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.
Pittsburgh, PA
Analytics, Tracking & Data in Pittsburgh, PA
Tracking that survives a sales process which starts on the storefront and finishes in an ERP.
Delivered remotely for brands across Pittsburgh and Pennsylvania.
Scale · Pittsburgh
Why Pittsburgh brands come to us for this
- Quote requests and phone orders tracked with click identifiers and imported back as offline conversions when they close
- ERP revenue reconciled against Shopify and GA4, with every legitimate difference documented rather than argued about
- Server-side event collection and Conversions API with correct deduplication, so blocked browsers stop erasing purchases
- Long-cycle attribution that survives a sixty-day gap between first visit and purchase order
- Seasonal reporting built on same-period-last-year comparisons rather than rolling windows that misread a quiet March
The Pittsburgh measurement problem is structural rather than technical. A large share of the revenue this city's stores generate never touches a Shopify checkout: it arrives as a quote request that becomes a purchase order, a phone call to a sales desk, or a reorder placed against net terms. GA4 sees the visit and nothing else, so the channels that generate the most valuable demand look like the weakest ones, and budget gets moved away from them accordingly.
So the work starts by closing the loop rather than by rebuilding dashboards. Click identifiers get captured on quote and contact forms and stored against the record. When the deal closes in the ERP, the value comes back as an offline conversion so bidding optimises toward orders that actually happened. Phone orders get matched where numbers and timestamps allow. It is unglamorous plumbing and it changes which channels look profitable more than any reporting layer ever will.
The conventional work runs alongside it: server-side event collection so purchases are not lost to browser blocking, Conversions API with correct deduplication, a GA4 event schema that means the same thing in every report, and consent handling that is defensible without silently destroying your data. Then a reconciliation any finance person can check, because a Pittsburgh owner comparing GA4 against Shopify against the ERP and getting three answers has correctly concluded that none of them can be trusted.
Local context
When the ERP, not Shopify, holds the real revenue number
In most consumer businesses Shopify is the source of truth and everything reconciles to it. For a Pittsburgh distributor or manufacturer, the ERP holds the number that finance actually uses — it knows about the freight, the credit note, the partial shipment, the returned pallet and the order that was placed online and amended by phone the next morning. Any reporting layer that ignores that will produce a marketing dashboard the owner does not believe, and once trust breaks it does not come back. We build the reporting to reconcile in the direction the business runs: Shopify events for behaviour and optimisation, ERP data for revenue and margin, and a documented explanation of every place the two legitimately differ. That also makes seasonality legible — a merchandise brand comparing October to March learns nothing useful, so reporting gets built on same-period-last-year and on lead-time-adjusted cohorts instead of on a rolling thirty days that punishes every quiet month.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Pittsburgh 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 Pittsburgh store
We do not work off a rate card. Every Pittsburgh 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 Pittsburgh — your questions
It works if it is built for it. Standard lookback windows and cookie lifetimes will not survive a sixty-day cycle, so we persist a first-touch identifier on the lead record rather than relying on the browser to remember. That gives you channel attribution at the point the order closes, months after the visit that caused it, which is when the number actually matters.
Partially, and honestly labelled. Calls from the site can be tracked with dynamic number insertion so the source is captured, and orders placed against a known account can be matched to prior sessions. What cannot be attributed is a call from someone who never identified themselves, and we report that bucket as unattributed rather than distributing it across channels to make a chart look complete.
By agreeing which system owns which number before building any reports. Revenue, margin and returns belong to the ERP. Sessions, funnel behaviour and channel signals belong to the analytics layer. Then we document the specific reasons they differ — freight, tax treatment, amendments, cancellations — so the gap is explained once instead of relitigated every month.
Yes. Consent obligations follow the individual visitor, not their employer, and your traffic includes plenty of people who are not buying on behalf of a company at all. We implement consent mode properly so measurement degrades in a modelled, defensible way rather than collapsing, and so you are not left choosing between compliance and having any data at all.
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 Pittsburgh 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.