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E-CRM
E CRM is the process of maximising sales to the existing customer, encouraging
continuous relationships through the use of digital communications
technologies such as operational databases, personalized web messages,
Customer Service, Email and Social Media Marketing.
E –CRM : A changing perspective
The business goals of CRM have changed little over the past 10 years. While
many of the business goals of CRM have seen little change over the last 10
years, today most CRM programs, applications, and services depend more
heavily on IT than in the past. These programs, software applications, and
services constitute part of what is known as electronic CRM (e-CRM).
Electronic CRM (e-CRM) is the electronically delivered or managed subset of
CRM. It arises from the consolidation of traditional CRM with the e-business
applications marketplace and covers the broad range of information
technologies used to support a company’s CRM strategy.
Features of E –CRM
 eCRM implies capabilities like self service knowledge bases, automated
email response, personalization of web content, online product bundling
and pricing.
 eCRM gives Internet users the ability to interact with the business
through their preferred communication channel.
 It also allows business to offset expensive customer service agents with
technology.
 eCRM puts much emphasis on the customer satisfaction and reduced
cost through improved efficiency.
 eCRM use customer data for personalization, cross-selling and upselling.
 Sales Force Automation(SFA )and Enterprise Marketing
Automation(EMA) is integrated in the eCRM.
Advantages of e-CRM
 Monitoring, analysing and responding to conversations through social
listening tools.
 Find the best ways to get involved, influence sales and generate leads by
understanding social platforms.
 Develop customer relationship tools/self-help/service and support.
 Using CRM based conversations to enhance online offers.
 Supporting collaboration within the organisation through e-Business
processes.
 Enhance customer experience and add value to the business.
 Reducing costs in customer targeting (customising emails on large scale
therefore reducing costs for direct mail).
 Increasing the meaning of information.
 Better and relevant understanding of customers and relationship
dynamics.
 Encouraging customer relationship/customer development and
retention through loyalty programmes.
 Customer life-cycle management (CLM) encompasses a number of major
management activities. Different authorities have different views of the
specific activities falling under the CLM rubric. For many the cycle
includes:
◗ Acquire customers. Involves the processes of identifying and obtaining new
customers. Lead generation, sales promotions, marketing campaigns, and
customer registration are some of the services associated with customer
acquisition.
◗ Retain customers. Involves the processes of caring for customers and
maintaining long-term relationships. Technical support, loyalty marketing,
customer satisfaction assurance, and collections and billing are some of the
services associated with customer retention.
◗ Growth. Involves the processes of enhancing the value of customers to the
business. New product launches, account management, loyalty marketing,
affinity programs, and up-sells and cross-sells are services associated with
customer growth and development.
An e-CRM system is to be accessible to customers including company users and
administrators from all access support points, round-the-clock on a
fundamental to e-CRM, the system must also have 24/7 basis.
E-CRM in Business
In business, e-CRM is used in –
 Customer-facing applications. These include all the areas where
customers interact with the company: call centres, including help desks;
sales force automation; and field service automation. Such CRM
applications basically automate information flow or support employees
in sales or service. It allows customers to communicate and interact with
a company in whatever way they choose—voice, fax, e-mail, and Web
interactivity. Autoresponders, also called infobots are e-mail on
demand. Sales force automation (SFA) applications support the selling
efforts of a company’s sales force, helping salespeople manage leads,
prospects, and customers through the sales pipeline. Field service
automation applications support the customer service efforts of field
service reps and service managers. These applications manage customer
service requests, service orders, service contracts, service schedules, and
service calls.
 Customer-touching applications. In this category, customers interact
directly with the applications. Notable are self-service activities, such as
FAQs; campaign management; and general-purpose EC applications.
Many companies provide customers with tools to create their own
individual Web pages (e.g., MyYahoo!). Not only can a customer pull
information from the vendor’s site, but the vendor can also push
information to the consumer. In addition, these Web pages can record
customer purchases and preferences. Typical personalized Web pages
include those for bank accounts, stock portfolio accounts, credit card
accounts, and so on. On such sites, users can see their balances, records
of all current and historical transactions, and more. American Airlines is
an example of one company that uses personalized Web sites to help
increase the bottom line. The Web environment provides an opportunity
for customers to serve themselves known as Web self-service. Probably
the best known and most frequently used Web self-service systems are
the package tracking systems provided by FedEx and UPS. Self-tracking
refers to systems, like that of FedEx, where customers can find the
status of an order or service in real. Most Web sites provide a
“frequently asked questions” (FAQ) page. An FAQ page lists questions
that are frequently asked by customers along with the answers to those
questions.
 Customer-centric intelligence applications. These are applications that
analyse the results of operational processing and use the results of the
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analysis to improve CRM applications. Reporting, data warehousing, and
data mining are the prime topics here. Reporting - Reports can range
from simple lists or charts of data and information to more complex
analyses of CRM performance metrics. Usually, reports come in one of
two forms—standardized (predefined) or query-based (ad hoc). Online
analytical processing (OLAP) - Medium and large corporations often
organize and store data in a central repository called a data warehouse.
A data warehouse has a specialized (star schema or hierarchical)
structure that makes it easy to view and analyze measures from a variety
of dimensional perspectives (e.g., comparing sales data for different
products sold at different stores at different times). Data mining - Data
mining is another analytic activity that involves sifting through an
immense amount of data to discover hidden patterns.
Online networking applications. Online networking refers to methods
that provide the opportunity to build personal relationships with a wide
range of people. These include chat rooms, blogs, wikis, and discussion
lists. Representative online networking tools and methods include the
following:
Forums. Available from Internet portals, forums offer users the
opportunity to participate in discussions as well as to lead forums on
“niche” topics (see Badjatia 2009 for discussion of the role of forums in
online CRMs).
Chat rooms. Found on a variety of Web sites, they offer one-to-one or
many-to many real-time conversations.
Usenet groups. These are collections of online discussions grouped into
communities. Usenet groups existed well before the advent of the Web.
Blogs and wikis. Blogs and wikis are becoming a major online
networking tool. Blogs enable companies to approach focused segments
of customers.
Technologies of e-CRM
Voice Portal - A voice portal (sometimes called a vortal) is a Web portal that
can be accessed entirely by voice. Ideally, any type of information, service,
or transaction found on the Internet could be accessed through a voice
portal. mobile user with a cellular telephone might dial in to a voice portal
Web site and request information using voice or Touchtone keys and
receive the requested information from a special voice-producing program
at the Web site. Voice portal interaction may involve audible speech,
speech recognition or a telephone keypad interface. A consumer voice
portal provides general access to information; an enterprise voice portal
provides customized access to customer support. Consumer voice portals
appeared in the late 1990s. Tellme Networks and Quack.com were among
the first providers. Live-agent portals are introducing greater automation
through speech recognition and text-to-speech technology. Enterprise voice
portals manage inbound and outbound voice traffic and agent controls to
manage calls within the enterprise.
Web phones - WebPhone is a Voice over Internet Protocol (VoIP) service.
WEB PHONE: A cellphone with Web access. Most cellphones are Internet
capable and have access to the Web, e-mail and other Internet facilities. It
is a technology that allows you to make voice calls using a broadband
Internet connection instead of a regular (or analog) phone line. Some VoIP
services may only allow you to call other people using the same service, but
others may allow you to call anyone who has a telephone number including local, long distance, mobile, and international numbers.
BoTs - A bot (short for "robot") is a program that operates as an agent for a
user or another program or simulates a human activity. On the Internet, the
most ubiquitous bots are the programs, also called spiders or crawlers, that
access Web sites and gather their content for search engine indexes. A
shopbot is a program that shops around the Web on your behalf. A
knowbot is a program that collects knowledge for a user by automatically
visiting Internet sites and gathering information that meets certain
specified criteria.
Virtual Customer Representative - In customer relationship management
(CRM), a virtual agent (sometimes called an intelligent virtual agent, virtual
rep or v-rep) is a chatterbot program that serves as an online customer
service representative for an organization. Because virtual agents have a
human appearance and respond appropriately to customer questions, they
lend automated interactions a semblance of personal service. Combining
artificial intelligence (AI) with a graphical representation, virtual agents are
increasingly used in CRM to help people perform tasks such as locating
information or placing orders and making reservations. Virtual agents are
usually scripted to respond to a wide variety of questions and remarks.
Virtual agent is also used to refer to a human agent who works over the
Internet at some distance from the employer's organization.
Customer Relationship Portals - A CRM portal is a way to open up the CRM
system to people who are not CRM system users. This allows them to work
with and get information from the company in an efficient way, regardless
of the time of the day. CRM portals usually ask the user to log in. When this
is done, they are connected to the CRM system, and have access to those
items that the portal is set to allow. A Customer Portal may allow “SelfSupport.” A customer with a problem can search the company’s knowledge
base to see if they can find a solution. In CRM, it is important to record
information about each ‘touch’ that your company has with its prospects
and customers. The CRM portal makes this possible for each portal
interaction. You are able to track who enters the portal, as well as the
information and actions they initiate. This helps enrich the data collected
about each person. The idea is to provide a means for those constituencies
outside the CRM system to interact with your company and to get the
materials that they need, without involving a customer support agent or a
sales person. The company’s customers can log into the portal and access
such things as:
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Enter Support Tickets and check status.
•
Enter Returned Material Authorizations (RMA) and check status.
•
See order status from the Order system.
•
See invoice status from the Accounting system.
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Read or download Product literature and company news.
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Download software updates.
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Read and Print CRM reports, such as Service history report.
Functional Components of CRM (Stated in Unit -1 )
1. Operational: Because of sharing information, the processes in business
should make customer’s need as first and seamlessly implement. This
avoids multiple times to bother customers and redundant process.
2. Analytical: Analysis helps company maintain a long-term relationship
with customers.
3. Collaborative: Due to improved communication technology, different
departments in company implement (intraorganizational) or work with
business partners (interorganizational) more efficiently by sharing
information.
Database Management
A Database is a computer based software application (program) that allows
input of information in a related and structured way for processing and
reporting. A CRM is a database that has been designed to track interactions
/ interventions between an organisation and its ‘customers’. A CRM will
store details on organisations / projects we work with and support, it will
provide an effective way to communicate electronically and allow us to
record activities that people / organisations have participated in and most
importantly allow us to create reports quickly and dynamically to evidence
our impact. A database management system (DBMS) is system software for
creating and managing databases. The DBMS provides users and
programmers with a systematic way to create, retrieve, update and manage
data. Database Management consist of:
1. Database Construction: To have a 360 degree view of the customer, the
CSR would require the assistance of software tools such as next generation
integrations and transformation platforms that are capable of handling the
complexities of transforming bare facts into useful data for efficient
customer service. A unified view of customer would further mean
maintaining hierarchal views of customers, linked to their transactional
histories and enhanced with external demographics, dates & behavioural
patterns obtained through various interactions and transactions that the
customer previously had with the organisation.
2. Database Warehousing – A Data Warehouse is the main repository of
the organisation’s historical data. It contains the raw material for the
management decision support system. This technique is used to develop
and use customer data to check profile, retention & loyalty patterns. It
provides valuable input for retaining customers and developing products &
service for future. A typical CRM cycle consists of front end operations that
interact with the customers (call centres, target market initiatives etc.) and
obtain data about him/her. This is typically consolidated from various
contact points and fed into a data warehouse. The data warehouse
consolidates not only the transaction data but also the data obtained from
outside sources such as census and provides a fertile ground for analysis. By
using data warehouse, an analyst can perform complex queries & analysis
such as data mining without slowing down the operational system.
3. Data Warehouse Architecture – To design an effective and efficient
data warehouse, we need to understand and analyze the business needs
and construct a business analysis framework. Each person has different
views regarding the design of a data warehouse. These views are as follows:
• The top-down view - This view allows the selection of relevant
information needed for a data warehouse.
• The data source view - This view presents the information being
captured, stored, and managed by the operational system.
• The data warehouse view - This view includes the fact tables and
dimension tables. It represents the information stored inside the data
warehouse.
• The business query view - It is the view of the data from the viewpoint of
the end-user.
Three-Tier Data Warehouse Architecture
Generally a data warehouses adopts a three-tier architecture. Following are
the three tiers of the data warehouse architecture.
• Bottom Tier - The bottom tier of the architecture is the data warehouse
database server. It is the relational database system. We use the back end
tools and utilities to feed data into the bottom tier. These back end tools
and utilities perform the Extract, Clean, Load, and refresh functions.
• Middle Tier - In the middle tier, we have the OLAP Server that can be
implemented in either of the following ways.
By Relational OLAP (ROLAP), which is an extended relational database
management system. The ROLAP maps the operations on multidimensional
data to standard relational operations.
By Multidimensional OLAP (MOLAP) model, which directly implements the
multidimensional data and operations.
• Top-Tier - This tier is the front-end client layer. This layer holds the query
tools and reporting tools, analysis tools and data mining tools.
The following diagram depicts the three-tier architecture of data
warehouse:
Data Mining
Data mining, by its simplest definition, automates the detection of relevant
patterns in a database. It is the non-trivial extraction of novel, implicit, and
actionable knowledge from large datasets.
Data mining uses well-established statistical and machine learning
techniques to build models that predict customer behavior. Today,
technology automates the mining process, integrates it with commercial
data warehouses, and presents it in a relevant way for business users. Data
mining extracts information from a database that the user did not know
existed. Relationships between variables and customer behaviors that are
non-intuitive are the jewels that data mining hopes to find. And because
the user does not know beforehand what the data mining process has
discovered, it is a much bigger leap to take the output of the system and
translate it into a solution to a business problem.
Advantage of Data Mining
1. Data mining helps marketing users to target marketing campaigns more
accurately; and also to align campaigns more closely with the needs, wants,
and attitudes of customers and prospects.
2. If the necessary information exists in a database, the data mining
process can model virtually any customer activity. The key is to find
patterns relevant to current business problems.
3. Typical questions that data mining addresses include the following:
Which customers are most likely to drop their cell phone service? • What is
the probability that a customer will purchase at least $100 worth of
merchandise from a particular mail-order catalog? • Which prospects are
most likely to respond to a particular offer? Answers to these questions can
help retain customers and increase campaign response rates, which, in
turn, increase buying, cross-selling, and return on investment (ROI).
4. Data mining builds models by using inputs from a database to predict
customer behavior. This behavior might be attrition at the end of a
magazine subscription, cross-product purchasing, willingness to use an ATM
card in place of a more expensive teller transaction, and so on. The
prediction provided by a model is usually called a score. A score (typically a
numerical value) is assigned to each record in the database and indicates
the likelihood that the customer whose record has been scored will exhibit
a particular behavior. For example, if a model predicts customer attrition, a
high score indicates that a customer is likely to leave, whereas a low score
indicates the opposite. After scoring a set of customers, these numerical
values are used to select the most appropriate prospects for a targeted
marketing campaign.
5. Database marketing software enables companies to deliver timely,
pertinent, and coordinated messages and value propositions (offers or gifts
perceived as valuable) to customers and prospects. Today's campaign
management software goes considerably further. It manages and monitors
customer communications across multiple touch-points, such as direct mail,
telemarketing, customer service, point of sale, interactive web, branch
office, and so on.
6. Increasing Customer Lifetime Value - Consider, for example, customers of
a bank who use the institution only for a checking account. To persuade
these customers to keep their money in the bank, marketing managers can
use campaign management software to immediately identify large deposits
and trigger a response. The system might automatically schedule a direct
mail or telemarketing promotion as soon as a customer's balance exceeds a
predetermined amount. Based on the size of the deposit, the triggered
promotion can then provide an appropriate incentive that encourages
customers to invest their money in the bank's other products.
Data Mining Tools & Techniques
Association
Association is one of the best-known data mining technique. In
association, a pattern is discovered based on a relationship between items
in the same transaction. That’s is the reason why association technique is
also known as relation technique. The association technique is used in
market basket analysis to identify a set of products that customers
frequently purchase together.
Retailers are using association technique to research customer’s buying
habits. Based on historical sale data, retailers might find out that
customers always buy crisps when they buy beers, and, therefore, they
can put beers and crisps next to each other to save time for customer and
increase sales.
Classification
Classification is a classic data mining technique based on machine
learning. Basically, classification is used to classify each item in a set of
data into one of a predefined set of classes or groups. Classification
method makes use of mathematical techniques such as decision trees,
linear programming, neural network and statistics. In classification, we
develop the software that can learn how to classify the data items into
groups. For example, we can apply classification in the application that
“given all records of employees who left the company, predict who will
probably leave the company in a future period.” In this case, we divide the
records of employees into two groups that named “leave” and “stay”. And
then we can ask our data mining software to classify the employees into
separate groups.
Clustering
Clustering is a data mining technique that makes a meaningful or useful
cluster of objects which have similar characteristics using the automatic
technique. The clustering technique defines the classes and puts objects in
each class, while in the classification techniques, objects are assigned into
predefined classes. To make the concept clearer, we can take book
management in the library as an example. In a library, there is a wide
range of books on various topics available. The challenge is how to keep
those books in a way that readers can take several books on a particular
topic without hassle. By using the clustering technique, we can keep
books that have some kinds of similarities in one cluster or one shelf and
label it with a meaningful name. If readers want to grab books in that
topic, they would only have to go to that shelf instead of looking for the
entire library.
Prediction
The prediction, as its name implied, is one of a data mining techniques
that discovers the relationship between independent variables and
relationship between dependent and independent variables. For instance,
the prediction analysis technique can be used in the sale to predict profit
for the future if we consider the sale is an independent variable, profit
could be a dependent variable. Then based on the historical sale and
profit data, we can draw a fitted regression curve that is used for profit
prediction.
Sequential Patterns
Sequential patterns analysis is one of data mining technique that seeks to
discover or identify similar patterns, regular events or trends in
transaction data over a business period.
In sales, with historical transaction data, businesses can identify a set of
items that customers buy together different times in a year. Then
businesses can use this information to recommend customers buy it with
better deals based on their purchasing frequency in the past.
Decision trees
The A decision tree is one of the most common used data mining
techniques because its model is easy to understand for users. In decision
tree technique, the root of the decision tree is a simple question or
condition that has multiple answers. Each answer then leads to a set of
questions or conditions that help us determine the data so that we can
make the final decision based on it. For example, We use the following
decision tree to determine whether or not to play tennis:
Starting at the root node, if the outlook is overcast then we should
definitely play tennis. If it is rainy, we should only play tennis if the wind is
the week. And if it is sunny then we should play tennis in case the
humidity is normal.
We often combine two or more of those data mining techniques together to
form an appropriate process that meets the business needs.