Why Loops - with Japna Sethi

Enjoy the Product Analytics Leaders' Meetup sessions in this "Masters Mini-Series". First up is Tom Laufer on how to deal with the correlation / causation problem by leveraging causal inference models.

Generating hypotheses is a critical aspect of the data analysis team’s work at monday.com. However, assessing the worthiness of a hypothesis can be overwhelming, particularly for a team that conducts hundreds, if not thousands, of analyses daily.

Got two minutes? Check out this snappy overview of Loops, hosted by Loops CEO, Tom Laufer

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.

Wahi has always had a focus on product analytics, but quickly determined that their correlation-based approach of formulating hypotheses, trying to optimize campaigns and the product, and waiting weeks to months on A/B tests trying to identify causation, was not optimal.