ASPIRE: Air Shipping Recommendation for E-commerce Products via Causal Inference Framework

Speed of delivery is critical for the success of e-commerce platforms. Faster delivery promise to the customer results in increased conversion and revenue. There are typically two mechanisms to control the delivery speed - a) replication of products across warehouses, and b) air-shipping the product. In this paper, we present a machine learning based framework to recommend air-shipping eligibility for products. Specifically, we develop a causal inference framework (referred to as Air Shipping Recommendation or ASPIRE) that balances the trade-off between revenue or conversion and delivery cost to decide whether a product should be shipped via air. We propose a doubly-robust estimation technique followed by an optimization algorithm to determine air eligibility of products and calculate the uplift in revenue and shipping cost. We ran extensive experiments (both offline and online) to demonstrate the superiority of our technique as compared to the incumbent policies and baseline approaches. ASPIRE resulted in a lift of +79 bps of revenue as measured through an A/B experiment in an emerging marketplace on Amazon.