“Solving the Sales-Marketing Conflict with Data”—with Lena Dierolf (avenit)

Recorded in German—English translation of the original conversation.

Intro

Maria-Lena Matysik: A warm 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 lasting success—with concrete insights, actionable tips and no buzzwords at all.

Hello and welcome to a new episode—to the first or, rather, once again the first episode of Analytics Sprechstunde!

In this episode, I talk with Lena Dierolf. Lena is Marketing Director at avenit. avenit is a digital agency that helps mid-sized companies build digital strategies and digital marketing—from strategy through implementation to ongoing operations. avenit also sells its own product, namely an enterprise AI platform.

And that’s super exciting because Lena brings together several perspectives on marketing, which I find super, super exciting. On the one hand, the agency perspective, that is, the perspective of experts who build marketing for clients: lead generation, branding, content and so on. On the other hand, in-house marketing as well, namely for the company’s own AI platform. And on top of that, of course, tons of insights into the marketing and marketing teams of the agency’s clients.

You’ll notice it in the interview, too: With this background, Lena has a strong focus on lead gen or, rather, lead marketing. That led us to talk in this episode about how marketing and sales can work together in a data-driven way, which typical conflicts we all know arise between the two areas and how data can help close these gaps. And, of course, about what data is even needed for that.

Okay, let’s go!

Interview

Maria-Lena Matysik: Hi Lena, welcome to the podcast! And thank you so much for letting me pepper you with questions about data-driven work in marketing. First of all, welcome.

Lena Dierolf: Thank you so much. I’m really happy to be talking with you today.

Maria-Lena Matysik: So what learning or tip did you bring along?

Lena Dierolf: My most important learning or, rather, my most important tip—partly from my own experience over the past few years, but actually also from what you can take away from these two perspectives—is: Don’t look at things only through a marketing lens. So don’t be too marketing-focused, but always try to keep an eye on the bigger picture. Because in the end, what counts is the outcome—not just the click-through rate or that one little KPI.

Maria-Lena Matysik: Cool. Could you maybe walk us through it a bit: How did this learning take shape in your mind, and why do you think it’s especially important?

Lena Dierolf: This is again a learning from previous positions, from what I see with clients, but also from my current role. I’ve seen the starting situation very, very often, and it’s very similar in a lot of companies: Marketing worked very much in isolation. Paid looked at clicks and ROAS, SEO at rankings, content maybe at reach—everyone sort of had their own KPIs.

And in the end—I always had to laugh, I could almost have put it in my calendar as an appointment—by the end of the first quarter at the latest, sales would come along: “The leads are bad. We can’t do anything with the leads you’re throwing over to us.” That’s such a totally typical situation, one I’ve really experienced again and again over the years, every year all over again—maybe sometimes even every quarter all over again. In other words: In marketing, everyone is super happy on their own and celebrates the great numbers, and sales still comes along at the end, complains and is unhappy.

That was also the point at which, many years ago, it became clear to me: Okay, we have to look at this entire chain and dig into it. In marketing, we really have to look from the first touchpoint all the way to the end of the funnel, at the deal—whether won or lost. And we also have to understand together: Why are these leads bad? Why does sales think they’re bad? Where do they maybe drop off? Where did we hand them over too early? Where is the whole process getting stuck?

And then the challenge is to define together: What is a good lead for marketing? And what is a good lead for sales? Sometimes there are also intermediate functions like business development, inside sales and the like. That’s the first challenge.

On top of that, very often—and many people will probably be nodding here—the data foundation isn’t always consistent and one hundred percent satisfactory. And that’s then the next step toward a really great basis for understanding this funnel or, rather, this overall process. And that has to change.

Maria-Lena Matysik: So in your experience, maybe the full potential of data wasn’t being used to close this gap that, as you said, often exists between marketing and sales—or to create a holistic understanding so that, in the end, customers or, rather, leads are actually generated.

Lena Dierolf: I think we first really had to determine what data we even need. So: What data do we need to be able to qualify the lead—for example, to put scoring behind it that we can use to measure this quality? And not only, let’s say, quantitatively in the sense of demographics, but really qualitatively as well.

So one thing was that the data foundation wasn’t of one hundred percent quality and didn’t necessarily give us all the information we wanted. And on the other hand, just as you say, the information we did have maybe wasn’t being used one hundred percent either.

Maria-Lena Matysik: Okay, got it. Let’s start again from the beginning. If you say that’s the problem you saw, my question now is: How did you approach it or, rather, how would you recommend approaching it to fix this problem? What concrete steps would you recommend?

Lena Dierolf: I’d break it down into three parts. First of all, I’d look at the processes and KPIs—and do it together. For me, that’s really, really important in all of these steps to arrive at a satisfactory result: to really define the whole thing together with sales. In other words, just as I said earlier: We define together what a good lead is. When is it passed on? When is it no longer just a lead but a Marketing Qualified Lead? When is it maybe Sales Accepted or Sales Qualified? These stages are, of course, very company-specific. But you should simply know: What are the handover points, and what criteria need to be met for leads to be passed on and reach the next stage in the funnel? So that, first of all, you’re speaking a common language.

In the next step, it’s about the data foundation. That means cleaning up the CRM. Where is our data? What data do we have? What data will we need in the future? How can that improve our tracking, for example, if we suddenly use a clean, jointly defined data foundation? If we use one tool for it together and no longer shuffle Excel lists back and forth or some screenshots nobody can do anything with because they never show the holistic picture?

Maria-Lena Matysik: Could you maybe make that a bit more tangible with an example—how you could document it concretely? So: At what points do the handovers happen? Where would you record that, how would you define it? And what does it look like in the CRM in the end?

Lena Dierolf: Sure, I’d be glad to. We wrote SLAs, that is, service level agreements, together—again with everyone involved in the funnel. It totally depends on the product and the funnel, but among other things, we tied it to scoring. That means we evaluated certain criteria: Does the lead fit the ICP? Is it in the buying center? But also: How many interactions has someone had with us? If you look at form fills, for example, and want to use them to get some measure of intent, then a contact request or a trial—when someone starts a trial—is of course weighted much higher than, say, simply a whitepaper download. That’s how we enriched the scoring. The score was very, very important to us for these handover mechanisms, internally as well.

Maria-Lena Matysik: Okay, got it. Cool. So when you defined the processes and KPIs at the beginning, you sat down together as marketing and sales and looked at: What interactions are there in the customer journey, what point value do we assign to them, how do we want to assess and evaluate that? How do we put the score together in the end, how do we calculate it? And what point value does the score have to exceed in each case for the lead to move to the next stage?

Lena Dierolf: Exactly.

Maria-Lena Matysik: Cool, super exciting.

Lena Dierolf: Of course, that’s also a change at first. It may be that in the first step, right after the change, even fewer leads reach sales at first because they’re now prequalified in a completely different way. You do have to prepare the sales team for that a bit. We said: “Please don’t come to us two weeks from now and say we’re not getting enough leads anymore. We’re qualifying them according to the new criteria now—you’re simply only getting the right leads.”

That’s ultimately also step three: really sitting down regularly with the sales team—we did it weekly—and looking at the reports. We always looked very closely at the defined KPIs and handover points together. We could see: Was there a drop in a conversion rate from one measuring point to the next? If so: What happened, what caused it? So we could look very closely at every point in the funnel: How’s it going? Where are things maybe getting stuck? Did something work especially well? That way, the business development team could maybe tell us: “Hey, all of a sudden things are going great because we’re now really only getting the right leads. We don’t have to deal with so many leads that aren’t anywhere near ready yet, and we can now focus really well on the right ones.”

Maria-Lena Matysik: Mhm. I’m trying to picture in my head how exactly that works. So you sat down once a week and saw, for example: Last week, a ton of leads moved from stage, I don’t know, three to four. Why was that? And then you dig deeper and look: What kind of leads were they? What exactly made them convert so well? Where did they come from? What interactions did they have, for example, that gave them the high score? And then you look at what worked and what didn’t.

Lena Dierolf: Exactly. And especially at the beginning, these very conversations are super important because they also build mutual trust—to break down these silos and create a shared understanding. And because we could look at really concrete numbers we had jointly committed to, we moved away from this, let’s say, blame game: “We’re getting leads that are too poor, we’re getting too few leads.” But also from marketing saying: “Hey, we’re delivering top leads to you here, and you’re not making anything of them.” That way, we simply managed to reach a shared understanding, to look together at the jointly defined KPIs and to really keep an eye on revenue for both teams.

Maria-Lena Matysik: Yes. And I can also imagine: If you have a shared conversation format or forum like that where you exchange ideas, you can, of course, understand much better how the other side thinks—so marketing about sales and sales about marketing. How do the others work, what are their everyday problems, how do they optimize? You get a better understanding of what the other side’s levers are and can bring that together.

Lena Dierolf: After all, the result is then a shared result. I have a very simple example of that, too: We wanted to keep the form fields as short as possible—ideally no fields at all anymore or only the email address. And sales was regularly up in arms about it. We just couldn’t understand each other until at some point it came out: Well, they have a quota of calls they have to make with the new leads. That means they need a phone number. We just thought: “How old-school are you that you always ask us to collect this stupid phone number?” But once we understood that they’re, of course, measured on it, it was more understandable for us on the one hand, and on the other hand, the problem was basically put to rest quite easily: Then the phone number just goes in, done. After all, we don’t want to make your lives unnecessarily hard. And we simply take a look, or even run an A/B test, to see whether it’s really that bad if we ask for it as well.

Maria-Lena Matysik: I could also imagine that when you started this joint review, you adjusted either the scoring or the ICP again or kept revisiting them.

Lena Dierolf: Exactly. You learn a ton when you look at it together very regularly and really discuss the good and bad leads together. That gave us the opportunity to check very regularly how we could adjust the score and keep it flexible so that it also adapts to changing conditions. We almost had to be careful not to optimize too much.

Maria-Lena Matysik: Mhm. How did you structure this review, then? Did you have a report or a dashboard that you always used as the basis? Was there some format that worked well for you?

Lena Dierolf: We always started very clearly with the numbers. We had a report—at some point it got nicer and nicer and became a dashboard, but at the start it really was a report that showed us the individual stages and the conversion rates. At some point we expanded the whole thing to include duration, that is, how long it takes for a lead to get from one stage to the next. Because there, too: If the average is two or three weeks and suddenly it’s three months, you’ve got a break somewhere, and you have to understand where it’s coming from.

We always started with exactly that and first of all looked at the highlights and lowlights before getting into the, let’s say, less data-driven or sometimes more emotional discussions. But we always, always started with the report and the numbers.

Maria-Lena Matysik: I find it really exciting that you say: At the beginning, we just looked at a report, and at some point it became a dashboard. I actually think that’s a great learning, too. I sometimes get the feeling that a lot of marketing teams tend to say: We need a pretty dashboard before we can even look at numbers. But that’s a super quick way to get bogged down, and you end up looking at things that aren’t relevant at all in the conversation. That’s why I think it makes total sense to start by saying: Let’s just look at the—quote-unquote—raw data first, and whatever proves useful then goes on the dashboard, for example.

Lena Dierolf: Yes, totally. I’ve also seen people get lost in highly complex dashboards. And it takes time to get the whole thing set up. And in the end, you realize: Well, it looks good now, but we can’t even read it, can’t interpret it and—what I think is most important of all—can’t derive any actions from it.

Maria-Lena Matysik: Yes, and then we have 25 KPIs, and in the end you say: Well, we’re really only looking at the one because that’s the important one.

Lena Dierolf: Exactly. And we actually still always pulled that report. Of course, at some point you know which numbers you want to look at in the meeting, but we still always gave ourselves the detailed view or, rather, kept it up our sleeve. And the other side of it was, of course, also very nice: In the end, we could also turn it into an executive summary and share the highlights with the rest of the company or with the executive board.

Maria-Lena Matysik: Cool, also a good point.

Lena Dierolf: Yes.

Maria-Lena Matysik: Sure, this is, of course, a process that never ends. But if you look back on the beginnings of this process: Did you take any learnings away from it? Were there stumbling blocks you first had to overcome before things were running—or things you could have done differently from the start?

Lena Dierolf: I think one of them has to do with tools: Tools are super important, but not necessarily the core of the whole thing. Looking back, it turned out for me that the core is actually creating a shared goal and understanding. After that, you can decide which tools to use. So you should really make sure to bring clarity to both teams early on.

And how important it is to invest in a clean data foundation as early as possible—because the bigger it gets, the more effort it takes. If you’re already running a lot of activities up front that bring leads into the data foundation but haven’t yet managed to set the framework for what a good lead looks like or whom you even want to reach, then it gets harder and harder the more there is in it.

Maria-Lena Matysik: That’s the perfect segue to my last question: What would you recommend to other CMOs or heads of marketing who simply want to start using data more? What’s the first step for you?

Lena Dierolf: I’d actually pick up on this learning: Don’t start with the tool, start with talking. Start by getting the people you need for this on board and defining together: What’s your goal? What’s a good lead? What do we want to achieve together? I think that was my biggest learning of all: that this gets you to the goal together much faster and much more effectively, and that you also manage to get everyone pulling in the same direction very early on.

Maria-Lena Matysik: That’s the perfect closing note. Thank you so, so much for the insights and your learnings from this process on your end.

Lena Dierolf: Thank you. That was a lot of fun.

Maria-Lena Matysik: Super, great!

Outro

Maria-Lena Matysik: I found it super, super exciting how the conversation with Lena made it clear once again: What ultimately counts is the actual outcome—that is, which leads and results marketing and sales can achieve together. Because the much-talked-about and much-debated conflict between marketing and sales often arises when both sides work in isolation and sometimes have different ideas of what a good lead is. That means the key always lies in clearly defined metrics, KPIs and data points. So: What do we measure? How do we measure it? When does a lead change stages, which criteria have to be met? And what is our scoring based on?

I also found it super exciting that Lena pointed out again how important it is not to start right away with super pretty, fancy dashboards—but that a simple report that is then expanded step by step is far, far more valuable. And that it’s definitely worth investing early in a clean data foundation because otherwise, down the line, it just gets super, super complex and harder and harder to implement.

But if I want to really highlight one point from this conversation, it’s this one: Data is not an end in itself. It’s not there to be collected or displayed in some dashboard. Rather, data becomes valuable whenever people talk about it.

I really love this concept of “Data as a Conversation” because data has the potential to be an engine—for exchange, for understanding, for decisions. After all, it doesn’t speak for itself. The mere fact that it exists somewhere doesn’t create any added value yet. Rather, we have to look at it together, ask questions, discuss hypotheses, learn from it, put those learnings into practice—and always ask dumb questions, too. Because the goal isn’t to justify yourself or to find someone to blame, but to understand together: to understand the target audience, to understand the customer journey, to understand problems.

And the origin of this concept, “Data as a Conversation,” also says a lot about why we need it. It originally comes from the data literacy and data democratization movement, in which the usual suspects—I don’t know, Airbnb, Spotify and a few others—provided the impetus to build internal programs. The idea: Data shouldn’t be reserved for analysts only; rather, really all experts from different disciplines in the company should be able to use data themselves, discuss it together and question it. And the data should help teams identify problems, solve them and move forward. This very concept is called data democratization.

So if you take one thing away from this episode, I hope it’s this: Talk more about your data. Make it a joint meeting, look at the numbers together and ask yourselves: What exactly are we seeing here? What does it mean? And what do we do with it now? Because data on its own doesn’t solve problems. People solve problems—people who talk about data are the ones who do.

And I think that was a good closing note for this episode. I’d love for you to follow me on LinkedIn, and feel free to send me your feedback on this episode, comments and, of course, ideas and requests for all the episodes still to come. And with that: bye-bye!