DESIGN
Post-Launch Evaluation
Track Key Metrics
Tracking key metrics helps evaluate the product's performance and determine whether it meets user and business goals.
Why it's Important
Provides a data-driven way to measure success.
Identifies trends and patterns in user behavior.
Highlights areas requiring improvement.
How to Implement
Define KPIs: Identify metrics aligned with your objectives (e.g., DAUs, MAUs, retention, conversion rates).
Use Analytics Tools: Set up tracking with tools like Google Analytics, Mixpanel, or Amplitude.
Monitor Regularly: Schedule periodic reviews of data.
Segment Data: Break metrics down by demographics, usage patterns, and other factors.
Compare to Goals: Evaluate metrics against pre-launch targets or industry benchmarks.
Available Workshops
KPI Alignment Workshop: Ensure metrics reflect business and user priorities.
Dashboard Design Session: Create easy-to-read dashboards for tracking metrics.
Trend Analysis Exercises: Explore changes in data over time.
Retention Analysis: Focus on metrics like churn and user engagement.
Comparative Benchmarking: Compare metrics against similar products or industry standards.
Deliverables
Metrics dashboard.
Monthly or quarterly performance reports.
Insights and recommendations based on data.
How to Measure
Achievement of pre-defined KPIs.
Consistency of user engagement and retention.
Identification of underperforming areas.
Real-World Examples
Spotify
Tracks metrics like total listening time and premium subscriptions to gauge success.
Airbnb
Uses booking rates and host engagement as performance indicators.
Netflix
Analyzes user viewing patterns to refine recommendations and content strategies.
Get It Right
Focus on actionable metrics rather than vanity metrics.
Regularly review and update KPIs as product goals evolve.
Use visual dashboards for clarity.
Combine quantitative data with qualitative insights.
Share findings across teams for alignment.
Don't Make These Mistakes
Tracking too many irrelevant metrics.
Ignoring small but significant data trends.
Neglecting user segmentation in data analysis.
Focusing on short-term spikes over long-term trends.
Failing to act on data insights.