Case Study: Taboola increases revenue impact with Loops

Taboola boosts revenue 5.7% with Loops

Taboola, a leading ad-tech platform, needed to prove the revenue impact of its new dynamic recommendation features to justify further investment. They turned to Loops and its "Feature Impact" model to overcome the challenge of measuring this impact amidst constraints like user tenure and seasonality.

Using Loops' causal inference methodology, Taboola proved a single recommendation feature delivered a 3% revenue uplift. This validation allowed them to expand the feature's release, which subsequently achieved a 5.7% uplift in revenue. Loops provided Taboola with a scientific tool to measure feature impact, eliminating noise and enabling more advanced analytical discussions.

Taboola