reengineering,automation and
downsizing reduced the manpower costs. Financial restructuringand efficient fund
management reduced the financial costs. Production and operation costshave been
reduced through Total Quality Management (TQM), Just in Time (JIT) inventory,
Flexible Manufacturing Systems (FMS), and efficient supply chain management.
Studies haveshown that while manufacturing costs declined from 55% to 30% and
management costsdeclined from 25% to 15%, the marketing costs have increased
from 20% to 55% (Sheth,1998).
The practice of relationship marketing has the potential to improve
marketingproductivity through improved marketing efficiencies and effectiveness
(Sheth and Parvatiyar,1995).Still relationship marketing appears to be an expensive
alternative to firms practicing mass
marketing due to the relatively high initial investments. Firms would adopt
relationshipmarketing only if it has the potential to benefit them. The benefits
come through lower costsof retention and increased profits due to lower defection
rates (Reichheld and Sasser, 1990).When customers enter into a relationship with a
firm, they are willingly foregoing otheroptions and limiting their choice. Some of
the personal motivations to do so result fromgreater efficiency in decision-making,
reduction in information processing, achieving morecognitive consistency in d
ecisions and reduction of perceived risks with future decisions (Sheth& Parvatiyar,
1995).
In the context of service, relationship marketing has been defined as attracting,
maintainingand in multi-service organisations enhancing customer relationships
(Berry 1983). Hereattracting customers is considered to be an intermediary step in
the relationship buildingprocess with the ultimate objective of increasing loyalty of
profitable customers. This isbecause of the applicability of the 80-20 rule.
According to Market Line Associates, the top20% of typical bank customers
produce as much as 150% of overall profit, while the bottom20% of customers
drain about 50% from the bank's bottom line and the revenues from the rest just
meeting their expenses.
Berry (1983) recommended the following five strategies for practicing relationship
marketing i. Developing a core service around which to build a customer relationship,
ii. Customizing the relationship to the individual customer,
iii Augmenting the core service with extra benefits,
iv. Pricing services to encourage customer loyalty,
v. Marketing to employees so that they will perform well for customers.
Developments in information technology, data warehousing and datamining have
made itpossible for firms to maintain a 1to1 relationship with their customers.
Firms can now manageevery single contact with the customer from account
management personnel, call centers,interactive voice response systems, on-line
dial-up applications, and websites to build lastingrelationships. These interactions
can be used to glean information and insights about customerneeds and their
buying behavior to design and develop services, which help create value for
the customers as well as the firms. Although customised as well as off the shelf
technologicalsolutions are available in the marketplace, businesses need to do a lot
more than just adoptthese solutions to implement customer relationship
management (CRM) practices.
Successful implementation of CRM requires a strategic approach, which
encompasses developing customer centric processes, selecting and implementing
technology solutions,employee empowerment, customer information and
knowledge generation capabilities todifferentiate them, and the ability to learn
from best practices.
Research Objectives
The current research was aimed at determining the approach being adopted by
businesses in
India for relationship marketing. The research focused on the following major
issues –
1. Do managers in service firms believe that their processes are customer centric?
2. Do they select technology based on an understanding of customer needs?
3. Have they empowered their employees to deliver superior service?
4. Do they have a customer knowledge strategy? How well do they manage their
customer relationships?
It adopted the framework recommended by Peppers, Rogers and Dorf (1999) for
the survey to
understand the status of relationship marketing across service businesses in India.
Methodology
The research was exploratory in nature and adopted a two-stage process.
During the first stage, 73 managers of service firms operating in India were
surveyed throughrespondent administered questionnaires. These managers
belonged to the following three categories –
a. Hospitality industry encompassing hotels and restaurants
b. IT and Telecom industry, which included software firms and telecom service
providers
c. Financial services included commercial banks and mutual funds
The distribution across the sectors is as follows The survey focused on the quality and customer centric processes, technology
selection,employee empowerment, and customer knowledge strategy to gauge the
status of CRMpractices in these firms.
In the second stage, managers of select firms in each category of services were
interviewed tounderstand the relationship marketing practices adopted by them.
These interviews explored the following issues –
1. What are the various CRM initiatives undertaken by the firm?
2. How do they develop these programs?
3. How do they measure the effectiveness of these programs?
4. How successful are these programs in retaining customers?
Analysis of Findings
Processes - The managers reported a wide divergence with respect to the adoption
of qualityassurance across the three sectors.
The IT and telecom sector is at the forefront of adopting a formal quality
managementorganisation. Most of the players in the finance and hospitality sector
report having somemethods in place to ensure quality management initiatives.
About 8% of the overall samplehave indicated the absence of any quality
initiatives in their organisations.
At a broad level, most managers believe that they understand most of the
interactionsbetween customers and their business processes. About 50% of them h
ave indicated that theyhave a full understanding of all possible interactions
between customers and their business processes. Customer-centric marketing
emphasizes understanding and satisfying the needs,wants, and resources of
individual consumers and customers rather than those of massmarkets or market
segments (Sheth, Sisodia and Sharma 2000). In customer-centricmarketing,
marketers assess each customer individually to determine whether to serve that
customer directly or via a third party. Also, customer-centric marketers determine
whether tocreate an offering that customizes the product and/or some other
element(s) of the marketingmix or standardize the offering. Therefore, it is very
important to have an understanding of allthe linkages between the customers and
the business processes, which help fulfill thecustomer needs.
Technology Selection –
Information technology (IT) is a major facilitator for CRM implementation. In
response to the question on whether they take, consider customers’ needs when
selecting and implementing IT, about 30% of managers have indicated that they
consider customer needs. Only 14% of managers in financial services do customer
validation when selecting technology. While only23% the managers in IT &
telecom firms believe that their technology selections are customer centric whereas
this was over 50% in the other two sectors.
Employee Empowerment
When asked whether their employees are empowered to make decisions in favor of
thecustomers, less than a quarter of the managers across the three sectors indicated
that everyemployee is empowered to take actions to ensure the ultimate satisfaction
of the customer.
Most of them feel that their employees have been empowered to take independent
decisionswithin the guidelines. This aspect of limited empowerment gets
reinforced when one looks atthe linkage between the employee’s rewards with
customer centric behavior. Over 18% of therespondents across the sectors have
reported no linkages or use of ad hoc methods to rewardcustomer centric behavior.
Facilitation of employees for their role fulfillment through IT is another aspect of
employeeempowerment. IT helps employees respond to customer queries and
provide support in a fastand timely manner. It helps them access information which
is normally spread across theorganisation. Over 54% of IT and telecom firms have
provided the most effective technologyto all employees who interact with
customers. This reduces to 42% for the hospitality and19% for the financial
services sector.
Customer Knowledge Strategy
Customer knowledge gets built when information is collected systematically over a
period oftime. This can be done through regular surveys and during customer
interactions. Butimportantly this information has to be combined with the
organisation’s experiences withcustomers to build rich customer profiles, buying
behavior, preferences and usage patterns.
Over 60% managers in the hospitality industry have indicated that they have a
continuousstrategy for collecting customer information. In most of the services,
opportunities to come indirect contact with their customers are high in comparison
to other businesses who haveintermediaries and hence have a arms length
relationship with their customers. Therefore, it isnatural for service firms to collect
customer information on a regular basis.
But informationcollection is just the first step in generating customer knowledge.
This information has to becombined with experiences to develop consumer
insights, which help them serve theircustomers better.When it comes to combining
customer information with experiences, service firms seem to beeconomising.
Most of them seem to be doing it for select customers.
Hotels do it for theirregular guests specially those who have enrolled for their
membership schemes..comservice providers selectively do it for their high net
worth individuals who typically use multipleofferings of the service provider.
Most service firms rely on periodic surveys to understand their customers’
expectations andalso understand and anticipate their behaviors.
Over 40% of managers in the financialservices have indicated that they work with
customers as a team to ensure that theirexpectations are met or exceede. It is very
important to work with customers to understandtheir expectations as research has
consistently indicated that one of the major reasons forpoor service quality is the
gap between managers perceptions about customers expectationsand actual
customer expectations (Parasuraman, Zeithaml and Berry 1985).
The purpose of collecting customer information and developing knowledge is to be
able todifferentiate customers and meet their specific requirements. Peppers,
Rogers and Dorf (1999)have recommended a four-stage process of Identification,
Differentiation, Interaction, andCustomization for implementing one to one
relationships with customers. Over 50% managersin financial services have
indicated that they have critical business information about their
relationships with individual customers. This falls to about 40% in the hospitality
and ITservices.
Customer knowledge can be used to initiate customization of the service for
customers basedon their needs. By tailoring the elements of services marketing
mix, firms can customize theirofferings to all or select customers.A majority of the
marketing programs are targeted for smaller segments of the markets. Butthere is a
growing trend towards individualizing these programs. With the emergence of
ecommerce,this trend is going to further intensify.Some of the important findings
of the depth interviews with managers of these services are
–a. The relationship initiatives undertaken by firms have been directed towards
customerretention. The initiatives were mostly membership /privilege schemes
with gradations basedon frequency and value of usage / purchase.
b. Most of them also indicated that these schemes were table stakes i.e. they cannot
survivein the business without these schemes if everyone else offers them. But the
race is always todifferentiate based on convenience for customers.
c. The source and reasons for these programs were found to be diverse - frontline
initiatives,adaptation of successful programs in parent organisations abroad
especially for themultinational firms, or copying competitor’s offerings. Pioneers
in the industry like one of themultinational bank, which introduced the concept of
relationship manager, adopted thepractices of their parent organisation.
Unit v
Database marketing
Database marketing is a form of direct marketing using databases of customers or
potential customers to generate personalized communications in order to promote a
product or service for marketing purposes. The method of communication can be
any addressable medium, as in direct marketing.
The distinction between direct and database marketing stems primarily from the
attention paid to the analysis of data. Database marketing emphasizes the use of
statistical techniques to develop models of customer behavior, which are then used
to select customers for communications. As a consequence, database marketers
also tend to be heavy users of data warehouses, because having a greater amount of
data about customers increases the likelihood that a more accurate model can be
built.
There are two main types of marketing databases, 1) Consumer databases, and 2)
business databases. Consumer databases are primarily geared towards companies
that sell to consumers, often abbreviated as B2C or BtoC. Business marketing
databases are often much more advanced in the information that they can provide.
This is mainly due to the fact that business databases aren't restricted by the same
privacy laws as consumer databases.
The "database" is usually name, address, and transaction history details from
internal sales or delivery systems, or a bought-in compiled "list" from another
organization, which has captured that information from its customers. Typical
sources of compiled lists are charity donation forms, application forms for any free
product or contest, product warranty cards, subscription forms, and credit
application forms.
The communications generated by database marketing may be described as junk
mail or spam, if it is unwanted by the addressee. Direct and database marketing
organizations, on the other hand, argue that a targeted letter or e-mail to a
customer, who wants to be contacted about offerings that may interest the
customer, benefits both the customer and the marketer.
Some countries and some organizations insist that individuals are able to prevent
entry to or delete their name and address details from database marketing lists.

Background
Database marketing emerged in the 1980s as a new, improved form of direct
marketing. During the period traditional "list broking" was under pressure to
modernise, because it was offline and tape-based, and because lists tended to hold
limited data [1]. At the same time, with new technologies enabling customer
responses to be recorded, direct response marketing was in the ascendancy, with
the aim of opening up a two-way communication, or dialogue, with customers.
Kestnbaum collaborated with Shaw in the 1980s on several landmark online
marketing database developments - for BT (20 million customers), BA (10 million)
and Barclays (13 million). Shaw incorporated new features into the Kestnbaum
approach, including telephone and field sales channel automation, contact strategy
optimisation, campaign management and co-ordination, marketing resource
management, marketing accountability and marketing analytics. The designs of
these systems have been widely copied subsequently and incorporated into CRM
and MRM packages in the 1990s and later.[3]
The earliest recorded definition of Database Marketing was in 1988 in the book of
the same name (Shaw and Stone 1988 Database Marketing):
"Database Marketing is an interactive approach to marketing, which uses the
individually addressable marketing media and channels (such as mail,
telephone and the sales force): to extend help to a company's target
audience; to stimulate their demand; and to stay close to them by recording
and keeping an electronic database memory of the customer, prospect and all
commercial contacts, to help improve all future contacts and to ensure more
realistic of all marketing."
Growth and Evolution of Database Marketing
The growth of database marketing is driven by a number of environmental issues.
Fletcher, Wheeler and Wright (1991) [4] classified these issues into four main
categories: (1) changing role of direct marketing; (2) changing cost structures; (3)
changing technology; and (4) changing market conditions.
DRIVER #1: THE CHANGING ROLE OF DIRECT MARKETING
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The move to relationship marketing for competitive advantage.
The decline in the effectiveness of traditional media.
The overcrowding and myopia of existing sales channels.
DRIVER #2: CHANGING COST STRUCTURES
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The decline in electronic processing costs.
The increase in marketing costs.
DRIVER #3: CHANGING TECHNOLOGY
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The advent of new methods of shopping and paying.
The development of economical methods for differentiating customer
communication.
DRIVER #4: CHANGING ECONOMIC CONDITIONS
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The desire to measure the impact of marketing efforts.
The fragmentation of consumer and business markets.
Shaw and Stone (1988) noted that companies go through evolutionary phases in
the developing their database marketing systems. They identify the four phases of
database development as:
1.
2.
3.
4.
mystery lists;
buyer databases;
coordinated customer communication; and
integrated marketing.
Sources of data
Although organizations of any size can employ database marketing, it is
particularly well-suited to companies with large numbers of customers. This is
because a large population provides greater opportunity to find segments of
customers or prospects that can be communicated with in a customized manner. In
smaller (and more homogeneous) databases, it will be difficult to justify on
economic terms the investment required to differentiate messages. As a result,
database marketing has flourished in sectors, such as financial services,
telecommunications, and retail, all of which have the ability to generate significant
amounts transaction data for millions of customers.
Database marketing applications can be divided logically between those marketing
programs that reach existing customers and those that are aimed at prospective
customers.
Consumer data
In general, database marketers seek to have as much data available about
customers and prospects as possible.
For marketing to existing customers, more sophisticated marketers often build
elaborate databases of customer information. These may include a variety of data,
including name and address, history of shopping and purchases, demographics, and
the history of past communications to and from customers. For larger companies
with millions of customers, such data warehouses can often be multiple terabytes
in size.
Marketing to prospects relies extensively on third-party sources of data. In most
developed countries, there are a number of providers of such data. Such data is
usually restricted to name, address, and telephone, along with demographics, some
supplied by consumers, and others inferred by the data compiler. Companies may
also acquire prospect data directly through the use of sweepstakes, contests, on-line
registrations, and other lead generation activities.
Business data
For many business-to-business (B2B) company marketers, the number of
customers and prospects will be smaller than that of comparable business-toconsumer (B2C) companies. Also, their relationships with customers will often
rely on intermediaries, such as salespeople, agents, and dealers, and the number of
transactions per customer may be small. As a result, business-to-business
marketers may not have as much data at their disposal as business-to-consumer
marketer are accustomed.
One other complication is that B2B marketers in targeting teams or "accounts" and
not individuals may produce many contacts from a single organization.
Determining which contact to communicate with through direct marketing may be
difficult. On the other hand it is the database for business-to-business marketers
which often includes data on the business activity about the respective client.
These data become critical to segment markets or define target audiences, e.g.
purchases of software license renewals by telecom companies could help identify
which technologist is in charge of software installations vs. software procurement,
etc. Customers in Business-to-Business environments often tend to be loyal since
they need after-sales-service for their products and appreciate information on
product upgrades and service offerings. This loyalty can be tracked by a database.
Sources of customer data often come from the sales force employed by the
company and from the service engineers. Increasingly, online interactions with
customers are providing B2B marketers with a lower cost source of customer
information.
For prospect data, businesses can purchase data from compilers of business data, as
well as gather information from their direct sales efforts, on-line sites, and
specialty publications.
Analytics and modeling
Companies with large databases of customer information risk being "data rich and
information poor." As a result, a considerable amount of attention is paid to the
analysis of data. For instance, companies often segment their customers based on
the analysis of differences in behavior, needs, or attitudes of their customers. A
common method of behavioral segmentation is RFM, in which customers are
placed into subsegments based on the recency, frequency, and monetary value of
past purchases. Van den Poel (2003)[5] gives an overview of the predictive
performance of a large class of variables typically used in database-marketing
modeling.
They may also develop predictive models, which forecast the propensity of
customers to behave in certain ways. For instance, marketers may build a model
that rank orders customers on their likelihood to respond to a promotion.
Commonly employed statistical techniques for such models include logistic
regression and neural networks.
Laws and regulations
As database marketing has grown, it has come under increased scrutiny from
privacy advocates and government regulators. For instance, the European
Commission has established a set of data protection rules that determine what uses
can be made of customer data and how consumers can influence what data are
retained. In the United States, there are a variety of state and federal laws,
including the Fair Credit Reporting Act, or FCRA, (which regulates the gathering
and use of credit data), the Health Insurance Portability and Accountability Act
(HIPAA) (which regulates the gathering and use of consumer health data), and
various programs that enable consumers to suppress their telephones numbers from
telemarketing.
Advances In Database Marketing
While the idea of storing customer data in electronic formats to use them for
database-marketing purposes has been around for decades, the computer systems
available today make it possible to gain a comprehensive history of client behavior
on-screen while the business is transacting with each individual, producing thus
real-time business intelligence for the company. This ability enables what is called
one-to-one marketing or personalization.
Today's Customer Relationship Management (CRM) systems use the stored data
not only for direct marketing purposes but to manage the complete relationship
with individual customer contacts and to develop more customized product and
service offerings. However, a combination of CRM, content management and
business intelligence tools are making delivery of personalized information a
reality.
Marketers trained in the use of these tools are able to carry out customer nurturing,
which is a tactic that attempts to communicate with each individual in an
organization at the right time, using the right information to meet that client's need
to progress through the process of identifying a problem, learning options available
to resolve it, selecting the right solution, and making the purchasing decision.
Because of the complexities of B2B marketing and the intricacies of corporate
operations, the demands placed on any marketing organization to formulate the
business process by which such a sophisticated series of procedures may be
brought into existence are significant. It is often for this reason that large
marketing organizations engage the use of an expert in marketing process strategy
and information technology (IT), or a marketing IT process strategist. Although
more technical in nature than often marketers require, a system integrator (SI) can
also play an equivalent role to the marketing IT process strategist, particularly at
the time that new technology tools need to be configured and rolled out.
New advances in cloud computing and marketing's penchant for both outsourcing
services to third-party agencies and avoiding involvement in the creation of
complex technological tools has provided a fertile soil for Software as a Service
(SaaS) providers to centralize the marketing database under a hosting service
model that incorporates functions from CRM, content management and business
intelligence under one offering to automate the marketing
Data warehouse
A data warehouse (DW) is a database used for reporting. The data is offloaded
from the operational systems for reporting. The data may pass through an
Operational Data Store (ODS) for additional operations before it is used in the DW
for reporting.
A data warehouse maintains its functions in three layers: staging, integration and
access. A principle in data warehousing is that there is a place for each needed
function in the DW. The functions are in the DW to meet the users' reporting
needs. Staging is used to store raw data for use by developers (analysis and
support). The integration layer is used to integrate data and to have a level of
abstraction from users. The access layer is for getting data out for users.
This definition of the data warehouse focuses on data storage. The main source of
the data is cleaned, transformed, catalogued and made available for use by
managers and other business professionals for data mining, online analytical
processing, market research and decision support (Marakas & OBrien 2009).
However, the means to retrieve and analyze data, to extract, transform and load
data, and to manage the data dictionary are also considered essential components
of a data warehousing system. Many references to data warehousing use this
broader context. Thus, an expanded definition for data warehousing includes
business intelligence tools, tools to extract, transform and load data into the
repository, and tools to manage and retrieve metadata.
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Considerations
Data warehousing arises in an organization's need for reliable, consolidated, unique
and integrated analysis and reporting of its data at different levels of aggregation.
The practical reality of most organizations is that their data infrastructure is made
up by a collection of heterogeneous systems. For example, an organization might
have one system that handles customer-relationship, a system that handles
employees, systems that handle sales data or production data, yet another system
for finance and budgeting data, etc. In practice, these systems are often poorly or
not at all integrated and simple questions like: "How much time did sales person A
spend on customer C, how much did we sell to Customer C, was customer C happy
with the provided service, did Customer C pay his bills?" can be very hard to
answer, even though the information is available "somewhere" in the different data
systems.
Another problem is that enterprise resource planning (ERP) systems are designed
to support relevant operations. For example, a finance system might keep track of
every single stamp bought; When it was ordered, when it was delivered, when it
was paid and the system might offer accounting principles (like double entry
bookkeeping) that further complicates the data model. Such information is great
for the person in charge of buying "stamps" or the accountant trying to sort out an
irregularity, but the CEO is definitely not interested in such detailed information,
the CEO wants to know stuff like "What's the cost?", "What's the revenue?", "Did
our latest initiative reduce costs?" and wants to have this information at an
aggregated level.
Yet another problem might be that the organization is, internally, in disagreement
about which data are correct. For example, the sales department might have one
view of its costs, while the finance department has another view of that cost. In
such cases, the organization can spend unlimited time discussing who has the
correct view of the data.
It is partly the purpose of data warehousing to bridge such problems. In data
warehousing the source data systems are considered as given: Even though the data
source system might have been made in such a manner makes it difficult to extract
integrated information, the "data warehousing answer" is not to redesign the data
source systems but rather to make the data appear consistent, integrated and
consolidated despite the problems in the underlying source systems. Data
warehousing achieves this by employing different data warehousing techniques,
creating one or more new data repositories (i.e. the data warehouse) whose data
model(s) support the needed reporting and analysis.
History
The concept of data warehousing dates back to the late 1980s [1] when IBM
researchers Barry Devlin and Paul Murphy developed the "business data
warehouse". In essence, the data warehousing concept was intended to provide an
architectural model for the flow of data from operational systems to decision
support environments. The concept attempted to address the various problems
associated with this flow, mainly the high costs associated with it. In the absence
of a data warehousing architecture, an enormous amount of redundancy was
required to support multiple decision support environments. In larger corporations
it was typical for multiple decision support environments to operate independently.
Though each environment served different users, they often required much of the
same stored data. The process of gathering, cleaning and integrating data from
various sources, usually from long-term existing operational systems (usually
referred to as legacy systems), was typically in part replicated for each
environment. Moreover, the operational systems were frequently reexamined as
new decision support requirements emerged. Often new requirements necessitated
gathering, cleaning and integrating new data from "data marts" that were tailored
for ready access by users.
Key developments in early years of data warehousing were:
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1960s — General Mills and Dartmouth College, in a joint research project,
develop the terms dimensions and facts.[2]
1970s — ACNielsen and IRI provide dimensional data marts for retail
sales.[2]
1983 — Teradata introduces a database management system specifically
designed for decision support.
1988 — Barry Devlin and Paul Murphy publish the article An architecture
for a business and information systems in IBM Systems Journal where they
introduce the term "business data warehouse".
1990 — Red Brick Systems introduces Red Brick Warehouse, a database
management system specifically for data warehousing.
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1991 — Prism Solutions introduces Prism Warehouse Manager, software for
developing a data warehouse.
1991 — Bill Inmon publishes the book Building the Data Warehouse.
1995 — The Data Warehousing Institute, a for-profit organization that
promotes data warehousing, is founded.
1996 — Ralph Kimball publishes the book The Data Warehouse Toolkit.
2000 — Daniel Linstedt releases the Data Vault, enabling real time
auditable Data Warehouses.
Architecture
Architecture, in the context of an organization's data warehousing efforts, is a
conceptualization of how the data warehouse is built. There is no right or wrong
architecture, but rather there are multiple architectures that exist to support various
environments and situations. The worthiness of the architecture can be judged from
how the conceptualization aids in the building, maintenance, and usage of the data
warehouse.
One possible simple conceptualization of a data warehouse architecture consists of
the following interconnected layers:
Operational database layer
The source data for the data warehouse — An organization's Enterprise
Resource Planning systems fall into this layer.
Data access layer
The interface between the operational and informational access layer —
Tools to extract, transform, load data into the warehouse fall into this layer.
Metadata layer
The data directory — This is usually more detailed than an operational
system data directory. There are dictionaries for the entire warehouse and
sometimes dictionaries for the data that can be accessed by a particular
reporting and analysis tool.
Informational access layer
The data accessed for reporting and analyzing and the tools for reporting and
analyzing data — Business intelligence tools fall into this layer. The InmonKimball differences about design methodology, discussed later in this
article, have to do with this layer
Conforming information
Another important fact in designing a data warehouse is which data to conform and
how to conform the data. For example, one operational system feeding data into
the data warehouse may use "M" and "F" to denote sex of an employee while
another operational system may use "Male" and "Female". Though this is a simple
example, much of the work in implementing a data warehouse is devoted to
making similar meaning data consistent when they are stored in the data
warehouse. Typically, extract, transform, load tools are used in this work.
Master Data Management has the aim of conforming data that could be considered
"dimensions".
Normalized versus dimensional approach for storage of data
There are two leading approaches to storing data in a data warehouse — the
dimensional approach and the normalized approach.
In a dimensional approach, transaction data are partitioned into either "facts",
which are generally numeric transaction data, or "dimensions", which are the
reference information that gives context to the facts. For example, a sales
transaction can be broken up into facts such as the number of products ordered and
the price paid for the products, and into dimensions such as order date, customer
name, product number, order ship-to and bill-to locations, and salesperson
responsible for receiving the order. A key advantage of a dimensional approach is
that the data warehouse is easier for the user to understand and to use. Also, the
retrieval of data from the data warehouse tends to operate very quickly. The main
disadvantages of the dimensional approach are:
1. In order to maintain the integrity of facts and dimensions, loading the data
warehouse with data from different operational systems is complicated, and
2. It is difficult to modify the data warehouse structure if the organization
adopting the dimensional approach changes the way in which it does
business.
In the normalized approach, the data in the data warehouse are stored following, to
a degree, database normalization rules. Tables are grouped together by subject
areas that reflect general data categories (e.g., data on customers, products, finance,
etc.). The main advantage of this approach is that it is straightforward to add
information into the database. A disadvantage of this approach is that, because of
the number of tables involved, it can be difficult for users both to:
1. join data from different sources into meaningful information and then
2. access the information without a precise understanding of the sources of data
and of the data structure of the data warehouse.
These approaches are not mutually exclusive, and there are other approaches.
Dimensional approaches can involve normalizing data to a degree.
Top-down versus bottom-up design methodologies
Bottom-up design
Ralph Kimball, a well-known author on data warehousingis a proponent of an
approach to data warehouse design which he describes as bottom-up
In the bottom-up approach data marts are first created to provide reporting and
analytical capabilities for specific business processes. Though it is important to
note that in Kimball methodology, the bottom-up process is the result of an initial
business oriented Top-down analysis of the relevant business processes to be
modelled.
Data marts contain, primarily, dimensions and facts. Facts can contain either
atomic data and, if necessary, summarized data. The single data mart often models
a specific business area such as "Sales" or "Production." These data marts can
eventually be integrated to create a comprehensive data warehouse. The integration
of data marts is managed through the implementation of what Kimball calls "a data
warehouse bus architecture".The data warehouse bus architecture is primarily an
implementation of "the bus" a collection of conformed dimensions, which are
dimensions that are shared (in a specific way) between facts in two or more data
marts.
The integration of the data marts in the data warehouse is centered on the
conformed dimensions (residing in "the bus") that define the possible integration
"points" between data marts. The actual integration of two or more data marts is
then done by a process known as "Drill across". A drill-across works by grouping
(summarizing) the data along the keys of the (shared) conformed dimensions of
each fact participating in the "drill across" followed by a join on the keys of these
grouped (summarized) facts.
Maintaining tight management over the data warehouse bus architecture is
fundamental to maintaining the integrity of the data warehouse. The most
important management task is making sure dimensions among data marts are
consistent. In Kimball's words, this means that the dimensions "conform".
Some consider it an advantage of the Kimball method, that the data warehouse
ends up being "segmented" into a number of logically self contained (up to and
including The Bus) and consistent data marts, rather than a big and often complex
centralized model. Business value can be returned as quickly as the first data marts
can be created, and the method gives itself well to an exploratory and iterative
approach to building data warehouses. For example, the data warehousing effort
might start in the "Sales" department, by building a Sales-data mart. Upon
completion of the Sales-data mart, The business might then decide to expand the
warehousing activities into the, say, "Production department" resulting in a
Production data mart. The requirement for the Sales data mart and the Production
data mart to be integrable, is that they share the same "Bus", that will be, that the
data warehousing team has made the effort to identify and implement the
conformed dimensions in the bus, and that the individual data marts links that
information from the bus. Note that this does not require 100% awareness from the
onset of the data warehousing effort, no master plan is required upfront. The Salesdata mart is good as it is (assuming that the bus is complete) and the production
data mart can be constructed virtually independent of the sales data mart (but not
independent of the Bus).
If integration via the bus is achieved, the data warehouse, through its two data
marts, will not only be able to deliver the specific information that the individual
data marts are designed to do, in this example either "Sales" or "Production"
information, but can deliver integrated Sales-Production information, which, often,
is of critical business value. An integration (possibly) achieved in a flexible and
iterative fashion.
Top-down design
Bill Inmon, one of the first authors on the subject of data warehousing, has defined
a data warehouse as a centralized repository for the entire enterprise. Inmon is one
of the leading proponents of the top-down approach to data warehouse design, in
which the data warehouse is designed using a normalized enterprise data model.
"Atomic" data, that is, data at the lowest level of detail, are stored in the data
warehouse. Dimensional data marts containing data needed for specific business
processes or specific departments are created from the data warehouse. In the
Inmon vision the data warehouse is at the center of the "Corporate Information
Factory" (CIF), which provides a logical framework for delivering business
intelligence (BI) and business management capabilities.
Inmon states that the data warehouse is:
Subject-oriented
The data in the data warehouse is organized so that all the data elements
relating to the same real-world event or object are linked together.
Non-volatile
Data in the data warehouse are never over-written or deleted — once
committed, the data are static, read-only, and retained for future reporting.
Integrated
The data warehouse contains data from most or all of an organization's
operational systems and these data are made consistent.
Time-variant
The top-down design methodology generates highly consistent dimensional views
of data across data marts since all data marts are loaded from the centralized
repository. Top-down design has also proven to be robust against business
changes. Generating new dimensional data marts against the data stored in the data
warehouse is a relatively simple task. The main disadvantage to the top-down
methodology is that it represents a very large project with a very broad scope. The
up-front cost for implementing a data warehouse using the top-down methodology
is significant, and the duration of time from the start of project to the point that end
users experience initial benefits can be substantial. In addition, the top-down
methodology can be inflexible and unresponsive to changing departmental needs
during the implementation phases.
Hybrid design
Over time it has become apparent to proponents of bottom-up and top-down data
warehouse design that both methodologies have benefits and risks. Hybrid
methodologies have evolved to take advantage of the fast turn-around time of
bottom-up design and the enterprise-wide data consistency of top-down design.
Data warehouses versus operational systems
Operational systems are optimized for preservation of data integrity and speed of
recording of business transactions through use of database normalization and an
entity-relationship model. Operational system designers generally follow the Codd
rules of database normalization in order to ensure data integrity. Codd defined five
increasingly stringent rules of normalization. Fully normalized database designs
(that is, those satisfying all five Codd rules) often result in information from a
business transaction being stored in dozens to hundreds of tables. Relational
databases are efficient at managing the relationships between these tables. The
databases have very fast insert/update performance because only a small amount of
data in those tables is affected each time a transaction is processed. Finally, in
order to improve performance, older data are usually periodically purged from
operational systems.
Data warehouses are optimized for speed of data analysis. Frequently data in data
warehouses are denormalised via a dimension-based model. Also, to speed data
retrieval, data warehouse data are often stored multiple times—in their most
granular form and in summarized forms called aggregates. Data warehouse data
are gathered from the operational systems and held in the data warehouse even
after the data has been purged from the operational systems.
Evolution in organization use
Organizations generally start off with relatively simple use of data warehousing.
Over time, more sophisticated use of data warehousing evolves. The following
general stages of use of the data warehouse can be distinguished:
Offline Operational Data Warehouse
Data warehouses in this initial stage are developed by simply copying the
data off of an operational system to another server where the processing load
of reporting against the copied data does not impact the operational system's
performance.
Offline Data Warehouse
Data warehouses at this stage are updated from data in the operational
systems on a regular basis and the data warehouse data are stored in a data
structure designed to facilitate reporting.
Real Time Data Warehouse
Data warehouses at this stage are updated every time an operational system
performs a transaction (e.g. an order or a delivery or a booking).
Integrated Data Warehouse
These data warehouses assemble data from different areas of business, so
users can look up the information they need across other systems
Benefits
Some of the benefits that a data warehouse provides are as follows:
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A data warehouse provides a common data model for all data of interest
regardless of the data's source. This makes it easier to report and analyze
information than it would be if multiple data models were used to retrieve
information such as sales invoices, order receipts, general ledger charges,
etc.
Prior to loading data into the data warehouse, inconsistencies are identified
and resolved. This greatly simplifies reporting and analysis.
Information in the data warehouse is under the control of data warehouse
users so that, even if the source system data are purged over time, the
information in the warehouse can be stored safely for extended periods of
time.
Because they are separate from operational systems, data warehouses
provide retrieval of data without slowing down operational systems.
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Data warehouses can work in conjunction with and, hence, enhance the
value of operational business applications, notably customer relationship
management (CRM) systems.
Data warehouses facilitate decision support system applications such as
trend reports (e.g., the items with the most sales in a particular area within
the last two years), exception reports, and reports that show actual
performance versus goals.
Disadvantages
There are also disadvantages to using a data warehouse. Some of them are:
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Data warehouses are not the optimal environment for unstructured data.
Because data must be extracted, transformed and loaded into the warehouse,
there is an element of latency in data warehouse data.
Over their life, data warehouses can have high costs.
Data warehouses can get outdated relatively quickly. There is a cost of
delivering suboptimal information to the organization.
There is often a fine line between data warehouses and operational systems.
Duplicate, expensive functionality may be developed. Or, functionality may
be developed in the data warehouse that, in retrospect, should have been
developed in the operational systems.
Sample applications
Some of the applications data warehousing can be used for are:
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Decision support
Trend analysis
Financial forecasting
Churn Prediction for Telecom subscribers, Credit Card users etc.
Insurance fraud analysis
Call record analysis
Logistics and Inventory management
Agriculture [9]
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Data Warehouse is the most important component of business intelligence
solution for any organization. We can provide assistance in designing data
warehouse architecture, implementing ETL modules, data access front-end
and provide advance data analysis services.

Designing data warehouse architecture. Right data warehouse
architecture is critical in effectively managing knowledge in organizations.
It needs to be inclusive, flexible and expandable. We have implemented
many data warehousing solutions and our experience can be very beneficial
in setting the blues print for your business intelligence solution.

Design and deployment of ETL (Extract Transform and Load) modules.
On average 70 to 80% of effort would be spent on extracting, transforming
and then loading the data. The ETL module needs to meet many stringent
requirements; it needs to support complex transformations, be scalable,
expandable and provide low latency. This is why most often the failure in
data warehouse deployment lays in ETL implementation. The process of
extracting, transforming and loading data can be spec out and develop by our
ETL specialists who understand the ETL challenges. We are familiar with
different tools and languages such as: AbInitio, Oracle Warehouse Builder,
PL/SQL, Informatica, Decision Stream, SAP BW, … To mention a few

Front end development. Creating the front end, maintaining it and
responding to many new requests can be demanding and code intensive.
Many of these tasks can be also spec out and develop by our IT
professionals. We are very capable of creating sophisticated interfaces for
data reporting, OLAP tools, “score cards” or GIS data depictions. Also, we
can be very helpful the front end maintenance and up keep. Our specialists
are familiar with the following tools; MS Analytical Services, Cognos,
Business Objects and Microstrategy
Data mining
Data mining is the process of extracting patterns from data. Data mining is seen as
an increasingly important tool by modern business to transform data into business
intelligence giving an informational advantage. It is currently used in a wide range
of profiling practices, such as marketing, surveillance, fraud detection, and
scientific discovery.
The related terms data dredging, data fishing and data snooping refer to the use of
data mining techniques to sample portions of the larger population data set that are
(or may be) too small for reliable statistical inferences to be made about the
validity of any patterns discovered (see also data-snooping bias). These techniques
can, however, be used in the creation of new hypotheses to test against the larger
data populations.
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Background
The manual extraction of patterns from data has occurred for centuries. Early
methods of identifying patterns in data include Bayes' theorem (1700s) and
regression analysis (1800s). The proliferation, ubiquity and increasing power of
computer technology has increased data collection and storage. As data sets have
grown in size and complexity, direct hands-on data analysis has increasingly been
augmented with indirect, automatic data processing. This has been aided by other
discoveries in computer science, such as neural networks, clustering, genetic
algorithms (1950s), decision trees (1960s) and support vector machines (1980s).
Data mining is the process of applying these methods to data with the intention of
uncovering hidden patterns.[1] It has been used for many years by businesses,
scientists and governments to sift through volumes of data such as airline
passenger trip records, census data and supermarket scanner data to produce
market research reports. (Note, however, that reporting is not always considered to
be data mining.)
A primary reason for using data mining is to assist in the analysis of collections of
observations of behaviour. Such data are vulnerable to collinearity because of
unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s)
of data being analysed may not be representative of the whole domain, and
therefore may not contain examples of certain critical relationships and behaviours
that exist across other parts of the domain. To address this sort of issue, the
analysis may be augmented using experiment-based and other approaches, such as
Choice Modelling for human-generated data. In these situations, inherent
correlations can be either controlled for, or removed altogether, during the
construction of the experimental design.
There have been some efforts to define standards for data mining, for example the
1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0)
and the 2004 Java Data Mining standard (JDM 1.0). These are evolving standards;
later versions of these standards are under development. Independent of these
standardization efforts, freely available open-source software systems like the R
Project, Weka, KNIME, RapidMiner and others have become an informal standard
for defining data-mining processes. Notably, all these systems are able to import
and export models in PMML (Predictive Model Markup Language) which
provides a standard way to represent data mining models so that these can be
shared between different statistical applications. PMML is an XML-based
language developed by the Data Mining Group (DMG), an independent group
composed of many data mining companies. PMML version 4.0 was released in
June 2009.
Research and evolution
In addition to industry driven demand for standards and interoperability,
professional and academic activity have also made considerable contributions to
the evolution and rigour of the methods and models; an article published in a 2008
issue of the International Journal of Information Technology and Decision Making
summarises the results of a literature survey which traces and analyzes this
evolution.
The premier professional body in the field is the Association for Computing
Machinery's Special Interest Group on Knowledge discovery and Data Mining
(SIGKDD Since 1989 they have hosted an annual international conference and
published its proceedings, and since 1999 have published a biannual academic
journal titled "SIGKDD Explorations" Other Computer Science conferences on
data mining include:









DMIN - International Conference on Data Mining;
DMKD - Research Issues on Data Mining and Knowledge Discovery;
ECML-PKDD - European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases;
ICDM - IEEE International Conference on Data Mining;
MLDM - Machine Learning and Data Mining in Pattern Recognition;
SDM - SIAM International Conference on Data Mining
EDM - International Conference on Educational Data Mining
ECDM - European Conference on Data Mining
PAKDD - The annual Pacific-Asia Conference on Knowledge Discovery
and Data Mining
Process
Pre-processing
Before data mining algorithms can be used, a target data set must be assembled. As
data mining can only uncover patterns already present in the data, the target dataset
must be large enough to contain these patterns while remaining concise enough to
be mined in an acceptable timeframe. A common source for data is a datamart or
data warehouse. Pre-process is essential to analyse the multivariate datasets before
clustering or data mining.
The target set is then cleaned. Cleaning removes the observations with noise and
missing data.
The clean data are reduced into feature vectors, one vector per observation. A
feature vector is a summarised version of the raw data observation. For example, a
black and white image of a face which is 100px by 100px would contain 10,000
bits of raw data. This might be turned into a feature vector by locating the eyes and
mouth in the image. Doing so would reduce the data for each vector from 10,000
bits to three codes for the locations, dramatically reducing the size of the dataset to
be mined, and hence reducing the processing effort. The feature(s) selected will
depend on what the objective(s) is/are; obviously, selecting the "right" feature(s) is
fundamental to successful data mining.
The feature vectors are divided into two sets, the "training set" and the "test set".
The training set is used to "train" the data mining algorithm(s), while the test set is
used to verify the accuracy of any patterns found.
Data mining
Data mining commonly involves four classes of tasks:[11]




Clustering - is the task of discovering groups and structures in the data that
are in some way or another "similar", without using known structures in the
data.
Classification - is the task of generalizing known structure to apply to new
data. For example, an email program might attempt to classify an email as
legitimate or spam. Common algorithms include decision tree learning,
nearest neighbor, naive Bayesian classification, neural networks and support
vector machines.
Regression - Attempts to find a function which models the data with the
least error.
Association rule learning - Searches for relationships between variables. For
example a supermarket might gather data on customer purchasing habits.
Using association rule learning, the supermarket can determine which
products are frequently bought together and use this information for
marketing purposes. This is sometimes referred to as market basket analysis.
Results validation
The final step of knowledge discovery from data is to verify the patterns produced
by the data mining algorithms occur in the wider data set. Not all patterns found by
the data mining algorithms are necessarily valid. It is common for the data mining
algorithms to find patterns in the training set which are not present in the general
data set, this is called overfitting. To overcome this, the evaluation uses a test set of
data which the data mining algorithm was not trained on. The learnt patterns are
applied to this test set and the resulting output is compared to the desired output.
For example, a data mining algorithm trying to distinguish spam from legitimate
emails would be trained on a training set of sample emails. Once trained, the learnt
patterns would be applied to the test set of emails which it had not been trained on,
the accuracy of these patterns can then be measured from how many emails they
correctly classify. A number of statistical methods may be used to evaluate the
algorithm such as ROC curves.
If the learnt patterns do not meet the desired standards, then it is necessary to
reevaluate and change the preprocessing and data mining. If the learnt patterns do
meet the desired standards then the final step is to interpret the learnt patterns and
turn them into knowledge.
Notable uses
Games
Since the early 1960s, with the availability of oracles for certain combinatorial
games, also called tablebases (e.g. for 3x3-chess) with any beginning
configuration, small-board dots-and-boxes, small-board-hex, and certain endgames
in chess, dots-and-boxes, and hex; a new area for data mining has been opened up.
This is the extraction of human-usable strategies from these oracles. Current
pattern recognition approaches do not seem to fully have the required high level of
abstraction in order to be applied successfully. Instead, extensive experimentation
with the tablebases, combined with an intensive study of tablebase-answers to well
designed problems and with knowledge of prior art, i.e. pre-tablebase knowledge,
is used to yield insightful patterns. Berlekamp in dots-and-boxes etc. and John
Nunn in chess endgames are notable examples of researchers doing this work,
though they were not and are not involved in tablebase generation.
Business
Data mining in customer relationship management applications can contribute
significantly to the bottom line.[citation needed] Rather than randomly contacting a
prospect or customer through a call center or sending mail, a company can
concentrate its efforts on prospects that are predicted to have a high likelihood of
responding to an offer. More sophisticated methods may be used to optimise
resources across campaigns so that one may predict which channel and which offer
an individual is most likely to respond to — across all potential offers.
Additionally, sophisticated applications could be used to automate the mailing.
Once the results from data mining (potential prospect/customer and channel/offer)
are determined, this "sophisticated application" can either automatically send an email or regular mail. Finally, in cases where many people will take an action
without an offer, uplift modeling can be used to determine which people will have
the greatest increase in responding if given an offer. Data clustering can also be
used to automatically discover the segments or groups within a customer data set.
Businesses employing data mining may see a return on investment, but also they
recognise that the number of predictive models can quickly become very large.
Rather than one model to predict how many customers will churn, a business could
build a separate model for each region and customer type. Then instead of sending
an offer to all people that are likely to churn, it may only want to send offers to
customers. And finally, it may also want to determine which customers are going
to be profitable over a window of time and only send the offers to those that are
likely to be profitable. In order to maintain this quantity of models, they need to
manage model versions and move to automated data mining.
Data mining can also be helpful to human-resources departments in identifying the
characteristics of their most successful employees. Information obtained, such as
universities attended by highly successful employees, can help HR focus recruiting
efforts accordingly. Additionally, Strategic Enterprise Management applications
help a company translate corporate-level goals, such as profit and margin share
targets, into operational decisions, such as production plans and workforce levels.[
Another example of data mining, often called the market basket analysis, relates to
its use in retail sales. If a clothing store records the purchases of customers, a datamining system could identify those customers who favour silk shirts over cotton
ones. Although some explanations of relationships may be difficult, taking
advantage of it is easier. The example deals with association rules within
transaction-based data. Not all data are transaction based and logical or inexact
rules may also be present within a database. In a manufacturing application, an
inexact rule may state that 73% of products which have a specific defect or
problem will develop a secondary problem within the next six months.
Market basket analysis has also been used to identify the purchase patterns of the
Alpha consumer. Alpha Consumers are people that play a key roles in connecting
with the concept behind a product, then adopting that product, and finally
validating it for the rest of society. Analyzing the data collected on these type of
users has allowed companies to predict future buying trends and forecast supply
demands
Data Mining is a highly effective tool in the catalog marketing industryCatalogers
have a rich history of customer transactions on millions of customers dating back
several years. Data mining tools can identify patterns among customers and help
identify the most likely customers to respond to upcoming mailing campaigns.
Related to an integrated-circuit production line, an example of data mining is
described in the paper "Mining IC Test Data to Optimize VLSI Testing."[13] In this
paper the application of data mining and decision analysis to the problem of dielevel functional test is described. Experiments mentioned in this paper demonstrate
the ability of applying a system of mining historical die-test data to create a
probabilistic model of patterns of die failure which are then utilised to decide in
real time which die to test next and when to stop testing. This system has been
shown, based on experiments with historical test data, to have the potential to
improve profits on mature IC products.
Science and engineering
In recent years, data mining has been widely used in area of science and
engineering, such as bioinformatics, genetics, medicine, education and electrical
power engineering.
In the area of study on human genetics, an important goal is to understand the
mapping relationship between the inter-individual variation in human DNA
sequences and variability in disease susceptibility. In lay terms, it is to find out
how the changes in an individual's DNA sequence affect the risk of developing
common diseases such as cancer. This is very important to help improve the
diagnosis, prevention and treatment of the diseases. The data mining technique that
is used to perform this task is known as multifactor dimensionality reduction.
In the area of electrical power engineering, data mining techniques have been
widely used for condition monitoring of high voltage electrical equipment. The
purpose of condition monitoring is to obtain valuable information on the
insulation's health status of the equipment. Data clustering such as self-organizing
map (SOM) has been applied on the vibration monitoring and analysis of
transformer on-load tap-changers(OLTCS). Using vibration monitoring, it can be
observed that each tap change operation generates a signal that contains
information about the condition of the tap changer contacts and the drive
mechanisms. Obviously, different tap positions will generate different signals.
However, there was considerable variability amongst normal condition signals for
exactly the same tap position. SOM has been applied to detect abnormal conditions
and to estimate the nature of the abnormalities.
Data mining techniques have also been applied for dissolved gas analysis (DGA)
on power transformers. DGA, as a diagnostics for power transformer, has been
available for many years. Data mining techniques such as SOM has been applied to
analyse data and to determine trends which are not obvious to the standard DGA
ratio techniques such as Duval Triangle.
A fourth area of application for data mining in science/engineering is within
educational research, where data mining has been used to study the factors leading
students to choose to engage in behaviors which reduce their learning and to
understand the factors influencing university student retentionA similar example of
the social application of data mining is its use in expertise finding systems,
whereby descriptors of human expertise are extracted, normalised and classified so
as to facilitate the finding of experts, particularly in scientific and technical fields.
In this way, data mining can facilitate Institutional memory.
Other examples of applying data mining technique applications are biomedical
data facilitated by domain ontologies mining clinical trial data, traffic analysis
using SOM, et cetera.
In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since
1998, used data mining methods to routinely screen for reporting patterns
indicative of emerging drug safety issues in the WHO global database of 4.6
million suspected adverse drug reaction incidents Recently, similar methodology
has been developed to mine large collections of electronic health records for
temporal patterns associating drug prescriptions to medical diagnoses.
Spatial data mining
Spatial data mining is the application of data mining techniques to spatial data.
Spatial data mining follows along the same functions in data mining, with the end
objective to find patterns in geography. So far, data mining and Geographic
Information Systems (GIS) have existed as two separate technologies, each with its
own methods, traditions and approaches to visualization and data analysis.
Particularly, most contemporary GIS have only very basic spatial analysis
functionality. The immense explosion in geographically referenced data
occasioned by developments in IT, digital mapping, remote sensing, and the global
diffusion of GIS emphasises the importance of developing data driven inductive
approaches to geographical analysis and modeling.
Data mining, which is the partially automated search for hidden patterns in large
databases, offers great potential benefits for applied GIS-based decision-making.
Recently, the task of integrating these two technologies has become critical,
especially as various public and private sector organisations possessing huge
databases with thematic and geographically referenced data begin to realise the
huge potential of the information hidden there. Among those organisations are:




offices requiring analysis or dissemination of geo-referenced statistical data
public health services searching for explanations of disease clusters
environmental agencies assessing the impact of changing land-use patterns
on climate change
geo-marketing companies doing customer segmentation based on spatial
location.
Challenges
Geospatial data repositories tend to be very large. Moreover, existing GIS datasets
are often splintered into feature and attribute components, that are conventionally
archived in hybrid data management systems. Algorithmic requirements differ
substantially for relational (attribute) data management and for topological
(feature) data management Related to this is the range and diversity of geographic
data formats, that also presents unique challenges. The digital geographic data
revolution is creating new types of data formats beyond the traditional "vector" and
"raster" formats. Geographic data repositories increasingly include ill-structured
data such as imagery and geo-referenced multi-media.
There are several critical research challenges in geographic knowledge discovery
and data mining. Miller and Han offer the following list of emerging research
topics in the field:


Developing and supporting geographic data warehouses - Spatial
properties are often reduced to simple aspatial attributes in mainstream data
warehouses. Creating an integrated GDW requires solving issues in spatial
and temporal data interoperability, including differences in semantics,
referencing systems, geometry, accuracy and position.
Better spatio-temporal representations in geographic knowledge
discovery - Current geographic knowledge discovery (GKD) techniques
generally use very simple representations of geographic objects and spatial
relationships. Geographic data mining techniques should recognise more
complex geographic objects (lines and polygons) and relationships (non-

Euclidean distances, direction, connectivity and interaction through
attributed geographic space such as terrain). Time needs to be more fully
integrated into these geographic representations and relationships.
Geographic knowledge discovery using diverse data types - GKD
techniques should be developed that can handle diverse data types beyond
the traditional raster and vector models, including imagery and georeferenced multimedia, as well as dynamic data types (video streams,
animation).
Surveillance
Previous data mining to stop terrorist programs under the U.S. government include
the Total Information Awareness (TIA) program, Secure Flight (formerly known
as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis,
Dissemination, Visualization, Insight, Semantic Enhancement (ADVISE), and the
Multi-state Anti-Terrorism Information Exchange (MATRIX). These programs
have been discontinued due to controversy over whether they violate the US
Constitution's 4th amendment, although many programs that were formed under
them continue to be funded by different organisations, or under different names.
Two plausible data mining techniques in the context of combating terrorism
include "pattern mining" and "subject-based data mining".
Pattern mining
"Pattern mining" is a data mining technique that involves finding existing patterns
in data. In this context patterns often means association rules. The original
motivation for searching association rules came from the desire to analyze
supermarket transaction data, that is, to examine customer behaviour in terms of
the purchased products. For example, an association rule "beer ⇒ crisps (80%)"
states that four out of five customers that bought beer also bought crisps.
In the context of pattern mining as a tool to identify terrorist activity, the National
Research Council provides the following definition: "Pattern-based data mining
looks for patterns (including anomalous data patterns) that might be associated
with terrorist activity — these patterns might be regarded as small signals in a
large ocean of noise Pattern Mining includes new areas such a Music Information
Retrieval (MIR) where patterns seen both in the temporal and non temporal
domains are imported to classical knowledge discovery search techniques.
Subject-based data mining
"Subject-based data mining" is a data mining technique involving the search for
associations between individuals in data. In the context of combatting terrorism,
the National Research Council provides the following definition: "Subject-based
data mining uses an initiating individual or other datum that is considered, based
on other information, to be of high interest, and the goal is to determine what other
persons or financial transactions or movements, etc., are related to that initiating
datum.
Privacy concerns and ethics
Some people believe that data mining itself is ethically neutral. However, the ways
in which data mining can be used can raise questions regarding privacy, legality,
and ethics.[33] In particular, data mining government or commercial data sets for
national security or law enforcement purposes, such as in the Total Information
Awareness Program or in ADVISE, has raised privacy concerns.
Data mining requires data preparation which can uncover information or patterns
which may compromise confidentiality and privacy obligations. A common way
for this to occur is through data aggregation. Data aggregation is when the data are
accrued, possibly from various sources, and put together so that they can be
analyzed.[36] This is not data mining per se, but a result of the preparation of data
before and for the purposes of the analysis. The threat to an individual's privacy
comes into play when the data, once compiled, cause the data miner, or anyone
who has access to the newly compiled data set, to be able to identify specific
individuals, especially when originally the data were anonymous.
It is recommended that an individual is made aware of the following before data
are collected:





the purpose of the data collection and any data mining projects,
how the data will be used,
who will be able to mine the data and use them,
the security surrounding access to the data, and in addition,
how collected data can be updated.[
In the United States, privacy concerns have been somewhat addressed by their
congress via the passage of regulatory controls such as the Health Insurance
Portability and Accountability Act (HIPAA). The HIPAA requires individuals to
be given "informed consent" regarding any information that they provide and its
intended future uses by the facility receiving that information. According to an
article in Biotech Business Week, “In practice, HIPAA may not offer any greater
protection than the longstanding regulations in the research arena, says the AAHC.
More importantly, the rule's goal of protection through informed consent is
undermined by the complexity of consent forms that are required of patients and
participants, which approach a level of incomprehensibility to average
individuals.” This underscores the necessity for data anonymity in data aggregation
practices.
One may additionally modify the data so that they are anonymous, so that
individuals may not be readily identified However, even de-identified data sets can
contain enough information to identify individuals, as occurred when journalists
were able to find several individuals based on a set of search histories that were
inadvertently released by AOL.[
Marketplace surveys
Several researchers and organizations have conducted reviews of data mining tools
and surveys of data miners. These identify some of the strengths and weaknesses
of the software packages. They also provide an overview of the behaviors,
preferences and views of data miners. Some of these reports include:





Forrester Research 2010 Predictive Analytics and Data Mining Solutions
report
Annual Rexer Analytics Data Miner Surveys.
Gartner 2008 "Magic Quadrant" report.[
Robert Nisbet's 2006 Three Part Series of articles "Data Mining Tools:
Which One is Best For CRM?"
Haughton et al.'s 2003 Review of Data Mining Software Packages in The
American Statistician
Groups and associations

SIGKDD, the ACM Special Interest Group on Knowledge Discovery and
Data Mining.
Applications



Data Mining in Agriculture
Surveillance / Mass surveillance
National Security Agency





Quantitative structure-activity relationship
Customer analytics
Police-enforced ANPR in the UK
Stellar wind (code name)
Educational Data Mining
Methods







Association rule learning
Cluster analysis
Structured data analysis (statistics)
Java Data Mining
Data analysis
Predictive analytics
Knowledge discovery
Miscellaneous




Data mining agent
Data warehouse
PMML
Privacy preserving data mining
Data mining is about analysing data; for information about extracting information
out of data, see:





Information extraction
Web scraping
Named entity recognition
Profiling
Profiling practices
Data mining
Data mining is the process of extracting patterns from data. Data mining is seen as
an increasingly important tool by modern business to transform data into business
intelligence giving an informational advantage. It is currently used in a wide range
of profiling practices, such as marketing, surveillance, fraud detection, and
scientific discovery.
The related terms data dredging, data fishing and data snooping refer to the use of
data mining techniques to sample portions of the larger population data set that are
(or may be) too small for reliable statistical inferences to be made about the
validity of any patterns discovered (see also data-snooping bias). These techniques
can, however, be used in the creation of new hypotheses to test against the larger
data populations.

Background
The manual extraction of patterns from data has occurred for centuries. Early
methods of identifying patterns in data include Bayes' theorem (1700s) and
regression analysis (1800s). The proliferation, ubiquity and increasing power of
computer technology has increased data collection and storage. As data sets have
grown in size and complexity, direct hands-on data analysis has increasingly been
augmented with indirect, automatic data processing. This has been aided by other
discoveries in computer science, such as neural networks, clustering, genetic
algorithms (1950s), decision trees (1960s) and support vector machines (1980s).
Data mining is the process of applying these methods to data with the intention of
uncovering hidden patterns.[1] It has been used for many years by businesses,
scientists and governments to sift through volumes of data such as airline
passenger trip records, census data and supermarket scanner data to produce
market research reports. (Note, however, that reporting is not always considered to
be data mining.)
A primary reason for using data mining is to assist in the analysis of collections of
observations of behaviour. Such data are vulnerable to collinearity because of
unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s)
of data being analysed may not be representative of the whole domain, and
therefore may not contain examples of certain critical relationships and behaviours
that exist across other parts of the domain. To address this sort of issue, the
analysis may be augmented using experiment-based and other approaches, such as
Choice Modelling for human-generated data. In these situations, inherent
correlations can be either controlled for, or removed altogether, during the
construction of the experimental design.
There have been some efforts to define standards for data mining, for example the
1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0)
and the 2004 Java Data Mining standard (JDM 1.0). These are evolving standards;
later versions of these standards are under development. Independent of these
standardization efforts, freely available open-source software systems like the R
Project, Weka, KNIME, RapidMiner and others have become an informal standard
for defining data-mining processes. Notably, all these systems are able to import
and export models in PMML (Predictive Model Markup Language) which
provides a standard way to represent data mining models so that these can be
shared between different statistical applications. PMML is an XML-based
language developed by the Data Mining Group (DMG),[ an independent group
composed of many data mining companies. PMML version 4.0 was released in
June 2009.
Research and evolution
In addition to industry driven demand for standards and interoperability,
professional and academic activity have also made considerable contributions to
the evolution and rigour of the methods and models; an article published in a 2008
issue of the International Journal of Information Technology and Decision Making
summarises the results of a literature survey which traces and analyzes this
evolution
The premier professional body in the field is the Association for Computing
Machinery's Special Interest Group on Knowledge discovery and Data Mining
(SIGKDD).[citation needed] Since 1989 they have hosted an annual international
conference and published its proceedings, and since 1999 have published a
biannual academic journal titled "SIGKDD Explorations". Other Computer
Science conferences on data mining include:









DMIN - International Conference on Data Mining;[9]
DMKD - Research Issues on Data Mining and Knowledge Discovery;
ECML-PKDD - European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases;
ICDM - IEEE International Conference on Data Mining;[10]
MLDM - Machine Learning and Data Mining in Pattern Recognition;
SDM - SIAM International Conference on Data Mining
EDM - International Conference on Educational Data Mining
ECDM - European Conference on Data Mining
PAKDD - The annual Pacific-Asia Conference on Knowledge Discovery
and Data Mining
Process
Pre-processing
Before data mining algorithms can be used, a target data set must be assembled. As
data mining can only uncover patterns already present in the data, the target dataset
must be large enough to contain these patterns while remaining concise enough to
be mined in an acceptable timeframe. A common source for data is a datamart or
data warehouse. Pre-process is essential to analyse the multivariate datasets before
clustering or data mining.
The target set is then cleaned. Cleaning removes the observations with noise and
missing data.
The clean data are reduced into feature vectors, one vector per observation. A
feature vector is a summarised version of the raw data observation. For example, a
black and white image of a face which is 100px by 100px would contain 10,000
bits of raw data. This might be turned into a feature vector by locating the eyes and
mouth in the image. Doing so would reduce the data for each vector from 10,000
bits to three codes for the locations, dramatically reducing the size of the dataset to
be mined, and hence reducing the processing effort. The feature(s) selected will
depend on what the objective(s) is/are; obviously, selecting the "right" feature(s) is
fundamental to successful data mining.
The feature vectors are divided into two sets, the "training set" and the "test set".
The training set is used to "train" the data mining algorithm(s), while the test set is
used to verify the accuracy of any patterns found.
Data mining
Data mining commonly involves four classes of tasks:[


Clustering - is the task of discovering groups and structures in the data that
are in some way or another "similar", without using known structures in the
data.
Classification - is the task of generalizing known structure to apply to new
data. For example, an email program might attempt to classify an email as
legitimate or spam. Common algorithms include decision tree learning,
nearest neighbor, naive Bayesian classification, neural networks and support
vector machines.


Regression - Attempts to find a function which models the data with the
least error.
Association rule learning - Searches for relationships between variables. For
example a supermarket might gather data on customer purchasing habits.
Using association rule learning, the supermarket can determine which
products are frequently bought together and use this information for
marketing purposes. This is sometimes referred to as market basket analysis.
Results validation
The final step of knowledge discovery from data is to verify the patterns produced
by the data mining algorithms occur in the wider data set. Not all patterns found by
the data mining algorithms are necessarily valid. It is common for the data mining
algorithms to find patterns in the training set which are not present in the general
data set, this is called overfitting. To overcome this, the evaluation uses a test set of
data which the data mining algorithm was not trained on. The learnt patterns are
applied to this test set and the resulting output is compared to the desired output.
For example, a data mining algorithm trying to distinguish spam from legitimate
emails would be trained on a training set of sample emails. Once trained, the learnt
patterns would be applied to the test set of emails which it had not been trained on,
the accuracy of these patterns can then be measured from how many emails they
correctly classify. A number of statistical methods may be used to evaluate the
algorithm such as ROC curves.
If the learnt patterns do not meet the desired standards, then it is necessary to
reevaluate and change the preprocessing and data mining. If the learnt patterns do
meet the desired standards then the final step is to interpret the learnt patterns and
turn them into knowledge.
Notable uses
Games
Since the early 1960s, with the availability of oracles for certain combinatorial
games, also called tablebases (e.g. for 3x3-chess) with any beginning
configuration, small-board dots-and-boxes, small-board-hex, and certain endgames
in chess, dots-and-boxes, and hex; a new area for data mining has been opened up.
This is the extraction of human-usable strategies from these oracles. Current
pattern recognition approaches do not seem to fully have the required high level of
abstraction in order to be applied successfully. Instead, extensive experimentation
with the tablebases, combined with an intensive study of tablebase-answers to well
designed problems and with knowledge of prior art, i.e. pre-tablebase knowledge,
is used to yield insightful patterns. Berlekamp in dots-and-boxes etc. and John
Nunn in chess endgames are notable examples of researchers doing this work,
though they were not and are not involved in tablebase generation.
Business
Data mining in customer relationship management applications can contribute
significantly to the bottom lineRather than randomly contacting a prospect or
customer through a call center or sending mail, a company can concentrate its
efforts on prospects that are predicted to have a high likelihood of responding to an
offer. More sophisticated methods may be used to optimise resources across
campaigns so that one may predict which channel and which offer an individual is
most likely to respond to — across all potential offers. Additionally, sophisticated
applications could be used to automate the mailing. Once the results from data
mining (potential prospect/customer and channel/offer) are determined, this
"sophisticated application" can either automatically send an e-mail or regular mail.
Finally, in cases where many people will take an action without an offer, uplift
modeling can be used to determine which people will have the greatest increase in
responding if given an offer. Data clustering can also be used to automatically
discover the segments or groups within a customer data set.
Businesses employing data mining may see a return on investment, but also they
recognise that the number of predictive models can quickly become very large.
Rather than one model to predict how many customers will churn, a business could
build a separate model for each region and customer type. Then instead of sending
an offer to all people that are likely to churn, it may only want to send offers to
customers. And finally, it may also want to determine which customers are going
to be profitable over a window of time and only send the offers to those that are
likely to be profitable. In order to maintain this quantity of models, they need to
manage model versions and move to automated data mining.
Data mining can also be helpful to human-resources departments in identifying the
characteristics of their most successful employees. Information obtained, such as
universities attended by highly successful employees, can help HR focus recruiting
efforts accordingly. Additionally, Strategic Enterprise Management applications
help a company translate corporate-level goals, such as profit and margin share
targets, into operational decisions, such as production plans and workforce levels.
Another example of data mining, often called the market basket analysis, relates to
its use in retail sales. If a clothing store records the purchases of customers, a datamining system could identify those customers who favour silk shirts over cotton
ones. Although some explanations of relationships may be difficult, taking
advantage of it is easier. The example deals with association rules within
transaction-based data. Not all data are transaction based and logical or inexact
rules may also be present within a database. In a manufacturing application, an
inexact rule may state that 73% of products which have a specific defect or
problem will develop a secondary problem within the next six months.
Market basket analysis has also been used to identify the purchase patterns of the
Alpha consumer. Alpha Consumers are people that play a key roles in connecting
with the concept behind a product, then adopting that product, and finally
validating it for the rest of society. Analyzing the data collected on these type of
users has allowed companies to predict future buying trends and forecast supply
demands[
Data Mining is a highly effective tool in the catalog marketing industry[Catalogers
have a rich history of customer transactions on millions of customers dating back
several years. Data mining tools can identify patterns among customers and help
identify the most likely customers to respond to upcoming mailing campaigns.
Related to an integrated-circuit production line, an example of data mining is
described in the paper "Mining IC Test Data to Optimize VLSI Testing." In this
paper the application of data mining and decision analysis to the problem of dielevel functional test is described. Experiments mentioned in this paper demonstrate
the ability of applying a system of mining historical die-test data to create a
probabilistic model of patterns of die failure which are then utilised to decide in
real time which die to test next and when to stop testing. This system has been
shown, based on experiments with historical test data, to have the potential to
improve profits on mature IC products.
Science and engineering
In recent years, data mining has been widely used in area of science and
engineering, such as bioinformatics, genetics, medicine, education and electrical
power engineering.
In the area of study on human genetics, an important goal is to understand the
mapping relationship between the inter-individual variation in human DNA
sequences and variability in disease susceptibility. In lay terms, it is to find out
how the changes in an individual's DNA sequence affect the risk of developing
common diseases such as cancer. This is very important to help improve the
diagnosis, prevention and treatment of the diseases. The data mining technique that
is used to perform this task is known as multifactor dimensionality reduction.
In the area of electrical power engineering, data mining techniques have been
widely used for condition monitoring of high voltage electrical equipment. The
purpose of condition monitoring is to obtain valuable information on the
insulation's health status of the equipment. Data clustering such as self-organizing
map (SOM) has been applied on the vibration monitoring and analysis of
transformer on-load tap-changers(OLTCS). Using vibration monitoring, it can be
observed that each tap change operation generates a signal that contains
information about the condition of the tap changer contacts and the drive
mechanisms. Obviously, different tap positions will generate different signals.
However, there was considerable variability amongst normal condition signals for
exactly the same tap position. SOM has been applied to detect abnormal conditions
and to estimate the nature of the abnormalities.
Data mining techniques have also been applied for dissolved gas analysis (DGA)
on power transformers. DGA, as a diagnostics for power transformer, has been
available for many years. Data mining techniques such as SOM has been applied to
analyse data and to determine trends which are not obvious to the standard DGA
ratio techniques such as Duval Triangle.
A fourth area of application for data mining in science/engineering is within
educational research, where data mining has been used to study the factors leading
students to choose to engage in behaviors which reduce their learningand to
understand the factors influencing university student retention. A similar example
of the social application of data mining is its use in expertise finding systems,
whereby descriptors of human expertise are extracted, normalised and classified so
as to facilitate the finding of experts, particularly in scientific and technical fields.
In this way, data mining can facilitate Institutional memory.
Other examples of applying data mining technique applications are biomedical
data facilitated by domain ontologies mining clinical trial data,[ traffic analysis
using SOM, et cetera.
In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since
1998, used data mining methods to routinely screen for reporting patterns
indicative of emerging drug safety issues in the WHO global database of 4.6
million suspected adverse drug reaction incidents. Recently, similar methodology
has been developed to mine large collections of electronic health records for
temporal patterns associating drug prescriptions to medical diagnoses.[22]
Spatial data mining
Spatial data mining is the application of data mining techniques to spatial data.
Spatial data mining follows along the same functions in data mining, with the end
objective to find patterns in geography. So far, data mining and Geographic
Information Systems (GIS) have existed as two separate technologies, each with its
own methods, traditions and approaches to visualization and data analysis.
Particularly, most contemporary GIS have only very basic spatial analysis
functionality. The immense explosion in geographically referenced data
occasioned by developments in IT, digital mapping, remote sensing, and the global
diffusion of GIS emphasises the importance of developing data driven inductive
approaches to geographical analysis and modeling.
Data mining, which is the partially automated search for hidden patterns in large
databases, offers great potential benefits for applied GIS-based decision-making.
Recently, the task of integrating these two technologies has become critical,
especially as various public and private sector organisations possessing huge
databases with thematic and geographically referenced data begin to realise the
huge potential of the information hidden there. Among those organisations are:




offices requiring analysis or dissemination of geo-referenced statistical data
public health services searching for explanations of disease clusters
environmental agencies assessing the impact of changing land-use patterns
on climate change
geo-marketing companies doing customer segmentation based on spatial
location.
Challenges
Geospatial data repositories tend to be very large. Moreover, existing GIS datasets
are often splintered into feature and attribute components, that are conventionally
archived in hybrid data management systems. Algorithmic requirements differ
substantially for relational (attribute) data management and for topological
(feature) data management Related to this is the range and diversity of geographic
data formats, that also presents unique challenges. The digital geographic data
revolution is creating new types of data formats beyond the traditional "vector" and
"raster" formats. Geographic data repositories increasingly include ill-structured
data such as imagery and geo-referenced multi-media.
There are several critical research challenges in geographic knowledge discovery
and data mining. Miller and Han offer the following list of emerging research
topics in the field:



Developing and supporting geographic data warehouses - Spatial
properties are often reduced to simple aspatial attributes in mainstream data
warehouses. Creating an integrated GDW requires solving issues in spatial
and temporal data interoperability, including differences in semantics,
referencing systems, geometry, accuracy and position.
Better spatio-temporal representations in geographic knowledge
discovery - Current geographic knowledge discovery (GKD) techniques
generally use very simple representations of geographic objects and spatial
relationships. Geographic data mining techniques should recognise more
complex geographic objects (lines and polygons) and relationships (nonEuclidean distances, direction, connectivity and interaction through
attributed geographic space such as terrain). Time needs to be more fully
integrated into these geographic representations and relationships.
Geographic knowledge discovery using diverse data types - GKD
techniques should be developed that can handle diverse data types beyond
the traditional raster and vector models, including imagery and georeferenced multimedia, as well as dynamic data types (video streams,
animation).
Surveillance
Previous data mining to stop terrorist programs under the U.S. government include
the Total Information Awareness (TIA) program, Secure Flight (formerly known
as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis,
Dissemination, Visualization, Insight, Semantic Enhancement (ADVISEand the
Multi-state Anti-Terrorism Information Exchange (MATRIX). These programs
have been discontinued due to controversy over whether they violate the US
Constitution's 4th amendment, although many programs that were formed under
them continue to be funded by different organisations, or under different names.
Two plausible data mining techniques in the context of combating terrorism
include "pattern mining" and "subject-based data mining".
Pattern mining
"Pattern mining" is a data mining technique that involves finding existing patterns
in data. In this context patterns often means association rules. The original
motivation for searching association rules came from the desire to analyze
supermarket transaction data, that is, to examine customer behaviour in terms of
the purchased products. For example, an association rule "beer ⇒ crisps (80%)"
states that four out of five customers that bought beer also bought crisps.
In the context of pattern mining as a tool to identify terrorist activity, the National
Research Council provides the following definition: "Pattern-based data mining
looks for patterns (including anomalous data patterns) that might be associated
with terrorist activity — these patterns might be regarded as small signals in a
large ocean of noise." Pattern Mining includes new areas such a Music Information
Retrieval (MIR) where patterns seen both in the temporal and non temporal
domains are imported to classical knowledge discovery search techniques.
Subject-based data mining
"Subject-based data mining" is a data mining technique involving the search for
associations between individuals in data. In the context of combatting terrorism,
the National Research Council provides the following definition: "Subject-based
data mining uses an initiating individual or other datum that is considered, based
on other information, to be of high interest, and the goal is to determine what other
persons or financial transactions or movements, etc., are related to that initiating
datum."
Privacy concerns and ethics
Some people believe that data mining itself is ethically neutral. However, the ways
in which data mining can be used can raise questions regarding privacy, legality,
and ethics.[33] In particular, data mining government or commercial data sets for
national security or law enforcement purposes, such as in the Total Information
Awareness Program or in ADVISE, has raised privacy concerns.
Data mining requires data preparation which can uncover information or patterns
which may compromise confidentiality and privacy obligations. A common way
for this to occur is through data aggregation. Data aggregation is when the data are
accrued, possibly from various sources, and put together so that they can be
analyzed. This is not data mining per se, but a result of the preparation of data
before and for the purposes of the analysis. The threat to an individual's privacy
comes into play when the data, once compiled, cause the data miner, or anyone
who has access to the newly compiled data set, to be able to identify specific
individuals, especially when originally the data were anonymous.
It is recommended that an individual is made aware of the following before data
are collected:





the purpose of the data collection and any data mining projects,
how the data will be used,
who will be able to mine the data and use them,
the security surrounding access to the data, and in addition,
how collected data can be updated.
In the United States, privacy concerns have been somewhat addressed by their
congress via the passage of regulatory controls such as the Health Insurance
Portability and Accountability Act (HIPAA). The HIPAA requires individuals to
be given "informed consent" regarding any information that they provide and its
intended future uses by the facility receiving that information. According to an
article in Biotech Business Week, “In practice, HIPAA may not offer any greater
protection than the longstanding regulations in the research arena, says the AAHC.
More importantly, the rule's goal of protection through informed consent is
undermined by the complexity of consent forms that are required of patients and
participants, which approach a level of incomprehensibility to average
individuals.” This underscores the necessity for data anonymity in data aggregation
practices.
One may additionally modify the data so that they are anonymous, so that
individuals may not be readily identified.[36] However, even de-identified data sets
can contain enough information to identify individuals, as occurred when
journalists were able to find several individuals based on a set of search histories
that were inadvertently released by AOL.
Marketplace surveys
Several researchers and organizations have conducted reviews of data mining tools
and surveys of data miners. These identify some of the strengths and weaknesses
of the software packages. They also provide an overview of the behaviors,
preferences and views of data miners.
Some of these reports include:





Forrester Research 2010 Predictive Analytics and Data Mining Solutions
report.
Annual Rexer Analytics Data Miner Surveys.
Gartner 2008 "Magic Quadrant" report.
Robert Nisbet's 2006 Three Part Series of articles "Data Mining Tools:
Which One is Best For CRM?"
Haughton et al.'s 2003 Review of Data Mining Software Packages in The
American Statistician.
CRM Technology Benefits
An effective Customer Relationship Management system incorporates
software and database applications to identify and respond to customer needs.
CRM software can automate and facilitate the functions of an organization's
sales force, marketing team and customer service specialists, allowing them to
best anticipate and meet the needs of their customers. The goal of any CRM
system is to personalize customer transactions and foster high levels of
customersatisfactionandretention
Data Consolidation & Analysis
The backbone of every CRM system is the customer database. Data must be
consolidated into one database to allow sales and customer service professionals
immediate access to comprehensive data. Applications offer specific functions that
refine the raw data into meaningful information that provides sales and customer
service professionals with the information they need to personalize customer
transactions. Any raw data collected during these transactions can be incorporated
into the existing customer database.
Customer Identification & Retention
CRM technology can utilize data gathered from the Internet to identify potential
customers and customize the company's offerings to their particular needs. Data
from search engines, surveys and e-commerce is analyzed to determine what
products and services are most appropriate for individual customers. Providing
personalized sales offerings facilitates making the initial sale as well as
engendering loyalty from existing customers.
Cross-Selling Opportunities
CRM technology gives support personnel the opportunity to contribute to company
sales. The delivery of pertinent information to customer support personnel enables
them not only to more effectively resolve customer issues, but also to offer
personally tailored product offerings during the service transaction. Cross-selling
capabilities can also be incorporated into automated devices and websites,
providing additional avenues of profit during those transactions.
Portability
CRM technology can distribute customer information to portable devices such as
PDAs to give sales and customer service specialists a great deal of mobility. When
they're not tied to a physical call center, service personnel can resolve customer
issues on site while using the information provided by CRM technology to identify
product offerings specifically tailored to that particular customer. Sales
professionals can similarly suggest additional services or product bundles while at
a customer's location based upon the customer's previous purchases and inquiries.
Improved Forecasting
The consolidation and steady acquisition of customer data that CRM technology
provides offers organizations valuable forecasting information. Analysis of
customer data can identify both short- and long-term trends in customer activity
that can be used to generate custom product and service offerings. The more
accurate forecasting that is enabled by CRM technology's data collection and
analysis functions improves an organization's decision-making capabilities by
removingaconsiderableamountofguesswork.
Despite the success of the Internet, direct mail continues to be the absolutely
best way for many companies to acquire new customers. During the past two
decades, mailers have developed very efficient and profitable direct mail
methods. There are thousands of lists for rent, many of which contain
households that will respond to promotions for credit cards, insurance,
publications, and scores of products and services. Intense competition has
reduced the price of rental names to make direct mail highly profitable with the
right offer to the right audience. Most people think that after twenty years of
direct mail experience we have tried everything, but they are wrong.
What I am going to outline in this article will astonish some long time direct
mail practitioners. It represents a new direction that breaks the mold of
traditional merge purge mailing. The technique is the creation of a prospect
database.
How large mailings are done today
In traditional direct mail, a mailer may rent names from 300 or more different
lists. He has a service bureau conduct a merge purge to come up with a clean
unduplicated mailing file. In selecting names, he would like to use criteria like
age and income, but usually is limited to selecting names by what list they are
on.
Traditional prospect mailings are typically stand-alone events. They require:



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Campaigns based on lists, rather than on households or individuals
Detailed back-end analysis of responders.
Measurement of the ROI of each campaign.
Erasure of prospect names after each campaign. No analysis of nonresponders is possible.
How a prospect database works
Once the names are in a prospect database, he can afford to append data to
each household. He will know the income, age, family composition, house
type, lifestyle, length of residence, and mail responsiveness. He may be able to
know how much they spend in his category. He may have credit data. He will
know a lot more about each prospect than he ever can discover with traditional
merge purge, which is usually limited to which lists their name appears on.
Each name in the prospect database has previous promotion history appended
to it. It may have channel history -- did this person buy from someone over the
web? The data can be updated frequently from appended data, from hot line
data, from NCOA and from other sources.
In traditional merge purge, the mailer never gets his hands on the actual data.
With a prospect database the mailer accesses his prospects over the web using
software like Brio or E.piphany. With this rich data on the prospects available
on his desktop, he can develop acquisition models, which are impossible with
traditional merge purge. With a prospect database, it is possible to determine
how many times a particular person has been mailed in the past, and to develop
a strategy for mailing that person in the future. This is almost impossible with
monthly rented lists. Some marketers can develop matrices like this:
Probability to Purchase Based on Behavior
High
High
Medium Low
Priority Priority Priority
A
B
C
Probability
Based
on
Priority Priority Priority
Demographics Medium B
B
C
Low
Priority Priority Priority
C
C
C
Every name can be assigned a priority, based on previous purchase behavior
and based on demographics. Here for the first time is a really scientific and
profitable way of categorizing prospects before they are mailed. Is your
method working? You can tell this by the results of your promotion. Your
models get better and better. Your response rates and sales get better and
better. Your costs go down.
A prospect database permits:




Better selectivity and segmentation based on better information
Ability to manage complex campaigns
Identifying "offer addicts" who are not qualified, but respond
anyway
Faster time to market. Since the merge purge is eliminated, a
hot line name can be mailed in a week or two instead of four or
five weeks.

Management of names by contact frequency
 Ongoing use of modeling and analytics
 Significant cost efficiencies
A prospect database is particularly useful in situations in which the responder
fills out an application, which has to be reviewed and approved. In many cases,
the merge purge process does not catch these applications, and the prospect is
mailed another time while his application is under review. This is not only
costly, it may complicate the application review process, particularly if the
second mailing contains a different offer. With a prospect database, all
responses are posted to the database immediately. The prospect does not get
mailed a second time.
A case study
A major mailer has 35 campaigns per year selling six related products. The
mailer asked KnowledgeBase Marketing to create a prospect database that
included the results from their customer-marketing database, which his
marketing staff could access on line.
The resulting prospect database covered roughly 90% of the 277 million
individuals in the U.S. and was built based on these three core strategies.
They:
1. Used advanced hygiene and processing capabilities to dramatically
reduce duplicates and wasted mail
2. Rethought their list acquisition strategy
3. Implemented an innovative approach to modeling to improve the results
The modeling program included response and performance models, which
predicted conversion to sales, not just response. They focused on what really
mattered. The model increased not only the ROI from each campaign, also
the Lifetime Value resulting from the prospect database.
After a year of operation, list rental costs had dropped by more than 70%,
merge purge costs decreased and response rates to each campaign increased
significantly. There was a major favorable impact on the bottom line.
Conclusion
Building a prospect database will work for you if:







You’re marketing to consumers (rather than B2B)
You’re a high-volume mailer
Mail drives your business
You use a wide variety of lists
You manage multiple campaigns each year
You aren’t locked into long-term, unbreakable list
deals
You’re willing to negotiate with list managers and
owners
The Indian CRM Market
This report is an Executive Survey of about 70 CRM Consultants, Users, and
Vendors involved with CRM practices and technology in India. The report is
restricted to the Indian market, and the purpose is to give an overview of the
market before investigating different aspects in further depth in subsequent
research programs.
1. CRM Market Size
QuickGlance:
• The Indian CRM market can be sized at Rs. 50-100 Crores (1 Crore=10 million)
• The CRM market can be segmented into the market for software and services
• The services segment includes outsourced CRM services, integration, training,
and
consultancy
• The market for CRM services is considerably larger than the market for CRM
software
Fig. 1. Sizing the Indian CRM Market
Observations and Inferences
 A clear majority of our respondents size the Indian CRM Market at the Rs.
50-100 Crore range but with 33% of our respondents putting the market at a
size greater than Rs. 100 Crore; there could be a higher benchmark for the
market size applicable than the Rs. 100 Crore mark.
 Our findings are in agreement with the figure most published in the media
stated by Denis Collart, the global head of PWC’s CRM practice who, in an
interview in November 2000, stated that the Indian Market for CRM
Software and Services would grow to about Rs. 100 Crore by 2001.
Fig. 2. CRM Market Segments
The market segments for CRM can be broadly out as the Software, Services, and
Hardware market. Our study has been restricted to the Software and Services
markets.
Fig. 3. Breakup of the Global CRM Market
This chart gives the breakup of the Global CRM Software and Services market.
The projected revenues for each of the segments for the year 2001 from past
research have been used to arrive at the relative percentages. This breakup is
merely indicative, as the revenue projections have been taken from more than one
source.
Observations and Inferences
 The breakup between revenues from various segments in the India n context
is not expected to vary from global market to a significant degree. With this
assumption, the size of the market for CRM implementations (including
Software, Integration, Consulting and Training) in India lies in the 40-60
Crore range.
 Given the small market, a local vendor looking for business is going to find
himself up against tough competition. Majority of the CRM solution
providers in India do not have a product but act as consultants and
integrators for software like Siebel, Oracle, SAP etc. providing consulting,
software deployment and integration, and training.
 Outsourced CRM Services has the maximum potential for growth, but the
number of players entering this market is growing at a significant rate.
Telemarketing Firms, Direct Marketing Firms, Data Collection firms,
Market Research firms, and even Advertising Agencies have begun to add
the CRM tag to their services. With the Call Center market finding the
international market tough going, they are increasingly turning to the
domestic market to supplement revenues.
2. Market Prospects
Quick
Glance:
• Indian firms are aware of CRM, but are yet to take concrete steps towards
implementation
• The market is expected to catch on, but slower than anticipated
• The overall sentiment is ‘wait-and-watch’
The next two charts indicates what our respondents feel is the stage of evolution of
the Indian CRM market and what they feel are the market prospects.
Fig. 4. Stage of evolution of the Indian Market
Fig. 5. CRM Market Prospects
Observations and Inferences
 While there has been a great deal of attention on CRM technology and
practices in recent times, when it comes to putting it in practice, the market
is in a very early stage of evolution. Most respondents felt that the Indian
firms were either unaware, or unconvinced about the benefits and
applicability of CRM.
 The overall sentiment when it comes to growth prospects is upbeat in the
sense that people are convinced that it shall take off, albeit slower than
anticipated. Signals for Solution and Service providers are that they are
going to have to stick through this early stage till the market matures in
terms of awareness and acceptance, and the number of implementations
increase.
 Media reports have put the annual growth rate for the CRM Software market
in India at 25-30%, and Services market at about 50-60%. Our respondents
however feel the going shall be slower than projected.
3. Market Drivers and Inhibitors
Quick
Glance:
• The need for improved customer service and high global adoption shall drive the
Indian
CRM
market
• The high cost of implementation and low awareness of benefits is going to prove
a major deterrent
The next two charts indicate the factors our respondents feel will drive acceptance
of CRM in India, and the factors that will hold back acceptance.
Fig. 7. Market Inhibitors
Observations and Inferences
 A need for improved Customer Service shall be the main driver for Industry
sectors that depend on the quality of their customer interactions to retain
existing customers and win new ones. High Global adoption is likely to
drive the MNCs to adopt CRM first in line with Global implementations.
 While the first hurdle holding back the market is a lack of awareness,
respondents have put high cost of implementation as the main inhibitor.
Complete and comprehensive CRM packages such as those of Siebel and
Oracle costing in the range of Rs. 1 to 2.5 Crores (and more) are too
expensive for most Indian firms. However, with software vendors bringing
down prices and offering relatively affordable packages bundled with
integration and consulting services, this could soon change.
 In the Indian context, lack of customer orientation and poor existing IT
infrastructure can prove major factors. Firms need to evolve their customer
thinking by a significant extent before they accept CRM as the strategic
imperative it is, and internal systems and database management practices
need to be upgraded before CRM software can be used to any effect.
 Another major inhibitor indicated by respondents was that Indian firms lack
the skills and strategic vision required to successfully implement CRM.
4. Buyer Sectors and Vendor Recall
Quick
Glance:
• Banking, Insurance, and Financial Services are the sectors that shall benefit most
from
CRM
practices
and
technology
• Siebel emerges as the most top-of-mind CRM package, followed by Oracle and
Talisma
Fig. 8. Best-fit sectors for CRM practices and packages
Fig. 9. Top-of-mind CRM Packages
Observations and Inferences
 Our respondents voted overwhelmingly in favor of the Financial Services
sector as the best fit sector for CRM. Recent implementations in the banking
and financial services sector, especially those of ICICI and Citibank, have
clearly grabbed attention.
 The best-fit sectors as expressed by our respondents gives an indication as to
how closely CRM is associated with improvement in customer service.
 Siebel is the global leader when it comes to CRM software and has clearly
grabbed mindshare in the Indian market as well. While 77% of the
respondents mentioned Siebel as a known CRM vendor, Siebel was the first
CRM package that came to mind for 64% of the respondents.
 SAP and Oracle have recently entered the Indian market with aggressive
plans targeting the SME market in particular. Both firms are targeting a
growth in the market for their products of about 30%.
5. Respondent Profile
Respondent Profile
Fig. 11. Respondent Involvement with CRM
Total respondents: 71