Structural analysis outputs

The level set output is usually a google spreadsheet with multiple tabs. Each tab represents a metric like Customer Counts, Revenue, AOV, ARPU, etc and contains many cohort triangles, each of which represent a segment like Overall, By product, By Region, Demographics, etc.

See 108.10.50 Data Analysis - Step 5 outputs and presentations for the way to think about outputting/presenting the level set data. It is useless in a database, it must be viewable, understandable, in front of a human, and it needs to be reliable.

Outputs

## Deliverables 108.30.10.20.30 Data Analysis - Structural analysis deliverables

Segments and metrics

• Segmentation is important because averages are misleading. [link to the averages are misleading page when it exists]
• Metrics are important because you view information through many different and important lenses.
• 108.30.10.20.20 Data Analysis - Segments and metrics for outputs

Growth Accounting Metrics

1. Gross Retention
2. Quick Ratio
3. Net churn

Cohort Analysis Metrics

1. Customer counts
2. Revenue
3. Customer quality (LTV)
4. Other metrics as appropriate for the business like AOV, ARPU, customer distribution

Reading a cohort triangle

108.30.10.20.10 Data Analysis - How to read a cohort triangle

Other stuff

### Communication/documentation is key Cohorts (or any analysis) should not stand on its own. They should have some kind of header/communication to explain what is important about it. - Quantify cohort metrics. Instead of just saying “cohorts are getting better”, say “cohorts are getting 18% better”. Quantify it on a true weighted basis, or a simple average, or by recency. - Export cohorts with both numbers and percentages

2. Quantitative Approach to PMF: https://tribecap.co/a-quantitative-approach-to-product-market-fit/