[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718NV4GAKC4CYJF4VVQ8X11":3,"skill-same:01M718NV4GAKC4CYJF4VVQ8X11":86},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M718NV4GAKC4CYJF4VVQ8X11","meta-ads-campaign-builder",null,"Build and configure Meta Ads campaigns across all ODAX objectives — from objective selection through audience strategy, placement, budget, and creative launch.","narayan-metaflow\u002Fmetaflow-marketing-skills","narayan-metaflow","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F181216517?v=4","01M718MB0Y27AYKTC9ZKT16V86","narayan","01M718MB209XRYRTS3ZZFK0JXS",20,"MIT","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmeta-ads-campaign-builder\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000040","财务",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"meta ads campaign builder","Meta","campaign","meta ads campaign","ads","meta ads","builder","Budget","creative","launch","build","across","objective","configure","strategy","selection","campaigns","2026-11-10 10:12:30",false,"---\nname: meta-ads-campaign-builder\ndescription: \"Build and configure Meta Ads campaigns across all ODAX objectives — from objective selection through audience strategy, placement, budget, and creative launch.\"\n---\n\n## Process\n\n1. **Define business goal** — Sales, leads, app installs, awareness, or traffic\n2. **Select objective and conversion location** — Use the ODAX Selector below\n3. **Choose campaign structure** — ASC vs manual (see Decision Tree)\n4. **Configure ad set** — Audience, placements, budget, schedule, optimization goal\n5. **Build creative** — Ad format, copy, media, CTA\n6. **Launch and protect learning phase** — No major changes for 5-7 days\n\nRead `references\u002Fmeta-campaign-types.md` for detailed setup per objective × conversion location.\nRead `references\u002Faudience-strategy.md` for complete audience targeting guidance.\n\n## ODAX Objective Selector\n\n**Meta's 6 objectives (ODAX system):**\n\n| Objective | Use When | Optimize For | Typical Conversion Location |\n|-----------|---------|-------------|---------------------------|\n| **Awareness** | Brand reach, video views, store traffic | Reach, Ad Recall Lift, ThruPlay | N\u002FA (upper funnel) |\n| **Traffic** | Driving website\u002Fapp visits | Link Clicks, Landing Page Views | Website, App |\n| **Engagement** | Post engagement, video views, messages | Post Engagement, Video Views, Messages | Facebook, Instagram, Messenger |\n| **Leads** | Lead form submissions, calls, sign-ups | Leads, Conversions | Instant Forms, Website, Messenger, Calls |\n| **App Promotion** | App installs, app engagement | App Installs, App Events | App (iOS\u002FAndroid) |\n| **Sales** | Purchases, catalog sales, ROAS-driven | Conversions, Value, ROAS | Website, App, Shop, Messenger |\n\n**Common mistake:** Using Traffic when you want Sales or Leads. Traffic optimizes for clicks, not conversions. Meta's algorithm will find clickers, not buyers. Always optimize for the deepest meaningful conversion event.\n\n**Objective alignment rule:** If the goal is revenue → Sales. If the goal is leads → Leads. Only use Traffic if you genuinely just want page visits (rare for performance marketers).\n\n## Campaign Structure Decision Tree\n\n**Q1: Ecommerce with 100+ conversions\u002Fweek?**\n- Yes → **Advantage+ Shopping Campaigns (ASC)** — typically reduces cost per purchase ~12% vs manual\n- No → Manual campaign structure\n\n**Q2: How should the manual account be structured?**\n\nUse the **simplified 3-campaign architecture** (2025 best practice):\n\n| Campaign | Purpose | Audience | Budget Share |\n|----------|---------|----------|-------------|\n| **1. Prospecting** | New customer acquisition | Broad \u002F Lookalikes \u002F Advantage+ | 60-70% |\n| **2. Retargeting** | Convert warm audiences | Website visitors, engagers, cart abandoners | 20-30% |\n| **3. Testing** | Test new creative, audiences, offers | Varies per test | 10% |\n\n**Why consolidated?** Meta's algorithm in 2025 performs better with fewer, larger ad sets that accumulate 50+ conversions\u002Fweek. Hyper-segmentation starves ad sets of data.\n\n## ASC (Advantage+ Shopping) Setup\n\n| Setting | Configuration |\n|---------|-------------|\n| Targeting | Country-level only (no interest\u002Fbehavior targeting) |\n| Customer ratio | Set existing vs new customer budget split (e.g., 70% new \u002F 30% existing) |\n| Creative | Up to 150 ads per campaign (use this ceiling strategically) |\n| Catalog | Required for dynamic product ads |\n| Budget | Enough for 50+ conversions\u002Fweek minimum |\n| Pixel + CAPI | **Must be strong** — ASC relies entirely on algorithm learning from signals |\n\n**When ASC works best:** Ecommerce with 100+ conversions\u002Fweek, strong Pixel + CAPI, diverse creative library, sufficient budget.\n\n**When to skip ASC:** Low conversion volume (\u003C50\u002Fweek), weak tracking, need granular audience control, non-ecommerce.\n\n## Ad Set Configuration\n\n### Budget & Bid Strategy\n\n| Strategy | When to Use | How It Works |\n|----------|------------|-------------|\n| **Highest Volume** (default) | No specific CPA\u002FROAS target, want maximum results | Meta spends full budget to get most conversions |\n| **Cost Cap** | Have a target CPA | Sets maximum average CPA — may underspend if target is tight |\n| **Bid Cap** | Have a hard maximum per-result bid | Caps individual auction bids — most restrictive |\n| **ROAS Goal** | Have a target ROAS (ecommerce) | Optimizes for return on ad spend target |\n\n**Budget sufficiency rule:** Each ad set needs enough budget for 50+ conversions\u002Fweek (optimal) or 25+ (minimum). If your target CPA is $50, that means $250-$350+\u002Fweek per ad set minimum.\n\n**60\u002F30\u002F10 allocation:**\n- 60% to best-performing audiences\u002Fad sets\n- 30% to scaling tests\n- 10% to experimental\n\n**Scaling rule:** Increase budgets by 20% increments maximum. Larger jumps risk resetting learning phase.\n\n### Placement Strategy\n\n| Approach | When | Configuration |\n|----------|------|--------------|\n| **Advantage+ Placements** (default) | Most campaigns | Let Meta optimize across all placements. Recommended default |\n| **Manual placements** | Specific creative constraints | Select specific placements (Feed only, Reels only, etc.) |\n\n**Monitor placement performance:** Even with Advantage+, review placement-level ROAS. If Audience Network drives clicks but no conversions, consider excluding it.\n\n**Creative specs by placement:**\n- Feed: 4:5 vertical (best) or 1:1 square\n- Stories\u002FReels: 9:16 full vertical\n- Right Column: 1:1 square\n- In-stream video: 16:9 landscape\n- 4:5 vertical outperforms 1:1 by ~15% in Feed (Billo data)\n\n### Attribution Windows\n\n| Window | Best For |\n|--------|---------|\n| 7-day click, 1-day view (default) | Most campaigns. Standard attribution |\n| 1-day click | Short purchase cycles, low-ticket items |\n| 7-day click | High-ticket, longer consideration (removes view-through noise) |\n\n**Critical (October 2025):** `7d_view` and `28d_view` windows removed from Insights API. Don't build reporting dependencies on these.\n\n## Ad Creative Setup\n\n### Format Selection\n\n| Format | Best For | Specs |\n|--------|---------|-------|\n| Single image | Simple messaging, product shots | 1080x1350 (4:5), \u003C30MB |\n| Single video | Storytelling, demos, UGC | 4:5 or 9:16, 15-30 sec, \u003C4GB |\n| Carousel | Multiple products, feature walkthrough | Up to 10 cards, 1:1 or 4:5 |\n| Collection | Ecommerce catalog browsing | Cover image\u002Fvideo + catalog products |\n| Reels-native | Mobile-first vertical video | 9:16, 15-60 sec |\n\n### Ad Copy Structure\n\n| Element | Guideline |\n|---------|----------|\n| Primary text | Under 125 characters (truncation point). Hook → benefit → CTA |\n| Headline | Under 40 characters. Clear value prop or offer |\n| Description | Under 30 characters. Supporting detail |\n| CTA button | Match objective: Shop Now, Learn More, Sign Up, Get Offer |\n\n**Copy framework:** Hook (stop the scroll) → Problem\u002FPain → Solution\u002FBenefit → CTA\n\n**Video rules:**\n- First 3 seconds = hook (determines if user watches)\n- Bold, high-contrast captions mandatory (85% of Facebook videos watched without sound)\n- Front-load the value proposition\n- Graphic CTAs outperform text-only CTAs\n\nFor creative testing frameworks, use the **meta-ads-creative-engine** skill.\n\n## Output Format\n\n```\n## Meta Ads Campaign Build Plan\n\n### Campaign Overview\n- Campaign name: [naming convention]\n- Objective: [ODAX objective]\n- Conversion location: [Website\u002FApp\u002FInstant Forms\u002Fetc.]\n- Structure: [ASC \u002F Manual 3-campaign \u002F Custom]\n- Total monthly budget: $[X]\n\n### Ad Set Configuration\n| Ad Set | Audience | Budget | Optimization | Attribution |\n|--------|----------|--------|-------------|-------------|\n| [name] | [targeting] | $[X]\u002Fday | [event] | [window] |\n\n### Audience Strategy\n- Prospecting: [approach]\n- Retargeting: [approach]\n- Exclusions: [who to exclude]\n\n### Creative Plan\n| Ad | Format | Hook | CTA | Landing Page |\n|----|--------|------|-----|-------------|\n| [name] | [format] | [hook concept] | [CTA] | [URL] |\n\n### Launch Checklist\n- [ ] Pixel + CAPI verified (EMQ ≥ 6.0)\n- [ ] Conversion event configured and firing\n- [ ] Audience exclusions set (existing customers from prospecting)\n- [ ] Creative meets spec requirements (4:5 vertical, captions on video)\n- [ ] Budget sufficient for 50+ conversions\u002Fweek\u002Fad set\n- [ ] Attribution window set (7d click \u002F 1d view default)\n- [ ] No changes planned for 5-7 day learning phase\n```\n\n## Example\n\n**Input:** \"I sell premium candles DTC, average order value $45, target CPA $18. I'm spending $4K\u002Fmonth on Meta. Currently have Pixel + CAPI active with 7.2 EMQ. Getting about 55 purchases\u002Fweek. Want to set up a proper campaign structure.\"\n\n**Output:**\n\n## Meta Ads Campaign Build Plan\n\n### Campaign Overview\n- Objective: **Sales** (Purchase optimization)\n- Conversion location: Website\n- Structure: **ASC + Manual retargeting** — 55 purchases\u002Fweek qualifies for ASC. Run ASC for prospecting + manual for retargeting\n- Total monthly budget: $4,000\n\n### Campaign Architecture\n\n**Campaign 1: ASC — Prospecting (70% = $2,800\u002Fmo \u002F ~$92\u002Fday)**\n| Setting | Configuration |\n|---------|-------------|\n| Customer ratio | 80% new \u002F 20% existing |\n| Creative | 15-20 best-performing ads (diverse formats: static, video, carousel) |\n| Catalog | Connected — enable dynamic product ads |\n| Optimization | Purchase |\n| Budget | $92\u002Fday (enough for ~35+ purchases\u002Fweek at $18 CPA) |\n\n**Campaign 2: Manual — Retargeting (20% = $800\u002Fmo \u002F ~$26\u002Fday)**\n| Ad Set | Audience | Budget | Optimization |\n|--------|----------|--------|-------------|\n| Hot retarget | Website visitors 0-14 days + cart abandoners | $18\u002Fday | Purchase |\n| Warm retarget | Website visitors 15-60 days + engagers (50%+ video viewers) | $8\u002Fday | Purchase |\n\nExclude: past purchasers (30 days) from both ad sets to avoid wasted spend.\n\n**Campaign 3: Testing (10% = $400\u002Fmo \u002F ~$13\u002Fday)**\n| Ad Set | Purpose | Budget | Duration |\n|--------|---------|--------|----------|\n| Creative test | Test 2-3 new creative concepts against control | $13\u002Fday | 7-10 days per test |\n\n### Audience Strategy\n- **Prospecting (ASC):** Country-level (US). Algorithm handles targeting using your strong Pixel + CAPI signals (7.2 EMQ — solid)\n- **Retargeting:** Website visitors by recency (14-day warmer than 60-day), cart abandoners, video engagers (50%+ view)\n- **Exclusions:** Upload customer purchase list → exclude from prospecting. Exclude 30-day purchasers from retargeting. Note: detailed targeting exclusions removed March 2025 — use customer list suppression instead\n\n### Creative Plan\n| Ad | Format | Hook | CTA | Placement |\n|----|--------|------|-----|-----------|\n| Hero product | 4:5 static image | Lifestyle shot — candle in styled room | Shop Now | Feed, Stories |\n| UGC unboxing | 9:16 video (20 sec) | \"This is the best candle I've ever owned\" | Shop Now | Reels, Stories |\n| Scent carousel | Carousel (5 cards) | \"Find your signature scent\" — one scent per card | Shop Now | Feed |\n| Social proof | 4:5 static | Customer quote + product image | Shop Now | Feed |\n| Process video | 9:16 video (15 sec) | \"Hand-poured in small batches\" — behind-the-scenes | Shop Now | Reels |\n\n**Copy example (UGC ad):**\n- Primary text: \"This candle completely transformed my living room. The scent lasts 60+ hours.\" (89 chars)\n- Headline: \"Premium Hand-Poured Candles\"\n- CTA: Shop Now\n\n### Launch Checklist\n- [x] Pixel + CAPI verified (EMQ 7.2 — above 6.0 threshold)\n- [ ] Purchase event configured as primary conversion\n- [ ] Customer list uploaded → excluded from ASC prospecting\n- [ ] 30-day purchaser exclusion on retargeting ad sets\n- [ ] All creative in 4:5 vertical (Feed) and 9:16 (Reels\u002FStories)\n- [ ] Video ads have captions (85% watch without sound)\n- [ ] ASC budget = $92\u002Fday (allows ~35+ purchases\u002Fweek at $18 CPA)\n- [ ] No changes for 5-7 day learning phase after launch\n\n## Guidelines\n\n- **Don't use Traffic when you want Sales.** This is the most common Meta Ads mistake. Traffic finds clickers. Sales finds buyers. Always optimize for the deepest meaningful conversion.\n- **Don't launch with weak tracking.** If EMQ is below 6.0 or CAPI isn't active, fix tracking first. Use the **meta-ads-tracking-setup** skill before building campaigns.\n- **Don't hyper-segment audiences.** 2025 Meta algorithm prefers fewer, larger ad sets with 50+ conversions\u002Fweek. Combine warm + cold in single ad sets. Let the algorithm sort.\n- **Don't skip customer exclusions.** Detailed targeting exclusions were removed March 2025, but customer list exclusions still work and are essential. Upload your buyer list and exclude from prospecting.\n- **Don't ignore learning phase.** 5-7 days minimum. Changes during learning (budget >20%, audience, creative swaps, bid changes) reset the clock. Advertisers under 20% learning spend see up to 68% lower CPA.\n- **Don't forget captions on video.** 85% of Facebook videos are watched without sound. No captions = invisible message.\n- **Don't exceed 150 ads in ASC.** Hard ceiling. Curate your best creative, don't dump everything in.\n- **Instant Experience change (June 2025):** No longer counts as a landing page view. Don't include in funnel metrics.\n- **Offline Conversions API (May 2025):** Discontinued. If someone needs offline conversion tracking, direct to CAPI with `action_source: physical_store` or `system_generated`.\n- **Cross-references:** For tracking setup, use the **meta-ads-tracking-setup** skill. For creative testing, use the **meta-ads-creative-engine** skill. For ongoing optimization after launch, use the **meta-ads-optimizer** skill. For Google campaign building, use the **google-ads-campaign-builder** skill. For audience deep-dives, read `references\u002Faudience-strategy.md`.\n- **Confidence:** Budget sufficiency estimates are based on target CPA math. Actual performance depends on creative quality, offer strength, and tracking accuracy. ASC's ~12% CPA improvement is Meta's benchmark — results vary.\n","## Process\n\n1. **Define business goal** — Sales, leads, app installs, awareness, or traffic\n2. **Select objective and conversion location** — Use the ODAX Selector below\n3. **Choose campaign structure** — ASC vs manual (see Decision Tree)\n4. **Configure ad set** — Audience, placements, budget, schedule, optimization goal\n5. **Build creative** — Ad format, copy, media, CTA\n6. **Launch and protect learning phase** — No major changes for 5-7 days\n\nRead `references\u002Fmeta-campaign-types.md` for detailed setup per objective × conversion location.\nRead `references\u002Faudience-strategy.md` for complete audience targeting guidance.\n\n## ODAX Objective Selector\n\n**Meta's 6 objectives (ODAX system):**\n\n| Objective | Use When | Optimize For | Typical Conversion Location |\n|-----------|---------|-------------|---------------------------|\n| **Awareness** | Brand reach, video views, store traffic | Reach, Ad Recall Lift, ThruPlay | N\u002FA (upper funnel) |\n| **Traffic** | Driving website\u002Fapp visits | Link Clicks, Landing Page Views | Website, App |\n| **Engagement** | Post engagement, video views, messages | Post Engagement, Video Views, Messages | Facebook, Instagram, Messenger |\n| **Leads** | Lead form submissions, calls, sign-ups | Leads, Conversions | Instant Forms, Website, Messenger, Calls |\n| **App Promotion** | App installs, app engagement | App Installs, App Events | App (iOS\u002FAndroid) |\n| **Sales** | Purchases, catalog sales, ROAS-driven | Conversions, Value, ROAS | Website, App, Shop, Messenger |\n\n**Common mistake:** Using Traffic when you want Sales or Leads. Traffic optimizes for clicks, not conversions. Meta's algorithm will find clickers, not buyers. Always optimize for the deepest meaningful conversion event.\n\n**Objective alignment rule:** If the goal is revenue → Sales. If the goal is leads → Leads. Only use Traffic if you genuinely just want page visits (rare for performance marketers).\n\n## Campaign Structure Decision Tree\n\n**Q1: Ecommerce with 100+ conversions\u002Fweek?**\n- Yes → **Advantage+ Shopping Campaigns (ASC)** — typically reduces cost per purchase ~12% vs manual\n- No → Manual campaign structure\n\n**Q2: How should the manual account be structured?**\n\nUse the **simplified 3-campaign architecture** (2025 best practice):\n\n| Campaign | Purpose | Audience | Budget Share |\n|----------|---------|----------|-------------|\n| **1. Prospecting** | New customer acquisition | Broad \u002F Lookalikes \u002F Advantage+ | 60-70% |\n| **2. Retargeting** | Convert warm audiences | Website visitors, engagers, cart abandoners | 20-30% |\n| **3. Testing** | Test new creative, audiences, offers | Varies per test | 10% |\n\n**Why consolidated?** Meta's algorithm in 2025 performs better with fewer, larger ad sets that accumulate 50+ conversions\u002Fweek. Hyper-segmentation starves ad sets of data.\n\n## ASC (Advantage+ Shopping) Setup\n\n| Setting | Configuration |\n|---------|-------------|\n| Targeting | Country-level only (no interest\u002Fbehavior targeting) |\n| Customer ratio | Set existing vs new customer budget split (e.g., 70% new \u002F 30% existing) |\n| Creative | Up to 150 ads per campaign (use this ceiling strategically) |\n| Catalog | Required for dynamic product ads |\n| Budget | Enough for 50+ conversions\u002Fweek minimum |\n| Pixel + CAPI | **Must be strong** — ASC relies entirely on algorithm learning from signals |\n\n**When ASC works best:** Ecommerce with 100+ conversions\u002Fweek, strong Pixel + CAPI, diverse creative library, sufficient budget.\n\n**When to skip ASC:** Low conversion volume (\u003C50\u002Fweek), weak tracking, need granular audience control, non-ecommerce.\n\n## Ad Set Configuration\n\n### Budget & Bid Strategy\n\n| Strategy | When to Use | How It Works |\n|----------|------------|-------------|\n| **Highest Volume** (default) | No specific CPA\u002FROAS target, want maximum results | Meta spends full budget to get most conversions |\n| **Cost Cap** | Have a target CPA | Sets maximum average CPA — may underspend if target is tight |\n| **Bid Cap** | Have a hard maximum per-result bid | Caps individual auction bids — most restrictive |\n| **ROAS Goal** | Have a target ROAS (ecommerce) | Optimizes for return on ad spend target |\n\n**Budget sufficiency rule:** Each ad set needs enough budget for 50+ conversions\u002Fweek (optimal) or 25+ (minimum). If your target CPA is $50, that means $250-$350+\u002Fweek per ad set minimum.\n\n**60\u002F30\u002F10 allocation:**\n- 60% to best-performing audiences\u002Fad sets\n- 30% to scaling tests\n- 10% to experimental\n\n**Scaling rule:** Increase budgets by 20% increments maximum. Larger jumps risk resetting learning phase.\n\n### Placement Strategy\n\n| Approach | When | Configuration |\n|----------|------|--------------|\n| **Advantage+ Placements** (default) | Most campaigns | Let Meta optimize across all placements. Recommended default |\n| **Manual placements** | Specific creative constraints | Select specific placements (Feed only, Reels only, etc.) |\n\n**Monitor placement performance:** Even with Advantage+, review placement-level ROAS. If Audience Network drives clicks but no conversions, consider excluding it.\n\n**Creative specs by placement:**\n- Feed: 4:5 vertical (best) or 1:1 square\n- Stories\u002FReels: 9:16 full vertical\n- Right Column: 1:1 square\n- In-stream video: 16:9 landscape\n- 4:5 vertical outperforms 1:1 by ~15% in Feed (Billo data)\n\n### Attribution Windows\n\n| Window | Best For |\n|--------|---------|\n| 7-day click, 1-day view (default) | Most campaigns. Standard attribution |\n| 1-day click | Short purchase cycles, low-ticket items |\n| 7-day click | High-ticket, longer consideration (removes view-through noise) |\n\n**Critical (October 2025):** `7d_view` and `28d_view` windows removed from Insights API. Don't build reporting dependencies on these.\n\n## Ad Creative Setup\n\n### Format Selection\n\n| Format | Best For | Specs |\n|--------|---------|-------|\n| Single image | Simple messaging, product shots | 1080x1350 (4:5), \u003C30MB |\n| Single video | Storytelling, demos, UGC | 4:5 or 9:16, 15-30 sec, \u003C4GB |\n| Carousel | Multiple products, feature walkthrough | Up to 10 cards, 1:1 or 4:5 |\n| Collection | Ecommerce catalog browsing | Cover image\u002Fvideo + catalog products |\n| Reels-native | Mobile-first vertical video | 9:16, 15-60 sec |\n\n### Ad Copy Structure\n\n| Element | Guideline |\n|---------|----------|\n| Primary text | Under 125 characters (truncation point). Hook → benefit → CTA |\n| Headline | Under 40 characters. Clear value prop or offer |\n| Description | Under 30 characters. Supporting detail |\n| CTA button | Match objective: Shop Now, Learn More, Sign Up, Get Offer |\n\n**Copy framework:** Hook (stop the scroll) → Problem\u002FPain → Solution\u002FBenefit → CTA\n\n**Video rules:**\n- First 3 seconds = hook (determines if user watches)\n- Bold, high-contrast captions mandatory (85% of Facebook videos watched without sound)\n- Front-load the value proposition\n- Graphic CTAs outperform text-only CTAs\n\nFor creative testing frameworks, use the **meta-ads-creative-engine** skill.\n\n## Output Format\n\n```\n## Meta Ads Campaign Build Plan\n\n### Campaign Overview\n- Campaign name: [naming convention]\n- Objective: [ODAX objective]\n- Conversion location: [Website\u002FApp\u002FInstant Forms\u002Fetc.]\n- Structure: [ASC \u002F Manual 3-campaign \u002F Custom]\n- Total monthly budget: $[X]\n\n### Ad Set Configuration\n| Ad Set | Audience | Budget | Optimization | Attribution |\n|--------|----------|--------|-------------|-------------|\n| [name] | [targeting] | $[X]\u002Fday | [event] | [window] |\n\n### Audience Strategy\n- Prospecting: [approach]\n- Retargeting: [approach]\n- Exclusions: [who to exclude]\n\n### Creative Plan\n| Ad | Format | Hook | CTA | Landing Page |\n|----|--------|------|-----|-------------|\n| [name] | [format] | [hook concept] | [CTA] | [URL] |\n\n### Launch Checklist\n- [ ] Pixel + CAPI verified (EMQ ≥ 6.0)\n- [ ] Conversion event configured and firing\n- [ ] Audience exclusions set (existing customers from prospecting)\n- [ ] Creative meets spec requirements (4:5 vertical, captions on video)\n- [ ] Budget sufficient for 50+ conversions\u002Fweek\u002Fad set\n- [ ] Attribution window set (7d click \u002F 1d view default)\n- [ ] No changes planned for 5-7 day learning phase\n```\n\n## Example\n\n**Input:** \"I sell premium candles DTC, average order value $45, target CPA $18. I'm spending $4K\u002Fmonth on Meta. Currently have Pixel + CAPI active with 7.2 EMQ. Getting about 55 purchases\u002Fweek. Want to set up a proper campaign structure.\"\n\n**Output:**\n\n## Meta Ads Campaign Build Plan\n\n### Campaign Overview\n- Objective: **Sales** (Purchase optimization)\n- Conversion location: Website\n- Structure: **ASC + Manual retargeting** — 55 purchases\u002Fweek qualifies for ASC. Run ASC for prospecting + manual for retargeting\n- Total monthly budget: $4,000\n\n### Campaign Architecture\n\n**Campaign 1: ASC — Prospecting (70% = $2,800\u002Fmo \u002F ~$92\u002Fday)**\n| Setting | Configuration |\n|---------|-------------|\n| Customer ratio | 80% new \u002F 20% existing |\n| Creative | 15-20 best-performing ads (diverse formats: static, video, carousel) |\n| Catalog | Connected — enable dynamic product ads |\n| Optimization | Purchase |\n| Budget | $92\u002Fday (enough for ~35+ purchases\u002Fweek at $18 CPA) |\n\n**Campaign 2: Manual — Retargeting (20% = $800\u002Fmo \u002F ~$26\u002Fday)**\n| Ad Set | Audience | Budget | Optimization |\n|--------|----------|--------|-------------|\n| Hot retarget | Website visitors 0-14 days + cart abandoners | $18\u002Fday | Purchase |\n| Warm retarget | Website visitors 15-60 days + engagers (50%+ video viewers) | $8\u002Fday | Purchase |\n\nExclude: past purchasers (30 days) from both ad sets to avoid wasted spend.\n\n**Campaign 3: Testing (10% = $400\u002Fmo \u002F ~$13\u002Fday)**\n| Ad Set | Purpose | Budget | Duration |\n|--------|---------|--------|----------|\n| Creative test | Test 2-3 new creative concepts against control | $13\u002Fday | 7-10 days per test |\n\n### Audience Strategy\n- **Prospecting (ASC):** Country-level (US). Algorithm handles targeting using your strong Pixel + CAPI signals (7.2 EMQ — solid)\n- **Retargeting:** Website visitors by recency (14-day warmer than 60-day), cart abandoners, video engagers (50%+ view)\n- **Exclusions:** Upload customer purchase list → exclude from prospecting. Exclude 30-day purchasers from retargeting. Note: detailed targeting exclusions removed March 2025 — use customer list suppression instead\n\n### Creative Plan\n| Ad | Format | Hook | CTA | Placement |\n|----|--------|------|-----|-----------|\n| Hero product | 4:5 static image | Lifestyle shot — candle in styled room | Shop Now | Feed, Stories |\n| UGC unboxing | 9:16 video (20 sec) | \"This is the best candle I've ever owned\" | Shop Now | Reels, Stories |\n| Scent carousel | Carousel (5 cards) | \"Find your signature scent\" — one scent per card | Shop Now | Feed |\n| Social proof | 4:5 static | Customer quote + product image | Shop Now | Feed |\n| Process video | 9:16 video (15 sec) | \"Hand-poured in small batches\" — behind-the-scenes | Shop Now | Reels |\n\n**Copy example (UGC ad):**\n- Primary text: \"This candle completely transformed my living room. The scent lasts 60+ hours.\" (89 chars)\n- Headline: \"Premium Hand-Poured Candles\"\n- CTA: Shop Now\n\n### Launch Checklist\n- [x] Pixel + CAPI verified (EMQ 7.2 — above 6.0 threshold)\n- [ ] Purchase event configured as primary conversion\n- [ ] Customer list uploaded → excluded from ASC prospecting\n- [ ] 30-day purchaser exclusion on retargeting ad sets\n- [ ] All creative in 4:5 vertical (Feed) and 9:16 (Reels\u002FStories)\n- [ ] Video ads have captions (85% watch without sound)\n- [ ] ASC budget = $92\u002Fday (allows ~35+ purchases\u002Fweek at $18 CPA)\n- [ ] No changes for 5-7 day learning phase after launch\n\n## Guidelines\n\n- **Don't use Traffic when you want Sales.** This is the most common Meta Ads mistake. Traffic finds clickers. Sales finds buyers. Always optimize for the deepest meaningful conversion.\n- **Don't launch with weak tracking.** If EMQ is below 6.0 or CAPI isn't active, fix tracking first. Use the **meta-ads-tracking-setup** skill before building campaigns.\n- **Don't hyper-segment audiences.** 2025 Meta algorithm prefers fewer, larger ad sets with 50+ conversions\u002Fweek. Combine warm + cold in single ad sets. Let the algorithm sort.\n- **Don't skip customer exclusions.** Detailed targeting exclusions were removed March 2025, but customer list exclusions still work and are essential. Upload your buyer list and exclude from prospecting.\n- **Don't ignore learning phase.** 5-7 days minimum. Changes during learning (budget >20%, audience, creative swaps, bid changes) reset the clock. Advertisers under 20% learning spend see up to 68% lower CPA.\n- **Don't forget captions on video.** 85% of Facebook videos are watched without sound. No captions = invisible message.\n- **Don't exceed 150 ads in ASC.** Hard ceiling. Curate your best creative, don't dump everything in.\n- **Instant Experience change (June 2025):** No longer counts as a landing page view. Don't include in funnel metrics.\n- **Offline Conversions API (May 2025):** Discontinued. If someone needs offline conversion tracking, direct to CAPI with `action_source: physical_store` or `system_generated`.\n- **Cross-references:** For tracking setup, use the **meta-ads-tracking-setup** skill. For creative testing, use the **meta-ads-creative-engine** skill. For ongoing optimization after launch, use the **meta-ads-optimizer** skill. For Google campaign building, use the **google-ads-campaign-builder** skill. For audience deep-dives, read `references\u002Faudience-strategy.md`.\n- **Confidence:** Budget sufficiency estimates are based on target CPA math. Actual performance depends on creative quality, offer strength, and tracking accuracy. ASC's ~12% CPA improvement is Meta's benchmark — results vary.\n",{"name":5,"description":7},"main","c137446804eb85b2cc0ba009c62068253a7c47be","skills\u002Fmeta-ads-campaign-builder",2,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow","engineer by education, marketer by profession, building rn","2026-09-16 13:41:44",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill meta-ads-campaign-builder",{"tool":68,"command":69},"bunx","bunx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill meta-ads-campaign-builder",{"tool":71,"command":72},"pnpm","pnpm dlx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill meta-ads-campaign-builder","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmeta-ads-campaign-builder\u002FSKILL.md",true,[76,82],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Faudience-strategy.md","REFERENCE","参考文档",10484,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmeta-ads-campaign-builder\u002Freferences\u002Faudience-strategy.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fmeta-campaign-types.md",8373,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmeta-ads-campaign-builder\u002Freferences\u002Fmeta-campaign-types.md",[87,114,142,163,192,219],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":95,"publishedAt":49},"01M718Q16XB7D7ZMGNNA1226G5","technical-seo-ai-crawler-audit","Diagnose and prioritize the technical issues blocking a site from being crawled, indexed, ranked, and cited. Modern technical SEO has two audiences: Google's crawler\u002Findexer and AI answer engines (GPTBot, PerplexityBot, ClaudeBot, etc.). Both need HTML-visible content and a clean crawl path.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-seo-ai-crawler-audit\u002FSKILL.md",[93,94],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"seo","technical","technical seo","technical seo ai crawler audit","audit","crawler","crawl","diagnose","answer","issues","clean","indexer","google","prioritize","site","content","html","path",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":125,"publishedAt":49},"01M718PY2PKA7BYS5KNJ2A9DRT","seo-reporting-measurement","Build reports that stakeholders trust, rooted in data sources that are actually reliable. Modern SEO measurement has to reckon with a very specific constraint: **the primary data source (GSC) is ~75% incomplete**, and the AI visibility layer is **probabilistic**, not deterministic. Building reports that ignore these realities produces false confidence.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-reporting-measurement\u002FSKILL.md",[120,121,124],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":122,"categoryName":123,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[126,127,128,129,96,130,131,132,133,134,135,136,137,138,139,140,42,141],"seo reporting","seo reporting measurement","reporting","measurement","sources","gsc","false","reports","data sources","data","layer","source","deterministic","trust","with","constraint",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":150,"publishedAt":49},"01M718PVF0SP35J6S9EHRGQZQ1","seo-max-kt","Long-form SEO and AEO methodology bundle; read SEO_AND_AEO_KT.md for the full corpus.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-max-kt\u002FSKILL.md",[148,149],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[151,96,152,153,154,155,156,157,158,159,160,161,162],"kt","seo max kt","max","aeo","long-form","methodology","form","for","bundle","full","read","long",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":174,"publishedAt":49},"01M718PRQNX2JF71GBFTH3MV19","seo-keyword-research-intent-mapping","Build a keyword universe that reflects how both humans *and* AI answer engines search. Traditional keyword research looks at standalone terms; modern research also maps the **fan-out sub-queries** AI tools generate when breaking down a user prompt.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-keyword-research-intent-mapping\u002FSKILL.md",[169,170,173],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[96,175,176,177,178,179,180,181,182,183,104,184,185,186,187,188,189,42,190,191],"seo keyword research","seo keyword research intent mapping","research","intent","keyword research","mapping","ai tools","standalone","down","search","prompt","when","maps","queries","generate","how","at",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":206,"publishedAt":49},"01M718PNDT3A19SV7KVWC9083P","seo-aeo-content-strategy","Design content systems that earn both Google rankings *and* AI citations. The core insight: AI answer engines read content differently than humans — they extract the highest-information-gain sentences and treat H2 headings as prompts. The \"ski ramp\" structure optimizes for both.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-aeo-content-strategy\u002FSKILL.md",[198,201,202,203],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},{"categoryNo":204,"categoryName":205,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[96,154,46,207,208,111,209,210,211,158,104,212,213,214,215,161,216,217,108,218],"seo aeo content strategy","content strategy","Citations","as","core","design","information","earn","structure","systems","ramp","prompts",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":228,"publishedAt":49},"01M718PJM103BE7581G6NRCC7T","prompt-picker","Build a prompt library that reveals how a brand shows up across the full buyer journey in AI search systems. The library is designed for actual LLM visibility tracking, not theoretical SEO keyword research.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fprompt-picker\u002FSKILL.md",[225],{"categoryNo":226,"categoryName":227,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[229,185,230,231,96,158,175,184,232,233,234,43,177,42,190,216,179,235,236,160],"prompt picker","llm visibility","brand","up","journey","prompt library","library","tracking"]