Customer Success Story
15%
Immediate sales uplift within 2 wks
13.5%
Increase in the checkout conversion rate
10%
Increase in average cart value
4
YesPlz AI Tools Leveraged
Kolon Mall wanted to help shoppers find clothes that truly matched their personal taste. But their old recommendation system couldn’t recognize how fashion items were similar in design. It only looked at simple similarities, such as the same brand or price points. Completely missed small but important details — silhouette, pattern, vibe, occasion — that made items feel alike. Because of this, even with a huge, growing catalog, Kolon Mall struggled to suggest products that truly felt right for each shopper.
That changed when the team started adopting YesPlz AI’s style-focused recommendation engine. The AI continuously updates which design elements each shopper is drawn to, then suggests other pieces with a similar look and feel. The results came fast. In just two weeks, sales went up 15%. Checkout conversion rates increased by 13.5%. And average cart value grew by 10%, as shoppers found more fashion items that matched their unique sense of style.
Rule-based recommendations without style matching: Kolon Mall featured several types of product recommendations on its detail pages, but none were driven by style. The system relied on simple rules, suggesting items from the same brand or category rather than those with a similar look or feel. For a large catalog, manual curation was also not a scalable solution.
Shoppers couldn’t find what they wanted, even with hundreds of products added daily: Product descriptions weren’t detailed and updated often enough. They didn’t highlight what made each item special, such as cut, fabric, or any design details. Even though the catalog kept growing, finding the right item felt harder, not easier.
Shoppers preferred brands they already knew, so hidden gems were overlooked: Many shoppers stayed loyal to big, well-known brands. Kolon Mall’s private labels and smaller fashion brands were often ignored, even when they offered great designs and value. New or lesser-known brands struggled to get noticed.
15% increase in sales within two weeks of adopting AI-driven recommendations.
13.5% rise in checkout conversion rates within just two weeks.
10% growth in average cart value
4 YesPlz AI Tools Leveraged
Style-focused recommendations that match shoppers’ tastes: YesPlz AI looks at the design elements of each item and how shoppers interact with them. Based on those insights, it figures out which features matter most to each shopper. It then recommends other items with similar styles. Shoppers get suggestions that actually match their personal taste.
Customized AI recommendations to Kolon Mall’s unique collection: The AI quickly adapted to Kolon Mall’s mix of brands and unique styles. The fine-tuning enabled it to understand what Kolon Mall fashion-savvy shoppers wanted at a micro level, making recommendations more accurate, relevant, and personal.
Solving cold-start challenges with style-based curation: Traditional recommendation systems often struggle with cold-start problems—when new or lesser-known products lack enough data to be recommended. YesPlz AI’s style-based engine solves this by focusing on visual and design attributes rather than user behavior alone. At Kolon Mall, this approach made it possible to surface hidden gems, older inventory, and emerging brands that matched shoppers’ style preferences, increasing both product discovery and sales opportunities.
AI-Powered Curation: Before and After
Before
Before
Before
By focusing on style attributes unique to each shopper and tailoring the AI model to Kolon Mall’s diverse brand mix, the solution greatly enhanced product discovery and increased conversion rates—even for less-known or newly added inventory. This demonstrates that enterprise fashion retailers can unlock new growth opportunities and enrich the customer experience by investing in AI solutions customized to their specific product assortments and shopper behaviors, rather than relying on one-size-fits-all recommendation engines.
