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.
New York, NY
Analytics, Tracking & Data in New York
New York buyers are the most numerate in the country. Give them a data layer that holds up under the scrutiny it will get here.
Delivered remotely for brands across New York and New York.
Scale · New York
Why New York brands come to us for this
- Reporting built for boards and PE owners, not for a dashboard screenshot in a monthly deck.
- Channel separation so DTC, wholesale and retail revenue stop being averaged into one misleading line.
- Contribution margin net of returns — non-negotiable in NYC's apparel and accessories categories.
- Server-side tracking and CAPI match quality above 8 before anyone scales spend at New York CPMs.
- Every figure reconciles to Shopify's own order data, so the weekly meeting is a decision not a debate.
Somewhere in this city, in a conference room, a founder is being asked why GA4 says one number, Meta claims more sales than the store took, and the finance model says something else entirely. New York brands are closer to capital than most — investors, boards, retail partners, sometimes a parent company — and that means the measurement layer gets audited by people who audit things for a living.
We rebuild it underneath everything else. Server-side tracking through a first-party endpoint, Conversions API with match quality above 8, a documented GA4 event schema, consent mode wired to your CMP, and a reporting layer that reconciles to Shopify's own order numbers rather than arguing with them.
It is boring work and it is the precondition for everything else you might buy. There is no point running a test programme or scaling media in the most expensive market in the country on numbers three people in the room already distrust.
Local context
Data that survives a board meeting, a retail partner and a returns rate
Two New York realities put unusual pressure on measurement. The first is proximity to capital: a large share of consumer brands headquartered here are venture-backed, PE-owned or reporting into a parent, and the reporting cadence that comes with that is monthly at minimum with contribution margin by cohort expected rather than requested. The second is channel complexity. NYC brands typically run DTC alongside wholesale, a showroom and often physical retail, so 'revenue' means four different things depending on who is asking, and returns in the dominant apparel and accessories categories are high enough that gross numbers mislead badly. We build the reporting layer to reflect that: channel-separated revenue, contribution margin net of returns and fees, and cohort views that a CFO can reconcile against the accounting system without a translation layer.
Scope
What Analytics & Data includes
The same standard of work we run for every client — applied to a New York 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 New York store
We do not work off a rate card. Every New York 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 New York — your questions
Yes, and it is one of the more common asks we get from New York brands because of how many are venture-backed or PE-owned. It requires clean order data, COGS and shipping at SKU level, returns attached to the original order, and channel separation. Most of the work is in getting those inputs right; the dashboard is the easy part.
Usually a mix of ad blockers, ITP, consent handling and client-side events that never fire. On a heavily mobile, heavily Safari audience — which describes most New York consumer traffic — that gap widens. Server-side tracking through a first-party endpoint plus proper deduplication typically brings variance inside five percent, which is the point where the number becomes usable.
Separately, always, with a blended view available but never the default. Wholesale and DTC have different margins, different cash cycles and different growth constraints, and averaging them hides whichever one is struggling. Since both run on one Shopify store for most of our New York clients, the separation is a tagging and reporting design decision made up front.
More than it used to. If you sell into Europe at all it is mandatory, and US state privacy laws are converging on similar requirements. Beyond compliance, consent mode with modelled conversions recovers reporting coverage you are otherwise simply missing, which matters when media decisions at New York spend levels rest on that data.
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 New York 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.