DTC Brand — Building a Full Analytics Stack from Zero

Scaling spend with no ability to verify if it was working

The brand was spending $500k/month in paid media across Meta, TikTok, and Google with zero independent tracking infrastructure. Every conversion number came directly from the platforms — self-reported, unverified, and not cross-referenced with anything.

Platform-reported numbers had no validation layer. If Meta said a campaign drove 400 purchases, there was no way to confirm that against the actual order data. Attribution was entirely last-click, entirely platform-side.

As spend scaled, the team had growing pressure to justify budget decisions to stakeholders. They couldn't. Every report was "what the platforms told us" — and that was no longer sufficient.

Full-stack implementation in 3 weeks — GTM, GA4, and all paid platforms

Designed and built the complete tracking architecture from scratch: GTM container with full trigger and variable structure, GA4 with complete e-commerce event schema (view_item, add_to_cart, begin_checkout, purchase), and all paid media pixels with server-side supplementation.

Meta Pixel + Conversions API via sGTM for browser + server coverage. TikTok Pixel + Events API. Google Ads conversion tracking with enhanced conversions. All platforms configured with consistent event naming and parameter structure so cross-platform comparison was apples-to-apples.

GA4 configured as the independent source of truth — separate from all platform-reported numbers. Every purchase in GA4 cross-referenced against the e-commerce platform's order data during QA before going live.

Full documentation written for the internal team: what each tag does, how to test it, how to interpret the numbers in each platform, and what to do if something breaks.

Full stack went live and verified within 3 weeks. Within 30 days of having real data, the team discovered that 60% of TikTok budget was concentrated in a single ad set with a 0.4% CVR — which had looked acceptable under platform-reported numbers but was actually the worst performer in the account.

That budget was reallocated. Results improved within one week of the change. The team now had an independent data layer that could surface problems the platforms had no incentive to show.

Stakeholder reporting shifted from "what the platforms say" to "what our GA4 data shows, cross-referenced with order data." Budget conversations became evidence-based for the first time.