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A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign

Author

Listed:
  • Meltem Sanisoglu

    (Istanbul Technical University
    Istanbul Technical University)

  • Sebnem Burnaz

    (Istanbul Technical University)

  • Tolga Kaya

    (Istanbul Technical University)

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.

Suggested Citation

  • Meltem Sanisoglu & Sebnem Burnaz & Tolga Kaya, 2024. "A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 909-924, December.
  • Handle: RePEc:pal:jmarka:v:12:y:2024:i:4:d:10.1057_s41270-023-00254-2
    DOI: 10.1057/s41270-023-00254-2
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    References listed on IDEAS

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    1. Georgios Marinakos & Sophia Daskalaki, 2017. "Imbalanced customer classification for bank direct marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(1), pages 14-30, March.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Friederike Paetz & Winfried J. Steiner & Harald Hruschka, 2022. "Correction to: Advanced data analysis techniques with marketing applications," Journal of Business Economics, Springer, vol. 92(4), pages 563-564, May.
    4. Friederike Paetz & Winfried J. Steiner & Harald Hruschka, 2022. "“Advanced data analysis techniques with marketing applications”," Journal of Business Economics, Springer, vol. 92(4), pages 557-561, May.
    5. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
    6. Arno de Caigny & Kristof Coussement & Wouter Verbeke & Khaoula Idbenjra & Minh Phan, 2021. "Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach," Post-Print hal-03599615, HAL.
    7. Eugenia Y. Huang & Chia-jung Tsui, 2016. "Assessing customer retention in B2C electronic commerce: an empirical study," Journal of Marketing Analytics, Palgrave Macmillan, vol. 4(4), pages 172-185, December.
    8. Daniel Baier & Björn Stöcker, 2022. "Profit uplift modeling for direct marketing campaigns: approaches and applications for online shops," Journal of Business Economics, Springer, vol. 92(4), pages 645-673, May.
    9. Maria Petrescu & Anjala S. Krishen, 2023. "A decade of marketing analytics and more to come: JMA insights," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 117-129, June.
    10. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    Full references (including those not matched with items on IDEAS)

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