Personalized Pricing Based on Behavioral Signals: Revenue Uplift and Fairness Tradeoffs in E-Commerce |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Authors : Anmol Aggarwal | ||
| DOI : 10.14445/22312803/IJCTT-V73I4P116 | ||
How to Cite?
Anmol Aggarwal, "Personalized Pricing Based on Behavioral Signals: Revenue Uplift and Fairness Tradeoffs in E-Commerce," International Journal of Computer Trends and Technology, vol. 73, no. 4, pp. 114-119, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I4P116
Abstract
Personalized pricing strategies increasingly rely on machine learning to tailor discounts based on user behavior. While these methods promise improved conversion and revenue, their fairness and targeting precision remain underexplored. In this study, we simulate two behavior-based pricing policies, one broad and one selective, using session-level e-commerce data and estimate their impact using uplift modeling techniques.We implement and compare the TwoModel (T-Learner) and Class Transformation frameworks to evaluate treatment effectiveness. Our results show that stricter treatment rules, though applied to fewer users, yield higher conversion uplift and revenue per treated session. However, they also exclude many high-opportunity users, raising fairness concerns.By integrating causal inference, pricing simulation, and behavioral fairness analysis, this paper highlights the tradeoffs between targeting precision, business value, and equitable incentive distribution. We propose uplift modeling as a robust foundation for building fair and profitable personalized pricing systems.
Keywords
Behavioral pricing, Conversion uplift, Machine learning, Personalized pricing, Revenue optimization.
Reference
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