When 18Birdies started working with Loops, the platform presented an opportunity to improve on 18Birdies’ pre-set KPIs with the “Goal Driver” analysis. This analysis automatically identifies the features that maximize a company’s KPIs using causal inference models, which enable you to get significant results with minimal traffic.
Why Loops
“The main reason I signed with Loops was to have a set of non-biased eyes on our data that could provide insights I may have missed. In addition to their data analysis capabilities, Loops delivers actionable growth opportunities that have helped us improve our product.”
The Case
When 18Birdies started working with Loops, the platform presented an opportunity to improve on 18Birdies’ pre-set KPIs with the “Goal Driver” analysis. This analysis automatically identifies the features that maximize a company’s KPIs using causal inference models, which enable you to get significant results with minimal traffic.
Loops’ unique causal inference models isolate the real impact of a feature by taking into account all the other influencing parameters (e.g., new users, seasonality, etc.) to understand the real impact of the new feature or product changes.
Within hours, Loops presented 18Birdies with two fascinating findings:
- A feature that was perceived to be negligible, adopted by only 1% of users, was causing a tremendous lift in one of the 18Birdies’ most essential KPIs – Day 2 retention, among adopters.
- Loops also identified that the feature impacts other KPIs, such as conversions to trial and overall retention.
This finding allowed 18Birdies to take a concrete action item to improve these KPIs, and they started to give the feature more exposure during their user onboarding.
The Results
After adopting Loops recommendation, 18Birdies presented the feature during users onboarding and saw a 15% uplift in Day two retention, their most important KPI.
A once-perceived negligible feature significantly contributed to 18Birdies KPIs, thanks to Loops’ unique “Goal Driver” analysis that identified this impact even with an initially low adoption rate.
In Conclusion
To grow KPIs, it’s essential to identify the factors that drive their improvement. Utilizing algorithms to determine causality is valuable for discovering, testing, and implementing these factors. And with so many features and possible journeys users take Loops causal models are a must-have in the product managers tool kit. With Loops, you can identify where to focus on maximizing your KPIs.
