Ann Taylor × Mapp Fashion

Founded in 1954, Ann Taylor is a premium, tailored brand serving modern, career-minded women, where fit, fabrication, and polish drive purchase.

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.

• 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.

• 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.

• 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 ›