Revenue Maximization using Multitask Learning for Promotion Recommendation

Abstract

This paper proposes and evaluates a multitask transfer learning approach to collectively optimize customer loyalty, retail revenue, and promotional revenue. Multitask neural network is employed to predict a customer's propensity to purchase within fine-grained categories. The network is then fine-tuned using transfer learning for a specific promotional campaign. Lastly, retail revenue and promotional revenue are jointly optimized conditioned on customer loyalty. Experiments are conducted using a large retail dataset that shows the efficacy of the proposed method compared to baselines used in the industry. A large retailer is currently adopting the proposed methodology in promotional campaigning owing to significant overall revenue and loyalty gains.