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Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use for analyzing social media performance, calculating engagement rate, measuring campaign ROI, comparing platform metrics, or benchmarking against industry standards.
How this skill is triggered — by the user, by Claude, or both
Slash command
/marketing-skills:social-media-analyzerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.
Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.
Analyze social media campaign performance:
| Field | Required | Description |
|---|---|---|
| platform | Yes | instagram, facebook, twitter, linkedin, tiktok |
| posts[] | Yes | Array of post data |
| posts[].likes | Yes | Like/reaction count |
| posts[].comments | Yes | Comment count |
| posts[].reach | Yes | Unique users reached |
| posts[].impressions | No | Total views |
| posts[].shares | No | Share/retweet count |
| posts[].saves | No | Save/bookmark count |
| posts[].clicks | No | Link clicks |
| total_spend | No | Ad spend (for ROI) |
Before analysis, verify:
Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100
| Metric | Formula | Interpretation |
|---|---|---|
| Engagement Rate | Engagements / Reach × 100 | Audience interaction level |
| CTR | Clicks / Impressions × 100 | Content click appeal |
| Reach Rate | Reach / Followers × 100 | Content distribution |
| Virality Rate | Shares / Impressions × 100 | Share-worthiness |
| Save Rate | Saves / Reach × 100 | Content value |
| Rating | Engagement Rate | Action |
|---|---|---|
| Excellent | > 6% | Scale and replicate |
| Good | 3-6% | Optimize and expand |
| Average | 1-3% | Test improvements |
| Poor | < 1% | Analyze and pivot |
Calculate return on ad spend:
| Metric | Formula |
|---|---|
| Cost Per Engagement (CPE) | Total Spend / Total Engagements |
| Cost Per Click (CPC) | Total Spend / Total Clicks |
| Cost Per Thousand (CPM) | (Spend / Impressions) × 1000 |
| Return on Ad Spend (ROAS) | Revenue / Ad Spend |
| Action | Value | Rationale |
|---|---|---|
| Like | $0.50 | Brand awareness |
| Comment | $2.00 | Active engagement |
| Share | $5.00 | Amplification |
| Save | $3.00 | Intent signal |
| Click | $1.50 | Traffic value |
| ROI % | Rating | Recommendation |
|---|---|---|
| > 500% | Excellent | Scale budget significantly |
| 200-500% | Good | Increase budget moderately |
| 100-200% | Acceptable | Optimize before scaling |
| 0-100% | Break-even | Review targeting and creative |
| < 0% | Negative | Pause and restructure |
| Platform | Average | Good | Excellent |
|---|---|---|---|
| 1.22% | 3-6% | >6% | |
| 0.07% | 0.5-1% | >1% | |
| Twitter/X | 0.05% | 0.1-0.5% | >0.5% |
| 2.0% | 3-5% | >5% | |
| TikTok | 5.96% | 8-15% | >15% |
| Platform | Average | Good | Excellent |
|---|---|---|---|
| 0.22% | 0.5-1% | >1% | |
| 0.90% | 1.5-2.5% | >2.5% | |
| 0.44% | 1-2% | >2% | |
| TikTok | 0.30% | 0.5-1% | >1% |
| Platform | Average | Good |
|---|---|---|
| $0.97 | <$0.50 | |
| $1.20 | <$0.70 | |
| $5.26 | <$3.00 | |
| TikTok | $1.00 | <$0.50 |
See references/platform-benchmarks.md for complete benchmark data.
python scripts/calculate_metrics.py assets/sample_input.json
Calculates engagement rate, CTR, reach rate for each post and campaign totals.
python scripts/analyze_performance.py assets/sample_input.json
Generates full performance analysis with ROI, benchmarks, and recommendations.
Output includes:
See assets/sample_input.json:
{
"platform": "instagram",
"total_spend": 500,
"posts": [
{
"post_id": "post_001",
"content_type": "image",
"likes": 342,
"comments": 28,
"shares": 15,
"saves": 45,
"reach": 5200,
"impressions": 8500,
"clicks": 120
}
]
}
See assets/expected_output.json:
{
"campaign_metrics": {
"total_engagements": 1521,
"avg_engagement_rate": 8.36,
"ctr": 1.55
},
"roi_metrics": {
"total_spend": 500.0,
"cost_per_engagement": 0.33,
"roi_percentage": 660.5
},
"insights": {
"overall_health": "excellent",
"benchmark_comparison": {
"engagement_status": "excellent",
"engagement_benchmark": "1.22%",
"engagement_actual": "8.36%"
}
}
}
The sample campaign shows:
references/platform-benchmarks.md contains:
| When you ask for... | You get... |
|---|---|
| "Social media audit" | Performance analysis across platforms with benchmarks |
| "What's performing?" | Top content analysis with patterns and recommendations |
| "Competitor social analysis" | Competitive social media comparison with gaps |
All output passes quality verification:
npx claudepluginhub alexbramall/claude-code-skills --plugin marketing-skillsGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.