| name | social-media-analyzer |
| description | Analyzes social media campaign performance across platforms with engagement metrics, ROI calculations, and audience insights for data-driven marketing decisions |
Social Media Campaign Analyzer
This skill provides comprehensive analysis of social media campaign performance, helping marketing agencies deliver actionable insights to clients.
Capabilities
- Multi-Platform Analysis: Track performance across Facebook, Instagram, Twitter, LinkedIn, TikTok
- Engagement Metrics: Calculate engagement rate, reach, impressions, click-through rate
- ROI Analysis: Measure cost per engagement, cost per click, return on ad spend
- Audience Insights: Analyze demographics, peak engagement times, content performance
- Trend Detection: Identify high-performing content types and posting patterns
- Competitive Benchmarking: Compare performance against industry standards
Input Requirements
Campaign data including:
- Platform metrics: Likes, comments, shares, saves, clicks
- Reach data: Impressions, unique reach, follower growth
- Cost data: Ad spend, campaign budget (for ROI calculations)
- Content details: Post type (image, video, carousel), posting time, hashtags
- Time period: Date range for analysis
Formats accepted:
- JSON with structured campaign data
- CSV exports from social media platforms
- Text descriptions of key metrics
Output Formats
Results include:
- Performance dashboard: Key metrics with trends
- Engagement analysis: Best and worst performing posts
- ROI breakdown: Cost efficiency metrics
- Audience insights: Demographics and behavior patterns
- Recommendations: Data-driven suggestions for optimization
- Visual reports: Charts and graphs (Excel/PDF format)
How to Use
"Analyze this Facebook campaign data and calculate engagement metrics"
"What's the ROI on this Instagram ad campaign with $500 spend and 2,000 clicks?"
"Compare performance across all social platforms for the last month"
Scripts
calculate_metrics.py: Core calculation engine for all social media metricsanalyze_performance.py: Performance analysis and recommendation generation
Best Practices
- Ensure data completeness before analysis (missing metrics affect accuracy)
- Compare metrics within same time periods for fair comparisons
- Consider platform-specific benchmarks (Instagram engagement differs from LinkedIn)
- Account for organic vs. paid metrics separately
- Track metrics over time to identify trends
- Include context (seasonality, campaigns, events) when interpreting results
Limitations
- Requires accurate data from social media platforms
- Industry benchmarks are general guidelines and vary by niche
- Historical data doesn't guarantee future performance
- Organic reach calculations may vary by platform algorithm changes
- Cannot access data directly from platforms (requires manual export or API integration)
- Some platforms limit data availability (e.g., TikTok analytics for business accounts only)
