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Customer Lifetime Value Measurement using Machine Learning

Techniques

Tarun Rathi

Mathematics and Computing

Department of Mathematics

Indian Institute of Technology (IIT), Kharagpur -721302

08MA2027@iitkgp.ac.in

Project guide: Dr. V Ravi

Associate Professor, IDRBT

Institute of Development and Research in Banking Technology (IDRBT)

Road No. 1, Castle Hills, Masab Tank, Hyderabad – 500 057

http://www.idrbt.ac.in/

July 8, 2011

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Certificate

Date: July 8, 2011

This is to certify that the project Report entitled “Customer Lifetime Value

Measurement using Machine Learning Techniques” submitted by Mr. TARUN

RATHI, 3 rd

year student in the Department of Mathematics, enrolled in its 5

year integrated MSc. course of Mathematics and Computing, Indian Institute of

Technology, Kharagpur is a record of bonafide work carried out by him under

my guidance during the period May 6, 2011 to July 8, 2011 at Institute for

Development and Research in Banking Technology (IDRBT), Hyderabad.

The project work is a research study, which has been successfully completed as

per the set of objectives. I observed Mr. TARUN RATHI as sincere, hardworking

and having capability and aptitude for independent research work.

I wish him every success in his life.

Dr. V Ravi

Associate Professor, IDRBT

Supervisor

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Declaration by the candidate

I declare that the summer internship project report entitled, “Customer

Lifetime Value Measurement using Machine Learning Techniques” is my own

work conducted under the supervision of Dr. V Ravi at Institute of

Development and Research in Banking Technology, Hyderabad. I have put in 64

days of attendance with my supervisor at IDRBT and awarded project

fellowship.

I further declare that to the best of my knowledge the report does not contain

any part of any work, which has been submitted for the award of any degree

either by this institute or in any other university without proper citation.

Tarun Rathi

III yr. Undergraduate Student

Department of Mathematics

IIT Kharagpur

July 8, 2011

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Acknowledgement

I would like to thank Mr B. Sambamurthy, director of IDRBT, for giving me this

opportunity.

I gratefully acknowledge the guidance from Dr. V. Ravi, who helped me sort

out all the problems in concept clarifications; and without whose support, the

project would not have reached its present state. I would also like to thank Mr.

Naveen Nekuri for his guidance and sincere help in understanding important

concepts and also in the development of the WNN software.

Tarun Rathi

III yr. Undergraduate Student

Department of Mathematics

IIT Kharagpur

July 8, 2011

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Abstract: Customer Lifetime Value (CLV) is an important metric in relationship marketing

approaches. There have always been traditional techniques like Recency, Frequency and

Monetary Value (RFM), Past Customer Value (PCV) and Share-of-Wallet (SOW) for

segregation of customers into good or bad, but these are not adequate, as they only

segment customers based on their past contribution. CLV on the other hand calculates the

future value of a customer over his or her entire lifetime, which means it takes into account

the prospect of a bad customer being good in future and hence profitable for a company or

organisation. In this paper, we review the various models and different techniques used in

the measurement of CLV. Towards the end we make a comparison of various machine

learning techniques like Classification and Regression Trees (CART), Support Vector

Machines (SVM), SVM using SMO, Additive Regression, K-Star Method, Multilayer

Perceptron (MLP) and Wavelet Neural Network (WNN) for the calculation of CLV.

Keywords : Customer lifetime value (CLV), RFM, Share-of-Wallet (SOW), Past Customer

Value (PCV), machine learning techniques, Data mining, Support Vector Machines,

Sequential Minimal Optimization (SMO), Additive Regression, K-star Method, Artificial

Neural Networks (ANN), Multilayer Perceptron (MLP), Wavelet Neural Network (WNN).

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Contents

Certificate

Declaration by the candidate

Acknowledgement 1

Abstract 2

1. Introduction 4

2. Literature Review 5

2.1 Aggregate Approach 5

2.2 Individual Approach 8

2.3 Models and Techniques to calculate CLV 10

2.2.1 RFM Models 10

2.2.2 Computer Science and Stochastic Models 12

2.2.3 Growth/Diffusion Models 15

2.2.4 Econometric Models 15

2.2.5 Some other Modelling Approaches 17

3. Estimating Future Customer Value using Machine Learning Techniques 19

3.1 Data Description 19

3.2 Models and Software Used 20

3.2.1 SVM 20

3.2.2 Additive Regression and K-Star 21

3.2.3 MLP 22

3.2.4 WNN 22

3.2.5 CART 24

4. Results and Comparison of Models 27

5. Conclusion and Directions of future research 28

References 29

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1. Introduction: Customer Lifetime Value has become a very important metric in Customer

Relationship Management. Various firms are increasing relying on CLV to manage and

measure their business. CLV is a disaggregate metric that can be used to find customers who

can be profitable in future and hence be used allocate resources accordingly (Kumar and

Reinartz, 2006). Besides, CLV of current and future customers is a also a good measure of

overall value of a firm (Gupta, Lehmann and Stuart 2004).

There have been other measures as well which are fairly good indicators of customer

loyalty like Recency, Frequency and Monetary Value (RFM), Past Customer Value (PCV) and

Share-of-Wallet (SOW). The customers who are more recent and have a high frequency and

total monetary contribution are said to be the best customers in this approach. However, it

is possible that a star customer of today may not be the same tomorrow. Matlhouse and

Blattberg (2005) have given examples of customers who can be good at certain point and

may not be good later and a bad customer turning to good by change of job. Past Customer

Value (PCV) on the other hand calculates the total previous contribution of a customer

adjusted for time value of money. Again, PCV also does not take into account the possibility

of a customer being active in future (V. Kumar, 2007). Share-of-Wallet is another metric to

calculate customer loyalty which takes into account the brand preference of a customer. It

measures the amount that a customer will spend on a particular brand against other brands.

However it is not always possible to get the details of a customer spending on other brands

which makes the calculation of SOW a difficult task. A common disadvantage which these

models share is the inability to look forward and hence they do not consider the prospect of

a customer being active in future. The calculation of the probability of a customer being

active in future is a very important part in CLV calculation, which differentiates CLV from

from these traditional metrics of calculating customer loyalty. It is very important for a firm

to know whether a customer will continue his relationship with it in the future or not. CLV

helps firms to understand the behaviour of a customer in future and thus enable them to

allocate their resources accordingly.

Customer Lifetime Value is defined as the present value of all future profits obtained

from a customer over his or her entire lifetime of relationship with the firm (Berger and

Nassr, 1998). A very basic model to calculate CLV of a customer is (V. Kumar, 2007) :

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