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

TL;DR

This paper introduces Incremental Profit per Conversion (IPC), a new uplift measure for e-commerce promotions with response-dependent costs, enabling efficient profit estimation using only converted data and a single model.

Contribution

The paper proposes IPC and a response transformation that simplifies uplift modeling for response-dependent cost promotions, reducing model complexity and bias.

Findings

IPC effectively estimates profit using only converted data.

The response transformation reduces noise and class imbalance issues.

Validation on synthetic data shows improved profit estimation accuracy.

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.