Solutions / Attribution modeling

Multi-touch attribution modeling

Every touch a customer had is stitched into one journey and credited by its real contribution, so budget decisions stop being an argument between platform dashboards.

Marketing Analytics Data
One journey per customer
across every channel
Credit by contribution
not by last click
Revenue, not conversions
tied back to CRM
Who it is for

Marketing teams spending across several channels where each platform claims the same conversions and nobody can reconcile them.

Short answer

We join web analytics, ad platforms, CRM and offline events into a single customer journey, then distribute credit across touches with a model you can inspect rather than a black box. The output is channel and campaign contribution against real revenue from your CRM, plus a view of which combinations of touches actually convert.

The problem

Every ad platform claims the same sale, and the sum of their reports is more revenue than you actually made.

01

Last click flatters the last channel

Branded search and retargeting take the credit for demand that something else created. Budget follows the credit, so the channels that build demand get cut first.

02

Platform reports cannot be added up

Each platform uses its own window and its own claim rules. Adding them gives a number larger than reality, and everyone knows it while still using it in the meeting.

03

Conversions are not revenue

A conversion counted at the form is not a paid invoice. Without the CRM outcome joined back in, you optimize toward whichever channel produces the most cheap leads that never close.

How it works

From trigger to result, step by step.

01

Fix the identity plumbing first

Server-side events, consistent IDs, UTM discipline, CRM keys. Attribution built on broken tracking produces confident numbers about a journey that never happened, so this comes before any modeling.

02

Stitch touches into journeys

Web, ads, email, calls and offline events joined per customer across devices where consent allows, with the gaps documented rather than quietly interpolated.

03

Credit with a model you can inspect

Data-driven credit where volume supports it, position-based rules where it does not — and we say which you are getting. A model nobody can explain does not survive its first budget meeting.

04

Join it back to CRM revenue

Credit is expressed against closed revenue, not form fills, so a channel producing cheap leads that never pay is visible instead of celebrated.

05

Deliver decisions, not another dashboard

Contribution by channel and campaign, which touch combinations convert, and where the next unit of budget should go — reviewed monthly against what actually happened.

Before / after

What changes on the ground.

Today, by hand
×Every platform claiming the same conversions
×Last click deciding the budget
×Conversions counted, revenue unknown
×Reports that cannot be reconciled
With the automation running
One stitched journey per customer
Credit spread by contribution across touches
Channel contribution measured against CRM revenue
A monthly budget decision with evidence behind it
What you get

Delivered, not demoed.

A tracking and identity audit with the fixes needed before modeling
Journey stitching across web, ads, email, calls and offline events
An attribution model you can inspect, with its assumptions written down
Contribution reporting joined to closed revenue from your CRM
A monthly review comparing the recommendation to what actually happened
Built with

We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.

Python BigQuery dbt PostgreSQL Metabase GA4 HubSpot n8n
Time to production6–10 weeks
Build priceFixed price
First stepFree mini-audit
Honest limits

When this is not the right solution.

·Below a few hundred conversions a month there is not enough data to model credit, and simple position-based rules will do the job honestly.
·If tracking and CRM data are broken, that is the project. Attribution on bad inputs is worse than last click because it looks authoritative.
·If one channel drives almost everything, attribution will tell you what you already know and the money is better spent on testing new channels.

Questions we get about this one

No. Attribution works from individual journeys and needs user-level tracking; MMM works from aggregate spend and outcomes and needs none. They answer different questions, and teams with the data often run both.

Bring us the process that hurts.

The mini-audit is free: we take your version of this process apart and tell you plainly whether automating it pays. If it does, you get a scope and a fixed price.