Counting your customers: Compounding customer’s in-store decisions, interpurchase time and repurchasing behavior

Abstract

Consumer buying decision is an important element of any company’s efforts to count its customers. Stochastic models have been used extensively in previous studies of marketing and are usually constructed so as to easily fit the data and make predictions. However, the regularity of customer interpurchase time, in-store decisions and repeat buying behavior are not considered simultaneously in the literature. Since the empirical evidence suggests that all the above factors do indeed influence the firm’s sales, we here present a methodology which takes all these elements into account. The methodology employed is illustrated by taking the customer purchase data for tea as an example. By not eliminating light buyers, the integrated model we developed achieves precise results and addresses the relative importance of each element while predicting firm sales.