Groen Data B.V. · Zoetermeer, NL
Marketing data you can make decisions on.
Analysis, measurement, data models and reporting for marketing and e-commerce teams. Sometimes a scoped assignment that runs a few weeks, sometimes a domain I take independent responsibility for over much longer.
- GA4
- GTM
- BigQuery
- Databricks
- SQL
- dbt
- Power BI
- Looker Studio
Stefan Groen in 't Woud
Senior marketing data specialist
Owner, Groen Data B.V.
- Work
- Analysis, measurement, data modelling, reporting
- Daily
GA4 · GTM · BigQuery · Databricks
SQL · dbt · Power BI- Engagement
- Scoped assignment or long-term independent collaboration
- Based in
- The Netherlands, remote and on site
- Status
- Active, reachable by email
Experience at Landal, Nike and citizenM, among others. Marketing and analytics work on data platforms with serious volume and the complexity that comes with it.
01 / Work
Four domains
What I help with
The work starts with the question, not with the tool. What it takes after that differs per assignment.
01 · Analysis & reporting
From a number to a decision
Campaign analysis, customer behaviour and recurring marketing reporting. The work is rarely in the chart, but in the question before it: which definition of revenue, measured against which period, and what do you decide off the back of it.
02 · Measurement & tracking
Knowing what is actually measured
GA4, Tag Manager, server-side tagging and attribution questions. Connecting ad platforms, on-site behaviour and back-end data, so you don't end up with three numbers that are all called "revenue".
03 · Data modelling
One place where a KPI is defined
Bringing sources together and standardising definitions in BigQuery or Databricks. SQL in version control, tests on the output, and a model someone else can take over without calling me.
04 · Dashboards & data products
Reporting you can explain
Power BI or Looker Studio on top of a model where every number traces back to its source table. An argument about a figure then ends at the data, not with whoever talks loudest.
02 / Engagement
Two forms
What can I help with?
Pick whichever is closest to your situation. The rest of this page stays the same. This only decides which detail you see below.
Form 01 · A specific question
A clear problem. A concrete solution.
Your reporting doesn't add up, tracking is fragmented, definitions diverge, or you want to finally bring marketing data together. I work out what is needed and build something your organisation can run itself afterwards.
Not every problem calls for a large programme. Sometimes it's a data model, sometimes a dashboard, sometimes just the tracking. If that turns out to be the case, I'll say so.
- marketing dashboard
- campaign analysis
- GA4 & BigQuery
- tracking and measurement
- data model
- marketing data warehouse
- reporting
Form 02 · Long-term collaboration
Independently responsible for a marketing data domain.
For larger and more complex questions I work independently over a longer period on analytics, measurement, reporting and the underlying data. That role is as much substantive as it is technical: from KPIs and definitions to data models, reporting and how it gets used inside the organisation.
My own approach, my own technical choices, my own planning. In collaboration with your organisation.
- commerce analytics
- marketing measurement
- reporting
- data modelling
- attribution
- KPI frameworks
- adoption inside the organisation
03 / Cases
Where I have done this
From practice
Three assignments that together show what the work is about: holding an overview of a domain, handling the technology myself, and making sure decisions actually get made on the data.
Landal · ongoing
Commerce analytics during the integration of Landal and Roompot
Leading analytics role across the commerce domain: Marketing, Brand, Digital and Revenue. From HQ, together with the countries.
- Context
- I joined during the merger of Landal and Roompot, while the marketing and data organisation was being restructured at the same time. Two organisations with their own sources, reporting and ways of working had to become one commerce organisation.
- Question
- Reporting was spread across Data Studio and Tableau, definitions did not line up, and there was no basis on which Marketing, Brand, Digital, Revenue and the countries could steer together. Underneath sat concrete questions: does GA4 agree with the back end, and which online session belongs to which booking?
- What I do
- Set up a single basis for commerce reporting, migrating reporting to Power BI on Databricks. Harmonised and maintained KPIs and definitions. Validated GA4 against back-end data and matched transactions on transaction ID. Took ownership of attribution through Billy Grace, making that data available via Delta Sharing. And built a UTM framework that gets eight countries, three agencies and every channel speaking the same language.
Alongside that I train country managers and local teams, so the numbers actually land in campaign planning and commercial decisions. Reporting nobody uses solves nothing. - Effect
- One commerce-wide basis for Marketing, Brand, Digital and Revenue, with HQ and the countries using the same KPIs and definitions. The reporting carries through to C-level decision-making, including CMO and trade meetings.
8 countries · 3 agencies · Marketing, Brand, Digital and Revenue · HQ and local teams
citizenM · 12 months
Customer data and measurement
Worked on customer and marketing data from within the Customer Experience domain. The work ran across the full width of the chain: implementing a CDP, digital analytics, reporting in Power BI, database marketing, app measurement with AppsFlyer and marketing orchestration in Redpoint.
The kind of assignment where measurement, customer data and activation cannot be looked at separately. Fix only the dashboard and you miss where it actually breaks.
Nike · data governance
Metrics at enterprise scale
As Senior Data Governance Analyst, made visible how metrics and KPIs move through the organisation and the data chain: upstream and downstream lineage, origin and definitions documented, data quality and quality checks put in place, and that governance information surfaced through Power BI.
That is where the principle running through this site comes from: a number is only usable once it is clear where it came from and how it was defined. The idea does not start at a marketing dashboard.
04 / Practice
Recurring problems
Where it usually goes wrong
Three things I run into on almost every assignment.
01 · Ownership
Your data is locked inside the tool
If the connector hangs straight off your dashboard, the data never lands anywhere that belongs to you. Cancel the subscription and the history goes with it. You pay monthly for access to your own figures, and outside that one dashboard you can do nothing with them.
02 · Analysis
Numbers without an explanation
A chart shows revenue falling, but not which campaign or which segment caused it. That calls for a data model where cost, behaviour and revenue are linked to each other, not for another chart.
03 · Data quality
The dashboard doesn't match the ad platform
Standard connectors only load the new day, while platforms assign conversions retroactively. Someone clicks on Monday and buys on Thursday: the platform writes that conversion back to Monday, but in your warehouse Monday is already fixed. The gap grows every week.
Standard connector
42
conversions · new day only
Google Ads interface
50
conversions · assigned retroactively
The fix isn't complicated: reload the last 30 days every night instead of just yesterday. It costs a little more compute and saves a lot of argument on Monday morning.
05 / Warehouse
One concrete solution
Marketing data warehouse
One of the assignments that comes up most often. Your marketing data sits scattered across platform exports, spreadsheets and separate dashboards. I set up one environment where ad spend, on-site behaviour and revenue live in the same tables, with fixed definitions and a daily refresh.
You don't need to know up front what that data model should look like. We start with the questions you currently can't get an answer to and work back to the sources you need for them.
-
Fivetran
Source
Managed connectors to Google Ads, Meta, GA4, your shop and your back end. No home-grown scripts that fall over on an API change.
-
BigQuery
Storage
One warehouse in your own Google Cloud project. You own the data and the invoice.
-
dbt
Transformation
All logic as SQL in version control, with tests on the output. A KPI is defined in exactly one place.
-
Looker Studio / Power BI
Reporting
Dashboards on top of the model, so every number traces back to its source table.
What's there when it's done
- A BigQuery project in your own company's name, with all your sources in it.
- A data model in dbt where every KPI is defined once.
- A dashboard in Looker Studio or Power BI, refreshed every morning.
- Documentation and access, so somebody else can take it over.
- All accounts and subscriptions in your company's name, not mine.
- If you stop, everything loaded up to that point simply stays.
What it costs per month
Everything is in your own company's name, so you see exactly where the money goes. No subscription runs through me.
- BigQuery
- Usage-based. Marketing data is small compared to what BigQuery handles.
- Ads, GA4
- Free export. You only pay the BigQuery side.
- Other
- Meta, LinkedIn, shop or CRM through Fivetran, in your name.
- Reporting
- Looker Studio is free. Power BI charges per user.
- Groen Data
- Only during the build, nothing after.
Not every company needs this. Sometimes tracking is the real problem, sometimes one data model or one analysis is enough. Google Ads and GA4 also have their own free export to BigQuery, so with few sources it can work without Fivetran. I'll say so when that's the case, even though it makes the assignment smaller.
06 / Approach
Five principles
How I work
Definitions first
If three departments count revenue differently, no tool is going to fix that. We pin those definitions down before a dashboard appears.
Everything in your environment
Cloud project, repository and dashboards are in your company's name. I get access, not ownership.
Version control and tests
All SQL lives in Git and is documented. Tests run before figures reach a dashboard.
Ship small
One finished report first, then expand. What else is needed usually emerges from the questions that first piece raises.
AI as a tool
I use it daily for SQL, documentation and checking my own work. It is not a line item in the quote.
07 / Tools
Free to use
Tools
Two things I built for my own work. Free to use, they run entirely in your browser. No sign-up, no email address. The interface is in Dutch.
Tool 01 · UTM
Multi-platform UTM builder
Generates consistent UTM links for Google Ads, Meta, DV360, LinkedIn, TikTok and email, including the platform macros and the hash correction for GA4.
Tool 02 · Architecture
Dataflow drawer
Pick your sources and tools and the diagram draws itself: source, extraction, storage, transformation, reporting. Built because I kept redrawing this diagram on every assignment.
08 / About
Stefan Groen in 't Woud
About me
I'm Stefan Groen in 't Woud, marketing data specialist and owner of Groen Data B.V. There is no agency or sales department in between: the person you talk to is the one doing the work.
My work sits equally on the marketing side and the data side. Technical enough to build the data model myself, and close enough to marketing to know which question sits underneath a request for a report. I build data models, measurement and reporting, not applications.
At larger organisations I work on data platforms with serious volume and the complexity that comes with it: Databricks, BigQuery at enterprise scale, reporting that several departments steer on. What is standard practice there often works just as well at a smaller scale.
Business
- The assignment is entered into with Groen Data B.V., not with me as a private individual.
- The work is carried out for the account and risk of Groen Data B.V.
- Own equipment, software licences and tooling.
- Own planning, working methods and professional judgement.
- Chamber of Commerce 95832386 · VAT NL867328952B01