content-performance-analyzer

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Analyzes content marketing metrics to identify top performers, trends, and optimization opportunities. Use when reviewing blog posts, social media, or campaign performance. Accepts CSV data with engagement metrics and provides actionable insights.

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When & Why to Use This Skill

The Content Performance Analyzer is a specialized Claude skill designed to transform raw marketing data into strategic insights. By processing CSV files containing engagement metrics, it automatically identifies top-performing content, calculates performance benchmarks, and detects emerging trends. This tool is essential for content marketers and SEO specialists looking to optimize their strategy through data-driven recommendations and cross-channel performance comparisons.

Use Cases

  • Monthly Performance Audits: Automatically generate comprehensive reports for blog posts or social media campaigns to identify which topics and formats resonate most with your audience.
  • Content Strategy Refinement: Use correlation analysis to determine the ideal content length, publishing frequency, and topic clusters to maximize engagement and ROI.
  • ROI and Conversion Tracking: Analyze the relationship between views, engagement rates, and final conversions to identify high-value content and justify marketing spend.
  • Trend and Pattern Detection: Identify seasonal growth patterns or declining engagement early, allowing teams to pivot content tactics proactively based on data-backed evidence.
namecontent-performance-analyzer
descriptionAnalyzes content marketing metrics to identify top performers, trends, and optimization opportunities. Use when reviewing blog posts, social media, or campaign performance. Accepts CSV data with engagement metrics and provides actionable insights.

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