pm-root-cause

To find the root cause of a conversion drop, you need structured anomaly diagnosis using funnel analysis, segmentation, and hypothesis testing to separate symptoms from actual causes. This approach guides you through measurement validation, change discovery, and candidate cause mapping to prevent premature conclusions.

Diagnosing a sudden churn spike requires evidence-based anomaly investigation that separates confirmed evidence from untested hypotheses. Using methods like 5 Whys and differential diagnosis, you rank potential causes, define confirmation tests, and determine corrective actions rather than jumping to conclusions.

Investigating low feature adoption involves running a structured diagnostic workflow that includes segmentation analysis and root-cause drilling. You map candidate causes, apply hypothesis ranking, and validate tests to identify why users are not engaging with the feature.

Hypothesis testing in product analytics is used to validate or reject candidate causes during anomaly diagnosis. By ranking hypotheses and running confirmation tests, teams ensure that identified root causes are backed by confirmed evidence rather than assumptions.

You need 5 Whys analysis when drilling into candidate causes during product anomaly investigation to move past surface-level symptoms. It is used alongside fishbone analysis and differential diagnosis to separate confirmed evidence from untested hypotheses.

Yes, structured anomaly diagnosis applies to operational incidents, retention declines, churn spikes, and feature adoption issues. The workflow handles any unexpected product behavior by tracking evidence, performing segmentation analysis, and validating corrective actions.