A generalized framework for estimating customer lifetime value when customer lifetimes are not observed

Singh, S S and Borle, S and Jain, D C (2009) A generalized framework for estimating customer lifetime value when customer lifetimes are not observed. QME, 7 (2). pp. 181-205.

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Measuring customer lifetime value (CLV) in contexts where customer defections are not observed, i.e. noncontractual contexts, has been very challenging for firms. This paper proposes a flexible Markov Chain Monte Carlo (MCMC) based data augmentation framework for forecasting lifetimes and estimating customer lifetime value (CLV) in such contexts. The framework can be used to estimate many different types of CLV models---both existing and new. Models proposed so far for estimating CLV in noncontractual contexts have built-in stringent assumptions with respect to the underlying customer lifetime and purchase behavior. For example, two existing state-of-the-art models for lifetime value estimation in a noncontractual context are the Pareto/NBD and the BG/NBD models. Both of these models are based on fixed underlying assumptions about drivers of CLV that cannot be changed even in situations where the firm believes that these assumptions are violated. The proposed simulation framework not being a model but an estimation framework allows the user to use any of the commonly available statistical distributions for the drivers of CLV, and thus the multitude of models that can be estimated using the proposed framework (the Pareto/NBD and the BG/NBD models included) is limited only by the availability of statistical distributions. In addition, the proposed framework allows users to incorporate covariates and correlations across all the drivers of CLV in estimating lifetime values of customers.

Affiliation: Indian School of Business
ISB Creiators:
ISB Creators
Singh, S S
Item Type: Article
Additional Information: The research article was published by the author with the affiliation of Rice University.
Uncontrolled Keywords: Customer lifetime value, Forecasting, Simulation, Data augmentation, MCMC
Subjects: Marketing
Depositing User: Ilayaraja M
Date Deposited: 15 Apr 2019 19:05
Last Modified: 15 Apr 2019 20:20
URI: http://eprints.exchange.isb.edu/id/eprint/843
Publisher URL: https://dx.doi.org/10.1007/s11129-009-9065-0
Publisher OA policy: http://sherpa.mimas.ac.uk/romeo/issn/1570-7156/
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