E-commerce & Digital
Returns prediction
Score orders by likelihood of return based on product category, customer history, order composition, and fulfilment signals. Enables proactive intervention.
10–20%
reduction in return rate for targeted orders
Use case assessment
Significant impact. Material effect on profitability, revenue, or cost base, visible at business level.
Challenging. Significant technical work required. Likely needs an AI engineer or specialist vendor. Not a quick win.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Zalando's AI-powered virtual try-on and GPT-powered assistant reduced return rates by up to 7% among users. Prior AI size tools had already reduced wrong-size returns by 21% (Reruption).
Requires a unified view of order history, customer history, and return history. If these sit in separate systems, the data integration work precedes the AI work.
Model training needs a substantial labelled dataset of past orders with return outcomes, typically 12-24 months of history minimum.
Define the intervention: what does the team do with a high-return-risk score? The prediction is only valuable if there's an action attached to it.
Design the intervention before building the model. What does the business do with a high-return-risk prediction? Options include a proactive fit recommendation, a customer service flag, or a targeted email. The action is where the value is.
Experiment starter: Test the intervention hypothesis first, without any ML. Have a merchandiser manually flag 100 orders they would consider high return risk based on experience. Trigger a proactive communication or intervention for those customers. If the return rate on that group is materially lower than the baseline, the intervention works. Now build the model to automate the identification.
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