Ranking customers for marketing actions with a two‐stage Bayesian cluster and Pareto/ NBD models

Autor: Ignasi Puig-de-Dou, Daniel González Ibáñez, Xavier Puig
Přispěvatelé: Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. ADBD - Anàlisi de Dades Complexes per a les Decisions Empresarials
Rok vydání: 2022
Předmět:
Zdroj: Applied Stochastic Models in Business and Industry. 38:609-619
ISSN: 1526-4025
1524-1904
Popis: 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.
Databáze: OpenAIRE
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