Case Study: Taboola achieves revenue uplift with Loops

Taboola Increased Revenue Impact

Taboola, one of the leading ad-tech platforms, wanted to improve its Recommendation Center and prove that exposing more advertisers to recommendation content would drive business value. The team needed a reliable way to measure impact despite seasonality, traffic variance, and other confounding factors, and they used Loops to help validate whether the feature was truly affecting revenue.

With Loops’ Feature Impact model, Taboola was able to run a zero-code causal analysis that compared users across time and similar cohorts, giving the product team a clear read on performance. Loops showed that the original recommendation drove more than a 3% revenue uplift, and after expanding the feature to more campaigns and an in-context placement, Taboola saw a 5.7% uplift in revenue, helping the team justify broader rollout and sharpen its internal analytics.

Taboola

Amir Engel

Product Analytics Director