Data-Informed Decisions
Use data as a guide, not a dictator, in your product decisions
Your Progress
Section 4 of 5Data-Informed Decisions
Data-informed (not data-driven) means using data to inform decisions while considering context, intuition, and qualitative insights. Data is a tool, not a dictator.
Great product thinkers know how to interpret data correctly, distinguish correlation from causation, and balance quantitative metrics with qualitative understanding.
Can You Interpret This Data?
E-commerce Conversion Rate
Your product has 10,000 visitors per month with a 2% conversion rate (200 purchases). After a redesign, you have 8,000 visitors with a 3% conversion rate (240 purchases).
What should you conclude?
Leading vs Lagging Indicators
📈Leading Indicators
Predict future outcomes. Help you act before problems happen.
- • User engagement rate
- • Feature adoption speed
- • Time to first value
- • Active user growth
📊Lagging Indicators
Show past results. Validate your strategy worked.
- • Revenue and profit
- • Churn rate
- • Customer satisfaction (NPS)
- • Market share
Build Your Metrics Dashboard
Select metrics for a product dashboard. A good dashboard balances leading and lagging indicators:
Total Revenue
Daily Active Users
Net Promoter Score
New Feature Adoption
Churn Rate
Time to First Value
Data-Informed Principles
Context Matters
Numbers without context are meaningless. Always ask "why" behind the data.
Balance Quant & Qual
Combine quantitative data with qualitative insights from user research.
Test Assumptions
Use A/B testing and experiments to validate hypotheses before big bets.
Avoid Vanity Metrics
Focus on actionable metrics that drive decisions, not just impressive numbers.
Common Pitfall
Correlation does not equal causation. Just because two metrics move together does not mean one causes the other. Always dig deeper to understand why.
Key Takeaways
- •Use data to inform decisions, not dictate them
- •Balance leading indicators (predictive) with lagging indicators (results)
- •Always consider context and avoid correlation/causation mistakes
- •Combine quantitative data with qualitative insights