Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations

Abstract

How to acquire the most valuable consumers to grow your recommender system? We propose a dynamic consumer acquisition model to enable value-driven acquisition decisions. We build a model of consumer acquisition that takes into account the value that a consumer contributes to the recommender system, the cost of their participation (e.g., privacy loss), and the value of their participation to other consumers (via network externality). We also propose data-driven procedures to estimate this model to enable informed, value-driven acquisition decisions. On three different data sets, we perform comprehensive simulation-based evaluations to demonstrate the performance of this dynamic consumer acquisition model. We find nuanced relationships between the firm’s choice of incentive strategies and acquisition outcomes. Neither a constant pricing strategy nor a greedy pricing strategy may be optimal. Instead, under a moderately greedy strategy, where the firm only partially extracts the network externality from consumers, the dynamic acquisition sequence can outperform random acquisition sequences on firm utility, recommender system performance, and consumer surplus simultaneously. Our work contributes a novel theoretical framework, practical insights, and design artifacts to facilitate effective consumer acquisition in recommender systems.