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

TL;DR,

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Abstract

Speed of delivery is critical for the success of e-commerce platforms. Faster delivery promise to the customer results in increased conver- sion 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 bal- ances 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 demon- strate the superiority of our technique as compared to the incum- bent 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.

物流速度对于电商平台而言至关重要,物流速度有保证会增加平台的转化和收益。通常有两种办法提升物流速度:在前置仓铺货或者空运。

本文中,我们提出一种基于机器学习的框架来决定商品是否有空运的资格,我们利用ASPIRE(Air Shipping Recommendation)来平衡是否将一个商品空运所带来的转化收益和运输成本。我们采用doubly-robust的预估技术来预测收益和成本的增量,并通最优化算法来决定产品的空运资格。

我们通过离线和在线的实验,都表明我们的方法相比基线策略和baseline方法具有优越性。AB实验结果,

ASPIRE最终为Amazon带来了0.79%的收益增长。

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