Your Meta pixel is missing half your conversions.
That is not a theory. It is what I find every time I audit a DTC brand’s stack.
Broken tracking does not just mean bad dashboards. It means wrong bids, inflated CAC, and retargeting audiences built on incomplete data. You are spending real money optimizing against numbers that are quietly lying to you.
What broken tracking actually costs.
// 01
Wrong bid optimization
Your Meta algorithm is optimizing against 50-70% of the data. Every campaign decision downstream of that gap is a guess.
// 02
Inflated CAC
You think you are paying $45 per acquisition when it is actually $28. Your real performance is better than you know, but you are making cuts based on phantom numbers.
// 03
Wasted retargeting
Retargeting audiences built on incomplete data miss your best customers. The people who actually converted never make it into the lookalike seed.
Browser-only tracking was missing half the story.
An ecommerce brand was making ad spend decisions against incomplete data. iOS 14.5 had silently broken browser-only pixel tracking. I built the entire server-side Meta CAPI infrastructure from scratch in 48 hours: GTM web and server containers, Stape cloud hosting, custom loader domain for first-party cookies, 11+ event tags with full event_id deduplication and external_id hashing.
~35% more conversions tracked. Accurate ROAS for the first time in 18 months.
+35%
Match quality improvement
9.1
Post-implementation MQ score
DTC Stack Audit
Four audit modules that show you exactly where your DTC stack is leaking revenue.
Most DTC brands are flying blind on at least two of these four: server-side tracking, analytics coverage, theme performance, and attribution accuracy. The data you see in Shopify, Meta, and GA4 rarely agree. You are making spend decisions against numbers that are missing 30 to 50 percent of your actual conversions. This audit shows you exactly where the gaps are, scored and prioritized.
// what gets audited
- Meta CAPI event coverage and deduplication
- Match quality score and external_id hashing
- GTM container health (web + server)
- GA4 and Shopify data alignment
// what you get back
- $Tracking Audit module (CAPI, dedup, match quality)
- $Analytics Gap Scan module (data vs. decisions)
- $Theme Performance Check module (Core Web Vitals, mobile friction)
- $Attribution Reality Check module (Shopify vs. Meta vs. GA4)
- $Scored diagnostic report with prioritized fixes
- $Claude Code prompts for each audit module
+35%
Conversions visible post-fix
9.1
Match quality score (typical result)
48h
Scratch to production
0
Revenue interruption
What if your problem is bigger than a $129 audit?
Some tracking problems are symptoms of a deeper tracking and measurement gap. If you need someone to build the fix, not just diagnose it, retainer slots are open through 2026.
// what a retainer looks like
- $Server-side CAPI implementation (full build, not a guide)
- $GTM container architecture and event deduplication
- $Analytics dashboards connecting Shopify, GA4, Meta, Klaviyo
- $Ongoing optimization and campaign creative
Is this another course?
No. It is a diagnostic. You run it against your actual store and get a specific report on what is broken and what to fix first.
Do I need to be a developer?
If you can navigate GTM and follow a checklist, you can implement this. The Claude Code prompts handle any code generation required.
Stop guessing. Fix the signal.