Why Loops - with Gad Allon

How to leverage causal inference models. Understand causal insight accuracy versus A/B tests. Learn how to make impactful decisions with less traffic, time, and cost.

We asked product and analytics leaders to share an analysis they worked on that led to an incredible insight and growth. The result is this inspiring collection of experiences and outcomes, drawn from the leading companies they helped grow. Enjoy!

“If you torture the data long enough, it will confess to anything" said British Economist, Ronald H. Coase. One of the things that really separates "good" versus "great" analytics teams is their ability to deal with data biases, in particular Confirmation bias.

A Loops-empowered R&D Team pinpoints the specific code release behind a core KPI change

A customer's own dashboards indicated their A/B test experiment variant was losing. Fortunately, they looked at the Loops validation analysis. Ultimately, it drove a 12% lift in Conversion to Paid

This case study highlights that many firms may be losing money without knowing it. In this case, a customer of Loops avoided a $5M revenue loss by quickly identifying an issue before it impacted the majority of their client base.