Segment Discovery: Enhancing E-commerce Targeting
This addresses the challenge of inefficient customer engagement for e-commerce companies, though it appears incremental as it builds on existing uplift modeling methods.
The paper tackles the problem of suboptimal customer targeting in e-commerce by proposing a policy framework that uses uplift modeling and constrained optimization to identify customers for interventions, demonstrating improvements over state-of-the-art approaches in large-scale experiments and production.
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.