CAPTURING CUSTOMER EVOLVING BUYING-BEHAVIOUR IN CONSUMER PACKAGED GOODS DATA
Akomolafe Abayomi A*., Amahia G.N.**, and Chuckwu A.U.***
* Department of Mathematics and Statistics, Joseph Ayo Babalola University, Ilesa, Osun State
** Department of Statistics, University of Ibadan, Nigeria
e-mail: akomolafea@yahoo.com
Abstract
Many retailers monitor customer buying-behaviour as a measure of their stores’ success. However, summary measures such as the total buying-behaviour per month provide little insight about individual-level shopping behaviour. Additionally, behaviour may evolve over time, especially in a changing environment like the Internet. Understanding the nature of this evolution provides valuable knowledge that can influence how a retail store is managed and marketed. This paper develops an individual-level model for store visiting behaviour based on juice drink and packed chicken buying-behaviour data. We capture cross-sectional variation in store-visit behaviour as well as changes over time as visitors gain experience with the store. That is, as someone makes more visits to an outlet, her latent rate of buying may increase, decrease, or remain unchanged as in the case of static, mature markets. So as the composition of the customer population changes (e.g., as customers mature or as large numbers of new and inexperienced Internet shoppers enter the market), the overall degree of buyer heterogeneity that each store faces may change. We also examine the relationship between visiting frequency and purchasing pro pensity. Previous studies suggest that customers who shop frequently may be more likely to make a purchase on any given shopping occasion. As a result, frequent shoppers often comprise the preferred target segment. We find evidence supporting the fact that people who visit a store more frequently are more likely to buy. However, we also show that changes (i.e., evolution) in an individual’s visit frequency over time provides further information regarding which customer segments are more likely to buy. Rather than simply targeting all frequent shoppers, our results suggest that a more refined segmentation approach that incorporates how much an individual’s behaviour is changing could more efficiently identify a profitable target segment.
Keywords: Consumer Packaged Goods (CPG) data, Buying Behaviour, Duration models, Heterogeneity, Nonstationarity