ANALYTICS & ATTRIBUTION · BOARD-READY

One number the board actually trusts.

MTA, MMM, and server-side tagging — joined to pipeline in your warehouse, not a platform dashboard. Every channel decision runs on the same numbers finance uses.

94%Event match rate
31%Budget reallocated
6wkTo first model
/ THE PROBLEM

Every dashboard tells a different story.

01

Last-click is lying to you

It hands credit to brand search and retargeting — the channels that show up last — and starves the ones that started the deal. Budget follows the lie.

02

GA4 and the CRM don't agree

Marketing reports one number, sales reports another, and every pipeline review starts with a fight about whose spreadsheet is right.

03

Signal loss broke your tracking

Cookies, iOS, ad blockers, consent banners. Client-side tags now miss 20–40% of conversions — and every downstream decision inherits the gap.

04

Reporting isn't board-ready

Screenshots of platform dashboards aren't a forecast model. When the CFO asks what CAC payback looks like next quarter, nobody has an answer.

/ WHAT WE RUN

Measurement built like infrastructure.

Server-side tagging

sGTM on your own subdomain, first-party collection, consent-aware. Match rates move from ~60% to 90%+ and every platform gets cleaner signal.

Multi-touch attribution

Warehouse-native MTA mapped to your CRM stages, first touch to closed-won. Position-aware models you can interrogate — not a black box.

Media mix modeling

MMM for incrementality and budget planning where user-level tracking can't reach. Rebuilt quarterly and validated against holdouts.

Warehouse joins

Spend, traffic, and pipeline joined in BigQuery or Snowflake with dbt models you own. One source of truth, versioned like code.

Board-ready reporting

CAC payback, pipeline coverage, forecast vs actual — one view for the QBR. The deck builds itself from the warehouse.

Incrementality testing

Geo holdouts and conversion-lift studies to sanity-check the models. When attribution and incrementality disagree, we find out why.

/ FIRST 90 DAYS

First 90 days: pipes first, models second.

01
WK 1–2

Audit and measurement plan

Tracking teardown, event taxonomy, gap analysis. We document what's actually measured today versus what the board is being told.

02
WK 3–6

Plumbing

Server-side tagging live, warehouse pipes built, spend and CRM data joined. Match rates get verified before anything is modeled.

03
WK 7–10

Models live

MTA running on CRM stages and the first budget reallocations made. Channels get judged on pipeline contribution for the first time.

04
WK 11–12

MMM and the QBR

First mix-model read, forecast model set, board deck automated. From here the system compounds — every quarter of data makes it sharper.

/ PROOF

Measured, then proven.

All case studies
0%

Event match rate, server-side

0%

Of budget reallocated in yr 1

0wk

To first working model

/ FAQ

Fair questions.

Both, at different altitudes. MTA guides in-quarter channel and campaign calls; MMM sets the budget envelope and catches what user-level tracking misses. Under roughly $100k/month in spend, we usually start with MTA plus geo holdouts.
No. We stand up BigQuery or Snowflake as part of the engagement if you don't have one — typically inside the first month, and it's yours. If you already run a warehouse and dbt, we build in your stack.
First-party, server-side collection with consent mode wired in — plus MMM and holdout tests that don't depend on user-level identifiers at all. The design assumption is that signal keeps degrading.
No — and they shouldn't. GA4 misses what server-side collection catches and models sessions its own way. We reconcile the deltas once, document them, and then you stop arguing about whose number is right.
You do. Everything ships in your warehouse, your repos, your dashboards — documented and versioned. No proprietary black box, nothing held hostage.
/ RELATED SERVICES
/ WORK WITH US

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