Enables TFWW to scale past organic/SMS into profitable paid acquisition by optimizing for actual closed revenue per ad rather than cheap leads that don't show or close.
Implement systematic Meta Ads attribution tracking in AIAS CRM to optimize for closed revenue per creative rather than just lead volume.
Business Applications
HIGH Meta Ads account structure and attribution tracking (meta_ads)Configure AIAS CRM to capture fbclid, campaign_name, adset_name, ad_name on lead creation; build dashboard view showing cost per booked appointment vs cost per showed appointment vs cost per closed deal by ad creative
HIGH CAPI implementation (meta_ads)Complete TFWW TODO: Implement Meta CAPI (Conversions API) to pass back offline conversion events (showed, closed) to Meta Ads manager for algorithmic optimization on actual revenue events not just leads
MEDIUM Creative testing methodology (general)When launching TFWW paid ads, create 15-35 ad variations per test batch using the '5 angles x 3 hooks' methodology (5 body/headline variants), run ABO test, then iterate top 3 into 15 variations for next batch
David Wehner appears to be a media buyer/agency owner specializing in B2B client acquisition. Low engagement (5 likes) suggests early account or niche audience, but content reflects experienced media buying operator.
Hook: Hook promises competitive advantage: "How I Scale B2B Ads that my competitors have nightmares about"
- Campaign structure: Lead objective, ABO (Ad Set Budget Optimization), budget split evenly between ad sets
- Testing volume: 15-35 ads per ad set, with only 5 body/headline variations (same across all ads), only targeting changes between ad sets
- Targeting stack: Interest stack, broad targeting, and 1% lookalike audiences
- Winner identification: Look for consistent winners across all three targeting types OR different winners per audience pocket
- Quality optimization: Don't optimize on booking cost alone—wait for sales team feedback on lead quality, show rates
- Attribution tracking: Custom fields populated with campaign, ad set, and ad name for every lead to isolate quality sources
- Winner migration: Move winning ads from testing campaigns to a dedicated "winners campaign" with one ad set per ad to force spend
- Winner scaling: For each winner, duplicate into two ad sets (interest stack + broad), split budget, kill underperformer, shift spend to winner tiered by ROAS
- Campaign hygiene: Create fresh testing campaigns for new batches (don't add to existing), keep winners campaign always running
- Creative iteration: Generate 15 new variations from top 3 winning ads (not random new concepts) for subsequent tests
“We don't just base off of the booking costs... we wait until we get feedback from the sales team on who's the most quality people coming in”
“We take any of the winners across three campaigns and we duplicate that over to a winners campaign where it's separated into one ad set per ad where we can force spend into those winner ads”
“All of the new ads we test are based off of basically putting together our winners and then we just create variations on those... 15 variations off of for example the top three winners”
What it is: A systematic Meta Ads account structure for scaling B2B lead generation while maintaining lead quality through sales-attributed feedback loops rather than front-end metrics alone.
How it helps us: Critical for TFWW's next phase. We currently have basic Meta Pixel (3032047526979670) but lack CAPI and deep attribution tracking. This provides the exact framework to structure campaigns when we scale past organic/SMS into paid acquisition.
Limitations: Requires active sales team feedback loop—we have AI qualification but human sales verification is lighter. Need to adapt 'sales team feedback' to 'show rate' and 'close rate' by ad creative in our CRM.
Who should see this: Dylan + TFWW ops team for paid media strategy; AIAS dev team for CRM attribution tracking implementation
✅ [SOLID] "Wait for sales team feedback before determining winners rather than booking cost alone" — This is best practice for high-ticket B2B. Front-end metrics (CPL) often inversely correlate with back-end revenue in B2B—cheap leads frequently don't convert. TFWW's AI qualification can simulate this by tagging lead quality scores that correlate to show/close rates.
Instead: null
🤔 [PLAUSIBLE] "Launch 15-35 ads per ad set" — Standard for aggressive B2B scaling but requires significant budget—$50-100/day minimum per ad to exit learning phase. For TFWW's初期 phase, start with 5-10 ads and scale up as budget increases.
Instead: Start with 5-10 ad variations per ad set until daily spend reaches $500+/day, then expand to 15-35
✅ [SOLID] "Always run one interest stack, one broad, and one 1% lookalike" — This is the classic 'B2B trifecta' testing structure. Broad often wins in 2024-2025 Meta algorithm landscape, but testing all three ensures you don't miss audience pockets where specific angles resonate.
Instead: null