Content Analytics Expert

Overview

This skill enables Claude to analyze content analytics data, generate comprehensive reports, identify performance trends, calculate ROI and revenue attribution, and provide actionable insights for content optimization.

When to Use This Skill

This skill activates automatically when users:

• Ask analytics questions or request performance reports
• Need help analyzing content performance data
• Want ROI calculations or revenue attribution analysis
• Request trend identification from analytics data
• Need content optimization recommendations based on data
• Want to understand which content performs best and why

Core Capabilities

1. Generate Analytics Reports

To generate comprehensive analytics reports:

1. Collect Analytics Data

   • Access analytics platform data (discover from project)
   • Aggregate performance metrics across platforms
   • Gather engagement data (views, likes, comments, shares)
   • Collect conversion and revenue data (if available)

2. Create Report Structure

   • Weekly/Monthly performance reports
   • Platform-specific performance analysis
   • Content type performance comparison
   • Audience engagement reports
   • ROI and revenue attribution reports

3. Generate Report Content

   • Summarize key metrics and insights
   • Create data visualizations (charts, graphs)
   • Identify top-performing content
   • Highlight trends and patterns
   • Provide actionable recommendations

Example User Request: “Generate a monthly performance report for my content”

Integration (discover from project):

• Analytics Platform: Access performance data
• Content Management Platform: Store and share reports
• Publishing Platform: Use insights for scheduling optimization

2. Identify Top-Performing Content Patterns

To identify patterns in top-performing content:

1. Analyze Performance Data

   • Review content performance metrics
   • Identify top-performing content pieces
   • Analyze common characteristics of successful content

2. Extract Patterns

   • Content topics and themes
   • Content formats and types
   • Posting times and frequencies
   • Platform-specific patterns
   • Engagement drivers (hooks, CTAs, visuals)

3. Generate Insights

   • Document successful content patterns
   • Recommend content strategies based on patterns
   • Suggest content replication opportunities

Example User Request: “What patterns do you see in my top-performing content?”

Integration (discover from project):

• Analytics Platform: Analyze performance data
• Content Creation Tools: Apply patterns to new content generation
• Content Management Platform: Store pattern insights

3. Predict Content Performance

To predict content performance before publishing:

1. Analyze Historical Data

   • Review similar content performance
   • Identify factors that correlate with success
   • Build performance prediction models

2. Evaluate New Content

   • Compare new content to historical patterns
   • Assess content against success factors
   • Calculate predicted performance scores

3. Provide Recommendations

   • Suggest content improvements
   • Recommend optimal posting times
   • Identify best platforms for content
   • Predict viral potential

Example User Request: “Predict how well this content will perform before I publish it”

Integration (discover from project):

• Analytics Platform: Use historical data for predictions
• Content Creation Tools: Optimize content before generation
• Publishing Platform: Optimize scheduling based on predictions

4. ROI Analysis and Attribution

To calculate ROI and revenue attribution:

1. Track Revenue Metrics

   • Link content to conversions and revenue
   • Track attribution through project’s tracking links (discover format from project docs)
   • Calculate cost per content piece (API costs, time)

2. Calculate ROI

   • Revenue per content piece
   • Cost to create content
   • ROI percentage calculation
   • Revenue per platform/channel

3. Generate ROI Reports

   • Content-level ROI analysis
   • Platform ROI comparison
   • Campaign ROI tracking
   • Revenue optimization recommendations

Example User Request: “Calculate the ROI for my content and show me which pieces drive the most revenue”

Integration (discover from project):

• Analytics Platform: Track conversions and revenue
• Content Management Platform: Store ROI data and reports
• Publishing Platform: Optimize distribution based on ROI

5. Trend Identification

To identify trends from analytics data:

1. Analyze Time-Series Data

   • Review performance trends over time
   • Identify growth or decline patterns
   • Detect seasonal trends

2. Identify Emerging Trends

   • Content topics gaining traction
   • Platform trends and shifts
   • Audience behavior changes
   • Engagement pattern shifts

3. Provide Trend Insights

   • Document identified trends
   • Recommend actions based on trends
   • Predict future trend directions

Example User Request: “What trends do you see in my content performance over the last 3 months?”

Integration (discover from project):

• Analytics Platform: Analyze time-series data
• Content Management Platform: Store trend insights
• Content Creation Tools: Apply trends to content generation

Project Context Discovery

Before analyzing analytics, discover the project’s context:

1. Scan Project Documentation:

   • Check .agents/SYSTEM/ARCHITECTURE.md for analytics platform details
   • Review .agents/SYSTEM/SUMMARY.md for analytics capabilities
   • Look for analytics-related documentation

2. Identify Analytics Platform:

   • Check for analytics service integrations in codebase
   • Look for analytics API endpoints or SDKs
   • Review environment variables for analytics services

3. Discover Available Metrics:

   • Review analytics API documentation if available
   • Check for analytics data models or schemas
   • Identify what metrics the project tracks

Common Analytics Data Types (adapt based on discovery):

• Post-level metrics: Views, Likes, Comments, Shares, Engagement Rate
• Platform-specific metrics: Performance by platform
• Time-based metrics: Performance over time (7d, 30d, 90d)
• Conversion metrics: Clicks, signups, revenue (via tracking links)
• Content type metrics: Performance by content type

Key Metrics:

• Engagement Rate: (Likes + Comments + Shares) / Views
• ROI: (Revenue – Cost) / Cost × 100
• Conversion Rate: Conversions / Clicks
• Average Performance: Aggregate metrics across content

Best Practices

1. Data-Driven Insights: Base all recommendations on actual analytics data
2. Context Matters: Consider platform, timing, and audience when analyzing data
3. Actionable Recommendations: Provide specific, actionable insights, not just data
4. Comparative Analysis: Compare performance against benchmarks and historical data
5. Continuous Monitoring: Recommend regular analytics review and optimization

Resources

references/

• analytics-api-reference.md: Project analytics API endpoints and data structures (discover from project docs)
• roi-calculation-guide.md: ROI calculation methods and formulas
• performance-benchmarks.md: Industry benchmarks for content performance

assets/

• analytics-report-template.md: Template for analytics reports
• roi-report-template.md: Template for ROI analysis reports
• trend-analysis-template.md: Template for trend identification reports

Complementary Skills (External)

For A/B testing and analytics tracking, pair with coreyhaines31/marketingskills:

/plugin marketplace add coreyhaines31/marketingskills