Comparative analysis of selected probabilistic customer lifetime value models in online shopping
Abstract
The selection of a suitable customer lifetime value (CLV) model is a key issue for companies that are introducing a CLV managerial approach in their online B2C relationship stores. The online retail environment places CLV models on several specific assumptions, e.g. non-contractual relationship, continuous purchase anytime, variable-spending environment. The article focuses on empirical statistical analysis and predictive abilities of selected probabilistic CLV models that show very good results in an online retail environment compared to different model families. For comparison, eleven CLV models were selected. The comparison has been made to the online stores’ datasets from Central and Eastern Europe with annual revenues of hundreds of millions of euros and with almost 2.3 million customers. Probabilistic models have achieved overall good and consistent results on the majority of the studied transactional datasets, with BG/NBD and Pareto/NBD models that can be considered stable with significant lifts from the baseline Status quo model. Abe's variant of Pareto/NBD have underperformed multiple criterions and would not be fully useful for the studied datasets without further improvements. In the end, the authors discuss the deployment implications of selected CLV models and propose further issues for future research to address.
Keyword : online retail, marketing management, e-commerce, probabilistic model, CEE region, B2C
How to Cite
Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2019). Comparative analysis of selected probabilistic customer lifetime value models in online shopping. Journal of Business Economics and Management, 20(3), 398-423. https://doi.org/10.3846/jbem.2019.9597
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Fader, P. S., & Hardie, B. G. S. (2009). Probability models for customer-base analysis. Journal of Interactive Marketing, 23(1), 61-69. https://doi.org/10.1016/j.intmar.2008.11.003
Fader, P. S., & Hardie, B. G. S. (2013). The Gamma-Gamma model of monetary value. Retrieved from http://www.brucehardie.com/notes/025/gamma_gamma.pdf
Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005a). Counting your customers the easy way: An alternative to the Pareto/NBD Modelmodel. Marketing Science, 24(2), 275-284. https://doi.org/10.1287/mksc.1040.0098
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Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139-155. https://doi.org/10.1177/1094670506293810
Haenlein, M. (2017). How to date your clients in the 21st century: Challenges in managing customer relationships in today’s world. Business Horizons, 60(5), 577-586. https://doi.org/10.1016/j.bushor.2017.06.002
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Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications, 26(2), 181-188. https://doi.org/10.1016/S0957-4174(03)00133-7
Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2018). Modeling and application of customer lifetime value in online retail. Informatics, 5(1), 2. https://doi.org/10.3390/informatics5010002
Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2019). Predictive performance of customer lifetime value models in ecommerce and the use of non-financial data. Prague Economic Papers (Accepted paper). https://doi.org/10.18267/j.pep.714
Kim, S-Y., Jung, T.-S., Suh, E.-H., & Hwang, H.-S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1), 101-107. https://doi.org/10.1016/j.eswa.2005.09.004
Knox, G., & van Oest, R. (2014). Customer complaints and recovery effectiveness: A customer base approach. Journal of Marketing, 78(5), 42-57. https://doi.org/10.1509/jm.12.0317
Kotler, P., & Keller, K. L. (2015). Marketing management (15th ed). New Jersey: Prentice Hall.
Kumar, V., & Pansari, A. (2016). National culture, economy, and customer lifetime value: Assessing the relative impact of the drivers of customer lifetime value for a global retailer. Journal of International Marketing, 24(1), 1-21. https://doi.org/10.1509/jim.15.0112
Kumar, V., Pozza, I. D., Petersen, J. A., & Denish Shah, D. (2009). Reversing the logic: The path to profitability through relationship marketing. Journal of Interactive Marketing, 23(2), 147-156. https://doi.org/10.1016/j.intmar.2009.02.003
Ma, S. H., & Liu, J. L. (2007). The MCMC approach for solving the Pareto/NBD model and possible extensions. In Proceedings of the 3rd International Conference on Natural Computation (pp. 505-512). Haikou, China. https://doi.org/10.1109/ICNC.2007.728
Nenonen, S., & Storbacka, K. (2016). Driving shareholder value with customer asset management: Moving beyond customer lifetime value. Industrial Marketing Management, 52, 140-150. https://doi.org/10.1016/j.indmarman.2015.05.019
Óskarsdóttir, M., Baesens, B., & Vanthienen, J. (2018). Profit-based model selection for customer retention using individual customer lifetime values. Big Data, 6(1), 53-65. https://doi.org/10.1089/big.2018.0015
Qi, J.-Y., Qu, Q.-X., Zhou, Y.-P., & Li, L. (2015). The impact of users’ characteristics on customer lifetime value raising: evidence from mobile data service in China. Information Technology and Management, 16(4), 273-290. https://doi.org/10.1007/s10799-014-0200-6
Pfeifer, P. E., Haskins, M. E., & Conroy, R. M. (2005). Customer life time value, customer profitability, and the treatment of acquisition spending. Journal of Managerial Issues, 17(1), 11-25.
Platzer, M. (2016). Customer base analysis with BTYDplus. Retrieved from https://cran.r-project.org/web/packages/BTYDplus/vignettes/BTYDplus-HowTo.pdf
Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing Science, 35(5), 779-799. https://doi.org/10.1287/mksc.2015.0963
Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17-35. https://doi.org/10.1509/jmkg.64.4.17.18077
Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis: An industrial purchase process application. Marketing Science, 13(1), 41-67. https://doi.org/10.1287/mksc.13.1.41
Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers – who are they and what will they do next. Management Science, 33(1), 1-24. https://doi.org/10.1287/mnsc.33.1.1
Singh, S. S., & Jain, D. C. (2010). Measuring customer lifetime value. In K. M. Naresh (Ed.), Review of marketing research, 6, 37-62. Emerald Group Publishing Limited, Bingley. https://doi.org/10.1108/S1548-6435(2009)0000006006
Singh, S. S., & Jain, D. C. (2013). Measuring customer lifetime value: Models and analysis. INSEAD Working Paper No. 2013/27/MKT. https://doi.org/10.2139/ssrn.2214860
Statista. (2018). Retail e-commerce sales worldwide from 2014 to 2021. Retrieved from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
Vojtko, V. (2014). Rethinking the concept of just noticeable difference in online marketing. Acta Informatica Pragensia, 3(2), 204-218. https://doi.org/10.18267/j.aip.49
Wieringa, R. J. (2014). Design science methodology for information systems and software engineering. Berlin: Springer. https://doi.org/10.1007/978-3-662-43839-8
Williams, C., & Williams, R. (2015). Optimizing acquisition and retention spending to maximize market share. Journal of Marketing Analytics, 3(3), 159-170. https://doi.org/10.1057/jma.2015.11
Wübben, M., & Wangenheim, F. (2008). Instant customer base analysis: Managerial heuristics often “Get It Right”. Journal of Marketing, 72(3), 82-93. https://doi.org/10.1509/jmkg.72.3.82
Abe, M. (2009). “Counting your customers” one by one: A hierarchical Bayes extension to the Pareto/NBD model. Marketing Science, 28(3), 541-553. https://doi.org/10.1287/mksc.1090.0502
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99
Batislam, E. M., Denizel, M., & Filiztekin, A. (2007). Empirical validation and comparison of models for customer base analysis. International Journal of Research in Marketing, 24(3), 201-209. https://doi.org/10.1016/j.ijresmar.2006.12.005
Borle, S., Singh, S. S., & Jain, D. C. (2008). Customer lifetime value measurement. Management Science, 54(1), 100-112. https://doi.org/10.1287/mnsc.1070.0746
Centre for Retail Research. (2017). Online Retailing: Britain, Europe, US and Canada 2017. Retrieved from http://www.retailresearch.org/onlineretailing.php
Chamberlain, B. P., Cardoso, A., Liu, C. H., Pagliari, R., & Deisenroth, M. P. (2017). Customer lifetime value prediction using embeddings. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1753-1762). Halifax, Canada. https://doi.org/10.1145/3097983.3098123
Chang, W., Chang, C., & Li, Q. (2012). Customer lifetime value: A review. Social Behavior and Personality, 40(7), e2243. https://doi.org/10.2224/sbp.2012.40.7.1057
Colombo, R., & Jiang, W. (1999). A stochastic RFM model. Journal of Interactive Marketing, 13(3), 2-12. https://doi.org/10.1002/(SICI)1520-6653(199922)13:3<2::AID-DIR1>3.0.CO;2-H
Damm, R., & Monroy, C. R. (2011). A review of the customer lifetime value as a customer profitability measure in the context of customer relationship management. Intangible Capital, 7(2), 261-279. https://doi.org/10.3926/ic.2011.v7n2.p261-279
Donkers, B., Verhoef, P. C., & De Jong, M. G. (2007). Modeling CLV: A test of competing models in the insurance industry. Quantitative Marketing and Economics, 5(2), 163-190. https://doi.org/10.1007/s11129-006-9016-y
Dresch, A., Lacerda, D. P., & Antunes Jr, J. A. V. (2015). Design science research: A method for science and technology advancement. Springer. https://doi.org/10.1007/978-3-319-07374-3
Dziurzynski, L., Wadsworth, E., & McCarthy, D. (2015). BTYD: Implementing buy’til you die models (R package version, 2.4). Retrieved from https://cran.r-project.org/web/packages/BTYD/BTYD.pdf
Ecommerce Europe. (2016). European B2C E-commerce Report 2016. Ecommerce Foundation, Brussels.Ehrenberg, A. S. (1959). The pattern of consumer purchases. Applied Statistics, 8(1), 26-41. https://doi.org/10.2307/2985810
Estrella-Ramón, A. M., Sánchez-Pérez, M., Swinnen, G., & VanHoof, K. (2013). A marketing view of the customer value: Customer lifetime value and customer equity. South African Journal of Business Management, 44(4), 47-64. https://doi.org/10.4102/sajbm.v44i4.168
Fader, P. S. (2012). Customer centricity: Focus on the right customers for strategic advantage. Wharton Digital Press.
Fader, P. S., & Hardie, B. G. S. (2001). Forecasting repeat sales at CDNOW: A case study. Interfaces, 31(3), S94-S107. https://doi.org/10.1287/inte.31.3s.94.9683
Fader, P. S., & Hardie, B. G. S. (2009). Probability models for customer-base analysis. Journal of Interactive Marketing, 23(1), 61-69. https://doi.org/10.1016/j.intmar.2008.11.003
Fader, P. S., & Hardie, B. G. S. (2013). The Gamma-Gamma model of monetary value. Retrieved from http://www.brucehardie.com/notes/025/gamma_gamma.pdf
Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005a). Counting your customers the easy way: An alternative to the Pareto/NBD Modelmodel. Marketing Science, 24(2), 275-284. https://doi.org/10.1287/mksc.1040.0098
Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005b). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. https://doi.org/10.1509/jmkr.2005.42.4.415
Gupta, S. (2009). Customer-based valuation. Journal of Interactive Marketing, 23(2), 169-178. https://doi.org/10.1016/j.intmar.2009.02.006
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139-155. https://doi.org/10.1177/1094670506293810
Haenlein, M. (2017). How to date your clients in the 21st century: Challenges in managing customer relationships in today’s world. Business Horizons, 60(5), 577-586. https://doi.org/10.1016/j.bushor.2017.06.002
Haenlein, M., Kaplan, A. M., & Schoder, D. (2006). Valuing the real option of abandoning unprofitable customers when calculating customer lifetime value. Journal of Marketing, 70(3), 5-20. https://doi.org/10.1509/jmkg.70.3.5
Hubka, V., & Eder, W. E. (1996). Design science: Introduction to the needs, scope and organization of engineering design knowledge. Springer. https://doi.org/10.1007/978-1-4471-3091-8
Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications, 26(2), 181-188. https://doi.org/10.1016/S0957-4174(03)00133-7
Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2018). Modeling and application of customer lifetime value in online retail. Informatics, 5(1), 2. https://doi.org/10.3390/informatics5010002
Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2019). Predictive performance of customer lifetime value models in ecommerce and the use of non-financial data. Prague Economic Papers (Accepted paper). https://doi.org/10.18267/j.pep.714
Kim, S-Y., Jung, T.-S., Suh, E.-H., & Hwang, H.-S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1), 101-107. https://doi.org/10.1016/j.eswa.2005.09.004
Knox, G., & van Oest, R. (2014). Customer complaints and recovery effectiveness: A customer base approach. Journal of Marketing, 78(5), 42-57. https://doi.org/10.1509/jm.12.0317
Kotler, P., & Keller, K. L. (2015). Marketing management (15th ed). New Jersey: Prentice Hall.
Kumar, V., & Pansari, A. (2016). National culture, economy, and customer lifetime value: Assessing the relative impact of the drivers of customer lifetime value for a global retailer. Journal of International Marketing, 24(1), 1-21. https://doi.org/10.1509/jim.15.0112
Kumar, V., Pozza, I. D., Petersen, J. A., & Denish Shah, D. (2009). Reversing the logic: The path to profitability through relationship marketing. Journal of Interactive Marketing, 23(2), 147-156. https://doi.org/10.1016/j.intmar.2009.02.003
Ma, S. H., & Liu, J. L. (2007). The MCMC approach for solving the Pareto/NBD model and possible extensions. In Proceedings of the 3rd International Conference on Natural Computation (pp. 505-512). Haikou, China. https://doi.org/10.1109/ICNC.2007.728
Nenonen, S., & Storbacka, K. (2016). Driving shareholder value with customer asset management: Moving beyond customer lifetime value. Industrial Marketing Management, 52, 140-150. https://doi.org/10.1016/j.indmarman.2015.05.019
Óskarsdóttir, M., Baesens, B., & Vanthienen, J. (2018). Profit-based model selection for customer retention using individual customer lifetime values. Big Data, 6(1), 53-65. https://doi.org/10.1089/big.2018.0015
Qi, J.-Y., Qu, Q.-X., Zhou, Y.-P., & Li, L. (2015). The impact of users’ characteristics on customer lifetime value raising: evidence from mobile data service in China. Information Technology and Management, 16(4), 273-290. https://doi.org/10.1007/s10799-014-0200-6
Pfeifer, P. E., Haskins, M. E., & Conroy, R. M. (2005). Customer life time value, customer profitability, and the treatment of acquisition spending. Journal of Managerial Issues, 17(1), 11-25.
Platzer, M. (2016). Customer base analysis with BTYDplus. Retrieved from https://cran.r-project.org/web/packages/BTYDplus/vignettes/BTYDplus-HowTo.pdf
Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing Science, 35(5), 779-799. https://doi.org/10.1287/mksc.2015.0963
Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17-35. https://doi.org/10.1509/jmkg.64.4.17.18077
Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis: An industrial purchase process application. Marketing Science, 13(1), 41-67. https://doi.org/10.1287/mksc.13.1.41
Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers – who are they and what will they do next. Management Science, 33(1), 1-24. https://doi.org/10.1287/mnsc.33.1.1
Singh, S. S., & Jain, D. C. (2010). Measuring customer lifetime value. In K. M. Naresh (Ed.), Review of marketing research, 6, 37-62. Emerald Group Publishing Limited, Bingley. https://doi.org/10.1108/S1548-6435(2009)0000006006
Singh, S. S., & Jain, D. C. (2013). Measuring customer lifetime value: Models and analysis. INSEAD Working Paper No. 2013/27/MKT. https://doi.org/10.2139/ssrn.2214860
Statista. (2018). Retail e-commerce sales worldwide from 2014 to 2021. Retrieved from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
Vojtko, V. (2014). Rethinking the concept of just noticeable difference in online marketing. Acta Informatica Pragensia, 3(2), 204-218. https://doi.org/10.18267/j.aip.49
Wieringa, R. J. (2014). Design science methodology for information systems and software engineering. Berlin: Springer. https://doi.org/10.1007/978-3-662-43839-8
Williams, C., & Williams, R. (2015). Optimizing acquisition and retention spending to maximize market share. Journal of Marketing Analytics, 3(3), 159-170. https://doi.org/10.1057/jma.2015.11
Wübben, M., & Wangenheim, F. (2008). Instant customer base analysis: Managerial heuristics often “Get It Right”. Journal of Marketing, 72(3), 82-93. https://doi.org/10.1509/jmkg.72.3.82