1. Introduction

In the context of customer relationship management, customer lifetime value (CLV) or customer equity (CE) becomes important because it is a disaggregate metric to evaluate marketing decisions [1], which can be utilised to allocate resources appropriately and identify profitable consumers [2]. Companies are looking forward to better approaches to create value and optimise their market offerings to appeal to customers and make profits [3]. Many firms are utilising CLV regularly to control and supervise the strategies of marketing as well as evaluate the business success. For companies, it is of interest to know how much net benefit it can expect from their customers. It is recognised that clv has become a significant component of

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

companies' central strategy [4, 5]. CLV of customers at present and in the future can be a good proxy of the general corporate value [6]. Meanwhile, at each point in each customer's lifetime with the firm, the firm would like to form some expectation regarding the lifetime value of that customer.

It is proposed by Gupta and other scholars [18] that CLV for a customer is [19, 36]:

Estimating Customer Lifetime Value Using Machine Learning Techniques

http://dx.doi.org/10.5772/intechopen.76990

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Another review of CLV model sees Jain and Singh [26]. Linear regression with the variance that stabilises the transformation forecasted with the ordinary least square is the first approach. Selecting a stable variance transformation can be informed by residual plots [27]. As shown by Neter et al. [28], the linear regression forecasted with iteratively reweighted least square is the second approach of regression. IRLS is another means to solve the heteroscedasticity issue.

For the sake of simplicity, the only predictor variables in these models are the recency, frequency and monetary (RFM) type, Buckinx and Van den Poel [29], and the variables of

The models of RFM have been utilised in direct marketing for three decades developed to target marketing programmes at specific customers with the objective to improve response rates. Studies show that customers' response rates vary the most by their recency, followed by their purchase frequency and monetary value [30]. Before these models, companies typically used demographic profiles of customers for targeting purposes. However, research strongly suggests that past purchases of consumers are better predictors of their future purchase

They have many restrictions though RFM, or other models of scoring try to forecast customers' behaviour in the future and are therefore associated with CLV implicitly [15, 16, 31]. Firstly, in the next periods, the behaviour can be predicted by the models. However, to estimate CLV, we need to estimate customers' purchase behaviour not only in Period 2 but also in Periods 3, 4, 5 and so on. Secondly, RFM variables are real underlying behaviour's imperfect index stemmed from a real distribution. The models of RFM have neglected this part. Thirdly, the previous behaviour of customers can be an outcome of the company's previous marketing promotion, which has been ignored by the models. In spite of the restrictions, due to the implementation in

One fundamental limitation of RFM models is that they are scoring models and do not explicitly provide a number for customer value. However, RFM is essential past purchase variables that should be good predictors of future purchase behaviour of customers. Fader et al. [15, 16] showed how RFM variables could be used to build a CLV model that overcomes

where:

= price paid by a consumer at time t.

T = time horizon for estimating CLV.

RFM are sound predictors for CLV [15, 16].

real practice, the models of RFM are the core of the industry.

behaviour than demographics.

many of its limitations.

AC = acquisition cost.

3.1.2. RFM model

= direct cost of servicing the customer at time t.

= probability of customer repeat buying or being 'alive' at time t.

= discount rate or cost of capital for the firm.
