Content Performance Analyzer

Content Performance Analyzer

Transform raw content metrics into actionable insights for improving your content marketing strategy.

Capabilities

• Analyze engagement metrics (views, clicks, shares, comments)
• Identify top-performing content patterns
• Calculate performance benchmarks
• Detect content trends over time
• Generate optimization recommendations
• Compare performance across channels/formats

Supported Metrics

| Metric | Description | Benchmark Calculation | |--------|-------------|----------------------| | Views/Impressions | Total reach | Average, growth rate | | Engagement Rate | (Likes+Comments+Shares)/Reach | Industry comparison | | Click-Through Rate | Clicks/Impressions | % benchmark | | Time on Page | Average reading time | Content length correlation | | Bounce Rate | Single-page sessions | Quality indicator | | Conversion Rate | Desired actions/Total visitors | Goal tracking |

Instructions

1. Import Data: Accept CSV or structured data with content metrics
2. Validate Fields: Ensure required metrics are present
3. Calculate KPIs: Compute averages, rates, and benchmarks
4. Identify Patterns: Find top performers and common traits
5. Trend Analysis: Detect performance changes over time
6. Generate Recommendations: Provide actionable next steps

Input Format

CSV with these columns (minimum):

content_id,title,publish_date,content_type,views,engagement,clicks

Optional enhanced columns:

channel,category,word_count,time_on_page,conversions,shares,comments

Output Format

# Content Performance Report

## Executive Summary
- Total content pieces analyzed: X
- Date range: [start] to [end]
- Overall engagement rate: X%

## Top Performers
| Rank | Title | Views | Engagement Rate | Key Success Factor |
|------|-------|-------|-----------------|-------------------|
| 1 | ... | ... | ... | ... |

## Performance by Category
[Chart/Table of metrics by content type]

## Trends Identified
1. [Trend 1 with data support]
2. [Trend 2 with data support]

## Recommendations
1. **Quick Win**: [Immediate action]
2. **Strategic**: [Medium-term improvement]
3. **Experiment**: [Test suggestion]

## Detailed Metrics
[Full breakdown tables]

Example Usage

Input: CSV file with 30 days of blog post metrics

Analysis Request:

Analyze this content performance data and identify:
1. Top 5 performing posts by engagement rate
2. Best performing content categories
3. Optimal publish day/time patterns
4. Content length vs performance correlation
5. Recommendations for next month's content calendar

Analysis Types

1. Performance Ranking

• Sort by chosen metric
• Calculate percentile rankings
• Identify outliers (over/under performers)

2. Comparative Analysis

• Content type comparison
• Time period comparison
• Channel/platform comparison

3. Correlation Analysis

• Length vs engagement
• Publish time vs views
• Topic vs conversion

4. Trend Detection

• Week-over-week changes
• Seasonal patterns
• Growth/decline indicators

Best Practices

1. Minimum Data: Need 10+ content pieces for meaningful analysis
2. Time Range: 30+ days provides better trend visibility
3. Consistent Metrics: Ensure same measurement methods
4. Segment Analysis: Break down by type for deeper insights
5. Action Focus: Every insight should lead to an action

Benchmarks Reference

| Content Type | Good Engagement | Great Engagement | |--------------|-----------------|------------------| | Blog Post | 2-3% | >5% | | Social Media | 1-3% | >5% | | Video | 3-5% | >8% | | Newsletter | 15-25% open | >30% open |

Limitations

• Requires structured data input
• Cannot access external analytics platforms directly
• Benchmarks are industry averages; your baseline may differ
• Correlation ≠ causation in trend analysis
• Historical data quality affects insight accuracy

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: fracabu
• Source: fracabu/claude-skill-factory
• License: MIT

Install and usage instructions live in the source repository linked above.