Customer-base analysis using repeated cross-sectional summary (RCSS) data

• Published in: European journal of operational research 2016-02, Vol.249 (1), p.340-350
• Main Authors: Jerath, Kinshuk, Fader, Peter S., Hardie, Bruce G.S.
• Format: Article
• Language: English
• Subjects: Customer-base analysis; Data aggregation; Data analysis; Data privacy and security; Estimating techniques; Histograms; Information loss; Information storage; Privacy; Probability models; Studies
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2015.09.002

•We conduct customer base analysis using summaries of individual-level data.•We use repeated cross-sectional summaries (RCSS), e.g., quarterly histograms.•RCSS are easy to create, visualize, and distribute, and preserve privacy•Four quarterly histograms are a good substitute for individual-level data. We address a critical question that many firms are facing today: Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers’ transaction activity? We address this question in the context of customer-base analysis. A number of researchers have developed customer-base analysis models that perform very well given detailed individual-level data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated cross-sectional summaries (RCSS) of the transaction data. Such summaries are easy to create, visualize, and distribute, irrespective of the size of the customer base. An added advantage of the RCSS data structure is that individual customers cannot be identified, which makes it desirable from a data privacy and security viewpoint as well. We focus on the widely used Pareto/NBD model and carry out a comprehensive simulation study covering a vast spectrum of market scenarios. We find that the RCSS format of four quarterly histograms serves as a suitable substitute for individual-level data. We confirm the results of the simulations on a real dataset of purchasing from an online fashion retailer.