Techathon Idea Paper

Reversing Customer Attrition with BigData Engine and Predictive Model Analytics
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Reversing Customer Attrition with
Big data Engine and Predictive Model
for Banking and Financial Domain
Dillip Kumar Majhi
BAO CoC Bigdata Architect
Email : dilmajhi@in.ibm.com
Date : 12- Nov- 2013

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Table of Contents
1.0 Abstract ..........................................................................................................................................................................3
2.0. Introduction ....................................................................................................................................................................3
3.0 Customer Churn..............................................................................................................................................................4
3.1. The customer lifetime value concept ...................................................................................................................4
3.2 Customer Attrition..................................................................................................................................................4
3.3. Potential Cause ......................................................................................................................................................5
3.4 Factors for Analysis...............................................................................................................................................5
4.0 BigData and Churn Analysis ..........................................................................................................................................6
4.1 Why Churn Analysis on BigData.....................................................................................................................................6
5.0 IBM’s Bigdata...................................................................................................................................................................6
6.0 Solution Approach for Churn and NBA.........................................................................................................................7
6.1 Solution Architecture ..........................................................................................................................................8
7.0 Predictive Model for Churn................................................................................................................................................9
7.1 The Tree-Based Method........................................................................................................................................9
7.2 The artificial neural network method..................................................................................................................12
7.3 Fault Measure ......................................................................................................................................................12
7.4 Logistic Regression.............................................................................................................................................14
7.4.1 Logistic Regression Model Build ...............................................................................................................15
7.4.2 Sample Data Input Format ...........................................................................................................................16
7.4.3 Sample Data Output.....................................................................................................................................17
7.5 GLM( Generalized Linear Model) – NBA.............................................................................................................17
7.5.1 GLM Model Build –NBA ..............................................................................................................................18
7.5.2 Sample Data Output.....................................................................................................................................18
8.0 Result ...............................................................................................................................................................................19
9.0 Reference.........................................................................................................................................................................20
10 .0 Abbreviations ................................................................................................................................................................21

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1.0 Abstract
Customer value analysis is critical for a good marketing and a customer relationship management
strategy. An important component of this strategy is the customer retention rate. Customer retention rate
has a strong impact on the customer lifetime value, and understanding the true value of a possible
customer churn will help the company in its customer relationship management. The goal of this study is
to apply logistic regression techniques to predict a customer churn and analyze the churning and no
churning customers.
Conventional statistical methods were very successful in predicting a customer churn so far, However,
since the nature of data is very high and very much unstructured in todays’ Social Networking Age where
customer's view and rating play’s a vital role , Hence it is time to analyze the churn and retention data
with the help of Bigdata and Predictive Model.
This paper presents a customer churn study and NBA(Next best action) in Banking and Finance Industry.
Biginsights, The IBM Bigdata Technology stack and Logistic Regression Model is being coherently used
to arrest this problem. The Results of the Study, and Future directions, are then discussed
2.0. Introduction
The subject of customer retention, loyalty, and churn is receiving attention in many industries. This is
important in the customer lifetime value context.
A company will have a sense of how much is really being lost because of the customer churn and the
scale of the efforts that would be appropriate for retention campaign. The mass marketing approach
cannot succeed in the diversity of consumer business today. Customer value analysis along with
customer churn predictions will help marketing programs target more specific groups of customers.
Companies experience what is labeled “churn”, losing customers only to replace them with new
Customers.
Companies can often experience churn in excess of 50-90%, yet do little or nothing about it. Such
companies maintain volume and revenues by paying the costs necessary to constantly acquire new
customers.
Some do not even know whether these new customers are in fact returning customers. This will inevitably
be a more costly proposition than retaining the same number of existing customers instead of losing
them.
Retaining customers maintains volume and revenues at lower cost, increases customer lifetime value,
and should create loyalty from limited experience and habit Not spending to recruit new customers saves
money
Some companies believe they can avoid the planning, effort, and upfront expense of such prospective
modeling by waiting until the customer defects, and then studying the defectors. Such a process, based
on reviewing events associated with defection and surveying defectors is better than doing nothing, but is
plagued by glaring and potentially fatal design flaws.
First, getting information from defectors is likely to be more difficult than getting information from
customers. Defectors have already acted on their negative orientation(s) toward your company.
Why should they be willing to provide you with free or low-cost information about their experiences with
and evaluations of you and your offerings?
Second, information provided by defectors may be biased, unreliable, or invalid. Their ill will is likely to
provide a biased description of their experiences and orientation to your company, products,
and customer service. People generalize and stereotype. If unaffected enough to defect,

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many defectors are likely to project their current evaluation or orientation onto many or all other and
earlier aspects of their relationship with your company.
Third, the type of initial contact with customers matters, accounting for three additional years of average
customer lifetime (more than two times longer than others). When an “inquiry” customer first buys, she is
likely to remain a customer 3.98 times longer than a “neighborhood” customer, whereas a “telemarketing”
recruit is likely to remain a customer 1.22 times longer than a “neighborhood” customer. This indicates a
substantial advantage to signing and keeping customers who initiate inquiry compared to all others.
Unfortunately the company had no such initiative in place. An “inquiry” customer has an expected
duration 2.35 times longer than a “telemarketing” customer. This relationship remains after adjusting for
the other factors tested.
3.0 Customer Churn
The focus on customer churn is to determinate the customers who are at risk of leaving and if possible on
the analysis whether those customers are worth retaining. The churn analysis is highly dependent on the
definition of the customer churn. The business sector and customer relationship affects the outcome how
churning customers are detected. Example in credit card business customers can easily start using
another credit card, so the only indicator for the previous card company is declining transactions. the
company must address the value of a potential loss of a customer. The customer lifetime value analysis
will help to face the challenges.
3.1. The customer lifetime value concept
The customer lifetime value is usually defined as the total net income, expenditure Capacity from the
customer over his lifetime This type of customer analysis is done under several terms: customer value,
customer lifetime value, customer equity, and customer profitability. The underlying idea in LTV concept is
simple and measuring the lifetime value is easy after the customer relationship is over. The challenge in
this concept is to define and measure the customer lifetime value during, or even before, the active stage
of customer relationship.
For example Hoekstra et al. defines a conceptual LTV model as follows :
LTV is the total value of direct contributions and indirect contributions to overhead and profit of an
individual customer during the entire customer life cycle, that is from start of the relationship until its
projected ending.
Most LTV models stem from the basic equation, although there are also many other LTV models having
various application areas. The components of the basic
LTV model are :
• Customer net present value and Transaction over time (revenue and cost).
• Retention
• Rate or length of service (LoS).
• Discount factor.
3.2 Customer Attrition
The customer attrition can be categorized into two types; unavoidable and avoidable attrition.

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Unavoidable customer attrition includes, Economic downturn, shifts in consumer preferences or poor
business management on the part of customers.
Avoidable customer attrition is related to the relationship between customer and service provider. The
banking context for identification of Attrition is given in below.
1. Identification of preferred customers, those who have longer expected durations with, and therefore
greater lifetime value for, the company.
2. Modeling risk of defection to identify potential defectors in advance and take customer specific
remedial actions that will increase retention.
3. Modeling potential to, strategies for, and relative value of recovering lost customers and keeping
them once recovered.
Annual % attrition rate =
(Number of Customers one year ago ---- Number of Customers from same group Today)
Number of Customers in Portfolio one year ago
3.3. Potential Cause
Below is the potential reason of any churning/attrition of customer for banking/Financial Industry
• Customer Service issues and Price
• Over Promising and under Delivering
• SLA of Time delivery and Mismatch
• Poor Customer Service Interaction
• Switched to New Service Provider
• Customer changed mind/opened in error
• No Longer Processing and Account become Dormant
• Rates/Fees are highs
• Emotions – This plays greater role than quality or price in the decision to defect
3.4 Factors for Analysis
The key factor in customer attrition analysis is conversation between customer and service provider. This
source of unstructured data i.e. conversations of customer with service provider is a strong leading
indicator of future customer behavior.
Followings are few pointers which are important while doing Attrition Analysis
• Is the percentage of attrition trending up or down?
• Who is leaving you? (Include name, address, and SIC and MCC codes.)
• What types of customers are leaving, and from what neighborhoods or regions?
• What was the time between signing and leaving? How long had they been with you?
• Who trained the customers who left?

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• What terminals and applications were lost customers using?
• Who was the processor?
• Who is responsible for customer service and help desk for the customers that left?
• How long had each customer who left been in business?
• What was each customer's dollar and transaction volume over the past 12 months, and was there
a three- to five-year sales volume trend?
• What was each customer's profit during specified periods, and was the trend in profit up or down?
• What are business life expectancy norms for a given customer's industry?
• What are the norms within geographic areas covered in the portfolio, and how are the norms
trending?
• How long does it take for new customers in particular industries and geographic regions, and
even at certain volumes, to become profitable?
4.0 BigData and Churn Analysis
In 2009, the McKinsey Global Institute estimated that U.S. banks and capital markets firms collectively
had more than 1 exabyte -- or one quintillion bytes -- of stored data. In the subsequent years, that figure
surely has grown exponentially, as banks continue to amass massive amounts of data on customers. This
entire data means there is a prime opportunity for banks to glean information to improve their customer
experience and retention . So far only 30 % of data is being used to provide information and 70% of data
is un noticed and there is no such tool or product who can process this kind of data and provide valuable
business information. Hence there is demand of New technology who can have MPP Processing logic
with capable to take care below problems. NoSQL Database,Text Mining,Machine Learning capability
,Elastic Search ,Taxonomy search are the additional capability plugged with Bigdata.
Bigdata tries to solve the upcoming issues in form of 4V( Volume, Velocity, Variety, Veracity)
1) Volume-High Volume of Data ( Over Peta byte)
2) Velocity- Often time sensitive, Streaming data into enterprise system
3) Variety – Extends beyond Structured Data( RDBMS,DWH etc) including unstructured data like text,
audio, video, click streams, log files, email, PDF and many more..
4) Veracity – 1 in 3 business leaders don’t trust the information they use to make decisions. How can you
act upon information if you don’t trust it? Establishing trust in big data presents a huge challenge as the
variety and number of sources grows.
4.1 Why Churn Analysis on BigData
The huge amount of data available on social media like customer view, opinions which is in unstructured
form can be analyzed to identify what customer exactly looks for. The customer sentiment plays important
role in unstructured data which is key point to identify whether customer satisfaction.
Mining big data and leveraging analytics play a vital role in customer retention and to enhance the
customer service. It is best utilization of big data by harnessing it to improve service, detecting customer
attrition behavior and customer experience. Hence when we do churn Predictive analysis over above
nature of Data[4.0], it is imperative to use BigData Ecosystem to hold ,Process and support Predicative
Analysis and Visualization
5.0 IBM’s Bigdata
IBM has come up with suit of Product for Bigdata to cater various business sector like
energy,Telco,Finance ,Media,Retail and many more.. Along with IBM has many accelerator and Use case
based solution pertaining to various customer pain areas. I am giving very high level overview of those
Prodcuts on Bigdata on this paragraph before going specific use of those few product for this solution.
• Infosphere BigInsights :

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This is Core of the Bigdata Product for Storing ,Processing for high volume of data powered with Apache
Hadoop , an open source distributed computing Platform. It has various componenet like
HDFS,Mapreduce, JAQL, BigIndex, Hbase, Hive and Pig.there are component for extracting data from
external source and load into the BigInsights File system. For more details,
visit : https://www.ibm.com/developerworks/community/blogs/ibm-big-
data/entry/ibm_big_data_where_do_i_start?lang=en
• Infosphere Streams:
IBM® InfoSphere® Streams is an advanced computing platform that allows user-developed applications
to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources. The
solution can handle very high data throughput rates, up to millions of events or messages per second. It
is real time information system which analyze data in motion ,For More Details,
visit : http://www-03.ibm.com/software/products/us/en/infosphere-streams/
• BigSheets :
This is the extension of mashup paradigm which explore and visualize in specific user defined contexts
and shows the result like spread sheet and Graphics. Since the spread sheet is commonly user friendly
calculation sheet, hence BigSheets is widely accepted and very much handy for user to work on it.
This is the again a component of Infosphere BigInsights. For more details,
visit: http://www-01.ibm.com/software/ebusiness/jstart/bigsheets/
• SPSS Modeling Tool :
This is IBM Statistics and Modeling tool where you can do analytical model like ( Regression, Predictive
Model, Liner Mixed Model etc) to bring business meaning of data and can applied to any industry like
health care, Telco ,Banking and Finance , Media etc..
6.0 Solution Approach for Churn and NBA
Below is the IBM’s Bigdata technology selection for conducting this test. I have taken the dataset for retail
Banking whose data input format is given in section[ ].
1) Infosphere BigInsights
2) Sqoop
3) HDFS
4) Mapreduce
5) Hbase
6) Pig
7) R
8) Pentaho
Below is the Process flow for this solution
• The Data is fed to BigInsights Hadoop file system by UDF/SQOOP from various Input system show in
the Architecture section [6.1 ]. GNIP, Social mention, Real time data can feed to this file system in
form of JSON/XML with help of any connector. However, I am loading data from CSV file into the
HDFS through SQOOP
• Data is being cleansed by PIG and invalid records are being dropped in this stage
• Look up and major dimensional data stores in Hbase for validation purpose of incoming data
• Final good set up data is maintained in HDFS which pass through for various purpose
• SPSS statistical modeler hits this HDFS File system to process the Data Set for Analytic purpose
• The model we are using LR is built with this set up Data set. Refer section [7.4 ].
• The model is being validated over subset of this data as well ,Refer section [7.4.1 ].
• After Validation of model data , which is close proximate, fed to second Model for NBA, Refer Section
[7.5 ]
• The data out put from first model and Second model is being stored in output file of HDFS File system

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• MapReduce Process this data based on the user requirement ,Slice and Dice and any reporting
purpose
• BigSheets invokes Mapreduce to present this final data for various reporting and spread sheet format
• Business users use BigSheets for view ,create, analyze and bring value of this reports.
6.1 Solution Architecture
Over view of the solution Architecture that combines BigInsights Hadoop Ecosystem with R Predictive
model .
(Figure 1)
Architecture of Churn - NBA Solution
Action
UI Presentation Layer
Action
UI Presentation Layer
Acquisition
Collection of Data
Acquisition
Collection of Data
Marshalling
Processing/Storing
Data
Marshalling
Processing/Storing
Data
Analysis
Predictive Modelling
Analysis
Predictive Modelling
XML/CSV
FILE
XML/CSV
FILE
Unstructured
data
Unstructured
dataDWHDWHInput SourcesInput Sources
HDFS File
system
HDFS File
system
PIG
Transformation
PIG
Transformation
MapReduceMapReduce
HBASE
1 2 3
SPSSSPSS 4
> User action
--- - Data flow
Legend
1 5
1
Biginsights Hadoop System
5

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7.0 Predictive Model for Churn
Followings are some data mining Methods to construct model to estimate Customer Attrition. ‘Fault
Measure’ is the annotation technology which is one of independent variable for below models
• Logistic Regression ( Cox )
• The Tree based Classification Method
• More recently, The artificial neural network Method
I am providing high level overview for Tree based Classification and ANNM Method here before going to
Logistic Regression in details which is used as the Predictive model for this Solution
7.1 The Tree-Based Method
The tree-based method employs the technique of recursive partitioning of data with respect to the
variable of interest. In the case of predicting customer attrition, it identifies subgroups of customers who
are relatively homogeneous with respect to the risk of service termination. Unlike the regression-based
method, recursive partitioning identifies subject subgroups based on Boolean combinations of variables.
A branching, algorithm-like "tree" is created, with the "trunk" (the entire sample) or major branches split
into two or more smaller branches based on the value of the single variable that minimizes a measure of
within-group heterogeneity. The tree terminates in two or more "nodes," each of which defines a subgroup
of relatively similar subjects with respect to the outcome of interest.

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The iterative process used by model to split the data into two partitions as Churn and Non-Churn is as
follows:
(Figure -2)
Check leaf
Data of
one class?
Iterate Next leaf
Node
End
Y
N
Start
Split Observation
at examined Node to
create child nodes
Each Node being
examined
Determine the best
Split

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(Figure -3)
The above tree splits the dataset based on one of values of variables, which help us to distinguish
between the different classes of interest. The same process is repeated for each variable until we reach
to condition that there are no more attributes on which further classification can be done.
The classification tree builds the rule which for response variable.
Model Outputs:
• The decision tree
• Predicted response that is whether customer will churn or non churn
• Misclassification rate
• Estimated importance of each variable which is used to build tree
The significant parameters from fitted model are as follows:
1. Sentiment Score
2. Number of Complaints
3. Credit Limit Utilized
4. Transaction Number Decrease
5. Transaction Size Change
6. Customer Age
7. Gender
Model Output
view in Tree
Fashion(
Details
mentioned
below)

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7.2 The artificial neural network method
This method automatically identifies patterns in data by a computer procedure. The artificial neural network
method distinguishes two types of self-learning processes: supervised and unsupervised learning. Unsupervised
learning is used to identify patterns in data, such as clustering customers based on certain criterion variables. In
supervised learning, the goal is to predict one or more target variables from one or more input variables.
Supervised learning is usually some form of nonlinear regression or discriminate analysis. The artificial neural
network method has been increasingly used in business modeling as a powerful tool to examine large data sets
with many variables.
The neural Network model calculates weights for each covariate. The calculated weights express the effect of
each covariate on response variable. The covariates having weights close to 0 are insignificant variables,
whereas the covariates with higher weights are most significant.
The model can be used to predict the probabilities of churn and non-churn.
The significant parameters obtained from fitted Neural Network Model are as follows:
1. Customer Age
2. Customer Bank Age
3. Gender
4. Change in Occupation
5. Income
6. Change in Income
7. Transaction Number Decrease
8. Transaction Size Decrease
9. Credit Limit Utilized
10. Change in Marital Status
11. Number of Complaints
12. Sentiment Score
7.3 Fault Measure
Organizations use natural language to communicate with employees, customers, partners and public, as
well as to organize information internally for future reference. Over 80% of useful information is being
stored in the form of text. It is not affordable to ignoring the dominant portion of data. Emails, web pages,
memos, call center transcripts, survey responses, claims notes, legal cases, patent descriptions, research
articles, and incident reports - all hold valuable pieces of knowledge that enable analysts to discover
patterns, trends and inconsistency. This knowledge predict outcome of future situation, and makes better
business decisions. Obtaining knowledge from unstructured text represents a major technological
challenge.
The solution offered by ‘Big Data’ technology for automated knowledge discovery in large volumes of text
based on a combination of sentiment analysis and text mining.
Sentiment analysis means, to extract information from annotations of eyewitnesses and parties
involved. The ‘Sentiment Score’ computed by assigning positive & negative weights to each annotation
forms a part of the fault measure.

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‘Fault Measurement Life Cycle’
(Figure -4)
The ‘Fault Measure’, plays important role in predicting probabilities of success of Churn in first predictive
model. In first predictive model, ‘fault Measure’ treated as independent variable, so it has an effect on
predicted probabilities. It also supports to predict settlement amount in second predictive model via ‘Fault
Measure’ and predicted probabilities as independent variables.
The following graph show how we can reach to utmost accuracy of predictive models by using Big Data

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(Figure -5)
7.4 Logistic Regression
This is most popular method to build models to predict customer leaving. It regresses the outcome of the
variable of interest (such as attrition) on a number of variables that may co-vary with (predict) the
outcome variable. It defines the probability of an outcome by the magnitude of the score obtained by
adding or subtracting the coefficients assigned to the predicting variables.
Model Description:
The customer data is very large which may include unwanted variables, i.e. variables which are not
responsible for customer churn. So in order to find most significant variables we fitted stepwise regression
model. The survival model, Cox Proportional Hazard model is fitted for churn prediction.
This is survival model that incorporate the regression component, because regression model can be used
to examine the effect of predictors on event time. It relates the time that passes before some event
occurs to one or more covariates that may be associates with response.
The Sentiment Score which is nothing but customer sentiments about service, products etc. add value in
predicting survival probabilities and other factors. As most of customers report their complaints,
dissatisfaction before leaving service via mails, phone calls which is source of unstructured data.
The Model used for Churn Analytics is as below.
logit(Y) = ln(π/1-π)=ά+B1x1+B2X2……..
Π(probability)=(Y |X1)=x1.X1+x2.X2
=e ά+ᵝ1x1 + ᵝ2x2/(1+ e ά+ᵝ1x1 + ᵝ2x2)
Here X1,X2,X3 are Independent Variable like Transaction amount, Transaction Decrease etc,
The details Variables are defined in the below table 1
Π = Probability
Alpha(ά) is the Y intercept
Beta s (ᵝ) are regression Coefficient

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Predictive Model for Churn Probability
Predictive Model Logistic Regression Model
Data Type Churn Data for Customer Complaint and Transaction
Family Binomial
Link Function Logit
Response Variable ‘Churn’s Status
Independent Variable 1) Customer Age 2) Customer Bank Age 3) Gender 4) Income 5)
Change in Income 6) Change in Marital Status 7) Transaction Number
Decrease 8) Transaction Size Change 9) Credit Limit Utilization 10)
Number of Complaints
Additional Variable Sentiment Score from ‘Fault Measure’
Predictive Output Probability of Success or Failure
( Table 1)
7.4.1 Logistic Regression Model Build
( TextArea -1 )
fit_CASA<- glm(CASA_EVENT~
AGE_CATG+RES_ST+INCOME_CATG+OCCUP_CATG+ACT_COND+CASA_AGE_CATG
+GENDER_CATG+MAR_ST_CHNG+CASA_TRAN_NO_DEC_CATG+
CASA_AMT_DEC_CATG+CASA_SENT_CATG,data
=Model_Input_Data, family=binomial("logit"))
coefficient_CASA<- (fit_CASA$coefficient)
coefficient_CASA<- data.frame(coefficient_CASA)
summary(fit_CASA)
step_CASA<-step(fit_CASA,direction="backward")
coefficient_step_CASA<- data.frame(step_CASA$coefficients)
variables_CASA <- rownames(coefficient_CASA)
coeff_CASA <- data.frame(variables_CASA)
coeff_CASA$coefficient<- NA
coefficient_CASA$variables<- variables_CASA
variables_CASA<- rownames(coefficient_step_CASA)
coefficient_step_CASA$varaibles<- variables_CASA
threshold1=0.5
esti.res1=NULL
esti.res1[which(pp.fitted_CASA>=threshold1)]=1
esti.res1[which(pp.fitted_CASA<threshold1)]=0
PP_Estimates_CASA<-
data.frame(CASA_EVENT=Model_Input_Data$CASA_EVENT,EST_EVENT_CASA=esti.res
1, EST_PROB_CASA=pp.fitted_CASA)

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7.4.2 Sample Data Input Format
Attribute Name Description
Cust_Name Name of Customer
Cust_id Customer Id starting from 5001
Gender Gender of customer (F-Female, M-Male)
Cust_Age Customer current age
Maritial_Status Customer current marital status
Product1 Checking Account
Product2 Credit Card
Last_Trans_Amt Last Transaction amount in $
Curr_Trans_Amt Current transaction amount in $
Tran_Amt_decr The decrease in transaction amount as compared to last
transaction amount
(Last_Trans_Amt - Curr_Trans_Amt)
Credit_Limit Credit limit for customer in $
Credit_Limit_Utilized It is the number of transactions instances with credit limit
exploited.
Avg_Mnth_Bal_Amt Monthly average balance in customer account in $
Avg_Mnth_Purch_Amt Monthly average purchase amount in $
No_Complaints_CC Number of complaints for credit card
No_Complaints_CA Number of complaints for checking account
Total_No_Complaints Total number of complaints submitted by customer
Sent_Score_CA Sentiment score of CA complaint
Sent_Score_CC Sentiment score of CC complaint
Total_Sent_Score Total sentiment score =( Sent_Score_CA+ Sent_Score_CC)
( Table -2 )

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7.4.3 Sample Data Output
Cust ID Customer
Name
No of
Complaints
Trans Amt
Decrease
Sentiment
Score
Churn
Probability
RAG
5003 Alice
Levine
1 $224 0 34 %
5005 Jill Ayears 3 $3050 1 95.56%
5006 Daisy leavy 6 $8900 1 99.79%
5009 Sergio Cole 4 $1115 0 67.23%
(Table 3)
7.5 GLM( Generalized Linear Model) – NBA
We have defined our LR model for Churn Probability. Now I am using GLM for identification of Best
offering as per Business rules for churn out or near to churn out customer. There will be various offering
from business side ( Service, Product, Consulting etc) to various classification of Customer need. The
details of GLM is given as per Below. However, Logistic Regression can be implemented based on the
Key factors need for this offering selection.
Predictive Model for Churn Probability
Predictive Model Generalized Linear Model
Data Type Churn Data for Customer Complaint and Transaction
Family Gamma
Link Function Identity
Response Variable ‘Business Offering’
Independent Variable 1) Customer Age 2) Income 3) Change in Income 4) Change in Marital
Status 5) Number of valid Complaints 6) Product Offering Catalogue 7)
CB Score 8) Product Propensity( Intermediate Calculation)
Additional Variable ‘Fault Measure’ and Churn Probability
Predictive Output Product/Service Offering
( Table -4 )

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7.5.1 GLM Model Build –NBA
( TextArea -2 )
7.5.2 Sample Data Output
Cust ID Customer
Name
CB Score Product
Propensity
Sentiment
Score
Product
Offering
5003 Alice Levine 1 LOW 0 Do not
offer
5005 Jill Ayears 3 HIGH 1 Gold
Credit
Card
5006 Daisy leavy 6 HIGH 1 Gold
Credit
Card
5009 Sergio Cole 4 HIGH 0 Standard
Credit
Card
( Table -5 )
churn$CHURN_LIKELIHOOD[offer[which(churn$LIKELIHOOD[offer]>0.5)]] =
"HIGH"
churn$CHURN_LIKELIHOOD[offer[which(churn$LIKELIHOOD[offer]<=0.5)]] =
"LOW"
churn$ATTRITION_RATE<- NA
churn$ATTRITION_RATE[attrition[which(churn$LIKELIHOOD[attrition]>0.5)]
] = "HIGH"
churn$ATTRITION_RATE[attrition[which(churn$LIKELIHOOD[attrition]<=0.5)
]] = "LOW"
churn$PROPENSITY<- PROPENSITY
churn$CLTV<- NA

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8.0 Result
Below (Figure -6) is the Dashboard of Churn PoC/PoV being developed to prototype the Concept.
Here the customer who are already churned out are flagged with ‘Red’ Color with ‘Likelihood of churn’ .
Below (Figure -7) shows the Churn Probability with Various contribution factor for churning .
(Figure -8) shows the NBA offering based on the Analysis
(Figure -6)
Figure -7
Shows Churn
Probability
with
progression
of Time
frame.
(99.91 %)
Indicates
the
Customer is
Churning

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( Figure -7)
(Figure -8)
9.0 Reference:
Customer Churn Prediction and Prevention
http://www.optimove.net/churn-prediction-prevention.aspx
On Customer Attrition By Vladimir Stojanovski
http://it.toolbox.com/blogs/crm-realms/on-customer-attrition-29522
What is Customer Attrition
http://www.wisegeek.com/what-is-customer-attrition.htm
With Big Data Comes Great Responsibility
http://www.banktech.com/business-intelligence/with-big-data-comes-great-
responsibility/232600252
Allison, Paul D. 1984 Event History Analysis Regression for Longitudinal Event Data
Sage
University Papers Series on Quantitative Applications in the Social Sciences 07-046 Beverly
Hills and London: Sage Publications
Campbell, Donald t. and Julian Stanley 1963 Experimental and Quasi-Experimental
Designs for ResearchBoston: Houghton Mifflin Company
Przeworski, Adam and Henry Teune The Logic of Comparative Social Inquiry1970 New
York: Wiley-Interscience
Findings from the Field – Emily McRae, Research Analyst, The Olinger Group
Offering
based on NBA
Modeling

21
10 .0 Abbreviations
NBA -Next Best Action
LR - Linear Regression
GLM - Generalized Linear Model
ANNM- Artificial Neural Network Method
LTV - LifeTime Value
HDFS - Hadoop Distributed File System

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