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Stochastic Models for Predicting Customer Activity and Future Best Customers in Non-Contractual Settings

Abstract

After analyzing the drivers of relationship breadth, the next challenges the marketing executive is facing are predicting customer (in)activity and predicting the company’s future best customers. Based on a high number of recommendations within academic marketing literature, the stochastic Pareto/NBD (Schmittlein et al. 1987; Schmittlein and Peterson 1994) and BG/NBD model (Fader et al. 2005a) emerge as opportunities for replacing her allowedly simple heuristics that are currently used by her company.41 The models are attractive for several reasons: (1) They are based on a well established theoretical foundation (Ehrenberg 1988), (2) they solely operate on past purchase behavior, i.e., the information requirements are limited such that any customer database that tracks customers’ purchase behavior can be used, (3) the models make predictions about the customers’ future purchase-levels, and (4) the Pareto/NBD model, albeit not the BG/NBD model, generates probabilistic outputs about customer future activity.

Keywords

Stochastic Model Mean Absolute Percentage Error Simple Heuristic Cutoff Threshold Customer Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  1. Schmittlein, David C., Donald G. Morrison, and Richard Colombo (1987), “Counting Your Customers: Who Are They And What Will They Do Next?” Management Science, 33(1), 1–24.CrossRefGoogle Scholar
  2. Fader, Peter S., Bruce G. S. Hardie, and Ka Lok Lee (2005a), “‘Counting Your Customers’ the Easy Way: An Alternative to the Pareto/NBD Model,” Marketing Science, 24(2), 275–285.CrossRefGoogle Scholar

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© Gabler | GWV Fachverlage GmbH, Wiesbaden 2008

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