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.
Detroit, MI
Analytics, Tracking & Data in Detroit, MI
Measurement for Detroit stores where a large share of revenue never touches an online checkout.
Delivered remotely for brands across Detroit and Michigan.
Scale · Detroit
Why Detroit brands come to us for this
- Phone and counter orders brought into reporting through call tracking and offline conversion imports
- Fitment selector and site-search interactions instrumented, so failed vehicle and part-number lookups become visible
- Trade and retail revenue separated at source instead of blended into one misleading average
- Returns and RMA data joined back to orders so channels are judged on kept revenue, not gross
- Canadian and US market performance reported separately, with duty and freight cost carried into the margin view
Detroit breaks default analytics in a way consumer brands never see. A meaningful portion of revenue arrives by phone, over a counter, or as a purchase order emailed after someone researched the whole thing on your site. Standard GA4 counts none of it, so the channels that generated those orders look weak, budget shifts away from them, and the account slowly optimises toward your least valuable customers. Fixing that is usually worth more than any platform tactic.
The build starts with reconciliation: an event inventory across GA4, Meta, Google Ads, Klaviyo and Shopify, checked against actual order data to quantify exactly what is missing and where. Then server-side GTM on a first-party subdomain, Conversions API and Enhanced Conversions with properly hashed and normalised identifiers, and browser-to-server deduplication done correctly rather than approximately. Consent mode is configured properly too, which matters more each year as state privacy law expands.
On top of that comes the schema that makes this catalog legible. Fitment selector interactions tracked as events, so you can see how many sessions fail to find a vehicle. Site search tracked with the query preserved, because failed part-number searches are a revenue report. Trade and retail revenue split at source. Return and RMA data joined back to the order, so channel performance can be judged on kept revenue rather than gross.
Local context
Counting the orders that happen off the website
The most common measurement failure we find in this metro is a business whose website is enormously influential and barely credited. A shop foreman in Sterling Heights browses your catalog, confirms fitment, then calls the counter and reads the number out. That order lands in your accounting system with no channel attached, and every optimisation decision downstream is made on the fraction that self-served. We instrument the bridge: call tracking with dynamic number insertion so the source survives, quote and draft-order tracking, offline conversion imports back into Google and Meta with real order values, and a reconciliation view that shows total revenue by source rather than online revenue by source. The second Detroit-specific piece is cross-border. Ontario orders behave differently on identifiers, currency and returns, and a market split that treats them as domestic noise hides both a genuine growth channel and a cost line — duties, freight and return shipping — that quietly changes which products are actually profitable.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Detroit 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 Detroit store
We do not work off a rate card. Every Detroit 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 Detroit — your questions
With dynamic number insertion so the traffic source is attached to the call, call outcome captured by your counter team or CRM, and the resulting order value imported back into Google and Meta as an offline conversion. The important detail is value accuracy — a phone order in this market is often several times an average online order, and importing a flat placeholder value teaches the algorithms the wrong lesson.
By instrumenting it as a funnel of its own: selector opened, vehicle chosen, results returned, zero-result rate, and conversion rate for sessions with a saved vehicle versus without. Zero-result rate is the number most stores have never seen, and it is usually higher than anyone expects — which makes it one of the most actionable metrics we can hand you.
Not by default. Out of the box you get one blended conversion rate and one average order value across two populations that behave nothing alike. We pass customer type as a user-scoped dimension and split the reporting from the ground up, so trade repeat frequency and retail acquisition are analysed on their own terms rather than obscuring each other.
They affect the consent layer, and the compliance map keeps expanding as more states enact their own laws — with a national catalog you are exposed to all of them regardless of where you are based. We implement consent mode with proper defaults, ensure server-side tagging respects signals rather than bypassing them, and document what is collected so the answer exists before someone asks for it.
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 Detroit 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.