Case Study: EF Shop achieves 3X sales performance with Appier's AIQUA

Appier’s deep learning algorithms achieved a sales performance 3X better than conventional models

EF Shop is a Taiwanese fashion e-commerce platform with over 10,000 items and 1.5M+ loyal shoppers. Facing a fast‑moving, crowded market, the company needed to unify multichannel engagement (website, social, email) and build scalable recommendation capabilities as its catalog and user base grew, moving from basic suggestions to more refined, personalized recommendations.

Appier’s AIQUA delivered automated multichannel messaging and a staged rollout of recommendation engines—from a basic “viewed also viewed” model to five hybrid deep‑learning engines that analyze product text, images, and user behavior. The solution boosted web engagement (4x subscription rate and 3.6x active subscribers vs. benchmark), enabled 1–2 million marketing emails per month to re‑engage users, and produced a sales performance 3× better than conventional models while continuously improving personalization.

EF Shop

Grace Yang

Marketing Director