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.
Charlotte, NC
Analytics, Tracking & Data in Charlotte, NC
A measurement layer built to a standard a finance-trained Charlotte owner will actually sign off on.
Delivered remotely for brands across Charlotte and North Carolina.
Scale · Charlotte
Why Charlotte brands come to us for this
- Server-side tracking on a first-party endpoint, with browser and server events deduplicated rather than double-counted
- Freight in, freight out, liftgate and residential surcharges carried into contribution reporting on heavy and made-to-order lines
- Wholesale, DTC and trade-show-originated orders tagged and reported separately instead of blended into one average
- Return reasons split by cause — wrong fitment, wrong expectation, transit damage — because each has a different owner
- Reporting reconciled to Shopify's own order numbers, with any variance explained rather than quietly absorbed
This is the city where the analytics conversation goes differently. Ask three dashboards how many orders you took yesterday and you get three answers everywhere, but in Charlotte the person asking has usually spent years in a bank tower on Tryon Street reconciling numbers for a living, and they will not accept a weekly meeting that turns into a debate about the data. That raises the bar in a useful way: the measurement layer has to tie back to Shopify's own numbers, and every gap has to have an explanation rather than a shrug.
The technical work is the same boring work it always is, done properly. Server-side tracking through a first-party endpoint so events survive browser restrictions and ad blockers. Conversions API with high match quality and clean deduplication against the browser events. A documented GA4 schema so an event means the same thing in six months. Consent mode wired to whatever CMP you run. And a reporting layer that reconciles to Shopify rather than competing with it.
Where it gets Charlotte-specific is what has to be in the model. Freight is a real cost of goods here, not a rounding error, and a contribution report that ignores LTL, liftgate and residential surcharges will tell you a furniture line is profitable when it is not. Wholesale and DTC have to be separated all the way through, because blending a fifty-line programme order with consumer purchases makes both averages meaningless. And returns in fitment categories need to be attributed to a cause — wrong part, wrong expectation, damaged in transit — because those three have completely different fixes and only one of them is a shipping problem.
Local context
Contribution margin by cohort, because that is the question you will be asked
Charlotte founders are unusually likely to have been trained by a lender, a credit committee or a corporate finance function, and the reporting standard follows. So we build toward contribution rather than revenue: product cost, freight in and freight out, payment fees, fulfilment labour, returns and discount all subtracted before a channel is judged, with cohorts tracked so a customer acquired in the winter build season can be compared with one acquired at a race weekend eighteen months later. That also means separating channels properly — a wholesale reorder from a long-standing account should never be sitting in the same average order value as a first-time consumer purchase, and marketplace or trade-show-originated orders need their own tag rather than defaulting to direct. The other local requirement is sales tax clarity: North Carolina is destination-sourced with county-level rates on top of the state rate, and shipping charges are generally part of the taxable sale, so a report that treats gross as revenue will overstate the business. None of this is exotic. It is just the version of analytics that survives a conversation with someone who reads statements for a living.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Charlotte 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 Charlotte store
We do not work off a rate card. Every Charlotte 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 Charlotte — your questions
By treating shipping as a per-order cost pulled from the carrier or 3PL rather than as the flat rate the customer paid. We map actual LTL invoices, liftgate and residential surcharges back to orders, then include both what you charged and what you paid in the contribution calculation. On heavy catalogues this frequently reverses the ranking of your best-performing product lines, which is the entire point of doing it.
Yes, and you should. B2B orders on Shopify carry company and price-list context we can tag through into the reporting layer, so average order value, margin and repeat rate are calculated per channel. Blending them is what produces the false comfort of a rising AOV that turns out to be one large programme order, and any finance-minded owner spots that inconsistency within a quarter.
By cause, captured at the point of the return request rather than inferred later. Wrong-fitment returns point at your product data and compatibility experience, wrong-expectation returns point at imagery and copy, and transit damage points at packaging or carrier. Each has a different owner and a different fix, and a single return-rate percentage tells you which quarter was bad without ever telling you why.
Mostly in the gap between gross sales and actual revenue. North Carolina applies a state rate plus county-level local rates, sourcing is destination-based, and shipping and handling on a taxable sale is generally taxable too — so a dashboard reading gross will overstate what the business earned and misstate contribution by destination. We separate tax and shipping out of revenue in the reporting layer and leave filing itself to your accountant, which is where it belongs.
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 Charlotte 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.