moreminimore-service-system/skills/mm-analytics/SKILL.md

name, description

name	description
mm-analytics	Analyze website SEO performance (Google Search Console) and ad campaign performance (Google Ads, Meta Ads) for a client. Produces insights and recommendations for content and marketing strategy. Use when: "analytics", "วิเคราะห์", "ดูสถิติ", "performance report", "seo report", "ad performance", "mm analytics", "ดูผล".

mm-analytics

Analyzes SEO and ad performance for a client, produces insights and recommendations.

When This Must Trigger

• "analytics", "วิเคราะห์", "ดูสถิติ", "performance report"
• "seo report", "ad performance", "mm analytics", "ดูผล"
• "วิเคราะห์เว็บ", "ดู traffic", "ดู campaign"

Scripts Location

All scripts are in ~/Gitea/moreminimore-service-system/scripts/:

• mm_gsc.py — Google Search Console analytics
• mm_google_ads.py — Google Ads analytics
• mm_meta_ads.py — Meta Ads analytics

Process

Step 1: Determine client and analysis type

ต้องการวิเคราะห์อะไร?

1. 🌐 SEO (Google Search Console) — traffic, queries, rankings
2. 📢 Google Ads — campaign performance, keywords
3. 📱 Meta Ads — Facebook/Instagram ad performance
4. 📊 ทั้งหมด — comprehensive report

Step 2: Run analytics scripts

2a. SEO Analytics (GSC)

cd ~/Gitea/moreminimore-service-system/scripts
python3 mm_gsc.py --client <client-id> --days 90

Output:

• Top queries by impressions, clicks, CTR
• Top pages by traffic
• Position distribution (top 3, 4-10, 11-20, 21+)
• Traffic trends (increasing/decreasing)
• Content gap opportunities

2b. Google Ads Analytics

cd ~/Gitea/moreminimore-service-system/scripts
python3 mm_google_ads.py --client <client-id> campaigns
python3 mm_google_ads.py --client <client-id> keywords
python3 mm_google_ads.py --client <client-id> stats --days 30

Output:

• Active campaigns with performance metrics
• Keyword performance (impressions, clicks, CTR, CPC, QS)
• Campaign stats with cost per conversion

2c. Meta Ads Analytics

cd ~/Gitea/moreminimore-service-system/scripts
python3 mm_meta_ads.py --client <client-id> campaigns
python3 mm_meta_ads.py --client <client-id> stats --days 30

Output:

• Active campaigns with objectives and budgets
• Performance stats (impressions, clicks, spend, reach, CTR, CPC)

Step 3: Analyze and produce insights

After collecting data, analyze for:

SEO Insights:

• Queries with high impressions but low CTR → title/description optimization
• Queries ranking 4-10 → quick win opportunities
• Pages losing traffic → content refresh needed
• Content gaps → new article topics
• GEO opportunities → AI search optimization

Ad Insights:

• Campaigns with high CPA → optimize targeting/creative
• Keywords with low QS → improve ad relevance
• Ad fatigue signals → refresh creative
• Budget allocation → shift spend to better performers

Cross-channel Insights:

• SEO queries that match ad keywords → organic vs paid synergy
• Content topics that perform well in ads → create organic content
• Landing page performance → conversion optimization

Step 4: Generate recommendations

Produce actionable recommendations:

📋 Analytics Report: [Client Name]
📅 Period: [date range]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🌐 SEO Performance
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Top Queries: [list]
Top Pages: [list]
Traffic Trend: [↑/↓/→]

⚡ Quick Wins:
1. [Query] — position [X], [Y] impressions → optimize title
2. [Page] — losing traffic → refresh content

📝 Content Opportunities:
1. [Topic] — high search volume, no content → write article
2. [Topic] — ranking 11-20 → expand existing content

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📢 Google Ads Performance
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Campaigns: [N] active
Total Spend: ฿[X]
Avg CPA: ฿[X]
ROAS: [X]x

⚡ Recommendations:
1. [Campaign] — high CPA → adjust targeting
2. [Keyword] — low QS → improve ad relevance

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📱 Meta Ads Performance
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Campaigns: [N] active
Total Spend: ฿[X]
Reach: [X]
CTR: [X]%

⚡ Recommendations:
1. [Campaign] — low CTR → refresh creative
2. [Ad Set] — high frequency → new audience

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 Next Actions
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Content:
1. Write article about [topic] (from SEO gap)
2. Refresh [page] (losing traffic)

Ads:
1. Optimize [campaign] (high CPA)
2. Refresh [ad creative] (fatigue)

Strategy:
1. [Recommendation based on cross-channel data]

Step 5: Save report

Save analytics report to:

~/vault/60_Articles/<client-id>/analytics/YYYY-MM-DD-report.md

Integration with Content Pipeline

The analytics feed back into the content pipeline:

1. SEO gaps → become new article topics (mm-article-idea-extract)
2. Top performing content → inform future content strategy
3. Ad performance → inform CTA and messaging for ad copy
4. Cross-channel insights → optimize overall marketing strategy

Error Handling

If a script fails:

• Check if credentials are configured in clients-config.json
• Check if the API is enabled (Google Ads API, Meta Marketing API)
• Check if the access token is valid

❌ [Script] failed: [error]

สาเหตุที่เป็นไปได้:
- Credentials ไม่ถูกต้อง → ตรวจสอบ clients-config.json
- API ไม่ได้เปิดใช้งาน → เปิดใช้ใน Google Cloud / Meta Developer
- Token หมดอายุ → ต่ออายุ token

ต้องการ:
1. ลองใหม่
2. ข้าม → วิเคราะห์เฉพาะส่วนที่ใช้ได้

Notes

• All scripts read from ~/vault/99_System/clients-config.json
• GSC uses service account key from global.google.service_account_key
• Google Ads uses developer_token + gcloud ADC for authentication
• Meta Ads uses page_token or system_user_token from config
• Reports are saved to the client's vault folder for reference