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The MCMC Approach for Solving the Pareto/NBD Model and Possible Extensions

39 citations0/0 files storedInternational Conference on Natural Computation (ICNC)

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Customer churn and segmentation Statistical Distribution Estimation and Applications Markov Chains and Monte Carlo Methods

Abstract

Though the Pareto/NBD (developed by Schmittlein et al. 1987) is a powerful model for customer base analysis, it is difficult to implement especially in terms of parameter estimation. In this paper, the authors propose a MCMC algorithm for model estimation, and a Monte Carlo simulative approach to calculate key results of the model. The outcome of the method is a measure in which value is operationalized as a probability distribution, in contrast to previous studies has actually computed a spot estimation. The algorithm is applied into two direct marketing datasets and gets close parameter estimates with MLE. By implementing MC simulation, the study also shows a good interval predictive performance of the Pareto/NBD. Further more, the authors propose three possible extensions to the Pareto/NBD model and derive a GG/NBD model as a generalization to the Pareto/NBD.

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References

2000On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical Investigation and Implications for Marketing 1987Counting Your Customers: Who-Are They and What Will They Do Next?2001Customer Equity: Building and Managing Relationships As Valuable Assets 2005“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model 1973A Consumer Purchasing Model with Erlang Inter-Purchase Times 1987Counting Your Customers 2003A Bayesian Approach to Modeling Purchase Frequency

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2019COMPARATIVE ANALYSIS OF SELECTED PROBABILISTIC CUSTOMER LIFETIME VALUE MODELS IN ONLINE SHOPPING 2017Marketing Models for the Customer-Centric Firm 2020Leveraging purchase regularity for predicting customer behavior the easy way 2010Counting your customers from an “always a share” perspective 2017A MCMC approach for modeling customer lifetime behavior using the COM‐Poisson distribution 2009Deterministic and stochastic Customer Lifetime Value models. Evaluating the impact of ignored heterogeneity in non-contractual contexts

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