The retailer did not have a data team, an internal ML engineer, or a large transformation budget. What they did have was a costly returns problem and enough transaction history to understand the pattern.
The challenge
Returns were eating margin, straining staff time, and making inventory planning harder. The root causes were spread across product descriptions, purchasing behavior, and post-purchase support.
The approach
We focused on one measurable outcome instead of building a huge platform. The solution combined product-level signals, customer behavior, and fulfillment data to flag high-risk orders and improve product guidance before purchase.
The rollout
The team deployed the workflow in phases so they could compare behavior before and after each change. That made it easier to trust the recommendations and easier to refine the rules.
The result
Within three months, returns dropped by 34 percent. Support tickets tied to order confusion fell as well, and the business gained a repeatable process for testing new AI-driven operational improvements.