Abstract
It is evident that companies invest considerable resources in creating and implementing marketing initiatives mainly to attract customers and thereby increase their market presence. Determining if these marketing efforts are effective in convincing customers to take desired actions is challenging since companies need to ensure that their marketing campaigns are feasible in order to target the right customers who are most likely to respond to the marketing treatment. Prescriptive analytics provide better means to measure the impact of a marketing campaign on customer behavior when compared to conventional predictive analytics. Uplift modeling presents an opportunity to maximize the incremental impact of marketing treatment by determining the most responsive customer segment who will take positive action only because of receiving the treatment. This paper aims to suggest an uplift modeling approach in a marketing campaign in B2B context for better evaluation of performance which has not gained enough attention as B2C in literature. By applying three uplift modeling techniques to a real-world B2B cross-sell campaign, it is demonstrated that the campaign effectiveness can be increased significantly by determining the customers who are truly responsive to the related campaign.
Similar content being viewed by others
References
Albert, J., and D. Goldenberg. 2021. E-commerce promotions personalization via online multiple-choice knapsack with uplift modeling. In Proceedings of the 31st ACM ınternational conference on ınformation and knowledge management, 2863–2872. New York: ACM.
Ascarza, E. 2018. Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research 55 (1): 80–98.
Athey, S., and G. Imbens. 2015. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences 113 (27): 7353–7360.
Baier, D., and B. Stöcker. 2022. Profit uplift modeling for direct marketing campaigns: Approches and applications for online shops. Journal of Business Economics 92: 645–673.
Börthas, L., and J.K. Sjölander. 2020. Machine learning based prediction and classification for uplift modeling (Degree project). Stockholm: KTH Royal Insitute of Technology School of Engineering Sciences.
Bose, I., and X. Chen. 2009. Quantitative models for direct marketing: A review from systems perspective. European Journal of Operational Research 195 (1): 1–16.
Cao, Y., Xu, C., and Hairong, G. 2017. Untangle customers’ incrementality using uplift modeling with a case study on direct marketing. In MWSUG, Paper BF03.
Chen, X., L. Ji, L. Jiang, S. Miao, and C. Shi. 2020. More bang for your buck: Effective kol marketing campaign in emerging short-video markets. SSRN Journal. https://doi.org/10.2139/ssrn.3655819.
De-Caigny, A., K. Coussement, W. Verbeke, K. Idbenjra, and M. Phan. 2021. Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach. Industrial Marketing Management 99: 28–39.
Devriendt, F., D. Moldovan, and W. Verbeke. 2018. A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics. Big Data 6 (1): 13–41.
Devriendt, F., J. Berrevoets, and W. Verbeke. 2021. Why you should stop predicting customer churn and start using uplift models. Information Sciences 548: 597–515.
Diemert, E., and C. Renaudin. 2018. A large scale benchmark for uplift modeling. In Proceedings of AdKDD & Target Ad, 603–621. Long Beach: KDD.
Goldenberg, D., Albert, J., Bernardi, L. and Estevez, P. 2020. Free lunch! Retrospective uplift modeling for dynamic promotions recommendation within ROI constraints. In Fourteenth ACM conference on recommender systems (RecSys’20), September 22–26, 2020, Virtual Event, Brazil.
Gubela, R., Beque, A., Gebert, F. and Lessmann, S. 2019. Conversion uplift in e-commerce: A systematic benchmark of modeling strategies, IRTG 1792 Discussion Paper, No. 2018-062, Humboldt-Universität zu Berlin, International Research Training Group 1792 “High Dimensional Nonstationary Time Series”, Berlin.
Gubela, R.M., and S. Lessmann. 2021. Uplift modeling with value-driven evaluation metrics. Decision Support Systems 150: 113648.
Guelman, L., M. Guillen, and A.M. Perez-Marin. 2012. Random forests for uplift modeling: an insurance customer retention case. In International conference on odeling and simulation in engineering, economics and management, 123–133. Cham: Springer.
Guelman, L., M. Guillen, and A.M. Perez-Marin. 2015. Uplift random forests. Cybernetics and Systems 46 (3–4): 230–248.
Guelman, L. 2014. “uplift: Uplift modeling”. R package version 0.3.5. https://cran.r-project.org/package=uplift. Accessed 06 Nov 2022.
Gutierrez, P., and J.Y. Gerardy. 2017. Causal inference and uplift modelling: A review of the literature. In International conference on predictive applications and APIs, 1–13. Cambridge: PMLR.
Hansotia, B., and B. Rukstales. 2002. Incremental value modeling. Journal of Interactive Marketing 16: 3.
Hu, J. 2023. Customer feature selection from high-dimensional bank direct marketing data for uplift modeling. Journal of Marketing Analytics 11: 160–171.
Huang, E.Y., and C. Tsui. 2016. Assessing customer retention in B2C electronic commerce: An empirical study. Journal of Marketing Analytics 4: 172–185.
Jaskowski, M. and Jaroszewicz, S. 2012. Uplift modeling for clinical trial data. In ICML 2012 workshop on clinical data analysis.
Kane, K., V.S. Lo, and J. Zheng. 2014. Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods. Journal of Marketing Analytics 82 (4): 218–238.
Karlsson, H. 2019. Uplift modeling: identifying optimal treatment group allocation and whom to contact to maximize return on investment. Sweden: University of Linköping.
Kondareddy, S.P., S. Agrawal, and S. Shekhar. 2016. Incremental response modeling based on segmentation approach using uplift decision trees. In Industrial Conference on Data Mining, 54–63. Cham: Springer.
Lo, V.S.Y. 2002. The true lift model—A novel data mining approach to response modeling in database marketing. ACM SIGKDD Explorations Newsletter 4 (2): 78–86.
Ludziejewski, J., Tomaszewska, P. and Zalewska A. 2020. XAI stories case studies for explainable artificial intelligence. Warsaw University and Warsaw University of Technology. https://pbiecek.github.io/xai_stories/index.html. Accessed 28 May 2022
Marinakos, G., and S. Daskalaki. 2017. Imbalanced customer classification for bank direct marketing. Journal of Marketing Analytics 5: 14–30.
Munting, M. 2020. An explorative study towards the feasibility of uplift modeling within a direct marketing setting and a web-based setting. Netherlands: University of Twente.
Nyberg, O., and A. Klami. 2023. Exploring uplift modeling with high class imbalance. Data Mining and Knowledge Discovery 37: 736–766.
Olaya, D., J. Vasquez, S. Maldonado, J. Miranda, and W. Verbeke. 2020. Uplift modeling for preventing student dropout in higher education. Decision Support Systems 134: 11320.
Paetz, F., W.J. Steiner, and H. Hruscha. 2022. Advanced data analysis techniques with marketing applications. Journal of Business Economics 92: 557–561.
Petrescu, M., and A.S. Krishen. 2023. A decade of marketing analytics and more to come: JMA insights. Journal of Marketing Analytics 11: 117–129.
Pok, W. 2020. How uplift modeling works. https://ambiata.com/blog/2020-07-07-uplift-modeling. Accessed 12 Dec 2021.
Pratt, S., S. McCabe, I. Cortes-Jimenez, and A. Blake. 2009. Measuring the effectiveness of destination marketing campaigns: Comparative analysis of conversion studies. Journal of Travel Research 49 (2): 179–190.
Proença, H.M., and F. Moraes. 2023. Incremental profit per conversion: a response transformation for uplift modeling in e-commerce promotions. In KDD’23, August 6–10. Long Beach: KDD.
Radcliffe, N.J., and R. Simpson. 2007. Identifying who can be saved and who will driven away by retention activity. Journal of Telecommunication Management 1 (2): 168–176.
Radcliffe, N.J., and P.D. Surry. 2011. Real-world uplift modelling with significance based uplift trees. Edinburgh: Portrait Technical Report TR-2011-1, Stochastic Solutions.
Robins, J., and A. Rotnitzky. 2004. Estimation of treatment effects in randomised trials with non-compliance. Biometrika 91 (4): 763–793.
Röbler, J., Tilly, R. and Schoder, D. 2021. To treat or not to treat: Reducing volatility in uplift modeling through weighted ensembles. In 54th Hawaii international conference on system sciences.
Rosset, S., Neumann, E., Eick, U., Vatnik, N. and Idan, I. 2001. Evaluation of prediction models for marketing campaigns. In KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 456–461.
Rudas, K., and S. Jaroszewicz. 2018. Linear regression for uplift modeling. Data Mining and Knowledge Discovery 32 (5): 1275–1305.
Rzepakowski, P., and S. Jaroszewicz. 2012. Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems 32: 303–327.
Sanisoglu, M., T. Kaya, and Ş Burnaz. 2022. Marketing campaign management using machine learning techniques: An uplift modeling approach. International Conference on Intelligent and Fuzzy Systems 505: 140–147.
Shimizu, A., R. Togashi, A. Lam, and N.V. Huynh. 2019. Uplift modeling for cost effective coupon marketing in c-to-c e-commerce. In IEEE 31st international conference on tools with artifical intelligence. New York: IEEE.
Shteingart, H., G. Oostra, O. Levinkron, N. Parush, G. Shabat, and D. Aronovich. 2022. ML prescriptive canvas for optimizing business outcomes. In KDD2022. Long Beach: KDD.
Siegel, E. 2011. Uplift modeling: Predictive analytics can’t optimize marketing decisions without it. San Francisco: Prediction Impact Inc.
Soltys, M., S. Jaroszewicz, and P. Rzepakowski. 2015. Ensemble methods for uplift modeling. Data Mining and Knowledge Discovery 29: 1531–1559.
Su, X., J. Kang, J. Fan, R.A. Levine, and X. Yan. 2012. Facilitating score and causal inference trees for large observational studies. Journal of Machine Learning Research 13: 2955–2994.
Wager, S., and S. Athey. 2018. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association 113 (523): 1228–1242.
Zaniewicz, L., and S. Jaroszewicz. 2013. Support vector machines for uplift modeling. In 2013 IEEE13th international conference on data mining workshops, 131–138. New York: IEEE.
Zhang, R., and T. Tran. 2011. An information gain-based approach for recommending useful product reviews. Knowledge and Information Systems 26: 419–434.
Zhao, Y., Fang, X. and Simchi-Levi, D. 2017. Uplift modeling with multiple treatments and general response types. In Proceedings of the 2017 SIAM international conference on data mining, 588–596, SIAM
Zhao, Z., and T. Harinen. 2019. Uplift modeling for multiple treatments with cost optimization. In IEEE international conference on data science and advanced analytics, 1–10. New York: IEEE.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sanisoglu, M., Burnaz, S. & Kaya, T. A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign. J Market Anal (2023). https://doi.org/10.1057/s41270-023-00254-2
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1057/s41270-023-00254-2