CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling
arXiv:2607.05242v1 Announce Type: cross Abstract: Personalized incentive allocation is vital for e-commerce, where uplift modeling is the standard for estimating Individual Treatment Effects (ITE). However, traditional models often fail in complex multi-seller environments with violations of the Stable Unit Treatment Value Assumption (SUTVA). We identify two critical challenges: Seller-level Cannibalization, where incentives shift expenditure between shops without growing the platform, and Incentive-level Cannibalization, where organic conversions or alternative rewards introduce significant n
The increasing complexity of e-commerce platforms and the drive for more efficient personalized incentive allocation is spurring advanced uplift modeling techniques.
Sophisticated e-commerce platforms will be able to optimize incentive spending, directly impacting profitability and market share in competitive environments.
This framework offers a refined approach to uplift modeling, addressing specific cannibalization issues that traditional methods overlook, allowing for more strategic incentive deployment.
- · E-commerce platforms
- · Data scientists
- · Consumers (potentially more relevant offers)
- · E-commerce platforms using naive incentive models
- · Traditional marketing consultancies
Improved ROI on marketing and incentive programs in multi-seller e-commerce environments.
Increased concentration of market power among e-commerce platforms that effectively implement advanced AI-driven optimization.
Further entrenchment of AI's role in core business strategic decision-making, moving beyond simple automation to complex economic modeling.
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Read at arXiv cs.AI