“Scaling Marketing with a Data-Driven System”—with Hannes Krause (rabau:x media)

Recorded in German—English translation of the original conversation.

Intro

Maria-Lena Matysik: Welcome to Analytics Sprechstunde. I’m Maria-Lena Matysik, analytics consultant and managing director of Analyticsfreaks. Here I share with you how marketing teams can really use their data for better decisions and sustainable success—with concrete insights, actionable tips and no buzzwords at all!

In this episode, I’m talking with Hannes Krause. Hannes is the founder and managing director of rabau:x media in Hamburg. rabau:x media is—I think you can put it that way—a super specialized digital media agency, so for performance and awareness marketing, but always with a focus on paid media.

Hannes has been working in online marketing for more than twelve years. He was a freelancer, later also got a taste of in-house life at a company and, as he says, then realized that he actually quite likes being a service provider and finds the perspective on the agency side really exciting. And so he founded Rabau:x Media. With their specialized media agency, he and his team also manage really large budgets and campaign setups.

That means that for his clients, it’s not just about starting small and trying out a little something. The focus is clearly on keeping track of performance, finding levers that can still be optimized—and, of course, the collaboration and communication with the clients. For exactly this, Hannes and his team have developed their own—I’d actually call it that—framework, or rather their own process: super, super structured, extremely data-driven and replicable. I find that super impressive, and that’s why I invited him to this episode.

So today we’re talking about why data-driven work needs a super clear strategy and what this strategy has to be able to do. And of course Hannes also explains how he does it in practice: from the joint KPI definition through traffic and detailed planning to the automated dashboard that enables plan-vs-actual comparisons over the course of the year and, of course, makes communication massively easier! And at the very end, we took a little off-topic detour to a subject that’s on a lot of people’s minds right now: Can we actually leave data analysis to AI yet?

Then I’d say: Let’s go!

Interview

Maria-Lena Matysik: Welcome, Hannes! It’s a great pleasure to have you on the podcast today. So nice that you’re here.

Hannes Krause: Thank you so much for the invitation. I’m really happy to be here.

Maria-Lena Matysik: Maybe first the most important question, which I always ask here on the podcast: What learning have you brought with you today?

Hannes Krause: You’re catching me right in the fourth quarter, and for us that basically means two things. One is that it’s a very intense time operationally. And the other—and that’s also what the learning we want to discuss today is about—is how important it is that we still take the time for our annual strategy now. Experience just shows this again and again: The entire next year depends to some extent on buckling down now and working out a really clean, well-thought-out and also quantifiable strategy for next year. And that’s exactly what the two of us want to talk about in a moment!

Maria-Lena Matysik: Very cool—super, super, super exciting. Before we get into the situation companies are in at that moment, maybe first a little preview: Why do you find this topic so important—and not at all in terms of avoiding problems, but more along the lines of: What would it make better?

Hannes Krause: I think I’ve noticed this again and again with our clients, but also back in my previous agency days: Ultimately, it’s the guardrails that help us steer in the right direction. In the annual strategy—and we’ll come back to this in detail in a moment—I find it really, really important that we detach ourselves to some extent from our specialist-department lens, which makes us lose ourselves in the individual details. Instead, we take a step back and consider together with our advertising clients: What are your overarching goals as a company? And our strategy is aligned with these overarching goals.

That’s exactly the positive outcome: that our work actually contributes exactly to where the company wants to go with its overall strategy. Of course, all of this is broken down into individual initiatives. But ultimately, the positive thing is that what we work on every day is also what we get a pat on the back for in the end, from management, for example: “Exactly right, that’s exactly what we needed!”

And then, when we’re in the middle of 2026, that gives us much, much more freedom and independence in how and why we do something. Because we always know exactly: This is contributing to the annual strategy right now. That’s why we can do it with self-assurance. And even when there are headwinds—over the course of a whole year, things obviously change—you have a completely different level of confidence.

And honestly, we also sometimes do things that aren’t perfect right away—especially when you test and try things out, as we all know, not everything is always perfect right away. But this gives you much better backing to say, for example: I can afford this right now. Let’s see: Within the annual strategy, we’re even x percent above—we can afford to take a risk right now. Or the other way around: We can’t allow ourselves any risk right now; we have to play it safe for once.

And it’s just as important that we, as service providers to our advertising clients, don’t lose ourselves too much in our, as I said, nerd topic, but always remember: We’re steering for the client here, and the client has overarching goals that we help contribute to. That really cements the entire collaboration. Honestly, when I look at our client projects, I always want to know: Is everything good for my client right now? And that’s regardless of whether a small individual initiative is working at the moment or not. I simply always want to know whether we have the same understanding of whether we’re in a good position right now or facing real challenges.

Maria-Lena Matysik: Really cool! I really like this image of guardrails, because it’s so vivid and shows so well: How much room to maneuver do you have, how far can you go to the right and left—and which direction are you heading in?

Hannes Krause: Honestly, it also gives you a completely different kind of stability, because you simply know exactly that not everyone has to keep asking themselves lots of questions. Within my guardrails, I sometimes move to the left and sometimes to the right, but I stay within these guardrails. That gives you a completely different continuity in your initiatives. I think that really helps us plan and think long term and not just talk about the next few months—but also to make the case: I’m now launching the following three initiatives; they’ll only materialize in six months, but then they’ll benefit my annual strategy because they have a bigger impact.

Maria-Lena Matysik: Yes, totally. Could you maybe share a few behind-the-scenes stories and bring an example case where you noticed in the past: Okay, if this overarching strategy and the guardrails are missing, what happens? So what situation did the learning you’ve brought today come out of?

Hannes Krause: Yes, we actually had a case with a client—a fairly large e-commerce company in Germany—where we worked on a concept for a really long time from our media vantage point and also together with the specialist department. As an e-commerce company, they had historically optimized all initiatives for revenue. We had all understood: One thing is of course revenue, but the other is how valuable that revenue actually is, i.e., what margin it generates. And we then developed concepts for how we could optimize directly for margin—both in reporting and tracking and in media management, so that we really pass the margin on to the algorithms. From our point of view and from the specialist department’s point of view, this really increased the value of our initiatives.

Of course, we also had the potential downsides on our radar. For example, heavily discounted items were of course always strong in revenue and really easy to sell through paid media. But the huge downside is that the margin on these items is extremely low. We had analyzed that beforehand as well and said: Okay, if we optimize for margin, we’ll improve the absolute margin. But that will lead to a drop in revenue—a drop in, quote unquote, maybe worthless revenue.

That’s why all of us, including the specialist department, were totally satisfied and had a total success story. But then suddenly management, including the shareholders, came knocking and said: “Guys, what happened to revenue? Where did the revenue go?” We said: “Well, of course, we optimized differently. We cut away the revenue that wasn’t doing anything for you anyway.” And then it came out: “Yes, but hold on, stop: At the shareholder or even investor level, only revenue is actually reported here.”

And that was exactly the moment we realized: It doesn’t have to be that one is right and the other is wrong. If, for example, you have clear growth ambitions—profitability or margin isn’t the focus right now at all; it’s about acquiring new customers, maximizing revenue and so on—then that’s all well and good. But what was missing—toward us as a service provider, but even within the internal specialist department—was transparency about which central KPI actually decides at the end of the year whether you say: The media agency did a really good job or not. And if you think the KPI is wrong, you can discuss that too.

But that was a typical example: If we’d had an annual strategy, we could have said: “Well, wait a minute, we agreed on margin, and we increased it in absolute terms in the end.” Then you have a completely different basis for discussion than: “Oh, we didn’t know that you only cared about revenue, because revenue is still what’s being reported.”

That’s one example where we clearly realized with this client: Especially with things that really touch the original KPI, we need buy-in from all relevant stakeholders—so that what we do is not only sensible from a subject-matter perspective but also the right path from the perspective of the company as a whole.

Maria-Lena Matysik: Mhm. What would you say—maybe not just in this specific case, but generally from your experience—where does this problem come from? In the end, I’d say it’s a communication problem: Either the strategy wasn’t developed or wasn’t communicated, or it didn’t get through. Maybe you can shed a bit more light on where, from your perspective, the problem often comes from.

Hannes Krause: I think the problem actually arises in different places. In this specific example, as you also said, it was clearly a communication problem from my point of view. There should have been different stakeholder management, where people at the various levels understand what we actually want to steer toward.

In other areas, though, I also see that—regardless of communication—people simply look at all kinds of different KPIs and there’s no shared understanding at all of what the KPI relevant to the company is and what might be secondary or contributing KPIs. For example, we sometimes get emails all the way up to the executive level: What happened to the traffic? Then you first ask yourself: Where is this question about traffic coming from now? Your company goal surely isn’t to have traffic on the website. For example, we optimized out display initiatives that shoveled lots and lots of traffic onto the site but where we found poor quality. That’s another example you’re surely familiar with: People log into GA4 and come with the question: What happened to the traffic?

And we notice again and again: It’s simply really important to create understanding. I think that’s a challenge especially in digital. We can measure a lot and may well have a lot of data, but you need a shared understanding of: Which number do I look at first? And if that number deviates from my plan or target, I have to look at a lot of upstream numbers to understand: Where is the break right now?

Those are the two central sticking points: One is communication. The other is understanding relevance. Because it’s not a KPI, a key performance indicator, if I have a hundred of them. Rather: How do I define a KPI that’s relevant to me?

Maria-Lena Matysik: Definitely very exciting. As you already hinted: We see similar things in the analytics world again and again. So how do you solve the problem? What steps would you recommend to make sure that the team has a shared understanding of the goal?

Hannes Krause: What has actually always proven very successful: starting a conversation and first interviewing the various stakeholders. Because depending on who you ask, you get very different answers. When we talk to the social department, for example, I quickly hear: “My goal for next year is plus 30 percent followers.” But that’s not a company goal. It has a purpose—namely, that it contributes to other, downstream company numbers.

That’s why we interview the relevant stakeholders to first collect all this input, and then we develop a proposal: From everything we’ve heard, the following KPIs emerge. Our job there is to help steer. One thing may be discipline-specific KPIs that are upstream. But we really have to break it down to: What is actually your overarching KPI that we’re contributing to?

Where you always have to play referee a bit is the separation between: Where do I have an annual strategy, and where am I talking about great individual initiatives that then have their very own KPIs? They contribute to the annual strategy but are not the annual strategy. That’s why we first collect all the input. And honestly, translating it into an annual strategy is then a big task. We’re talking about data here, after all, and of course there are also many quantitative aspects to translating it into an annual strategy.

Maria-Lena Matysik: Sorry to interrupt—may I ask again very briefly about gathering the information: How does that work? I could imagine that some stakeholders you’d like information from are very willing to give lots of info and say: “Hey, this is my idea, this is what I want to optimize for, and this would be another idea.” But I could also imagine that the more abstract it gets—meaning the higher up toward management—the harder it becomes to involve the stakeholders. What has your experience been with that?

Hannes Krause: That’s exactly where we help, namely by making things simpler. Because I really don’t want to discuss with management—I’m just picking something random here—what the SEO visibility index should look like next year. Management might not be interested in that either. Instead, it’s about starting more at the top and saying: Okay, you’re management—and our area in particular, media, is often a very high cost item for companies. I want to find a way with you so that at the end of next year, when I report something to you, we still have the same understanding of whether it was successful or not. And this also needs to be said: Just because you make an annual strategy doesn’t mean everything will happen exactly that way.

And then it’s about understanding on a simple level: What would you, as management, as CEO or managing director, say is required in the end for the company to have been successful? If it turns out, for example: For next year we have very strong growth ambitions—to come back to my example from earlier: We need to generate x in revenue—then of course it’s my job to also ask the relevant questions: Okay, revenue at any cost? Or does that also mean revenue with a certain share of new customers or a certain minimum margin? And then if they say: “Oh yes, good thing you mention that—new customers, absolutely!”—exactly.

That’s why these interviews are of course also prepared, and you think up certain interview guides. After all, you’re not doing this for the very first time: Typically, at the management level there are objectives that fall somewhere within the corridor of growth, increased profitability and so on. And then again, it’s exactly our job to break that down into the individual areas. If management says, for example: “I have to steer toward profitability,” I can translate that into certain channels of ours. In awareness initiatives, I can’t apply it that way.

But from my point of view, that’s not a management task either. That’s what the specialist department is for. And it has to build exactly that bridge: not always just talking about individual KPIs, where management tunes out at some point, but saying: We have an annual strategy; it gets reported and met—or not. Everything below that is essentially expertise that happens in the background. Otherwise, you also lose the line of communication, because it gets too complicated. You also can’t expect management to interpret, for every individual channel or every further development of GA4, why the number is now different from before. Management isn’t interested in that.

Maria-Lena Matysik: Totally, sure.

Hannes Krause: We have to sort that out among ourselves. But that’s exactly where consulting is especially beneficial.

Maria-Lena Matysik: I see. Okay, very good! Then we can move on: So you’ve gathered a lot of information about which KPIs are relevant and what management’s goals are.

Hannes Krause: Exactly. And then of course it’s our job—and this is the moment where you basically disappear under the hood—to implement it. Let’s say I’ve received a revenue target and a new customer target from management. I now translate that into a plan, for example. And then of course I look at: Okay, this year, or rather over the full calendar year, we achieved the following values from the following traffic channels and so on.

And then I first check: Is what we’ve been given even remotely realistic? Because that’s also a really important conversation: saying that it’s not realistic at all—or only realistic under the following assumptions. There, too, under the hood, things go down to a very deep level of detail. You really have to plan the channels on a monthly and even weekly basis, because only once I’ve done that at the detail level do I get an overall result again. The overall result is what people may be interested in, not the detailed planning. But I need the details to be able to assess at all whether the overall result is realistic or not.

And above all, I also have to plan the upstream KPIs along with it. When it’s about revenue, for example, I still plan my traffic as well. Why? I don’t report it as a primary KPI. But if I don’t hit my revenue in the end, I want to—and we may get to this in a moment—see automatically: What’s the cause? Is it because I’m getting significantly less traffic from channel X than last year? Is it because the conversion rate in that channel has dropped? Or is it because my average order value has collapsed?

So it’s like this: Even if I say we need a clear focus on individual KPIs in the annual strategy, I of course plan the rest along with them. As long as I hit my core KPI at the end, I don’t want to hear that something is deviating at the front—then it doesn’t matter. But if I don’t reach my goals or massively overachieve them—that can be positive too—I have to understand where it’s coming from in order to derive the appropriate actions. And that’s why it’s important that you’ve also planned everything upstream.

And again, it’s really important there: The detailed work is condensed and translated into a language the respective stakeholder speaks. I’ve kept mentioning management. But it’s just as important to talk to product management, to finance, maybe to marketing colleagues and marketing teams …

Maria-Lena Matysik: … maybe also to other marketing agencies from other disciplines.

Hannes Krause: Exactly, of course also, for example, to the creative agency, to find out what’s planned. That doesn’t mean you can anticipate every fluctuation of the coming year. But then you at least have the most educated guess you can make, and with it, ultimately, a very informed strategy.

Maria-Lena Matysik: Can you maybe say something about what this looks like for you during the year? You’ve built a largely automated dashboard that shows you these guardrails—ultimately, quote unquote, the customer journey from impressions or the first touchpoint to the conversion at the end. How do you handle that? Do you have regular meetings or check-ins? Do you then do deep-dive analyses with the data as well? Maybe you can say something about that.

Hannes Krause: When I started out, you had to do all of this manually. And that’s now something we map via a database. From my point of view, that’s also the be-all and end-all, and something clients should look for in agencies, too. We simply live in the digital space and have an incredible amount of data. If you start cobbling it together every time from twenty sprawling raw-data Excel sheets, then chances are there are errors in it, formula errors, and you’re also far too slow to get it done cleanly.

That’s why—speaking for ourselves now; there are of course all sorts of technical ways to do this—we built a data warehouse in which we tap into all relevant data silos via APIs. In our area, that’s mainly the media channels we pull all the data from, and the web analytics data. With many clients, though, we’re also connected directly to the client’s own DWH, from which we pull the data—simply so we can work flexibly with the data.

And that’s why what I mentioned earlier about annual planning at the channel and week level and so on is actually relatively doable when you have structured data. It’s not like you press a button once and everything is done. But all the data preparation is really relatively simple.

Then you have your annual strategy, you’ve gotten a thumbs-up from all stakeholders that you want to commit to doing next year exactly this way. And then the next step is to have an automated plan-vs-actual comparison. Ultimately, there’s also self-interest behind it for me as an agency: I don’t want our team, for example, to waste time constantly hunting down data. Instead, you should please log in in the morning and see directly in the dashboard: hopefully green—or red, shoot, we’re off track, we have an issue. And you also see at a glance where—that’s why it’s so important that the upstream KPIs are in the plan too. Where in my KPI chain is everything maybe deep green, so I know I’m ahead, or where is it red and heading down?

For example, we recently had a case where we didn’t hit the target, but saw: Man, the conversion is totally great. But at a glance you immediately saw: The AOV had totally sagged. It turned out that the client had launched a big promo through which everyone who placed an order got 30 percent off—so our AOV went through the floor. That, for example, was an issue that was solved really quickly. You saw at a glance: It’s red. I see: Oh, the AOV has dropped. We call the client and say: “The AOV has somehow dropped a lot—do you know what’s going on?”—“Yes, that’s the new customer promo that’s running right now.”—“Okay, got it!”

If I had had to hunt all of that down first, I might not have seen it at all or would have taken forever. This way, you have a completely different speed—and then also a completely different kind of communication with the client. Because at that moment we of course also said: “Ah, okay, a new customer promo, that’s great for the new customer goals.” That wasn’t exactly in the plan—no big deal, you can’t plan everything. “But that’s why, as an example, we didn’t quite reach our revenue target over the last two weeks.” And the client says: “No problem, we planned it that way, great.” So you had a really harmonious, wonderful communication where you saw: We have a deviation from the target that can be fully explained and still served the company’s goals.

Maria-Lena Matysik: Yes, super exciting. This might be a bit off-topic now, but it immediately warms my own service-provider heart. Because of course that’s also super transparent for the clients, for the collaboration and the communication: You always see what the plan is, what works, what doesn’t work, why and why not. You have a super foundation for communication. I think that’s super.

Hannes Krause: I mentioned briefly that with our database we’re often connected to the client’s DWH as well and receive the total revenue figures. That of course also has an effect on the client relationship—of course it’s covered by strict NDAs and so on. But we actually have full transparency into the overall result with almost all of our clients. We see it daily: Yesterday, for example, the entire shop generated the following revenue with the following margins. Down to the individual product level, we can see, for example, what the purchasing terms are. That’s highly sensitive data, but it has an effect on how we communicate with each other.

And that’s exactly what I mean: That’s the goal of this annual strategy—that we’re fully aligned and not stuck in that classic client-agency relationship, which can sometimes be a bit more difficult, especially in media. That’s exactly what I really wanted to avoid: us just working through services for our clients that we get paid for, instead of knowing together: This is the goal we’re steering toward, and we’ll do everything we possibly can to achieve it. That’s our shared goal. Because ultimately I know exactly: The meeting is harmonious, the client is happy—not just in the sense of “Great agency, Rabau:x Media,” but because we’ve reached our goal. And that’s kind of exactly the whole backbone of a strategy like this: that it really intensifies the client relationship and we’re aligned with each other.

Maria-Lena Matysik: Do you also still have items on your to-do list where you’d say: This and that should work even better? Where do you still see room for improvement in this whole process—on your side or generally, perhaps also on your clients’ side or in the collaboration?

Hannes Krause: It’s certainly the case that what I just described sometimes involves very considerable effort. In strategy especially, there’s a lot of scenario planning behind it: What if …? If we increase the budget by another 20 percent, the results go up a bit—but usually not by exactly 20 percent because of diminishing marginal returns. We can currently calculate all of that based on the data. The data preparation is now fully automated, but the calculation is still simply manual number crunching.

We’re experimenting there and have the feeling that in the near future, it should be possible to automate even more and become even more flexible. Because right now, the client may have scenarios one, two, three—plus 20, plus 30, plus 50 percent. Then they come back: “Oh, and what would happen at plus 70 percent?” And at some point something else again. It would of course be awesome if we could automate more there. We have the data, after all. There’s certain logic behind it—each channel has a different marginal return.

For example, I was a bit disappointed when we first wanted to have AI output our campaign reports and then also the interpretations of deviations from this annual strategy—so: “Okay, we’re deviating right now; here’s the raw data; interpret what could be causing it.” Honestly, that didn’t work all that well yet. But I think a lot is still going to happen there!

My dream, of course, is really this: We have this annual strategy, the actual figures flow in automatically, and then you notice: I have a deviation, positive or negative. And then, for example, the media manager can ask the AI: “Okay, what’s the cause?” And it tells them exactly: “This is the cause, and here are the potential actions: Impressions, for example, are stable, clicks are poor—that means the click-through rate has sagged. Aha! You need to do some kind of creative optimization in that channel, yada yada.”

Of course that’s a heuristic, I know that. But ultimately it’s a fairly quantifiable area. The recommendation is a mix of numbers plus a bit of history on top. I think that’s something AI can pull off. But we’re not there yet. And that’s why I think we can still get better there.

Maria-Lena Matysik: I’m glad you share my pain when it comes to how useful AI currently is in data analysis. I’m also pinning my hopes on the future.

Hannes Krause: Honestly, in our own hands-on work, in media for example, we also have lots of areas where AI is used. But wherever there’s an absolute right and wrong, we absolutely hit our limits. Two plus two is simply four—and it can’t come out as four one time and two the next; two plus two is four. That’s exactly the area where, at least in our applications, there are still challenges right now. But in reality, these are use cases where I’m firmly convinced it’s only a matter of time. That’s why we’re playing around with it—be it, this sounds basic now, the Gemini integration or other things. We experiment, all good, and then the results aren’t that great after all. But I don’t think this is a matter of decades more; it’s going to happen very quickly.

Maria-Lena Matysik: Over the last few years, have you seen any development in how structured companies, and especially marketing teams, are in working with data? Do you see that your request for strategic planning, concrete goal setting and KPIs is getting easier and easier in communication because more comes back—because a lot more has already been prepared within the companies?

Hannes Krause: Yes … so-so! Looking across all of them, I have to say: I’m sometimes amazed at how much we’re still at the very beginning when you say: “Let’s discuss next year.” But of course it’s gotten much better. Almost all of our clients are at the point where we can do it exactly this way. And still, you keep running into obstacles.

For example, we get the goal: increase new customers, absolutely. And then when we ask: “Okay, how do you actually measure new customers? We’d need to talk about that again to see how we can meet this goal.” That’s where the annual strategy was really valuable: When I’m told that new customers are the priority and then ask myself where we get the data for that, and it turns out: “Oh, they can’t even measure that properly!”—okay, then we’ve just discovered a really important issue. We absolutely have to establish measurability.

Of course it’s gotten much, much better over the years, and I’m glad about that, because at the same time it creates a certain clarity—sometimes a harsh one. In the past, there very often wasn’t a goal at all. I see big, big developments there, but I still think there’s a lot left to do. And sometimes you have to be careful that it doesn’t swing to the opposite extreme, because I observe that too: Because so much is measurable, suddenly you’re supposed to define twenty things as target KPIs. There, too, we have to step in and correct course. We can meet more than one target KPI, but maybe not twenty—because then you get everything, but nothing really.

Maria-Lena Matysik: Yes, super, super exciting. I find it really exciting to hear this from you, and with every question you answer, two more occur to me. But I’d say we’ll leave it there for now. Of course I have one final question: What would be your tip for marketing teams, or especially for heads of marketing and CMOs—what could they do first to work in a more data-driven way?

Hannes Krause: I think what’s really important—and maybe that also came through in my monologues here so far—is: You have to clarify priorities. You first have to clarify very clearly: We as a company have the following priorities. And translate that into data to get buy-in from the team: These are our guardrails. Because I believe the entire success depends on developing, when you work with data, a shared understanding of which data matters how much to us. And that everyone involved becomes aware: This data matters. These aren’t just some weird goals that someone came up with at some point; rather, you really have to understand the relevance of this data—and then also really put your weight behind making sure it can be captured technically and without manual processes.

Because on the one hand, you have to manage to make it, to some extent, part of the team’s DNA that this data is important. And there, too, it’s very important to me not to become data-blind: Just because the data is all great, please don’t switch off your brain—we still want to think for ourselves. But on the other hand, it really has to become part of the DNA, and that only works if this data is also understood at the decision-maker level. You don’t have to work with it operationally, but arguments have to be made with this data so the team understands: These are our priorities. And we in turn empower the team to take creative and also qualitative actions to optimize this data. But the data simply has to be centrally available and a central building block of day-to-day business.

Maria-Lena Matysik: That was a very good closing statement! Very nice. Thank you so, so much for all your insights and for sharing your experiences.

Hannes Krause: My pleasure. I can only repeat myself: Thank you so much for the invitation! I felt honored when you wrote to me. Thank you so much!

Maria-Lena Matysik: I’m glad to hear that.

Outro

Maria-Lena Matysik: My personal learning—or maybe rather a very valuable reminder—from the conversation with Hannes was definitely: Without a clean, clear strategy, there’s no meaningful data-driven marketing. Meaningful in the sense that it really contributes to the company goals and thus to real success. And for that, of course, we need the right KPIs—because not everything that’s somehow measurable is automatically important! The K in KPIs of course stands for key, meaning what really counts in the end.

We also touched on a few concepts in the conversation without naming them explicitly. I find them super helpful in general, and that’s why I want to name them very specifically here.

The first is the concept of the North Star Metric. It basically describes the one metric that shows whether a company is successful. If this one number grows, we’re successful, and everything you do should contribute to it. For a shop, this could be, for example, the number of first-time buyers, maybe also revenue from returning customers or simply revenue or margin.

To understand how you can influence and move this North Star Metric, there are KPI Trees—that’s basically the second concept. A KPI Tree shows what this one metric is made up of. Revenue, for example, isn’t simply revenue; it’s the number of customers times the average basket value, i.e., the AOV, as Hannes also mentioned in the conversation. And the number of customers in turn depends on metrics like traffic, conversion rate and so on. That way you can see super, super clearly which levers you can pull to influence your North Star Metric.

And the overarching concept is the so-called Metric Hierarchy, a kind of ordering of all KPIs. At the very top is the company strategy with the North Star Metric, below that the KPI Trees and below that, in turn, the operational metrics of the individual marketing disciplines, initiatives, campaigns and so on.

In the end, all these concepts help you work in a data-driven way because they provide clarity, define priorities and serve as a shared basis for communication within the company. And that’s exactly what, in the end, makes a clean, clear strategy that really holds up!

And with that, we’ve already reached the end. I’d be super, super happy if you followed me on LinkedIn, and if you have feedback or comments on this episode, or suggestions and ideas for future episodes—feel free to write to me. I look forward to hearing from you. Bye!