Case Study: Blue Tomato boosts revenue and basket size with Algonomy Recommendations

Blue Tomato triples revenue from product recommendations with Algonomy

Blue Tomato, an international boardsport and fashion retailer, faced a challenge with its existing recommendation engine, which could no longer handle its wide range of over 450,000 products. Their traditional tools required excessive manual effort and delivered limited results. They sought a new solution and chose the Algonomy Recommend product for its advanced core competency in personalization.

Algonomy implemented its Recommend solution, which uses competing machine learning algorithms in a 'King of the Hill' approach. This resulted in a significant measurable impact for Blue Tomato: revenue from orders containing recommendations tripled, the average shopping basket value increased by 20%, and customers purchased an average of one more product per order. The Algonomy solution also improved mobile revenues and successfully managed complex product matching scenarios.

Blue Tomato