Tracking Audit & Reconciliation
A full event inventory across GA4, Meta, Google Ads, Klaviyo and Shopify, reconciled against order data to quantify exactly where and how much data is lost.
Dallas, TX
Analytics, Tracking & Data in Dallas, TX
Server-side tracking, a clean GA4 event layer and reporting that separates buyer revenue from consumer revenue instead of averaging them into nonsense.
Delivered remotely for brands across Dallas and Texas.
Scale · Dallas
Why Dallas brands come to us for this
- Wholesale and consumer traffic separated at the event layer, not reconciled in a spreadsheet later
- Freight allocated by zone into contribution margin for heavy and oversized categories
- Season-to-date reporting aligned to market cycles rather than calendar quarters
- Server-side tracking and Conversions API with event match quality above 8 out of 10
- Reporting built to be read by a CFO who thinks in landed cost, not in attributed ROAS
Every measurement conversation in this metro eventually arrives at the same problem. The dashboard shows one conversion rate, one AOV and one customer count, and inside those numbers are two completely different businesses: a handful of retail accounts placing large seasonal orders, and thousands of consumers buying one thing. Blend them and the average describes nobody. Report the blend to a CFO who knows the wholesale book and you lose credibility in the first minute.
So the first job is separation. Logged-in company traffic and consumer traffic split at the event layer, with revenue, cohort and margin reporting for each. Then the standard repairs: server-side tracking, Conversions API with event match quality above 8 out of 10, a GA4 event schema that is not double-counting purchases, and consent mode configured properly.
Then we build the margin layer, which for Dallas brands is the part that changes decisions. Freight cost by zone, return rate by category, and case-pack economics folded into contribution margin, so when paid media asks for more budget the answer comes from a number that includes the cost of putting a sixty-pound box on a truck.
Local context
Reporting that survives a wholesale-literate CFO
The finance leadership at a Dallas consumer brand usually came up through a wholesale P&L. They think in landed cost, freight, chargebacks, allowances and season-to-date bookings, and they are unimpressed by a dashboard showing platform-attributed ROAS. That sets the bar for what we build: contribution margin by channel with freight allocated by zone, cohort revenue split between buyer accounts and consumers, season-to-date views that line up with market cycles rather than calendar quarters, and inventory-turn context so the direct channel's value is visible against the same yardstick as wholesale. We also model the sales-tax and nexus reality of a brand shipping nationally from a Texas warehouse into the reporting, because unrecognised tax and freight drag is the most common reason a DTC channel looks profitable in a dashboard and is not in the ledger.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Dallas brand’s realities.
Full service detailServer-Side Tracking
Server-side GTM on a first-party subdomain, resilient to ad blockers and ITP, with deduplication between browser and server events done properly.
Conversions API Integration
Meta CAPI, Google Enhanced Conversions and TikTok Events API with hashed identifiers, targeting event match quality of 8 or above.
GA4 Event Schema
A documented, consistent eCommerce event and parameter specification across every surface, so reports mean the same thing in six months as they do today.
Consent Mode & Privacy
Consent mode v2 wired to your CMP with modelled conversions, plus Shopify's customer privacy API and regional compliance handled correctly.
Executive Reporting Layer
One dashboard for blended MER, contribution margin, cohort LTV, new-versus-returning revenue and channel payback. Reconciled to Shopify, refreshed daily.
Scoped and quoted for your Dallas store
We do not work off a rate card. Every Dallas engagement starts with a fixed statement of work — named deliverables, named dates, one number — written after we have looked at your store, not before. If a smaller first step would serve you better, we will say so.
Get this scopedHow it runs
From kickoff to results
Audit & Quantify
We measure the gap between platform-reported and actual orders per channel. Most stores we audit are losing 15-30% of conversion signal before we start.
Specification
A written measurement plan: events, parameters, identifiers, consent states and destinations. Signed off before implementation begins.
Implement
Server-side container, CAPI, enhanced conversions and consent mode built in a staging environment and validated event by event.
Validate
Order-level reconciliation against Shopify for a full week, plus match-quality checks in each platform. We do not sign off on a screenshot of a tag firing.
Report & Maintain
Dashboards built, team trained, and monitoring in place to alert on event volume anomalies before someone spots them in a monthly report.
Proof
Analytics & Data results
Anonymised under NDA. Figures pulled from the client’s own analytics.
Consumer Electronics & Accessories
~$9M/yr, 210 SKUs, US + AU · Shopify Plus (migrated from BigCommerce)
Meta ROAS had slid from 3.6x to 1.9x in a year and the team had spent twelve months buying new creative to fix it. The real cause was measurement: the BigCommerce checkout dropped 22% of purchase events and the Conversions API had never been installed, so both ad platforms were optimising on incomplete data. The named constraint: peak season was 14 weeks out, and the replatform had to be live and stable well before Black Friday traffic arrived.
1.9x → 3.4xMeta ROAS, once the 22% event gap closed60 days after server-side tracking went live, spend up 18%. Most of that is signal we recovered, not performance we invented — the honest number is the blended CAC below, which is measured against Shopify orders
-32%customer acquisition cost$44 to $30 blended across Meta and Google
4.1s → 1.7smobile LCPdesktop went 2.9s to 1.2s over the same window
+47%peak-season revenueBlack Friday through Cyber Monday, year over year
In their words
Clients on this work
GA4/Shopify gap 14% → under 2%
“Paid audit, and worth every dollar. Forty pages on where our measurement was lying to us — duplicate purchase events, CAPI never configured, GA4 and Shopify off by 14% — each one ranked by the revenue it was hiding. No pitch deck at the end. We fixed six of the items ourselves before we ever signed a retainer.”
FounderHome goods brand, ~$3M/yr · Denver, CO
Verified client, 2026FAQ
Analytics & Data in Dallas — your questions
Because it distorts every headline metric. A logged-in buyer converts at a multiple of consumer rates and orders in case packs, so a blended conversion rate and AOV describe a customer who does not exist. We split the two at the event layer using Shopify company records, then report each against its own baseline and margin structure.
Yes, and for the bulky categories common here it is the change that matters most. We map carrier cost by zone and weight band against orders, add return rate by category, and produce contribution margin per order and per SKU. It routinely reorders which products deserve ad budget and which have been quietly subsidised.
Instrumentation, not insight. Month one is server-side tracking, purchase deduplication, consent configuration and the channel split. Clean data has to exist before the reporting layer means anything, and most stores we audit are double-counting purchases somewhere between the theme, an app and a tag manager container.
Where one exists, yes. We feed clean, documented data into whatever your finance team already uses rather than adding another dashboard nobody opens. If there is nothing in place, we build a lightweight warehouse and a small number of reports that answer specific decisions, because a large dashboard nobody trusts is worse than no dashboard.
Browser tracking loses 15-30% of conversions to ad blockers, ITP and consent rejections. Server-side sends events from your own infrastructure, which recovers most of that signal. Better signal means better algorithmic bidding, so it usually pays for itself in media efficiency within a quarter.
Four to six weeks for a typical Shopify store, including the validation week. Complex setups with subscriptions, multiple markets or a headless front end run six to ten. The audit and specification phase takes about a third of that and is the part that determines quality.
Yes. We use Shopify's Web Pixels API and customer events for checkout tracking, which is the supported path since checkout.liquid was retired. Order-level data comes through the server side, so checkout tracking no longer depends on scripts Shopify will not let you inject.
Next step
Analytics & Data for your Dallas brand.
Thirty minutes with the strategist who would actually run your account. We screen-share your store, read your data live, and tell you the three highest-value things we can see from the outside.
Shopify or Shopify Plus stores doing $150k/mo or moreFounder, CEO or eCommerce lead on the callNo deck and no pitch — we open your store instead
Prefer to write it out? [email protected] gets a real reply the same business day, Mon-Fri, 9am-6pm MT.