Predictive analytics uses historical patterns to estimate future events. For returns, predictive models estimate which orders are likely to be returned based on product characteristics, customer history, order value, and shipping addresses. High-risk orders can trigger quality verification, proactive communication, or return policy adjustments. Effective prediction reduces net return costs by enabling preemptive intervention.
← Glossary
Predictive Analytics
Statistical techniques using historical data to forecast future outcomes like return likelihood.
Related terms
- Machine Learning Model — A mathematical system trained on historical data to make predictions or classifications on new data.
- Return Rate — The percentage of orders that customers return within a given period, a key metric for product and operational health.
- Training Data — The labeled examples used to teach an AI model how to categorize returns correctly.
- Feature Engineering — The process of selecting and transforming raw data into the most useful inputs for an AI model.