Google Ads says it drove forty sales. Meta says thirty-five. Together you sold fifty.
Every platform is happy to count its own contribution. Added up, that is more sales than you actually had, so every budget you base on it is wrong too.
It is almost never a complicated problem. Usually it is one tag firing twice, one conversion set up wrong, or three departments defining revenue differently. You just need to know which one.
You can see your client’s numbers are wrong. You just have no appetite for working out why.
If you work as an SEA, social or marketing consultant, you run into this with clients whose measurement you would rather not touch. Send them over; I deliver the report and you keep the relationship.
Your client hears the same price as everyone else, gets the same report, and comes back to you afterwards. I do not take over work that you do.
From GA4 validation, attribution and web and data analysis to KPI definitions, data quality and Power BI reporting that marketing and management steer on.
Landalcommerce analytics
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.
More about this engagementHide the details
Joined during the merger of Landal and Roompot, while the marketing and data organisation was being restructured. What changed since then is set out below.
Was
Data Studio and Tableau, with definitions that didn’t line up
→
Now
Power BI on Databricks, the same KPIs up to CMO and trade meetings
Measurement
GA4 validated against back-end data, matched on transaction ID
Attribution
Billy Grace, made available via Delta Sharing
UTM
One framework across three brands, two agencies, five countries and every channel
Adoption
Country managers and local teams trained
Worked with
GA4
Google Tag Manager
Power BI
Databricks
Billy Grace
Delta Sharing
More engagementsHide engagements
citizenMcustomer data platform
CDP implementation from the marketing side
Came in as temporary cover for the marketing data analyst who went on leave, and stayed on after she returned because the CDP implementation needed attention from the marketing side. That is where the questions sit that have to be asked before go-live: which definition do we use, where does a field come from, and is the data quality good enough for management to steer on. Deduplicating accounts was part of it, as was the orchestration plan for marketing and communication, run in Redpoint.
More about this engagementHide the details
Alongside that: GTM implementation and GA4 analyses, and implementation and analysis in AppsFlyer. Connecting that to the CDP later made it possible to add SMS to the journeys.
Before
A purchased CDP that was not yet usable for marketing or reporting
→
After
Journeys running, data quality monitored, and reporting on real CDP users
Sources
More source systems connected, which made orchestration possible
Journeys
Set up together with the CX, marketing and data teams
Data quality
Deduplication, a front-end email check against new duplicates, and continuous DQ checks
Reporting
On real CDP users instead of online users or sessions
Worked with
Redpoint
GA4
Google Tag Manager
AppsFlyer
Nikedata governance
Setting up Collibra and de-duplicating metrics
The assignment was to set up Collibra and connect it to the existing reports, dashboards and data. Documenting definitions, origin and lineage made visible how many metrics and reports existed twice.
More about this engagementHide the details
That opened the conversation with business and technical stakeholders: do we keep one definition, or several variants with a clear distinction. Those decisions were documented and surfaced through Power BI.
Before
The same KPI name from different sources, and dashboards that did not say which variant you were seeing
→
After
Per KPI: what it means, where it comes from and who owns it
Definition
One recorded definition per KPI, with the distinction stated where variants remain
Lineage
From dashboard back to the source where the metric originates
Ownership
A technical and a business owner named per KPI
Usage
From the source, see which metrics are used where and by whom
Worked with
Collibra
Power BI
B2B Onlineweb and data analysis
Web and data analysis for the agency’s clients
As a Web & Data Analyst consultant, worked for several clients of this agency, which works entirely with freelancers.
Stefan heeft ons geholpen met de creatie van een complex Google Data Studio-dashboard op basis van Google Analytics-data. Daarnaast heeft hij geholpen met het instellen van cross-domain tracking en de nodige tags via Google Tag Manager. Zeer goede samenwerking en een hoog kennisniveau. We huren hem zo weer in.Stefan provided training for our e-commerce clients on how to set up Google Analytics for their business and how to use the data to grow their business. I really enjoyed working with him. Stefan has a wealth of knowledge and is very good at sharing it.
The person you speak to does the work
There is no agency and no sales department in between. Technical enough to build the data model myself, and close enough to marketing to know which question sits underneath a reporting request.
On larger engagements I work with a few regular people I know. You hear that up front, and you know who does what.
I use AI daily as a tool for SQL, documentation and reviewing my own code. Definitions, models and conclusions I check myself.
