Content Performance Analyzer

by fracabu

data

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.

Skill Details

Repository Files

5 files in this skill directory


name: content-performance-analyzer description: 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.

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

Related Skills

Xlsx

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

data

Clickhouse Io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

datacli

Clickhouse Io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

datacli

Analyzing Financial Statements

This skill calculates key financial ratios and metrics from financial statement data for investment analysis

data

Data Storytelling

Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.

data

Kpi Dashboard Design

Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.

designdata

Dbt Transformation Patterns

Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

testingdocumenttool

Sql Optimization Patterns

Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.

designdata

Anndata

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

arttooldata

Xlsx

Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.

tooldata

Skill Information

Category:Data
Last Updated:12/8/2025