The Challenge of KPI Change Attribution
In the fast-evolving world of product analytics, understanding why Key Performance Indicators (KPIs) change is one of the most challenging yet essential tasks for data-driven teams. A sudden spike in user engagement, a drop in retention, or an unexplained shift in conversion rates - these changes can make or break business strategies. However, traditional Root Cause Analysis (RCA) methods often fall short, not providing the precise, actionable insights that product growth and analytics teams need to drive business impact.
The Root Cause Analysis Process
At Loops, our Root Cause Analysis (RCA) is designed to uncover the real reasons behind changes in Key Performance Indicators (KPIs) - whether they are spikes, drops, or gradual shifts. Our approach goes deeper, using an Explainability Score that prioritizes the most meaningful segments contributing to KPI fluctuations.
Explainability Scoring
Loops' Explainability Scoring goes beyond basic contribution to change by quantifying the disproportionate influence of specific segments on KPI fluctuations. It ensures that teams focus on the most relevant drivers of change, balancing a segment’s impact, size, and statistical significance. This approach not only enhances RCA but also transforms how businesses react to data fluctuations, making explainability a competitive advantage.
By balancing a segment's impact, size, and statistical significance, Explainability Scoring ensures that insights are both statistically significant and practically useful. This means you can quickly identify the factors driving change - without getting lost in noise or misleading correlations.
Validated by Real-Life Examples
We developed the Loops Explainability Scoring methodology by using thousands of real-world examples and conducting extensive trial and error - to the point where we can identify the real cause, as indicated by weeks of research by our clients analysts, to hoiurs and minutes.
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