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With sales anticipated to reach $4 trillion by 2020, ecommerce is an unstoppable force. It’s no real mystery why — customers prefer the convenience of online stores compared with brick and mortar, countless surveys show — but it’s a mixed bag for companies that attempt to edge out rivals with liberal return policies. It’s estimated that as high as one-third of online orders are returned, which whittles down profit margins.

Researchers at Google and India-based ecommerce company Myntra Designs took a stab at the returns pain point in a new paper published on the preprint server Arxiv.org. Using a machine learning model trained on a data set of shoppers’ preferences, body shapes, product views, and more, they managed to predict per-customer return probability prior to purchase.

To identify which factors had a disproportionate effect on returns, the researchers conducted analyses on Myntra Designs’ ecommerce platform, which hosts approximately 600,000 products at any one time and facilitates millions of orders a week. They found that out of all returns, 4% occur when there are some similar products present in the cart. Additionally, they discovered that 53% of returns are due to size- and fit-related issues and that return rates are highly dependent on cart size, with cart sizes exceeding five products seeing return rates around 72% compared with 9% for carts with one product. And perhaps unsurprisingly, older inventory has an almost doubled return rate compared to newer items.

Armed with these insights, the team devised what they call a hybrid dual-model to forecast both cart and item return probability. A higher-level AI classifier classified returnable carts, while a second classifier (drawing on the carts classified as returnable by the first classifier) predicted return probability at an individual product level. Both trained on a data set containing samples across three categories — product-, cart-, and user-level features — including (but not limited to) things like brand, product age, cart size, order day and time, delivery city, order count, payment mode, and purchase frequency.

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So how’d it perform? In experiments, the best-performing return-predicting AI system achieved 83.2% area under the receiver operating characteristic (AUC) — a measure of detection accuracy — and 74% precision. In a live test conducted with 100,000 users, the number of orders dipped slightly (by 1.7%) compared with a control set, but the return percentage dropped by 3%.

The team notes that knowing which customers are likely to return an item enables a retailer to take preemptive actions, like personalizing shipping charges or making the product no-returnable by offering a coupon. “As future work, we plan to apply this model on more action items which can further help in reducing the overall returns,” they wrote.

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