We address a critical question that many firms are facing in this era of "big data": 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 (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 RCSS data 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. Our results consistently and convincingly establish that model performance associated with the use of three or four cross-sections of RCSS data (as judged by model fit, parameter recovery, and forward-looking metrics of customer value) can closely match the model performance associated with the use of individual-level data. We confirm the results of the simulations on a real dataset of purchases from an online fashion retailer. The thesis of our approach is that existing statistical models continue to have value in a "big data" world, but to harness this value one may want to approach estimation of these models in a different manner.
Jerath, Kinshuk, Peter Fader, and Bruce G. S. Hardie. "Customer-Base Analysis on a 'Data Diet': Model Inference Using Repeated Cross-Sectional Summary (RCSS) Data." Columbia Business School, 2014.
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