analytics-expert

What this skill does

# Content Analytics Expert

## Overview

This skill helps 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/memory/` for analytics platform details and architecture notes
   - 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

## Related Local Skills

| Skill | When to Use |
|-------|-------------|
| `copywriter` | Turn analytics findings into revised messaging |
| `funnel-architect` | Redesign the funnel when analytics shows structural drop-off |
| `traffic-architect` | Adjust channels and acquisition mix based on performance data |