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CRM Process
CRM Process is defined as any group of action that is instrumental in the achievement of the
output of an operation system in accordance with a specified measure of effectiveness.
The final objective of the CRM Process is to originate a powerful new tool for customer
retention
The CRM implementation and success rate purely depend upon the process, which includes the
future, revenue, customer value, customer retention, customer acquisition and profitability.
Benefits of CRM Process
Ability to retain loyal and profitable customers and channels for rapid growth of the business
project.
Acquiring the right customers based on known characteristics which drives growth and increased
profit margins
Increasing individual customer margins, while offering right products at the right time
A Closed Loop CRM Process
Gathering information - About customers and data comes from both external and internal
sources
Perform Data aggregation - Here the data is merged and compressed into a complete view of
the customer. A large customer data depository is produced.
Create exploration warehouses - These are the extracts of customer data needed to support
specific analysis. Such as customer profitability and predictive modeling.
Execution of strategies - Execute these strategies by developing and launching marketing
campaigns across targeted segments of customers. Campaign execution inevitably results in an
interaction with customers.
Finally, once a customer interaction takes place and the customer responds, capture that response
and “recycle” it to use in the on-going learning, analysis, and refinement process
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Necessities for an effective Process Planning
CRM Process is the systematic determination of the methods by which a project is to be
implemented, economically and competitively.
Process planning is the intermediate stage between project kick off and implementation.
Creatively translate business innovation, structural transformation and value measures into
CRM investment priorities
Continually refine iteration approach based on the following five major areas.
• Customer
• Competition
• Market
• Growth
• Technology innovation
Process Selection Procedure
• Major technological changes
• Minor technological changes
• Specific component choice
• Process flow choice
Major Technological Changes
Does technology exist or not ?
Are there competing technologies between which we should choose?
Should technology be developed in the country itself?
Should innovations be licensed from foreign countries?
Minor Technological Changes
Once the major technological choice is made there may be a number of minor technological
process available
Additional Modules – Customization of Software
Director should be involved in evaluating costs and for consistency of the desired product
Deciding the best combination of process in terms of costs and the operation process.
Specific Component Choice
What type of hardware is to be used?
How effectively the technology is to be used?
Process flow choice - The final process selection step determines how the CRM
product will move through the system
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An Insight into E-CRM
E-CRM/On Line CRM
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Online CRM implies an additional means of communication and level of interaction with
the customer where there is a real difference in the technology and its architecture, which
allows for ease and self service to customers
Need to Adopt E-CRM
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Optimize the value of inter-active relationships.
Enable business to extend its personalized messaging to the web and E-Mail.
Coordinate Marketing initiatives across all customer channels.
Leverage customer information for more effective e-marketing and e business
Focus Business on improving customer relationships and earning a greater share of each
of customers business through consistent measurement, assessment and “actionable”
customer contact strategies.
Basic requirements of E-CRM
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Electronic channels-Web Personalized E-Messaging medium for fast interaction
Enterprise-touch and shape a customers experience across the entire organization
Empowerment-Customer Empowerment
Economics-provides the greatest return customer communication initiatives.
Assessment-Evaluation results
Outside information-use of customer sanctioned external information.
Steps in building an E-CRM Solution
• Define business objectives
• Assess its current sophistication along the E-CRM continuum in terms of process and
technology.
• Define Business processes and changes needed to support its goals.
• Plan and timetable to implement processes.
• Erect the necessary data warehouse to attain a consolidated view of the customers
• Select implement and integrate the required E-CRM applications.
Three Dimensions of E-CRM
• Acquisition Getting
• Expansion
• Retention
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Key E-CRM Features
• Focused on process-reduces the technology gap.
• Driven by a Data warehouse
• Focused on a multi-channel view of customer actions Across channels.
• Based on consistent metrics to asses customer actions across channels.
• Built to accommodate the new market dynamics that place the customer in control.
• Structured to identify a customers profitability or profit potential and determine effective
investment allocation decisions accordingly
• Scalable to meet growth and performance needs
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Introduction to CRM
The approaches in marketing functions are constantly changing in tune with the
changing challenges that are faced at the market fronts. Today, marketers consider retaining of
customers as a much more challenging job than acquiring customers in the context of growing
competitive forces.
Thus, the traditional transactional approach of marketing became insufficient to
achieve the marketing goals and necessitated the emergence of a new approach namely
Relationship Marketing.
Traditional Transaction Approach:
The focus on this type of approach is on individual transaction and does not concern
continuous relationship with customers. It does not contain a strategic long term perspective. The
customers are viewed as outsiders to the business. Very little attention is paid to the customer
services and customer commitments.
Relationship Marketing:
This focuses on continuous multiple transactions rather than isolated individual
transactions. The customers are viewed as insiders to the business and aims to build a long -term
and never -ending relationship with them. It focuses on developing a hard core loyal customers
with the idea of retaining them forever.
The relationship Marketing approach has gradually taken the shape of Customer Relationship
Management.
Customer Relationship Management focuses more widely on customers and on the entire
functions connected.
Definition of Customer Relationship Management:
“a management process of acquiring customers by understanding their
requirements, retaining customers by fulfilling their requirements more than their expectations
and attracting new customers through customer specific strategic marketing approaches. The
process invites total commitment on the part of the entire org in evolving and implementing
relationship strategies that would be rewarding to all concerned”
Significance of CRM:
The following are some of the points that gives an overview about the importance of Customer
Relationship Management
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Reduction in customer recruitment cost.
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Generation of more and more loyal customers.
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Expansion of customer base.
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Reduction in advertisement and other sales promotion expenses.
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Increase in the number of profitable customers.
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Easy introduction of new products.
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Easy business expansion possibilities.
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•
Emerging Concepts of CRM
1. Conceptual Framework of Customer Relationship Management
2. CRM: A Research Agenda
3. Implications of Globalization on Customer Relationship Management
4. Effective Customer Relation Management Through Customer Knowledge Management
5. A Generalized Model for the Structure of Business Relationships: A Meta-analysis of
Relationship Literature
6. Regain Management: Issues and Strategies
7. Winning Markets through Effective Customer Relationship Management
CRM Portal
NEED FOR CRM
 Reduction in customer recruitment cost
 Generation of more and more royal customer
 Expansion of customer base
 Reduction in advertisement & other sales promotion expenses
 Benefiting customer selectivity approach
 Increase in number of profitable customers
 Easy introduction of new products
 Easy business expansion possibilities
 Increase in customer partnering
CRM APPLICATIONS
The CRM applications are a convergence of
1. Functional Components
2. Channels
And the Functional components include:
 Sales Applications
 Marketing Applications
 Customer Service and Support Application
SALES APPLICATIONS
 Sales Forecasting
 Proposal Generation and Management
 Compensation
 Pricing
 Territory Assignment and Management
 Expense Reporting
MARKETING APPLICATIONS
 Marketing Campaign Planning, Execution and Analysis.
 Budgeting and Forecasting
 A Marketing Encyclopedia
 List Generation and Management
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CUSTOMER SERVICE AND SUPPORT APPLICATIONS
 Customer care
 Field Service
 Problem and Solution Database
 Service agreements and Contracts
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Data Mining
• Applications of Data Mining
• Involvement of the three main groups participating in a data-mining project
• Overview of the Data Mining Process
• CRM at Work: Credite Est and Yapi Kredi
Applications of Data Mining
• Reducing churn with the help of predictive models, which enable early identification of
those customers likely to stop doing business with the company
• Increasing customer profitability by identifying customers with a high growth potential
• Reducing marketing costs by more selective targeting
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Involvement of Business, Data Mining and IT Resources in a Typical Data Mining Project
• Data mining group:
– Understand the business objectives and support the business group to refine and
sometimes correct the scope, and expectations
– Most active during the variable selection and modeling phase
– Share the obtained customer insights with the business group
• IT resources:
– Required for the sourcing and extraction of the required data used for modeling
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Business group:
– Involved in checking the plausibility and soundness of the solution in business
terms
– Takes the lead in deploying the new insights into corporate action such as a call
center or direct mail campaign
Manipulations to Data Set
• Column manipulations:
– Transformation
– Derivation
– Elimination
Row manipulations
– Aggregation
– Change detection
– Missing value detection
– Outlier detection
Data Preparation
For modeling, incoming data is sampled and split into various streams as:
• Train set: Used to build the models
• Test set: Used for out-of-sample tests of the model quality and to select the final model
candidate
• Scoring data: Used for model-based prediction , ‘large’ as compared to other data sets
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Define Business Objective
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Modeling of expected customer potential, in order to target acquisition of
customers who will be profitable over the whole lifetime of the business
relationship
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Distinguish between customers with a target variable equal to zero and customers with a
target variable equal to one
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Establish likelihood threshold levels above which business group think a prospect
should be included in the marketing campaign
• Define the set of business or selection rules for the campaign (e.g.: , the customers that
should be excluded from or included in the target groups)
• Define the details of project execution specifying the start and delivery dates of the data
mining process, and the responsible resources for each task
• Define the chosen experimental setup for the campaign
• Define a cost/revenue matrix describing how the business mechanics will work in the
supported campaign and how it will impact the data mining process
• Establish the criteria for evaluating the success of the campaign
• Find a benchmark to compare against results obtained in the past for the same or similar
campaign setups using traditional targeting methods, and not predictive models
Cost/Revenue Matrix
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Get Raw Data
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Identify, extract and consolidate raw data in a database (often called “Analytical Data
Mart”)
Check the quality of the analytical raw data - technical checks as well as ensuring that
the data makes sense in the given business context
Get Raw Data (contd.)
• Step 1: Looking for Data Sources
– Mixed top-down and bottom-up process, driven by business requirements (top)
and technical restrictions (bottom)
• Step 2 : Loading the Data
– Define how the data will be imported into the data mining environment
• Checking Data Quality
– Technical aspects of the data: primary keys, duplicate records, missing values
– Business context: realistic data
Step 1: Looking for Data Sources
• Data warehouse infrastructures with advanced data cleansing processes can help ensure
that you are working with high-quality data
• Build a (simple) relational data model onto which the source data will be mapped
Step 2: Loading the Data
• Define further query restrictions , prepared by IT teams , for execution at pre-defined
time windows in batch mode
• Deliver extracted data to the data mining environment in a pre-defined format
• Further processing and using data to fill previously defined data model in the data mining
environment as part of the ETL process (Extract-Transform-Load)
Step 3: Checking Data Quality
• Assess and understand limitations of data resulting from its inherent quality (good or
bad) aspects
• Create an analytical database as the basis for subsequent analyses
• Carry out preliminary data quality assessment
– To assure an acceptable level of quality of the delivered data
– To ensure that the data mining team has a clear understanding of how to interpret
the data in business terms
• Data miners have to carry out some basic data interpretation and aggregation exercises
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Identify Relevant Predictive Variables
Step 1: Create Analytical Customer View – “Flattening” the Data
Step 2: Create Analytical Variables
Step 3: Select Predictive Variables
Step 1: Create Analytical Customer View – “Flattening” the Data
• Individual customer constitutes an observational unit for data analysis and predictive
modeling
• All data pertaining to an individual customer is contained in one observation (row,
record)
• Individual columns (variables, fields) represent the conditions at specific points in time or
a summary over a whole period
• Definition of the target or dependent variable- values should be generated for all
customers and added to the existing data tables
Step 2: Create Analytical Variables
• Introduce additional variables derived from the original ones
• When needed, transform variables to get new and more predictive variables
• Increase normality of variable distributions to help the predictive model training process
• Missing value management is key for enhancing the quality of the analytical data set
Step 3: Select Predictive Variables
• Inspect the descriptive statistics of all univariate distributions associated to all available
variables
• Exclude those variables:
• which take on only one value (i.e. the variable is a constant)
• with mostly missing values
• directly or indirectly identifying an individual customer
• showing collinearities
• showing very little correlation with the target variable
• Containing personal identifiers
• Define a threshold missing value count level above which the field would
be excluded from further analysis (e.g. more than 95% missing values)
• Check if all variables have been mapped to the appropriate data types
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Gain Customer Insight
Step 1: Preparing data samples
Step 2: Predictive Modeling
Step 3: Select Model
Step 1: Preparing Data Samples
• Analyze if sufficient data is available to obtain statistically significant results
• If enough data is available, split the data into two samples:
– the train set to fit the models
– the test set to check the model’s performance on observations that have not been
used to build it
Step 2: Predictive Modeling
Two steps:
• The rules (or linear/non-linear analytical models) are built based on a training set
• These rules are then applied to a new dataset for generating the answers needed for the
campaign
Guidelines:
• Distinguish between different types of predictive models obtained through different
modeling paradigms: supervised and un-supervised modeling
• Find the right relationships between variables describing the customers to predict their
respective group membership likelihood: purchaser or non-purchaser, referred to as
scoring (e.g. between 0 and 1)
• Apply unsupervised modeling where group membership is not known beforehand
Step 3: Select Model
Compare relative quality of prediction by comparing respective misclassification rates obtained
on the test set
Example of misclassification error rate or confusion matrix:
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Act
Step 1: Deliver Results to Operational Systems
Step 2: Archive Results
Step 3 Learn
Step 1: Deliver Results to Operational Systems
• Apply the selected model to the entire customer base
• Prepare score data set containing the most recent information for each customer with the
variables required by the model
• The obtained score value for each customer and the defined threshold value will
determine whether the corresponding customer qualifies to participate in the campaign
• When delivering results to the operational systems, provide necessary customer
identifiers to unambiguously link the model’s score information to the correct customer
Step 2: Archive Results
• Each data mining project will produce a huge amount of information including:
– raw data used
– transformations for each variable
– formulas for creating derived variables
– train, test and score data sets
– target variable calculation
– models and their parameterizations
– score threshold levels
– final customer target selections
• Useful to preserve especially if the same model is used to score different data sets
obtained at different times
Step 3: Learn
• Referred to as “closing the loop”
• Obtain the facts describing performance of data mining project and business impact
• Obtained by monitoring campaign performance while it is running and from final
campaign performance analysis after the campaign has ended
• Detect when a model has to be re-trained
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