Measuring the full impact of a new feature or product version has traditionally required a proper A/B test.
The problem is - they require traffic, time and resources. They just aren’t feasible at times. Also, the pace of progress slows down way too much and is unsustainable to the business.
So often, features and version are rolled out blind. Vanity metrics, like adoption, subsequently fill the airspace. The team crosses their fingers and hopes for the best.
💡 The good news is that there’s a paradigm shift happening - leveraging causal inference models to measure the impact of product releases.
The approach has been proven to meet the gold standard of A/B testing, but with less traffic, time, and cost, and more than 90% matched as measured against real A/B tests.🎯
👉 It’s called @Loops Release Impact. It’s real, and does all the heavy lifting that’s really hard to do on your own - accounting over time for trends and seasonality, measuring segment and user mix changes, measuring the Full causal impact of your release. 🔥
So, really accurate and easy to use to measure every release you make. 🎯 We also have a white-paper that details our extensive research and work behind the high accuracy rate of the solution, i.e. how we know it works: Measuring the Causal Effect of Product Releases 🚀
Watch the Release Impact Video:
>>Read the Release Impact Whitepaper - Measuring the Causal Effect of Product Releases
