Startup Traction Scorecard
PROCalculate and score my startup's traction across 7 dimensions: viral coefficient, retention health, financial sustainability, growth engine type, and composite traction score with stage-specific benchmarks.
Example Usage
“I’m running a B2B SaaS at Seed stage with 3 months of data. Here are my metrics: 500 MAU, 150 DAU, 8% monthly churn, $45 CAC, $180 average revenue per user, 18-month average customer lifespan. Each user sends 2 invitations per month with 15% conversion rate. Calculate my viral coefficient, LTV:CAC ratio, DAU/MAU stickiness, and give me a composite traction score with recommendations for which growth engine to pursue.”
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| My business model type for benchmark calibration | B2B SaaS | |
| Current stage for context-appropriate thresholds | Seed | |
| Which dimension to weight most heavily | Balanced | |
| How much data I have available | Partial | |
| Analysis time window in months | 3 | |
| Currency for financial calculations | USD |
Research Sources
This skill was built using research from these authoritative sources:
- The 40% Rule: Sean Ellis Test Foundational framework establishing the 40% very disappointed threshold for PMF
- Growth Accounting and Retention for PMF Academic research on growth accounting frameworks and cohort retention analysis
- Mixpanel: Finding PMF with Data Analytics-driven approach to achieving PMF through retention curves
- Measuring Product Market Fit Comprehensive framework covering retention, NPS, TAM, and leading indicators
- Amplitude Cohort Retention Technical guide on behavioral cohorts and data-driven iteration
- Segmented Churn Analysis How segmentation reveals true PMF and hidden friction points
- LTV:CAC Ratio Guide Financial health metric where 3:1 indicates strong PMF
- Net Revenue Retention SaaS metric showing NRR 120%+ indicates strong PMF
- Sequoia Measuring Product Health DAU/MAU ratios and stickiness benchmarks by product category
- Productboard Growth Engines Framework for viral, sticky, and paid growth engine classification