Ranking customers for marketing actions with a two-stage Bayesian cluster and Pareto/NBD models
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Modelling customer behaviour to predict their future purchase frequency and value is crucial when selecting customers for marketing activities. The profitability of a customer and their risk of inactivity are two important factors in this selection process. These indicators can be obtained using the well-known Pareto/NBD model. Here we cluster customers based on their purchase frequency and value over a given period before applying the Pareto/NBD model to each cluster. This initial cluster model provides the customer purchase value and improves the predictive accuracy of the Pareto/NBD parameters by using similar individuals when fitting the data. Finally, taking the outputs from both models, the initial cluster and Pareto/NBD, we present some recommendations to classify customers into interpretable groups and facilitate their prioritisation for marketing activities. To illustrate the methodology, this paper uses a database with sales from a beauty products wholesaler.
CitationPuig-de-Dou, I.; González, D.; Puig, X. Ranking customers for marketing actions with a two-stage Bayesian cluster and Pareto/NBD models. "Applied stochastic models in business and industry", 25 Març 2022, vol. 38, núm. 4, p. 609-619.
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