Case Study – monday.com: Closing the Loop on Hypotheses Prioritization

CAUSAL INSIGHT DRIVES NEW EFFICIENCIES IN GROWTH About monday.com: monday.com (MNDY) monday.com is a work operating system (Work OS) where organizations of any size can create the tools and processes they need to manage every aspect of their work. The monday.com Work OS is a low code-no code platform that democratizes the power of software […]

Case Study – Atera: Insights from Feature-Impact Model led to 34% Lift – with no A/B Testing

Using Loops, the product analytics team at Atera, the world’s first AI-powered IT management platform, were able to drive a 34% lift in a core business KPI.

Initially, the naive effect (before/after) of a recent feature release appeared to have a positive outcome. However, digging deeper with the Loops feature release model, they discovered that the causal effect was actually negative. Using the model and the new insight, they were empowered to make a series of iterative changes to the feature in order to maximize the KPI, resulting in the substantial, 34% lift in conversion rates.

The False Sense of Confidence With “Before-After” Analysis

In this article, Iyar Lin, data science lead at Loops, goes through the “behind the scenes” of one of our most used types of analyses: Release Impact. A/B tests are the gold standard for estimating causal effects in product analysis. But in many cases, they aren’t feasible. In this post, I’d like to discuss one […]