Segment Discovery: Enhancing E-commerce Targeting

TL;DR

This paper introduces a new targeting policy framework for e-commerce that uses uplift modeling and constrained optimization to identify the most beneficial customers for interventions, improving business outcomes.

Contribution

It presents a novel policy framework combining uplift modeling with constrained optimization for targeted interventions in e-commerce.

Findings

Outperforms existing targeting methods in large-scale experiments

Demonstrates significant business value improvement

Validated through production implementation

Abstract

Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This customer engagement increases acquisition of more customers and retention of existing ones, leading to more business for the company while improving customer experience. Often, customers are either randomly targeted or targeted based on the propensity of desirable behavior. However, such policies can be suboptimal as they do not target the set of customers who would benefit the most from the intervention and they may also not take account of any constraints. In this paper, we propose a policy framework based on uplift modeling and constrained optimization that identifies customers to target for a use-case specific intervention so as to maximize the value to the business, while taking account of any given constraints. We demonstrate improvement over state-of-the-art targeting approaches using two large-scale experimental studies and a production implementation.