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Server-side tracking is a four-part series that guides you through the key steps of implementing server-side tracking in practice. We start with the foundations: where to run your server, how Cloud Run compares to solutions like Stape, and what you need before deployment. In the following parts, we will cover proper frontend GTM setup, server monitoring and maintenance, and advanced use cases such as anonymous tracking through your own server.
First-party data is becoming one of the most important foundations of modern digital measurement. In this series, we explain what first-party data is, how to collect it responsibly, how to send it securely to advertising platforms, and how to use it to improve attribution, campaign optimization, and lead quality. We cover consent, hashing, server-side tracking, CRM imports, platform-specific setup, and practical debugging so you can turn your own customer data into more reliable marketing signals.
Articles
In our day-to-day work, we rely on ClickUp for a large part of our operational know-how. ClickUp's native backup options weren't enough for us, so we built a workflow covering backup, recovery, and data viewing — because our goal was continuity of operations, not just data export.
If you are considering server-side tracking, you will face the question of where to actually run it sooner or later. Stape, your own Cloud Run setup, or a completely different path? For some, Stape makes sense. For others, their own Cloud Run setup in Google Cloud Platform is the better option. And that is exactly what we will focus on in this article. We will go through the differences between Stape and Cloud Run and look in detail at everything you need to deploy server-side tracking on Cloud Run. We will focus on the things that are often missing from standard tutorials: how to name your servers, which region to choose, how much does running the server cost, when a load balancer is worth it, and how not to get caught out when choosing the billing type.
In the first part of our series on using first-party data in online advertising, we explained why first-party data is important for media targeting and improving campaign performance. In this part, we will take a closer look at how to collect first-party data correctly and send it to media systems. We will go through the full journey of first-party data, from the dataLayer through normalization and hashing to sending it via server-side Google Tag Manager (sGTM) to Google Ads and Meta. The goal is to give you a practical guide that you can implement right away, including code snippets, edge cases, and the places where things most often break in practice.
As data analysts, we are constantly looking for ways to make our work more efficient. Since our company runs on ClickUp, we decided to test ClickUp MCP - a tool that lets you control ClickUp through AI assistants. In this article, we share our hands-on experience, the limitations we ran into, how secure access tokens really are, and who this solution actually makes sense for.
The cookie apocalypse and the many other attempts at coming up with an original name for the end of third-party cookies in Google Chrome thankfully stopped haunting our LinkedIn feeds sometime in early 2024. What it did do, however, was spark an industry-wide conversation about working with first-party data . That is proving to be a key step toward better measurement and stronger campaign performance today.
With the rise of server-side measurement, we are increasingly implementing server-side tracking for our clients not only for analytics, but also for advertising platforms. In this article, I want to share our experience with a server-side implementation of media tags for a larger client - what we learned along the way, which templates we used, and what to watch out for.
This year, I led several workshops at digital and media agencies. The scenario was usually similar: at some community data/analytics event (or more often at the afterparty), I'd connect with the team leader of their analytics department and we'd arrange a workshop aimed at helping their internal team level up. The goal was to share not only current technical know-how and best practices, but also how to demonstrate the value of digital analytics to clients and which measurement use cases deliver the greatest real-world impact.
I studied economics and spent many years working as a project manager in an agency. But coordinating other people’s work wasn’t enough for me — I wanted to truly master something myself. I’ve always enjoyed math, so I gradually, almost naturally, shifted into analytics. I started around 2014 as a self-taught analyst, later began working with Vašek Jelen, and in 2020 we founded MeasureDesign together. I quickly realized this field was exactly what I’d been looking for — it satisfies my curiosity, my need to dig into details, and my desire to bring a bit of “ordnung” into things. In the whirlwind of running a household, taking care of kids, and navigating global chaos, data feels oddly calming. At the same time, it lets me use my creativity when I play detective and hunt down measurement issues like a modern-day Miss Marple. I genuinely believe analytics is a great career for women in general. And yet, there still aren’t many of us in the field.
Sometimes you need to move historical data from a Google Analytics 4 export to a different BigQuery project – for example, when changing your project structure, switching to a new billing account, or consolidating data. In this article, we’ll show how to copy GA4 datasets using BigQuery Data Transfer Service (there are other methods as well).
Reshoper advisory zone and several hours of consultations for trade fair visitors - this year held in the pleasant surroundings of the Křižík Pavilions at the Prague Exhibition Grounds. This year, I had the opportunity to experience Reshoper both as an advisor in the advisory zone and as a participant in the Roundtables.