Most teams make decisions on broken data. We rebuild the foundation — clean tracking, honest attribution from click to sale, and live dashboards your whole team can actually read.
12 capabilities under one data pod. From the tag that fires on your landing page to the dashboard your whole team reads — every event instrumented, every dollar traced, every model defensible.
sGTM endpoints · deduplication · first-party
Events · conversions · audiences · BigQuery export
Conversion API · advanced matching · value
BigQuery · Snowflake · Postgres pipelines
Looker · Tableau · Metabase · Hex
Segment · RudderStack · identity stitching
MTA · MMM · last-click vs data-driven
Server-to-server · dedup keys · value match
Real-time pipelines · multi-stakeholder views
Anomaly detection · drift alerts · daily scans
Tracking gaps · dedup · cohort accuracy
GDPR · CCPA · consent mode · vendor audits
These are the patterns we see across the first 90-day audit of every new account. Most teams are flying blind on at least three of them — and optimizing budget on broken data they don't know is broken.
Across 23 audits we ran last year. Most clients had GTM events firing twice — counted as separate conversions, attributed to the wrong source.
Average across the first 100 accounts we audited. Server-side and client-side events firing without deduplication keys.
When we ran last-click, MTA, MMM, and platform-reported attribution on the same data, four out of five told different stories about which channels were working.
Average lag we measure on new accounts. Most teams find out about a leak two days after it started. We cut this to under one hour with the AI signal layer.
Cross-device, cross-platform identity stitching is broken on most ad accounts. Looks like new-user growth on paper — actually 60%+ duplicates.
Meta and Google optimization algorithms bid on the revenue signal you send back. Three out of four accounts send the wrong number — so the algo optimizes toward the wrong bid.
Server-side GTM, deduplication keys, value matching, identity stitching — done right in week one. The foundation everything else stands on. Most teams skip this and optimize on numbers that quietly lie to them.
AI scans every event stream 24/7. Tracking gaps, conversion drops, revenue lag, identity mismatches — flagged in Slack the day they happen, not in the MTD report when the budget is already gone.
Most agencies start optimizing on day one. We don't. The first 14 days are pure audit — every tracking gap surfaced, every duplicate counted, every attribution mismatch logged. Then we fix in order of dollar impact.
MTA, MMM, last-click, platform-reported — we run them in parallel and reconcile the gaps. One number per channel, defensible to anyone in the room, including the auditor and the board.
Three views of the same source data: ops (daily decisions), leadership (revenue + CAC + LTV), founder (the one number that matters this quarter). Each tuned for who reads it — not for what the BI tool defaulted to.
Once the foundation is right, every event captured improves the next attribution run, the next AI scan, the next budget decision. Your data layer gets smarter every month it runs — and the gap to teams without one keeps widening.
Every team walks in with a different version of the same problem — broken numbers, untrusted dashboards, blind spots. Eight scenarios we see most.
Most accounts have lost 30–50% of their conversion signal since iOS 14.5. Server-side GTM + CAPI rebuild brings 80%+ of it back — measured against pre-2021 baselines.
Most accounts set up CAPI without passing revenue values back. Meta's optimization algo bids on the wrong signal. We fix the value matching, dedup, and event quality score — usually a 20–40% efficiency lift.
The Universal Analytics sunset hit you mid-migration. Conversions don't match. Audiences don't sync. We finish the migration, reconcile the historical data, and lock the event schema.
Sales has HubSpot. Marketing has Looker. Leadership has a spreadsheet. We collapse them into one source of truth + three views — one per audience. Same data, no more reconciliation meetings.
Different currencies, different ad accounts, different attribution windows. We build a unified data layer that normalizes everything to one currency and one attribution model — so global ROAS finally means something.
Platform-reported attribution always inflates. GA4 always under-reports. We build a third, defensible model that splits the difference — and explains why each one was wrong.
Marketing reports ROAS. Sales reports closed-won. Nobody connects them. We pipe both into the warehouse and build the join — so you can finally say "this campaign drove this revenue" with a straight face.
The board wants a clean answer. Most marketing teams can't give one. We build a P&L-shaped marketing report: spend by channel, revenue by source, blended CAC, LTV trend. Leadership signs off in 30 minutes, not 3 days.
4 weeks to deploy. Then a system that gets sharper every month. Two phases, one team, one accountability line.
Full tracking audit. Every event mapped, every duplicate counted, every attribution mismatch logged. We measure your lag, your dedup gaps, your CAPI value-match rate against industry baselines.
An audit report ranking every tracking gap by dollar impact. You'll know exactly where the data is lying — and what it's costing you.
Server-side GTM stood up. Deduplication keys added. Meta and Google CAPI rebuilt with proper value matching. Warehouse pipeline configured for downstream BI.
A tracking foundation that survives audit: deduplicated events, defensible value signals, identity stitched across devices.
Attribution model built — last-click, MTA, MMM, platform-reported, all reconciled. Three dashboards shipped: ops, founders. AI signal layer connected to event streams.
Live revenue dashboards your whole team will open, a defensible attribution model, and daily drift alerts in Slack.
First full week of clean data. AI scans flag the first round of drift. Operators reconcile, dashboards stabilize, the first clear monthly revenue statement is in your inbox by Friday.
First clean monthly statement, a tuned attribution model already outperforming baseline, and the operating cadence that sharpens every event captured after.
AI scans every event stream 24/7 — tracking gaps, conversion drops, identity mismatches, value-match failures. Flagged in Slack before the day starts, not in next month's audit.
30-minute call. Last week's attribution reconciliation, top drift events caught, dashboard tweaks shipped, next week's audit items. Decisions and shipping — no status theater.
P&L-shaped marketing report: spend by channel, revenue by source, blended CAC, LTV trend, attribution-reconciled ROAS. Leadership signs off in 30 minutes — sent by the 3rd.
Strategy reset session. We re-test attribution windows, re-weight channel contributions, propose new tracking events, and reconcile what the platform-reported numbers got wrong this quarter.
Dedicated Slack channel with your data pod — analyst, data engineer, dashboard lead, attribution strategist. Same-day fixes when an event stream breaks, no 48-hour escalation queue.
Data governance built in from week one. Consent mode v2, GDPR/CCPA handling, vendor audit trails, data residency. Compliance reviews handled before procurement asks.
We’re a real performance marketing agency. We staff the team and fund the hours— then structure a success fee that makes sense for your business.
We partner selectively with growth-stage companies already running paid media.
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Data & Analyticspairs naturally with our other 4 services. Most clients run 3 together — that’s where the compounding starts.
Need a custom mix? Reach out →The growth engine itself. Paid acquisition across every channel.
SERVICEAds that win the auction. AI-tuned creative production at scale.
SERVICEConversions, end to end. Landing pages, funnel rebuild, A/B testing.
SERVICEStrategy that ships Monday. Channel tests, positioning, expansion.
SERVICEMost paid growth bottlenecks live at the seam between two services — creative bleeding into media, attribution missing the funnel. The seams are where 30% of the upside hides.
Most engagements run 6 months minimum — the first 4 weeks are audit + foundation rebuild; months 2–6 are where attribution sharpens and the AI signal layer pays for itself. 12+ months is common because every event captured improves the next attribution run.
Across the first 23 audits we ran in 2025, average uncovered mis-attributed spend was $1.4M per account. Most clients had 20–25% of conversions duplicated and at least one attribution model the team didn't trust. We'll show you exactly what's hidden during the audit, not promise a number before.
Both options. Pure handoff: our pod owns the tracking stack, dashboards, and attribution model end to end. Hybrid: we audit + architect, your data team implements. Slack-integrated either way, and we hand back full access and documentation whenever the engagement ends.
An Account Strategist (your lead), a Data & Analytics Lead, a Data Engineer, a Dashboard / BI Designer, and our AI Signal Monitoring layer. Five to seven humans, plus AI running daily event-quality scans.
Any stack. GA4, Mixpanel, Amplitude, Segment, RudderStack, Looker, Tableau, Metabase, Hex, BigQuery, Snowflake, Postgres, dbt — we've built on all of them. We'll tell you during the audit whether your current stack needs a swap or just a rebuild on top of what you have.
That's most of what we do. The "ops dashboard" / "revenue dashboard" / "founder dashboard" pattern means three views of the same source data, each tuned for who reads it. Leadership gets the P&L-shaped view. Founders get the one number that matters this quarter. Ops gets daily decisions.
No hard floor. Accounts under 10K monthly events get the tracking foundation built right (so the data layer scales when volume does). Above 100K events/month, attribution modelling and AI signal monitoring become the workhorses. We'll calibrate the approach during the audit.
How brands across D2C, logistics, healthcare, SaaS, and hospitality rebuilt their data foundation — and stopped optimizing on broken numbers.
A leading luxury tour operator competes against OTAs, marketplaces, and local tour resellers, where every booking won through a third party comes at the cost of lower margins.
Sell thousands of products across multiple brands and you hit one wall fast: you can't advertise everything to everyone.
One of the region's largest electrical distributors had spent decades building a trusted brand, but its online sales channel was barely contributing.
A regional hotel group was paying OTA commissions on bookings it should have owned. We turned paid search and social into a direct-booking engine that now outperforms the OTAs they used to depend on.
When your buyers are procurement teams and contractors, clicks don't pay the bills but a qualified inquiries do.
A premium healthcare network running paid acquisition across 4 markets with attribution that the CFO couldn't defend.
Real testimonials from clients we're still working with — the tracking rebuilds, attribution models, and revenue dashboards we ship.
Came to GTMLab for predictability over unreliable growth. They delivered in a quarter: revenue +27%, CPC -50%, bookings +20%. Highly recommend for anyone looking to grow and scale.
From the first call, GTMLab brought the professionalism that other agencies never did. The whole process was spot on, and in the first month, we were already getting 3–5 qualified leads a day.
They built our entire marketing funnel end-to-end and owned every deliverable. Not an ad-hoc agency you pay to run a few campaigns and hope it works.
Our corporate sales team had run 7–8 years without much structure. GTMLab brought the strategy, the CRM, and a more systematic way to manage the team — and the result was game-changing.
We weren't even sure it was possible, and we wanted GTMLab to validate whether there is a way to acquire partners predictably. GTMLab didn’t just answer 'yes'—they proved it.
We wanted a way to grow our paid ads, and GTMLab delivered. Our website bookings went up, and over a year later, they're still the team that we trust. We couldn't be happier with how it turned out.
