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The purpose of this paper is to discuss and assess the structural characteristics (conceptual utility) of the most popular classification and predictive techniques employed in customer relationship management and customer scoring and to evaluate their classification and predictive precision.

A sample of customers' credit rating and socio‐demographic profiles are employed to evaluate the analytic and classification properties of discriminant analysis, binary logistic regression, artificial neural networks, C5 algorithm, and regression trees employing Chi‐squared Automatic Interaction Detector (CHAID).

With regards to interpretability and the conceptual utility of the parameters generated by the five techniques, logistic regression provides easily interpretable parameters through its logit. The logits can be interpreted in the same way as regression slopes. In addition, the logits can be converted to odds providing a common sense evaluation of the relative importance of each independent variable. Finally, the technique provides robust statistical tests to evaluate the model parameters. Finally, both CHAID and the C5 algorithm provide visual tools (regression tree) and semantic rules (rule set for classification) to facilitate the interpretation of the model parameters. These can be highly desirable properties when the researcher attempts to explain the conceptual and operational foundations of the model.

Most treatments of complex classification procedures have been undertaken idiosyncratically, that is, evaluating only one technique. This paper evaluates and compares the conceptual utility and predictive precision of five different classification techniques on a moderate sample size and provides clear guidelines in technique selection when undertaking customer scoring and classification.

The objective of this paper is to present and discuss the development of a transaction‐based model for segmenting users of internet banking. It aims to employ a random sample of clients of a large Canadian bank in generating the hybrid segments.

The basic transactional profile of the bank's clients was merged with Mosaic's financial segments contained in the Generation5 database. A random sample of 3 percent of a large Canadian chartered bank's clients was drawn from its transaction database. The transaction database employed contains clients from Quebec and the Maritime provinces. The sampling frame consisted of close to one million clients. Two‐step cluster analysis was employed to generate the transaction segment and later merged with the Mosaic financial segment to produce hybrid segments.

Two‐step cluster analysis identified four generic transaction segments which, when cross‐tabulated with the Mosaic financial segments, produced highly stable and interpretable segments. These hybrid segments are clearly superior to conventional life style or psychographic segments produced by classical segmentation methodologies.

The results of this study clearly demonstrate the functional and analytical superiority of hybrid customer segments. Hybrid segmentation, by cross‐tabulating transaction and Mosaic's financial segments, provides banks and financial institutions with superior strategic insights in customer understanding, customer segmentation, customer communication, customer prospecting and targeting.

This paper is the first to present, explain and to demonstrate the nature and the operational procedure to develop hybrid customer/client segments. More importantly, it is the first that goes beyond conventional approaches to segmenting banks' clients who engage in internet banking by integrating clients' transaction profiles and Mosaic financial segments. The resulting hybrid segments are radically different than the conventional, one‐dimensional segments produced by conventional cluster‐based segmentation.