Date of this Version
European Journal of Operational Research
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.
Originally published in the European Journal of Operational Research © 2016 Elsevier
This is a pre-publication version. The final version is available at http://dx.doi.org/10.1016/j.ejor.2015.09.002
customer-base analysis, probability models, data aggregation, data privacy and security, information loss
Jerath, K., Fader, P. S., & Hardie, B. G. (2016). Customer-Base Analysis using Repeated Cross-Sectional Summary (RCSS) Data. European Journal of Operational Research, 249 (1), 340-350. http://dx.doi.org/10.1016/j.ejor.2015.09.002
Applied Statistics Commons, Business Administration, Management, and Operations Commons, Business Analytics Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Marketing Commons
Date Posted: 15 June 2018
This document has been peer reviewed.