Behind the metrics with Tim Kreienkamp

Reliable data doesn’t just happen. It takes real expertise to vet and curate data, clean inconsistencies and ensure every report reflects your business reality. That’s why our team doesn’t just rely on automation — we believe it’s important to apply human intelligence to uncover impactful insights and avoid the pitfalls of misleading data.

With so many solutions trying to “SaaS-ify” reporting, it’s easy to overlook the work that happens behind the curtain. However, the reality is that having a dedicated team working on the software businesses rely on to make critical decisions makes all the difference.

We sat down with Tim Kreienkamp, Director of Measurement and Data Science, to get his take on modern measurement and its critical role in getting direct insights for businesses.

Bringing science back to business reality

Tim is a pillar at Funnel, helping curate and translate data into actionable recommendations for clients. With seven years of experience at marketing measurement company Adtriba (before it joined Funnel), he has extensive experience in creating measurement solutions that help businesses get reliable insights they can use to drive actual growth.

So it might not surprise you to know that Tim didn’t only study data science. He also has a business background. 

Building a data science career

Tim’s journey into data science wasn’t exactly a straight path, but it was one that ultimately led to him playing a key role in shaping the future of marketing analytics.

After finishing his studies, Tim stepped into the world of data science. But like many early-career professionals, his first role wasn’t quite the right fit. He landed a job in fintech back in his hometown of Hamburg, but something was missing. “I didn't really like it,” he admits. Just a year into the role, he was already looking for something more exciting.

That’s when János, the founder of Adtriba, reached out on Xing — the German version of LinkedIn. “We connected and the rest is history,” Tim recalls.

He joined Adtriba as a senior data scientist and quickly proved his value. Over seven years, he climbed the ranks from Head of Data Science to Chief Data Scientist, playing a major role in shaping the company’s product and technology.

“That role entailed a bit more than the title would tell you,” Tim explains. “I was essentially managing almost everything that wasn't either sales, marketing or finance. Basically, everything product-related, tech-related, but also CSM solutions engineering.”

For Tim, data science wasn’t just about numbers — it was about solving real business challenges. That mindset led him to Funnel, where he now brings his expertise to an even bigger stage.

Heading up measurement and data science at Funnel

Tim’s expertise places him at the heart of measurement and marketing intelligence at Funnel, leading a team that ensures data accuracy, consistency and reliability — critical elements for any performance marketer.

His team plays a pivotal role in developing Funnel’s marketing intelligence solution by curating and cleaning data, identifying inconsistencies and making sure every report reflects real business performance. Their work ensures that every data point entering the model is correct and every model-generated report delivers actionable insights.

The structure behind measurement at Funnel

To make this possible, the Measurement and Data Science team operates within a broader structure with each function playing a key role:

• Measurement (the whole team) – Led by János (VP of Measurement)
• Measurement and Data Science – Led by Tim (Director), overseeing two key functions:
  • (Measurement) Engineering – Led by Daniel (Tech Lead), focusing on building and maintaining the data pipelines
  • Data Science – Led by Houssem (Tech Lead), responsible for refining and improving measurement algorithms

Within engineering, the team breaks down further:

• Data Engineering ("data plumbers") – Ensuring smooth data flow across systems
• Backend Engineering – Supporting infrastructure and server-side tracking
• Frontend Engineering – Handling the user-facing measurement tools

Meanwhile, measurement consultants led by Kalle work closely with clients to make sure that measurement insights align with business needs.

Tim explains the team’s impact:

“We work on the part that you can actually see. That means the triangulation app, the modeler — all that is handled by our team. Then there's another team led by Vasilii which takes care of server-side tracking and infrastructure-related aspects. Together our teams are in charge of everything related to data pipelines.”

From data cleaning to advanced modeling, every aspect of measurement at Funnel is built to give marketers complete confidence in their data so they can make smarter, faster decisions without second-guessing their numbers.

Overcoming the biggest challenges customers face when starting with measurement

One of the biggest challenges in measurement isn’t just applying the right technology — it’s making sure marketers can understand and trust the results. Every marketing mix model (MMM) is a unique abstraction of a client’s business, which means scaling measurement solutions requires both powerful technology and expert guidance.

Funnel’s approach is built for scale. It combines a SaaS solution with an analytics team working behind the scenes to ensure the models are accurate, the observational data is as complete as possible and marketers can easily translate data into actionable insights.

The challenge of observational data and bias

When using observational data to draw conclusions, there’s always a risk of hidden biases. If left unchecked, these biases can skew results, leading to flawed marketing decisions. That’s why validation is so critical to the Funnel team’s workflow.

“That’s why we bring in this unique triangulation approach.”

Measurement triangulation helps to validate the observational data that MMM relies on. By cross-referencing insights with testing and attribution models at the channel level, Funnel makes sure marketers get a clearer, more reliable picture of performance. This approach helps teams move beyond surface-level data to make smarter, data-backed decisions with confidence.

Upcoming measurement trends

Staying informed about the current trends in market modeling is a central part of driving measurement as more thought leaders and academics join the conversation.

The measurement landscape has changed dramatically over the past few years.

Emerging thought leaders like Dr Julian Runge, Igor Skokan from Meta, Eric Seufert and Google’s Ana Cerreira Vidal are shaping the future of marketing measurement. Staying up to date on their publications is also a critical way to stay ahead of the competition. 

But what interests Tim is how marketers themselves, without a specific data science background, are leveling up their ability to embrace the more technical side of measurement.

“One thing that we see is that there are more and more open source packages popping up and becoming more and more advanced and clients take notice of that, and they themselves are becoming more advanced in their application of marketing modeling.”

When it comes to MMM and MTA, to get the most accurate possible results and insights, you need to start with a large volume of accurate data. Getting the data in, to begin with, is one of the biggest challenges when it comes to measurement. 

Understanding how to get the data in, particularly from offline sources like TV and DOOH that have such unique data structures, requires a certain level of data maturity. You have to be able to collect everything, organize it, normalize it and then put it to use.

So, how do you increase your data maturity as the landscape of measurement changes? 

Marketing intelligence solutions like Funnel essentially take the load off by automating the process of organizing your data. 

Tips to get started in a data science career 

If you’re a data scientist looking to make an impact in marketing measurement, patience and a solid theoretical foundation are key.

As Tim says, “I guess one thing I would definitely tell myself is to be a bit more patient. So especially in the beginning of my career, I really had the feeling that I needed to be very fast, which in hindsight maybe I rushed a few things here and there.”

Tim emphasizes that while hands-on experience is valuable, ignoring theory can limit your long-term growth. Practical application can take you far, but without a strong grasp of the underlying principles, you’ll eventually hit a ceiling.

Why theory matters in real-world data science

Many data scientists assume that complex theoretical concepts — like numeric optimization — won’t be relevant outside of the university setting. Tim disagrees.

“I think one classic field that is a very hard subject in university is numeric optimization — linear, nonlinear, constraint optimization. For some people, the theory behind it is quite hard and then people think, ‘I'm never going to use this again.’ This is not true. That is definitely something that you’re quite likely to have to use in practice.”

For Tim and his team, constrained optimization plays a crucial role in the software they develop. Time and time again, they find themselves revisiting core concepts learned in school to solve real-world problems.

The pitfalls of shortcuts in data science

Bootcamp programs often promise to turn someone into a data scientist in just a few months, but Tim warns that this approach doesn’t hold up in practice.

“They claim to make a data scientist out of you in 10 weeks or three months, whatever. No. Just slow down. We don’t hire these people. We hired somebody for a front-end software engineering position and this person also hit their limits quite quickly. But in data science, we see it even more.”

The best path to a successful data science career? Take your time, build a deep understanding of the theory and be patient. The more solid your foundation, the more impact you’ll have in fields like marketing measurement, where precision and accuracy are everything.

Finding the meaning in measurement

When it comes to his role at Funnel, Tim finds enjoyment in seeing the direct insights Funnel uncovers to help drive growth for his clients. 

Seeing the theory driving a solution that helps clients grow their businesses is the best outcome for a data scientist.

“When you see that clients start to act on the insights and actually make the business more efficient due to the recommendations that we provide, that’s the fun part.”

Want to hear more from Tim? He hosts a weekly podcast, ‘Marketing Measurement Matters’ with János Moldvay, Funnel’s VP of Measurement, and Dr Tim Wiegels, Data Leader at Funnel.

Special guests have included Igor Skokan from Meta, Ana Carreira Vidal from Google and Twilio’s Rupali Singh.