Customer Relationship Management A Databased Approach

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Customer Relationship ManagementA Databased
Approach

• V. Kumar
• Werner J. Reinartz
• Instructors Presentation Slides

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Chapter Ten

• Data Mining

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Topics Discussed

• 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

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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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Overview of the Data Mining Process
Learn
Get Raw Data
Identify Relevant Variables
(Re)defineBusiness objectives
-
Gain Customer Insight
Act

• Extract
• descriptive and
• transactional data

• Rollup data

• Train predictive
• models

• Deploy
• models

• Define
• objectives
• and expectations

• Create analytical
• variables

• Compare
• models

• Monitor
• performance

• Define
• measurement
• of success

• Check quality

• Enhance
• analytical data

• Select
• models

• Enhance models

• Select relevant
• variables

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Timeframe of Data Mining Methodology
60
-
(Re
-
) Define
Raw
Relevant
Customer
insight
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Extent of Involvement of The Three Main Groups
Participating in a Data-Mining Project
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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
• 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

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Manipulations to Data Set

• Column manipulations
• Transformation
• Derivation
• Elimination
• Row manipulations
• Aggregation
• Change detection
• Missing value detection
• Outlier detection

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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 Objectives

• Modeling of expected customer potential, in
  order to target acquisition of
• customers who will be profitable over the
  whole lifetime of the business
• relationship
• Distinguish between customers with a target
  variable equal to zero and
• customers with a target variable equal to one
• Establish likelihood threshold levels above
  which business group think a
• prospect should be included in the marketing
  campaign

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Define Business Objectives (contd.)

• 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

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Cost/Revenue Matrix

• Will have an impact on the choice of model
• parameters such as the cut-off point for the
  selected model scores
• It will also give business users an immediately
  interpretable table

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Cost/Revenue Matrix
Cost/Revenue matrix In reality prospect did not purchase In reality prospect did purchase
Model predicts prospect will not purchase (not contacted) Cost 0 1st year revenue 0 Total 0 lost business opportunity of 895
Model predicts prospect will purchase (contacted) Cost -5 1st year revenue 0 Total -5 Cost -5-100 1st year revenue 1000 Total 895

• Assuming average cost per call is 5, each
  positive responder (purchaser) will generate
  additional cost due to
• administration work required to register him as a
  new customer
• the cost of the delivered phone handset (say,
  100)
• Customers, who respond positively will, generate
  average revenue of 1000 per year

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Get Raw Data

• 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

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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

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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

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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)

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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
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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

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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

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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
.
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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 models performance on
  observations that have not been used to build it

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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

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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
Input Node - Classification Neural Network (10)
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Act
Step 1 Deliver Results to Operational
Systems Step 2 Archive Results Step 3 Learn
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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 models score
  information to the correct customer

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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

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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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CRM at Work Credite Est

• Regional mid-tier bank in France use of data
  mining in marketing
• Uses segmentation scheme based on behavioral
  characteristics
• (e.g. product ownership), and an
  activity-based-costing system to identify
  individual customer level contribution margin
• Project
• Business goal to acquire new prospects
• Objective to identify the characteristics of
  profitable customers in Credite Ests mass-market
  segment to efficiently target similar profiles in
  the prospect pool

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Credite Est (contd.)

• Get Raw Data
• Response variable for current customers is
  customer contribution margin
• Customers sorted by operating contribution and
  profile of the top 20 of customers noted
• Transaction information on prospects purchased
  and then appended to individual records of
  existing customers
• Identify relevant variables
• To find the profile that best characterizes high
  value clients which is subsequently applied to
  prospects information
• Model attempts to predict customer operating
  margin as dependent variable with geodemographic
  information as independent variables
• Credite Est appended a total of 65 variables to
  existing customer records

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Credite Est (contd.)

• Select Predictive Variables
• All variables that were appended had almost 50
  missing data
• Assessing whether any of the missing data could
  be meaningfully replaced improved the overall
  rate of missing values from 42 to 21
• Investigation of univariate statistics (means,
  standard deviations, frequencies, outliers) for
  all variables brought reduction in variables from
  65 to 54
• Calculation of all bi-variate correlations (or
  mean analyses in case of categorical variables)
  of existing independent variables with the
  dependent variable customer value
• Data evaluation process resulted in a total of 17
  variables that had a reasonable correlation with
  the dependent variable. These were retained for
  the next step, the response model

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Credite Est (contd.)

• Gain Customer Insight
• Use logistic regression to classify the dependent
  variable as 0/1 the goal being to either target
  or not target a certain individual in the
  prospect pool
• Theory-based elimination variables that are
  highly collinear
• The ability of the model to correctly classify in
  a holdout sample was 75.5 in the estimation
  sample and 69.8 in the holdout sample, roughly
  20 higher than based on chance alone
• Result was deemed successful and it was decided
  to utilize this model for a prospecting campaign

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Credite Est (contd.)

• Act
• Final model was rolled out in sequential fashion
  to target prospect audience
• Credite Est purchased addresses from list brokers
  that had at least non-missing vales for 3 out of
  the 5 variables in the final model
• The prospects were scored with the model and then
  ranked by likelihood of being a high value
  customer
• Objective was to assess the receptivity of the
  two samples of customers for respective products
• Result Both target mailings were significantly
  more successful than the base line scenario

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CRM at Work Yapi Kredi Predictive Model Based
Cross-Sell Campaign

• Challenge To continue YAPI KREDIs development
  as the fastest growing retail bank in Turkey
• Capabilities required
• Advanced analytical customer segmentation
• Segment specific offering of product bundles
• Conversion of customers to more profitable
  segments via targeted campaigns using advanced
  CRM tools such as predictive modeling
• Project plan
• To carry out a set of pilot projects for
  cross-selling of consumer banking products
• A reduced selection of target customers with a
  high propensity to positively respond would be
  included in a multi-channel, two-step campaign

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Yapi Kredi - Define Business Objectives

• YAPI KREDIs B-type mutual funds, characterized
  by
• Being low risk investment instruments based on
  fixed income securities
• Easily purchased via the ATM, Web, and Telephone
  channels
• Offer to two customer groups
• Customers already having invested into B-type
  mutual funds to stimulate an increase of the
  assets
• Customers not yet owning any B-type fund to help
  increase product ratio and attract new money

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Yapi Kredi-Define business objectives (contd. )

• Communication channels two-channel approach
• Campaign sizing Contact 3000 customers by
  branch based out-bound calls and active marketing
  during customer branch visits
• Campaign Two-step
• Customers were first contacted with the B-type
  mutual fund offer
• Positive responders received a follow up call if
  they had not purchased until one week after their
  initial positive response
• Evaluation of results Based on response and
  purchase rates by contact channel (branch or call
  center)

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Yapi Kredi- Get Raw Data Identify Relevant
Variables

• Get Raw Data
• Data mart with data extracted from more than 50
  source system tables
• About 20 database tables were produced with 30
  Giga Bytes of disk space for the initial project
  phase
• Identify Relevant Variables - customer attributes
  describing
• Demographics
• Product Ownership
• Product Usage
• Channel usage
• Assets
• Liabilities
• Profitability

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Yapi Kredi - Gain Customer Insight

• Based on six months of historical customer data,
  five different predictive models were developed
• Best model logistic regression
• Yielding a lift value of 29 and a cumulative
  response rate of 14 for the top customer
  percentile
• Reaches 2.9 times more responders for the top
  customer percentile than a random selection of
  the same size
• A set of 4200 customers with the highest
  propensity to purchase was selected as the target
  group for the pilot campaign

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Yapi Kredi - Act

• A subset of 3000 customers was assigned to the 16
  branches holding the responsibility for the
  respective relationships
• The remaining 1200 customers were assigned to the
  call center
• The target list with the corresponding channel
  assignment was made available to the campaign
  management system

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Yapi Kredi - Result

• Result
• Impressive response rates of 6.5 and 12.2 were
  obtained with the branch based part of the
  campaign and the call center based part of the
  campaign respectively
• The pilot campaign acquired more than 1 million
  into B-type mutual funds

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Summary

• Data Mining can assist in selecting the right
  target customers or in identifying previously
  unknown customers with similar behavior and needs
• A good target list is likely to increase purchase
  rates, and have a positive impact on revenue
• In the context of CRM, the individual customer is
  often the central object analyzed by means of
  data mining methods
• A complete data mining process comprises
  assessing and specifying the business objectives,
  data sourcing, transformation and creation of
  analytical variables, and building analytical
  models using techniques such as logistic
  regression and neural networks, scoring customers
  and obtaining feedback from the field
• Learning and refining the data mining process is
  the key to success