Speaker
Description
Selecting customers for marketing actions is an important decision for companies. The profitability of a customer and his inactivity risk are two important aspects of this selection process. These indicators can be obtained using the known Pareto/NBD model. This work proposes clustering customers based on their purchase frequency and purchase value per period before implementing the Pareto/NBD model onto each cluster. This initial cluster model allows estimating the customers purchase value and improves the parameter estimation accuracy of the Pareto/NBD by using alike individuals in the fitting. Models are implemented using Bayesian inference as to determine the uncertainty behind the different estimates. Finally, using the outputs of both models, the initial cluster and the Pareto/NBD, the project developed a guideline to classify clients into interpretable groups to facilitate their prioritization for marketing actions. The methodology was developed and implemented on a set of 25,600 sales from a database of 1,500 customers from beauty products wholesaler.
| Keywords | Customer Base Analysis; Customer Lifetime Value; Marketing |
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