The Brand Context
Founded in 1954, Ann Taylor is a premium, tailored brand serving modern, career-minded women, where fit, fabrication, and polish drive purchase.
The Challenge
Ann Taylor wasn’t getting the results that it wanted from generic personalization solutions and was looking for an intent- focused, feature-based recommender. They wanted a tool that could interpret nuanced signals (e.g., fabric, fit, sleeve/neckline details, occasion) rather than lean on generic “people like you” logic.
The experiment leading to Mapp Fashion
Two 2-way tests compared Mapp Fashion against 2 other players, including Salesforce Einstein. The initial proof of value began with Similar Items on PDP.
The Solution
- Implementation of feature-based recommendations on PDP tuned to understand the real customer intent.
- Real-time weighting of availability by size and return prone attributes to bias toward keepworthy suggestions.
- Deep, detailed and consistent product attributes encompassing everything from the physical, the contextual and the trend data.
Results
- Test winner: Mapp Fashion was the overall winner and solidly outperformed the two other vendors, including Salesforce Einstein.
- Observed gains concentrated on high intent PDP sessions, where feature level matching to suiting and workwear families mattered most.
We appreciated Mapp Fashion’s specialist focus and product data-led approach, including Mapp Fashion’s native understanding of size fragmentation, returns and other factors unique to apparel. As we roll out more functionality together, we’re aligned on a partnership that delivers profitable growth.
Jordan Lustig
VP eCommerce @Ann Taylor
What’s Next
- Extend beyond PDP into homepage, basket, and themed email recommendations using the same feature-led propensity modelling.
- Continue optimizing toward profitable growth by factoring returns, size availability, and margin in real time.
Discover Mapp Fashion ›
