Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions

• URL: http://arxiv.org/abs/2306.13759v2
• Date: Wed, 9 Aug 2023 18:43:47 GMT
• Title: Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions
• Authors: Hugo Manuel Proen\c{c}a, Felipe Moraes
• Abstract summary: This paper focuses on promotions with response-dependent costs, where expenses are incurred only when a purchase is made. Existing uplift model approaches often necessitate training multiple models, like meta-learners, or encounter complications when estimating profit. We introduce Incremental Profit per Conversion (IPC), a novel uplift measure of promotional campaigns' efficiency in unit economics.
• Score: 1.7640556247739623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Promotions play a crucial role in e-commerce platforms, and various cost structures are employed to drive user engagement. This paper focuses on promotions with response-dependent costs, where expenses are incurred only when a purchase is made. Such promotions include discounts and coupons. While existing uplift model approaches aim to address this challenge, these approaches often necessitate training multiple models, like meta-learners, or encounter complications when estimating profit due to zero-inflated values stemming from non-converted individuals with zero cost and profit. To address these challenges, we introduce Incremental Profit per Conversion (IPC), a novel uplift measure of promotional campaigns' efficiency in unit economics. Through a proposed response transformation, we demonstrate that IPC requires only converted data, its propensity, and a single model to be estimated. As a result, IPC resolves the issues mentioned above while mitigating the noise typically associated with the class imbalance in conversion datasets and biases arising from the many-to-one mapping between search and purchase data. Lastly, we validate the efficacy of our approach by presenting results obtained from a synthetic simulation of a discount coupon campaign.

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