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.
Denver, CO
Analytics, Tracking & Data in Denver, CO
Server-side tracking, a clean GA4 event layer and attribution built for buyers whose path to purchase spans weeks and three devices.
Delivered remotely for brands across Denver and Colorado.
Scale · Denver
Why Denver brands come to us for this
- Attribution windows configured for multi-week consideration cycles instead of a seven-day default.
- Cross-device identity resolution so the September researcher and the November buyer are the same person.
- Return rate modelled into revenue reporting, because gross revenue overstates a fit-driven category badly.
- Freight cost per order in the contribution-margin view — non-trivial shipping bulky goods out of Colorado.
- Seasonal cohort reporting that compares this season to last season, not this month to last month.
Standard attribution windows were designed for impulse commerce. A seven-day click window applied to a Denver hardgoods buyer who researches for a month simply loses the sale, and the channel that actually created the demand shows up in your dashboard as a rounding error while branded search takes the credit.
That is the first thing we fix here: window configuration, cross-device identity resolution, and a data layer that survives a returning visitor arriving on a different device six weeks later. Then the standard rebuild — server-side GTM on a first-party subdomain, Conversions API with match quality above 8, a documented GA4 event schema, consent mode wired to your CMP.
And then the piece that changes decisions in this market: contribution margin loaded with freight and returns. In fit-driven and bulky-goods categories those two costs are the difference between a channel that looks profitable and one that is. Reports that reconcile to Shopify's own numbers, so the Monday meeting is about the business instead of about which dashboard is lying.
Local context
Long windows, high returns, expensive freight
Three measurement distortions hit Front Range brands harder than average. Consideration windows in gear and outdoor categories routinely exceed the default attribution settings, so upper-funnel channels get systematically under-credited and budget shifts toward whatever harvests demand last. Return rates in size-and-fit categories mean gross revenue in your analytics overstates what you actually kept, sometimes by double digits. And Denver's distance from both coasts makes outbound freight a real line item that never appears in a platform ROAS figure. Until all three are modelled, every optimisation decision downstream — media, CRO, retention — is being made against numbers that flatter the wrong channel. Fixing the measurement layer is unglamorous and it is the cheapest thing on this list.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Denver 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 Denver store
We do not work off a rate card. Every Denver 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 Denver — your questions
Because each platform reports on its own attribution model and none of them know about each other. Meta claims it, Google claims it, and the totals exceed what Shopify recorded. We reconcile everything against order data, quantify exactly where and how much is being double-counted, then report blended numbers that add up. It stops the weekly meeting being a debate about the data.
Widen the windows, resolve identity across devices, and stop reading last-click as truth. For a category where someone researches in September and buys in November, a default configuration credits whatever they touched last and hides the channel that created the demand. We pair widened windows with geo holdout testing, because at that consideration length modelled attribution alone is not evidence.
In size-and-fit categories, yes — often by double digits on net revenue, and disproportionately on the channels driving the least confident buyers. A campaign at 4x reported ROAS can be a loss once two-way freight and a 20% return rate are applied. We build return rate into the reporting layer by channel and by product so you can see which acquisition is producing customers who keep the thing they bought.
Before both. Testing against broken events produces confident wrong answers, and scaling spend against poor match quality means the algorithms are guessing at who converted. It is the least exciting engagement we run and the one everything else depends on. It also usually takes three to five weeks, so it does not hold up the rest of the roadmap for long.
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 Denver 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.