Abstract 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 customerbase 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 (e.g., four quarterly histograms). 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 privacy 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 * Corresponding author Email addresses: [email protected] (Kinshuk Jerath), [email protected] (Peter S. Fader), [email protected] (Bruce G.S. Hardie) URL: www.petefader.com (Peter S. Fader), http://www.brucehardie.com (Bruce G.S. Hardie) 1 The authors thank David Bell for providing the Bonobos data used in this paper. 2 The second author acknowledges the support of the Wharton Customer Analytics Initiative. serves as an suitable substitute for individual-level data. We confirm the results of the simulations on a real dataset of purchasing from an online fashion retailer
