A/B Test Ideation & Variant Prioritization

A UK online retailer of health and beauty products used Evolv AI’s computer vision and generative AI across various funnel touchpoints, including the homepage, product listing, and checkout pages, to evaluate digital experiences. The platform identified UX improvement opportunities tailored to specific audience segments and goals, ranked by impact, clarifying their roadmap. The client ran A/B/n tests that compared multiple variants of a web page, using the Evolv AI technology to recommend the best variants for specific user segments. Evolv AI directed traffic to the experiences most likely to convert for each user, learning from each interaction and retaining knowledge for future applications. The payoff: an 8.1% increase in conversions and $5 million increase in annual revenue.

Swiss Gear, the durable backpack and travel gear manufacturer, conducted A/B testing on their product detail pages, comparing their original cluttered design against an optimized version with a cleaner layout. The result was a 52% increase in conversion rates under normal conditions and a 137% increase during the holiday season [citation needed]. The success demonstrated how AI-assisted prioritization could identify high-impact design elements, as the testing system analyzed heat maps and user interaction data to suggest simplification strategies.

A 2024 McKinsey survey of 52 global Fortune 500 retail executives found that 90% had begun experimenting with generative AI solutions for scaling priority use cases, with 64% conducting pilots for internal value chain augmentation and 82% running pilots for customer service reinvention. While ecommerce companies have used A/B testing for a long time, traditional testing methods often take a lot of time to gather adequate data and struggle to compare several versions of a web page at the same time. According to Econsultancy, companies that use AI- powered A/B testing are 50% more likely to see a significant increase in conversions compared to those using traditional methods. 279 3.3 Design