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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

    [email protected]

    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) :

    ���� = ∑ �� �� �� ��� ��� ��������� � �� �� �� ���������� �� where,