Integrating consumer characteristics into the stochastic modelling of purchase loyalty

Abstract

Purpose – This paper aims to extend a widely used stochastic model of purchase loyalty to include covariates such as demographics, psychographics and geodemographics. Potentially, this allows covariates to explain variations in brand performance measures (BPMs) such as penetration/reach, average purchase frequency, sole buying, share of category requirements, repeat purchase and so forth. The result is to integrate consumer-based segmentation into previously unsegmented stochastic models of brand performance. Design/methodology/approach – This paper describes a model for predicting BPMs. Covariates are then introduced into the model, with discussion of model specification, model estimation, overall model assessment, and the derivation of generalised theoretical BPMs. The outcome is a practical procedure for behavioural loyalty segmentation. Findings – The implications for strategy and management in applying covariates to the BPMs are considerable. Where there are concentrations of consumers with high repeated purchase/consumption, then many aspects of the marketing mix will be affected. An investigation of the role of covariates in understanding BPMs in the laundry detergent market is presented as an example, and ways for market analysts to display results are demonstrated. Originality/value – Despite the fact that BPMs are the best operationalisation of behavioural loyalty, until now there has not been a model to evaluate the impact of consumer characteristics as covariates on these BPMs. This paper's original contribution includes a model that fits covariates to the BPMs. New statistical and graphical methods are described. Computer software for fitting the model and generating the output is available from the authors.