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The purpose of this paper is to provide a customer lifetime value (CLV) model to carefully assess and classify banking customers using individual measures and covering customers’ relationships with a portfolio of products of the company.

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 customers of a bank.

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.

Previous CLV models are mostly conceptual, generalisation is one of their main concerns, are usually focussed on single product categories using aggregated customer data, 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.

In the tourism sector it is often impossible for an travel agent to answer all the specific questions about destinations in a certain area. The tourist expects an expert in front of him, but a travel agent can't know everything about all the holiday resorts. Also taking into account his lack of time due to the enormous number of tourists, the travel agent will overwhelm the client with detailed brochures. Unfortunately, this service doesn't fulfill the requirements of quality management. The travel agent needs a tool to give better advice to the tourist. At the same time this can result in a strategic differentiation from competitors. Together with the students of the special licentiate in Tourism at the Limburg University Centre, Faculty of Applied Economics, we have designed such an advice system. It should lead to an improvement in the quality of advice given to the tourist who is looking for the appropriate holiday destination. We used the technology of an expert system. The experiment was limited to the 13 seaside resorts on the Belgian Coast.

Customer satisfaction continues to be an important topic in the financial services industry. However, there is an increasing awareness that customer satisfaction as such is not enough. Distinguishes between overall satisfied customers and latently dissatisfied customers; the latter being those customers who, although reporting satisfaction in a survey, have other characteristics (i.e. satisfaction with specific service items and/or socio‐demographic characteristics) that resemble dissatisfied customers. The identification of these latently dissatisfied customers may function as an early warning signal. Indeed, their probability to defect is relatively high and can be compared to that of dissatisfied customers. Proposes a data mining technique called “characteristic rules” to identify latently dissatisfied customers of a Belgian bank. Appropriate marketing actions (dissatisfaction management) may help to avoid these customers leaving. Therefore, the objective of this study is to provide scholars and business managers with theoretical, methodological and managerial insights into identifying latently dissatisfied customers.