Kavli Affiliate: Ting Xu
| First 5 Authors: Yoon Tae Park, Ting Xu, Mohamed Anany, ,
| Summary:
Uplift modeling is essential for optimizing marketing strategies by selecting
individuals likely to respond positively to specific marketing campaigns. This
importance escalates in multi-treatment marketing campaigns, where diverse
treatment is available and we may want to assign the customers to treatment
that can make the most impact. While there are existing approaches with
convenient frameworks like Causalml, there are potential spaces to enhance the
effect of uplift modeling in multi treatment cases. This paper introduces a
novel approach to uplift modeling in multi-treatment campaigns, leveraging
score ranking and calibration techniques to improve overall performance of the
marketing campaign. We review existing uplift models, including Meta Learner
frameworks (S, T, X), and their application in real-world scenarios.
Additionally, we delve into insights from multi-treatment studies to highlight
the complexities and potential advancements in the field. Our methodology
incorporates Meta-Learner calibration and a scoring rank-based offer selection
strategy. Extensive experiment results with real-world datasets demonstrate the
practical benefits and superior performance of our approach. The findings
underscore the critical role of integrating score ranking and calibration
techniques in refining the performance and reliability of uplift predictions,
thereby advancing predictive modeling in marketing analytics and providing
actionable insights for practitioners seeking to optimize their campaign
strategies.
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