152 lines
5.3 KiB
Markdown
152 lines
5.3 KiB
Markdown
# Analytics & Feedback Loop
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## Performance Tracking
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### Postiz Analytics API
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**Platform analytics** (followers, views, likes, comments, shares over time):
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```
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GET https://api.postiz.com/public/v1/analytics/{integrationId}
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Authorization: {apiKey}
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```
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Response:
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```json
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[
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{ "label": "Followers", "percentageChange": 2.4, "data": [{ "total": "1250", "date": "2025-01-01" }] },
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{ "label": "Views", "percentageChange": 4, "data": [{ "total": "5000", "date": "2025-01-01" }] },
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{ "label": "Total Likes", "data": [{ "total": "6709", "date": "2026-02-15" }] },
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{ "label": "Recent Likes", "data": [{ "total": "6354", "date": "2026-02-15" }] },
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{ "label": "Recent Comments", "data": [{ "total": "148", "date": "2026-02-15" }] },
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{ "label": "Recent Shares", "data": [{ "total": "119", "date": "2026-02-15" }] },
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{ "label": "Videos", "data": [{ "total": "43", "date": "2026-02-15" }] }
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]
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```
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**Per-post analytics** (likes, comments per post):
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```
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GET https://api.postiz.com/public/v1/analytics/post/{postId}
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Authorization: {apiKey}
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```
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Response:
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```json
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[
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{ "label": "Likes", "percentageChange": 16.7, "data": [{ "total": "150", "date": "2025-01-01" }, { "total": "175", "date": "2025-01-02" }] },
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{ "label": "Comments", "percentageChange": 20, "data": [{ "total": "25", "date": "2025-01-01" }, { "total": "30", "date": "2025-01-02" }] }
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]
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```
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Note: Per-post analytics availability depends on the platform. TikTok may return empty arrays for some posts — in this case, fall back to the **delta method**: track platform-level view totals before and after each post to estimate per-post views.
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**List posts** (to get post IDs for analytics):
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```
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GET https://api.postiz.com/public/v1/posts?startDate={ISO}&endDate={ISO}
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Authorization: {apiKey}
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```
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### RevenueCat Integration (Optional)
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If the user has RevenueCat, track conversions from TikTok:
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- Downloads → Trial starts → Paid conversions
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- UTM parameters in App Store link
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- Compare conversion spikes with post timing
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## The Feedback Loop
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### After Every Post (24h)
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Record in `hook-performance.json`:
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```json
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{
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"posts": [
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{
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"date": "2026-02-15",
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"hook": "boyfriend said flat looks like catalogue",
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"hookCategory": "person-conflict-ai",
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"views": 15000,
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"likes": 450,
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"comments": 23,
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"saves": 89,
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"postId": "postiz-id",
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"appCategory": "home"
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}
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]
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}
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```
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### Weekly Review
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1. Sort posts by views
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2. Identify top 3 hooks → create variations
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3. Identify bottom 3 hooks → drop or radically change
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4. Check if any hook CATEGORY consistently wins
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5. Update prompt templates with learnings
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### Decision Rules
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| Views | Action |
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|-------|--------|
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| 50K+ | DOUBLE DOWN — make 3 variations immediately |
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| 10K-50K | Good — keep in rotation, test tweaks |
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| 1K-10K | Okay — try 1 more variation before dropping |
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| <1K (twice) | DROP — radically different approach needed |
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### What to Vary When Iterating
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- **Same hook, different person:** "landlord" → "mum" → "boyfriend"
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- **Same structure, different room/feature:** bedroom → kitchen → bathroom
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- **Same images, different text:** proven images can be reused with new hooks
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- **Same hook, different time:** morning vs evening posting
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## Conversion Tracking
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### Funnel
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```
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Views → Profile Visits → Link Clicks → App Store → Download → Trial → Paid
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```
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### Benchmarks
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- 1% conversion (views → download) = average
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- 1.5-3% = good
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- 3%+ = great
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### Attribution Tips
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- Track download spikes within 24h of viral post
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- Use unique UTM links per campaign if possible
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- RevenueCat `$attribution` for source tracking
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- Compare weekly MRR growth with weekly view totals
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## Daily Analytics Cron
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Set up a cron job to run every morning before the first post (e.g. 7:00 AM user's timezone):
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```
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Task: node scripts/daily-report.js --config tiktok-marketing/config.json --days 3
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Output: tiktok-marketing/reports/YYYY-MM-DD.md
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```
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The daily report:
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1. Fetches all posts from the last 3 days via Postiz API
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2. Pulls per-post analytics (views, likes, comments, shares)
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3. If RevenueCat is connected, pulls conversion events (trials, purchases) in the same window
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4. Cross-references: maps conversion timestamps to post publish times (24-72h attribution window)
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5. Applies the diagnostic framework:
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- High views + High conversions → SCALE (make variations)
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- High views + Low conversions → FIX CTA (hook works, downstream is broken)
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- Low views + High conversions → FIX HOOKS (content converts, needs more eyeballs)
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- Low views + Low conversions → FULL RESET (try radically different approach)
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6. Suggests 3-5 new hooks based on what's working
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7. Updates `hook-performance.json` with latest data
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8. Messages the user with a summary
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### Why 3 Days?
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- TikTok posts peak at 24-48 hours (not instant like Twitter)
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- Conversion attribution takes up to 72 hours (user sees post → downloads → trials → pays)
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- 3-day window captures the full lifecycle of each post
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### RevenueCat Integration
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When connected, the daily report pulls:
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- **Trial starts** within 24-72h of each post → maps to which hooks drive installs
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- **Paid conversions** (initial purchase + trial converted) → maps to which CTAs convert
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- **Revenue** per period → tracks actual MRR impact of content
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This is the difference between "this post got 50K views" (vanity) and "this post generated $47 in new subscriptions" (intelligence).
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