Product usage statistics Summarization Prompt

# Product Usage Statistics Summarization Prompt ## Background & Context Product usage statistics reports track how customers interact with software, applications, websites, or physical products. These reports are typically generated by product teams, data analysts, or automated analytics platforms (like Google Analytics, Mixpanel, or Amplitude). The primary consumers are product managers, UX designers, executives, marketing teams, and development teams who use this data to make product improvement decisions, guide development priorities, and identify user experience issues. ## Report Structure & Components Most product usage statistics reports contain these common elements: - User acquisition metrics (new users, user growth rates) - Engagement metrics (active users, session duration, feature usage frequency) - Retention metrics (return rate, churn rate, user lifetime) - User behavior flows (common paths, drop-off points) - Feature adoption rates and usage patterns - Performance metrics (load times, error rates, crashes) - User segmentation data (usage by demographics, user types, platforms) - Conversion metrics (if applicable to the product) - Time-based comparisons (week-over-week, month-over-month, year-over-year) - Device/platform breakdown (mobile vs. desktop, iOS vs. Android, browser types) ## Critical Information to Extract Focus on summarizing these high-priority elements: 1. Key performance indicators (KPIs) and whether they're trending positively or negatively 2. Significant changes in user behavior or adoption patterns 3. Top-performing features and underutilized features 4. Major drop-off points or friction areas in the user journey 5. Actionable insights that suggest specific product improvements 6. Comparison to previous time periods to highlight trends 7. Any anomalies or unexpected patterns that require attention 8. User segments that show notably different behavior patterns 9. Correlation between feature usage and user retention/satisfaction 10. Metrics that are significantly above or below targets or benchmarks ## Stakeholder Priorities Different stakeholders need different information: - **Executives**: Focus on high-level KPIs, business impact, and strategic implications. Emphasize growth trends and areas needing resource allocation. - **Product Managers**: Highlight feature adoption rates, user flows, pain points, and specific improvement opportunities. - **UX/Design Teams**: Emphasize user behavior patterns, drop-off points, and usability metrics. - **Development Teams**: Focus on technical performance metrics, error rates, and feature-specific usage data. - **Marketing Teams**: Highlight user acquisition channels, user segments, and conversion metrics. - **Customer Success**: Emphasize usage patterns that correlate with customer satisfaction or churn risk. ## Output Format Guidelines Structure the summary as follows: 1. **Executive Summary** (3-5 sentences highlighting the most critical insights and changes) 2. **Key Metrics Dashboard** (bullet-point list of primary metrics with trend indicators) 3. **Notable Findings** (3-5 bullet points on significant insights, organized by importance) 4. **User Behavior Highlights** (key patterns in how users interact with the product) 5. **Areas for Attention** (prioritized list of potential issues or opportunities) 6. **Segment Analysis** (brief overview of how different user groups are using the product) 7. **Recommendations** (2-3 data-backed suggestions for improvement) Total length should be 500-750 words for a comprehensive yet concise summary. ## Special Considerations - Distinguish between correlation and causation when interpreting usage patterns - Be aware of sampling biases in data collection (such as survivorship bias) - Consider the product lifecycle stage when interpreting metrics - Note any recent product changes that might affect usage patterns - Understand that metrics like "time spent" can indicate either engagement or frustration - Consider seasonality effects for proper period-over-period comparisons - Be mindful of privacy regulations (GDPR, CCPA) when discussing user data - Differentiate between leading indicators (predictive) and lagging indicators (confirmatory) ## Sample Output Structure # Product Usage Statistics Summary: [Product Name] - [Time Period] ## Executive Summary [3-5 sentences capturing the most important trends and insights from the reporting period] ## Key Metrics Dashboard - DAU/MAU: 0.XX [↑/↓ X% from previous period] - New User Growth: X% [↑/↓ X% from previous period] - Feature X Adoption: X% [↑/↓ X% from previous period] - Retention (Day 7): X% [↑/↓ X% from previous period] - Average Session Duration: X minutes [↑/↓ X% from previous period] ## Notable Findings • [Primary insight with supporting data] • [Secondary insight with supporting data] • [Tertiary insight with supporting data] ## User Behavior Highlights [Brief paragraph on key usage patterns and changes in user behavior] ## Areas for Attention 1. [Highest priority issue or opportunity with brief explanation] 2. [Secondary issue or opportunity with brief explanation] 3. [Tertiary issue or opportunity with brief explanation] ## Segment Analysis [Brief analysis of how different user segments show varying usage patterns] ## Recommendations 1. [Primary recommendation based on data] 2. [Secondary recommendation based on data] 3. [Tertiary recommendation based on data]