Maximilian Groth, CEO at Decentriq discusses the fundamental problems most marketers make when choosing and deploying martech stacks in this Q&A with MarTechSeries:

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Hi Max, what’s a day at work like as a CEO in martech?

No two days look alike, which is both the challenge and the appeal. A lot of my time goes into conversations at the intersection of business and technology: understanding what marketing and data teams are actually struggling with, and translating that into product thinking. In martech specifically, the pace of change is relentless. New privacy regulations, shifting platform dynamics, the AI wave: you’re constantly having to update your mental model of the landscape.

I try to carve out time in the mornings for deep thinking before the meeting load kicks in. Running and skiing in the Alps help me reset. But the honest answer is that being a CEO in this space means you’re perpetually juggling the urgency of today with the strategy of tomorrow.

What’s wrong with how marketers today choose, deploy and integrate their martech stacks?

The most fundamental problem is that the customer is rarely the starting point. Martech decisions tend to be driven by internal logic (what the vendor promises, what the team already knows, what the budget cycle allows, etc.) rather than by asking: what does the person on the other end of this actually experience, and does our data infrastructure make that experience better or worse?

That sequencing problem has consequences that compound. The average enterprise today runs dozens of martech tools, each holding a fragment of the customer picture. But because those tools were chosen independently rather than as parts of a coherent whole, they rarely agree on who a customer is, what they’ve done, or what they need next. The result is a degraded customer experience. People receive irrelevant messages at the wrong moment through the wrong channel, because the system of record is too fragmented to know any better.

The deeper issue is that most stacks were built around third-party data assumptions that no longer hold. The architecture was designed for a world where you could fill gaps in your customer understanding by buying data about people from somewhere else. That world is contracting fast. What replaces it has to be built on genuine first-party relationships. Too many organizations are still patching over that gap rather than rethinking the foundation.

There’s also a governance blind spot that ultimately hurts the customer too. When tool decisions are made in marketing without legal, IT, and compliance in the room, you get a stack that looks commercially attractive but creates real risks around how customer data is handled. And these are risks that erode the trust that makes the customer relationship possible in the first place.

What martech stack optimization tips do you think more marketers need to pay closer attention to?

A few things I’d highlight:

Always start with your customer, not your tool wishlist. Before you add anything new to the stack, ask: do we have a clear, consistent picture of our customer data, or at least how we can obtain the data we need? If the answer is no, adding more tools will compound the mess.

Audit your existing stack ruthlessly. Most teams discover, when they actually sit down and map it out, that they’re paying for tools that overlap significantly or that nobody is using at full capacity. Consolidation (where it doesn’t compromise capability) almost always pays off.

Treat interoperability as a first-class requirement. When evaluating any new tool, the question shouldn’t just be “does it do what we need?” but “how cleanly does it plug into everything else?” Poor integrations are where data quality goes to die.

Invest in data quality before you invest in analytics. It sounds basic, but the signal-to-noise ratio in most marketing data environments is terrible. Better models and better campaigns both depend on better underlying data.

Finally, think carefully about where sensitive data flows, as this can present a serious business continuity problem in addition to the more obvious legal implications. Knowing where your customer data goes and who has access to it has become a core competency for modern marketing teams.

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How can modern marketing teams create better data cleaning and data unification processes?

The first shift is cultural: data quality has to stop being treated as someone else’s problem. In too many organizations it lives in a technical backwater, handled by a small engineering team that nobody pays much attention to until something breaks. Elevating data quality as a marketing concern as well as an IT concern changes what gets prioritized and resourced.

On the process side, you need standards before you need software. That means agreeing on what a “customer” is, how identity gets resolved across channels and devices, what counts as a valid email address, and so on. These decisions sound mundane but they’re foundational. You can’t clean data if you don’t have a shared definition of what clean means.

For unification specifically, the challenge is almost always organization at its core rather than technical. Data lives in different systems because different parts of the business own different relationships with the customer. The CRM has one slice, the e-commerce platform has another, the ad platform has a third. Unifying that requires not just technical connectors but trust between teams: agreement on who can see what, under what conditions, and for what purposes. Getting that governance layer right is actually the hard part.

Identity resolution has matured significantly, and the best approaches increasingly combine deterministic and probabilistic methods depending on the context. Many teams still apply a single method rigidly where a more flexible strategy would serve them better. The key is understanding which approach fits which use case, rather than treating it as one-size-fits-all.

A few thoughts on how AI-powered martech is leading to a complete rejig in marketing?

AI is accelerating a shift that was already underway: from campaigns built around broad segments to experiences shaped around individuals. That personalization around scale changes the fundamental unit of marketing strategy.

Here’s the thing that doesn’t get said enough: AI doesn’t create competitive advantage on its own. It multiplies what already exists. If your data is siloed or poorly governed, AI will only amplify the issue. The organizations seeing the best results from AI-powered martech aren’t necessarily those with the most sophisticated models. They’re the ones with the most solid, best connected first-party data foundations.

That’s driving a fundamental rethink of data strategy. For a long time, the dominant instinct was to stockpile and ring-fence proprietary datasets. Today’s marketers are realising that intelligence compounds when data is connected via secure networks. No single brand has a complete view of the customer journey. But through privacy-respecting collaboration across brands, retailers, and publishers, marketers can feed AI richer and more diverse signals without ever exposing raw data. That network effect is where the real AI advantage lives.

Five martech thoughts to leave us with before we wrap up?

1. First-party data is not optional. Every strategy that still depends significantly on third-party data has a shelf life, and that shelf is getting shorter. The teams who’ve invested in owning their customer relationships directly will have a structural advantage that compounds over time.
2. Less stack, more depth. The arms race of adding tools has to end somewhere. The best-performing marketing teams I see are the ones who’ve made fewer, better choices when it comes to their tools and actually mastered what they have.
3. Collaboration between data owners is the next competitive frontier. Some of the most interesting marketing use cases require combining data across organizations — retailer and advertiser, publisher and brand, etc. — without either party giving up control. This kind of privacy-respecting data collaboration is still early, but the teams that figure it out will unlock insights their competitors simply can’t access.
4. Treat compliance as a design constraint, not an afterthought. Privacy regulations aren’t slowing down, and neither is enforcement. The organizations building data practices around compliance from the start will spend far less time and money fixing things later.
5. The AI opportunity in martech is real, but it has to be earned. You don’t get the benefits of AI by adopting AI tools. You get them by doing the unglamorous work of building clean, unified, well-governed data foundations and then letting AI do what it’s actually good at on top of that.

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The company specializes in secure data collaboration and offers a platform for data clean rooms, as well as the Collaborative Audience Platform: a unified layer that adds CDP- and DMP-style capabilities to the clean room for real-time segmentation, identity, activation, and shared audience products.  Decentriq has secured significant funding, acquired international customers, and established partnerships with major technology companies such as Microsoft.

About Maximilian Groth

Maximilian Groth is co-founder and CEO of Decentriq, a technology company founded in Switzerland.