| Summary: | Purpose – The purpose of this study is to provide a model to assess and classify banking customers based on the concept of Customer Lifetime Value (CLV) in order to determine which kind of customers creates more value to the bank.
Design/methodology/approach – The proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the Latent Class regression. The model is tested using real transaction data of 1,357 randomly-selected customers of a bank.
Findings – This research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management.
Originality/value – Previous CLV models are mostly conceptual, generalization is one of their main concerns, are usually focused on single product categories, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision-makers to improve the understanding of the value of each customer and their behaviour towards different financial products.
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