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.
Orlando, FL
Analytics, Tracking & Data in Orlando, FL
Measurement that survives the hour when six thousand people arrive at once — and reconciles afterwards.
Delivered remotely for brands across Orlando and Florida.
Scale · Orlando
Why Orlando brands come to us for this
- Server-side tracking on a first-party subdomain that keeps recording when client-side tags degrade under spike load
- Post-launch order-level reconciliation with oversells, cancellations and refunds netted out before any number is reported
- Duplicate-transaction detection for payment retries, the most common source of an inflated drop-day figure
- Royalty-ready SKU, territory and period reporting built into the product and event schema, not rebuilt on request
- In-state versus out-of-state and wholesale versus consumer splits, so convention and storm weeks read correctly
Spiky traffic breaks measurement in ways steady traffic never reveals. Duplicate purchase events from a customer retrying a slow payment step. Cancelled and refunded oversell orders inflating a revenue figure nobody later corrects. Client-side tags dropping under load while server-side events keep arriving. A brand looks at its launch dashboard the next morning, sees a number that is 18% too high, and makes a media decision on it.
That is the first job here: a measurement layer that holds under the worst hour of the year. Server-side tracking through a first-party endpoint, proper browser-to-server deduplication, and a reconciliation process that runs after every launch — orders in Shopify against events in GA4, Meta and Google, with cancellations and refunds netted out. A drop-day number that has not been reconciled is a rumour.
The second job is specific to what Orlando merchants sell. Licensed product carries reporting obligations: royalty by SKU, by territory, by period, and it needs to be exportable and defensible rather than reconstructed from three exports and a pivot table. That is a data-model decision made when the catalogue is structured, not a report someone builds under pressure the week a licensor asks. We build it into the event and product schema so the answer already exists.
Local context
Reconcile the spike, and report what the licensor will ask for
We treat a launch as a measurement event with its own protocol. Before it: event volume alerting, a documented expected shape, and a check that server-side and browser paths are deduplicating correctly. During it: monitoring for the specific failure modes that only appear under load — duplicate transaction IDs, missing parameters on retried events, consent states not resolving. After it: an order-level reconciliation against Shopify with oversells, cancellations and refunds removed, so the reported number and the banked number agree before anyone reallocates budget. Alongside that we build the reporting Orlando merchants actually get asked for: royalty-relevant SKU and territory data, wholesale versus consumer revenue split for suppliers running both, and a view of in-state versus out-of-state demand that makes convention weeks and storm weeks legible instead of looking like unexplained anomalies.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a Orlando 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 Orlando store
We do not work off a rate card. Every Orlando 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 Orlando — your questions
Almost always oversells and payment retries. Orders that could not be fulfilled get cancelled, duplicate transactions from a retried checkout get refunded, and neither correction flows back into the platform dashboards that reported the original figure. We build reconciliation as a scheduled step after every launch so the corrected number is the one that reaches your reporting.
Yes, and it is one of the clearest cases for it. Browser tags compete with everything else loading on a stressed page and are the first thing to fail; server-side events are sent from infrastructure you control and are far less affected. You get more complete signal exactly when the volume is largest, which is also when the bidding algorithms are learning most.
It can if the schema was designed for it. That means licence identifier, territory and rate held as product metafields, order-level attribution preserved, and refunds and cancellations handled explicitly rather than ignored. Built that way, a royalty report is an export. Retrofitted afterwards, it is weeks of reconstruction and a defensibility problem.
By having geography and channel segmented in the reporting layer before the week happens. A local demand spike, a national creator moment and a paid budget increase all look identical in a blended revenue line. We build annotated reporting with in-state and out-of-state splits so the cause is visible at the time, not argued about a month later.
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 Orlando 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.