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.
Austin, TX
Analytics, Tracking & Data in Austin, TX
Server-side tracking and cohort reporting built for a board meeting, not for a dashboard screenshot.
Delivered remotely for brands across Austin and Texas.
Scale · Austin
Why Austin brands come to us for this
- Subscription revenue separated from one-time in every report, so renewal growth is never mistaken for acquisition.
- Wholesale and retail revenue modelled alongside DTC, even though it arrives as an invoice rather than an order.
- Contribution margin that includes summer cold-pack and expedited freight instead of hiding it in a footnote.
- Cohort payback and net revenue retention in the SaaS vocabulary your Austin investors already speak.
- Everything reconciled to Shopify's own order data, refreshed daily, before anyone argues about attribution again.
Ask three dashboards how many orders you took yesterday and you will get three answers. In most cities that is an annoyance. In Austin it is a board problem, because a venture-backed founder here is presenting cohort economics to people who have seen a hundred decks and will notice within thirty seconds that GA4 revenue and Shopify revenue do not reconcile.
There is a second gap specific to this market. A consumables brand has at least three revenue streams — one-time DTC, subscription, and wholesale into regional retail — and standard analytics setups capture one of them properly. Subscription renewals get attributed to whatever the customer last clicked years ago, or vanish. Wholesale never appears at all. The blended picture your investors actually asked for does not exist anywhere.
So we rebuild the layer underneath everything: server-side tracking on a first-party endpoint, Conversions API with real match quality, a documented GA4 event schema, consent mode wired to your CMP, and a reporting layer that reconciles to Shopify and separates new, returning and subscription revenue rather than averaging them into a number nobody can defend.
Local context
Reporting to a board that came from software
Austin's venture density changes what 'good analytics' means. Founders here often came out of SaaS, and they instinctively want the vocabulary of that world — cohorts, payback period, net revenue retention, contribution margin by acquisition month — applied to a physical product with COGS, co-packing runs and freight in it. That translation is most of the work. Subscription revenue has to be separable from one-time, retail sell-through has to sit next to DTC even though it arrives as a monthly invoice rather than an order, and the seasonal cost swing from summer cold-pack shipping has to be inside contribution margin rather than sitting in a footnote. When a board asks for payback by cohort, the honest answer should take an hour to pull, not a fortnight.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Austin 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 Austin store
We do not work off a rate card. Every Austin 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 Austin — your questions
Not on its own, and not from GA4 either. It needs order-level data joined to acquisition source and cost, with subscription renewals attributed to the original acquiring cohort rather than to a later click. We build that as a reconciled reporting layer on top of Shopify, refreshed daily, so the number is available before the meeting rather than after it.
Renewals are credited to the cohort that acquired the customer, not to whatever channel gets last-click credit years later. Otherwise your retention success shows up as email or direct performance and quietly makes your acquisition channels look worse than they are. It is the single most common distortion in a consumables brand's reporting.
Yes, if you want a real picture. Retail arrives as periodic invoices rather than as orders, so it needs a separate ingestion path, but keeping it out means your marketing spend is being judged against a fraction of the revenue it influences. We model it alongside DTC with the caveats stated plainly.
Because the platforms optimise on the signal you send them. Poor event match quality means Meta and Google are effectively guessing who converted, and every downstream decision inherits that guess. Fixing server-side tracking and CAPI match quality frequently lifts measured performance by double digits on its own, before a single budget changes.
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 Austin 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.