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.
St. Louis, MO
Analytics, Tracking & Data in St. Louis, MO
When the ERP, Shopify and Google all report a different number, the argument stops being about marketing and starts being about which system anyone believes.
Delivered remotely for brands across St. Louis and Missouri.
Scale · St. Louis
Why St. Louis brands come to us for this
- Shopify and ERP revenue reconciled first, with a written definition of which system owns which number
- Phone, email, purchase order and EDI orders imported with source attribution instead of being invisible
- Customer-type dimension built into the GA4 schema so trade and consumer orders never blend into one average
- Offline conversion imports fed back to Google Ads so bidding optimises to real revenue, not card checkouts only
- Contribution margin after freight and COGS surfaced by product line, since Midwest shipping economics vary widely
The measurement problem in this metro has a distinctive shape. It is not usually a broken pixel — it is two systems of record disagreeing. Shopify says one revenue figure, the ERP that has run the business since the nineties says another, and neither includes the orders that arrived by phone, email or purchase order. Until those reconcile, nobody senior trusts a marketing report, and rightly so.
So we start with reconciliation rather than instrumentation. Define which system owns which number, agree what counts as revenue and when, and build a bridge between the ERP's order records and Shopify's so that the difference between them is explained rather than argued about. That work is boring, it takes a couple of weeks, and it is the reason the rest of the reporting eventually gets used in a board meeting.
Then the tracking itself. Server-side event collection through the Shopify Customer Events layer, Conversions API for Meta, enhanced conversions for Google, a GA4 event schema that matches how you actually talk about the business, and consent handling that works. For companies with both a trade portal and a consumer storefront, the schema needs a customer-type dimension from day one — otherwise every average you calculate blends a $90 consumer order with a $9,000 pallet.
Local context
Offline orders are not noise, they are most of the revenue
For a manufacturer or distributor here, a meaningful share of orders never touch the storefront. They arrive as a purchase order, an emailed spreadsheet, a phone call to a rep, or an EDI drop from a large account. Standard ecommerce analytics simply cannot see them, which means every channel in the report looks unprofitable and the marketing spend looks indefensible. Fixing it means importing offline orders with source attribution, tagging orders by origin at entry, and running offline conversion imports back into Google Ads so bidding optimises toward the revenue that actually exists rather than the fraction that happened to check out with a card. The same logic applies to margin: with freight-heavy products shipped from a low-cost Midwest warehouse, blended ROAS hides enormous variation between product lines, and the only target worth bidding to is contribution margin after shipping and cost of goods. We build that into the reporting layer rather than leaving it in a spreadsheet on the controller's desktop.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a St. Louis 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 St. Louis store
We do not work off a rate card. Every St. Louis 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 St. Louis — your questions
Usually both, for different definitions. The ERP typically recognises revenue at shipment or invoice, Shopify at order placement, and the two treat taxes, freight, discounts and cancellations differently. The fix is not to force them to match but to document the reconciliation so the gap is explainable to the pound. Once that exists, the arguments about whose number is real stop.
By tagging it at entry and importing it with its source. Rep-taken orders get an origin tag and, where possible, a link to the customer record that was browsing beforehand. Then those orders are imported as offline conversions with their value and date, so the channel that generated the interest gets credit and the bidding algorithms see the real outcome. It is not perfect attribution, but it beats pretending the order never happened.
Not all of it. A single-channel consumer store with card checkout and no ERP has a much simpler problem, and the honest answer is often that a clean GA4 setup, working server-side events and a single reporting view is enough. We would rather scope you into the small version than sell an integration project you do not need. The complexity is driven by how many systems hold order truth, not by revenue.
It changes every decision that uses an average. Blend a trade pallet with a consumer order and your AOV, conversion rate, LTV and return rate all become numbers that describe no real customer. Split them and you can see that consumer acquisition is profitable at one target and trade acquisition at a completely different one. Most of the surprising findings in the first month of reporting come from that split alone.
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 St. Louis 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.