By using user’s purchase history across multiple shops together with behavioral tracking information we have build several algorithms that help us to identify high risk returns before they are made. We use over a hundred of different factors such as various return rates, overall performance of the brands and items across market as well as user’s fashion preferences and behavioural indicators.
All this helps us to leverage network effect and use every bit of relevant data we have to provide you with actionable insights in order for you to take necessary steps to prevent returns before they happen.
In the Score API reference will take you step by step through using the API.