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

Author

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  • Jerath, Kinshuk
  • Fader, Peter S.
  • Hardie, Bruce G.S.

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 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.

Suggested Citation

  • Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:1:p:340-350
    DOI: 10.1016/j.ejor.2015.09.002
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    as
    1. Jae Young Lee & David R. Bell, 2013. "Neighborhood Social Capital and Social Learning for Experience Attributes of Products," Marketing Science, INFORMS, vol. 32(6), pages 960-976, November.
    2. Jerry Rackley, 2015. "Marketing Analytics Roadmap," Springer Books, Springer, number 978-1-4842-0259-3, April.
    3. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    4. John R. Hauser, 1978. "Testing the Accuracy, Usefulness, and Significance of Probabilistic Choice Models: An Information-Theoretic Approach," Operations Research, INFORMS, vol. 26(3), pages 406-421, June.
    5. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    6. Zhiqiang Zheng & Balaji Padmanabhan & Steven O. Kimbrough, 2003. "On the Existence and Significance of Data Preprocessing Biases in Web-Usage Mining," INFORMS Journal on Computing, INFORMS, vol. 15(2), pages 148-170, May.
    7. Oecd, 2014. "Consumer Policy Guidance on Mobile and Online Payments," OECD Digital Economy Papers 236, OECD Publishing.
    8. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    9. Wu, Couchen & Chen, Hsiu-Li, 2000. "Counting your customers: Compounding customer's in-store decisions, interpurchase time and repurchasing behavior," European Journal of Operational Research, Elsevier, vol. 127(1), pages 109-119, November.
    10. Krieger, Abba M & Gastwirth, Joseph L, 1984. "Interpolation from Grouped Data for Unimodal Densities," Econometrica, Econometric Society, vol. 52(2), pages 419-426, March.
    11. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    12. Milad Sawiris, 2000. "Optimum grouping and the boundary problem," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(3), pages 363-371.
    13. Siddharth Singh & Sharad Borle & Dipak Jain, 2009. "A generalized framework for estimating customer lifetime value when customer lifetimes are not observed," Quantitative Marketing and Economics (QME), Springer, vol. 7(2), pages 181-205, June.
    14. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
    15. Huang, Chun-Yao, 2012. "To model, or not to model: Forecasting for customer prioritization," International Journal of Forecasting, Elsevier, vol. 28(2), pages 497-506.
    16. Li, Tongzhe & McCluskey, Jill J., 2017. "Consumer preferences for second-generation bioethanol," Energy Economics, Elsevier, vol. 61(C), pages 1-7.
    17. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    18. Fader, Peter S. & Hardie, Bruce G. S., 2002. "A note on an integrated model of customer buying behavior," European Journal of Operational Research, Elsevier, vol. 139(3), pages 682-687, June.
    19. Kinshuk Jerath & Peter S. Fader & Bruce G. S. Hardie, 2011. "New Perspectives on Customer "Death" Using a Generalization of the Pareto/NBD Model," Marketing Science, INFORMS, vol. 30(5), pages 866-880, September.
    20. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    21. Davies, J. B. & Shorrocks, A. F., 1989. "Optimal grouping of income and wealth data," Journal of Econometrics, Elsevier, vol. 42(1), pages 97-108, September.
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    2. Wieringa, Jaap & Kannan, P.K. & Ma, Xiao & Reutterer, Thomas & Risselada, Hans & Skiera, Bernd, 2021. "Data analytics in a privacy-concerned world," Journal of Business Research, Elsevier, vol. 122(C), pages 915-925.

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