Customer Behavior in an Online Ordering Application: A Decision Scoring Model

This research presents the development of behavioral scoring models to predict future customer purchases in an online ordering application. Internet retailing lowers many barriers for customers switching between retailers for repeat purchases; thus, retaining existing customers is a key challenge for achieving profitability. Survey data were collected from 1,089 online customers of two companies. The subjective survey data were then used to predict purchases over the ensuing 12 months based on data from the company databases. The analysis illustrates the general applicability of predictive models of future customer purchases while also demonstrating the need to develop specific models tailored for an individual company’s operating and marketing environment. The models provide insight on how companies can target marketing dollars more effectively and allocate investment across multiple operational areas for maximum return. The research answers a call for rigorous research in the area of predictive marketing, an area in which many companies are excelling but where there is a scarcity of detailed knowledge regarding application of such models. Subject Areas: E-Commerce, Grocery Home Delivery, Internet Ordering, Longitudinal Research, Order Fulfillment, and Service Quality.