Marketing data and attribution

One source of truth for your marketing data, and attribution you can explain.

I connect your CRM, ad platforms and revenue data, so your team works from the same numbers and can see what marketing drives.

Book a quick discovery call

Fixed scope. Two weeks.

The problem

There's no single source of truth, so every decision is harder than it should be.

Your data is siloed.

The CRM doesn't carry what happens in your ad platforms and marketing channels, so spend, marketing activity and revenue never connect.

I connect them, so you can follow a dollar of spend through to pipeline and revenue.

Everyone has a different story.

Each team pulls its own numbers, gets a different result, and reads it differently. Meetings go to arguing about whose number is right.

I unify it into a single source of truth that everyone works from and can trust.

Attribution is missing, or nobody believes it.

Either you have no way to show marketing's influence on revenue, or you have a tool that gives answers nobody can explain. It feels like a black box, so the results get ignored.

I open the box. You see how your attribution works and why it says what it says. If you don't have attribution, I build it.

Linear. Equal credit to every touch. illustrative split

How it works

Three steps, in order.

Review.I go through your tools, tracking, data flows, and any attribution you have today. I document what's broken, what's disconnected, and what can't be explained.
Recommend. You get a written plan: the infrastructure that fits your team and budget, how attribution should work for your sales cycle, what to build first, and what it costs to run. The plan is yours whether or not you hire me to build it.
Build. I build it: warehouse, pipelines, identity matching, attribution, and dashboards, documented so your team can maintain it.

What you end up with

Connected data. CRM, ad platforms, web analytics, and revenue in one place.
One set of definitions. A lead, an opportunity, and a dollar of pipeline mean the same thing in every report.
Attribution you can explain. Every number shows how it was calculated, and you can check it yourself.
Dashboards people use. Each figure traces back to its source, so nobody needs their own export.

The attribution explainer above is the point in miniature: pick a model and the credit changes, and you can see why. That is what “explainable” means in practice.

Proof

One example, as published.

A marketing ops team had email engagement in Marketo and web behaviour in GA4 and could not connect the two at the person level. I designed a Marketo lead ID to GA4 user ID mapping through UTM tokens and GTM, feeding one BigQuery layer that made post-email behaviour analysis possible for the first time. No more reconciling spreadsheets. One queryable data layer. TODO time frame

Who you'd work with

I'm Martin Guzman. I've spent twelve years building marketing measurement: warehouses, attribution, forecasting, and production machine learning. I currently run analytics for the marketing team at a B2B AI company. You work with me directly, with no account managers and no handoffs.

The offer

Data foundation audit

Two weeks · fixed scope

You get

A map of your current data flows and where they break
A review of your attribution: what it does, and whether it can be trusted
A recommended architecture with tools and running costs
A build plan with scope, timeline, and price

Book a quick discovery call

Questions

What if our data is a mess?

That’s the usual starting point, and the audit exists to sort it out.

We already have an attribution tool. Do we have to replace it?

Not necessarily. Often the tool is fine and the problem is the data going into it, or that nobody can see how it works. I check that first.

Do we have to change our other tools?

Only where a tool is the problem. I work with what you have first.

What access do you need?

Read access to your CRM, ad platforms, analytics, and warehouse if you have one.

What if we only want the audit?

You keep the plan and can build it with your own team.

What comes after

With the foundation in place, two things become possible:

Forecasting and budget planning
AI for marketing analytics

Start with the audit.

In two weeks you'll know where your data breaks and have a plan to fix it.

Book a quick discovery call