PurposeThe 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.Design/methodology/approachThe 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.FindingsTwo‐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.Practical implicationsThe 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.Originality/valueThis 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.
Abstract:The purpose of this paper is to develop and validate an instrument to measure the concept of e-readiness in the context of online learning environment. A rigorous method of scale development was undertaken to achieve this purpose: conceptual model development, item generation, content validation, exploratory study, and confirmatory study. The results of a study conducted in university, specializing in online education, suggest that the e-learning readiness (ELR) is a five-dimensional construct, namely Self Competence, Self-Directed Learning, Motivation, Financial, and Usefulness. Further psychometric tests show a parsimonious ELR measure with support for convergent, discriminant and predictive validities. In addition, these results show that the second-order factor model is the better fitting for the e-learning readiness construct. Based on the results, it can be concluded this instrument is useful and appropriate is assessing e-learning readiness construct. The theoretical and practical implications of the study as well as directions for future research are discussed.
Purpose -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. Design/methodology/approach -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). Findings -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. Originality/value -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.
All rights reserved including database rights. This electronic file is for the personal use of authorised users based at the subscribing company's office location. It may not be reproduced, posted on intranets, extranets or the internet, e-mailed, archived or shared electronically either within the purchaser's organisation or externally without express written permission from Warc.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.