Lars Grønnegaard, COO and Co-Founder of Dreamdata, joins Mike to discuss data utilization in B2B marketing and the importance of understanding what truly drives sales.
They talk about the complexities of attribution in B2B marketing, particularly with long buying cycles, and highlight the importance of collecting and analyzing data, even though multi-touch attribution can be controversial.
Lars explains how Dreamdata addresses the challenge of data overload in B2B marketing by building robust data models that connect various data sources, enabling marketers to analyze campaign effectiveness and make informed decisions.
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About Dreamdata
Dreamdata is the leading B2B Activation & Attribution Platform that provides the most complete customer journey map anywhere. We gather, join, and clean all revenue-related data, transforming it into transparent, actionable insights about what truly drives B2B revenue. Dreamdata empowers businesses to optimize their go-to-market activities, ensuring they can produce faster, better-performing customer journeys and deliver more revenue growth.
About Lars Grønnegaard
Lars Grønnegaard is a passionate problem-solver focused on revolutionizing B2B go-to-market strategies. As COO & Co-founder of Dreamdata, he’s dedicated to empowering B2B companies with holistic, actionable data to drive smarter decisions, optimize customer journeys, and accelerate revenue growth. His journey from VP Product at Trustpilot (where he was instrumental in its product growth to unicorn status) to building Dreamdata stems from a deep understanding that B2Bs are rich in data but often struggle to turn it into actionable insights.
Time Stamps
00:00:43 – Lars’s Career Journey and Founding Dreamdata
00:03:13 – Startup Environment in Denmark
00:05:30 – The Challenge of Data Clarity in Marketing
00:08:22 – Attribution vs. Incremental Sales Testing
00:10:39 – Long Buying Cycles in B2B
00:16:12 – Maintaining Data Models Over Time
00:19:17 – Freemium Model and Customer Trials
00:22:44 – Ad Spend and ROI Considerations
00:28:49 – Best Marketing Advice from Lars
00:29:39 – Advice for Aspiring Marketers
00:30:34 – Where to Learn More About Dreamdata
Quotes
“You can probably cancel somewhere between 25 and 75 percent of your ad spend and see no impact on your pipeline performance.” Lars Grønnegaard, COO and Co-Founder of Dreamdata
“The fundamental problem here is not lack of data or tracking… but the problem is all this data doesn’t really connect.” Lars Grønnegaard, COO and Co-Founder of Dreamdata
Follow Lars:
Lars Grønnegaard on LinkedIn: https://dk.linkedin.com/in/larsgroennegaard
Dreamdata’s website: https://dreamdata.io/
Dreamdata on LinkedIn: https://www.linkedin.com/company/dreamdata-io/
Follow Mike:
Mike Maynard on LinkedIn: https://www.linkedin.com/in/mikemaynard/
Napier website: https://www.napierb2b.com/
Napier LinkedIn: https://www.linkedin.com/company/napier-partnership-limited/
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Want more? Check out Napier’s other podcast – The Marketing Automation Moment: https://podcasts.apple.com/ua/podcast/the-marketing-automation-moment-podcast/id1659211547
Transcript: Interview with Lars Grønnegaard at Dreamdata
Speakers: Mike Maynard, Lars Grønnegaard
Lars: Thanks for listening to Marketing B2B Tech, the podcast from Napier, where you can find out what really works in B2B marketing today.
Welcome to Marketing B2B Technology, the podcast from Napier. Today, I’m joined by Lars Grønnegaard. Lars is the COO and co-founder of Dreamdata. Welcome to the podcast, Lars.
Lars: Thank you so much. Very, very happy to join the podcast. It’s great to have you on.
Mike: So before we talk about, you know, what you’re doing, we like to get a bit of background. So can you tell us a little bit about your career and why you decided to found Dreamdata?
Mike: Yeah, I started my career in what was back then known as web agencies. So back in the late 90s, early 2000s. And then I worked through different web agencies and advertising agencies always sort of lived in the advertising space, you can say. And at some point I drifted into what you call software development, product management of software and worked both on some sort of big, say, internal advertising platforms. I worked at a company called Ad People, which was like a WPP agency. So we ran all of Dell’s print ads on the product we built. And then later I went to a company called Trustpilot, pretty well known in the UK as well. and ran product there. And then in that role, became quite involved in the go-to-market of Trustpilot. So kind of back to, you know today, Trustpilot is in many ways also like a marketing product for the customers of Trustpilot. So drifted back into sort of working with marketing and go-to-market and found, basically we found in Trustpilot that, it was like, And it is still like a super successful business. So selling quite well. So how did Trustpilot make money? Well, we signed deals with customers. But when we sort of went one step back and said, OK, but where did they come from? We you know, there were a lot of opinions, but honestly, like nobody really knew. So we went sort of looking for answers is like, OK, where do all these customers come from? We had a free product. Did they come from there? We spent tons of marketing dollars. Did they come from there? And of course, we had like not a million, but quite a few salespeople, cold calling. And we basically, you know, collected the data, built a big data model and did the analysis, came up with answers and saw that it was hugely beneficial for the business to actually know where to invest. And the other side of it was maybe that, you know, there wasn’t a product that could solve this. It was a ton of very, very tedious work to get to those answers. And we felt that it was something that should be a product. So we went looking for whether it could be a product, and that was sort of the genesis of this company. So that’s how I ended up where I am, being a co-founder of a MarTech company.
Lars: That’s awesome. I mean, before we dive into what Dreamdata does to help people understand what drives their sales, I’m just interested to know, what’s it like founding a company in Denmark?
Mike: Founding a company in Denmark, I think you can say like, what’s the startup environment? It’s quite good. It’s easy to found companies in Denmark. So there’s not a lot of red tape. So I think that’s good. Used to be quite hard on sort of capital, like funding, what used to be hard, but is now, you know, your access to capital is fairly globalized. So it’s not an issue raising capital anymore. I think the biggest sort of pitfall is building it, if you build a product for the Danish market, it’s not very big. So at least then you need either a very expensive product or you need to cover a lot of the market. So that can be sort of a pitfall. On the other hand, for many companies, it means that the first thing you do is that you sell somewhere else. And that’s also what we did. So we never went for, say, the Danish, like maybe first five initial customers were in Denmark because it was a network. But since then, we have more like the size of a Danish segment is outsized, but 50 percent of our revenue and customers in the US and Canada and 35 percent are in non-Danish European countries, including the UK. So I think that that’s sort of like some of the stuff around founding companies in Denmark. But in general, Copenhagen is a good place to hire tech talent as well, I think.
Lars: I think that’s really interesting. Whenever I talk to Danish companies, there’s always this view that Denmark’s very small as a market, so they have to be international. I think it gives Danish companies or Danish startups a much more international view than maybe if you started in a large market, Germany or the US. So I think, in a way, it’s an advantage.
Mike: Definitely. I mean, I think the old recipe in Denmark was like, oh, win Denmark, go to the UK, win there, then go to the US. And there’s definitely a lot of successful Danish startups that are now going like, look, go to the world.
Lars: I love it. So we talked a little bit about Dreamdata earlier. So you’re helping your customers really understand what’s driving sales, which I think is, quite honestly, the holy grail in marketing to understand what works and what doesn’t. So can you talk about how you do it and what the technology is behind Dreamdata?
Mike: Yeah, absolutely. So you can say, in general, I’ll say most marketers would agree that they are not suffering from sort of lack of data. The big problem is actually sort of clarity and getting some answers from the data. So most companies have like, I don’t know, hundreds, thousands of dashboards. Like we sold to a fairly large enterprise customer recently, and they told us that in their data warehouse, they had 200,000 tables. Which is a lot. It just means that there’s so much that nobody knows where to look. So I think that is like the fundamental problem here is not lack of data or tracking or like all the products we use, you know, your marketing automation, all your ad platforms, your CRM, your web tracking, it all generates data. But the problem is we are exclusively for B2B, I want to say. So I only think about this from the B2B side. But the big problem is that all this data doesn’t really connect. So you’re looking at your Google Ad Spend data over here, your LinkedIn Ad Spend over here. LinkedIn is saying, oh, we generated $100,000 last week. Google also generated $100,000. You go and ask sales and they did $150,000. Okay, so it doesn’t add up, right? So how do you sort of make that into something meaningful? That’s a fundamental problem. And I think the job you want to do is get all the data together and build a data model where you can ask questions like this campaign. What did it actually create in terms of pipeline or revenue or all the revenue we had? Where did it come from? How much can we track back to our of content initiatives or our conference or whatever you’re doing as marketing channels. So that’s the job of the product. So collect all the data, build a data model, and then provide sort of a layer on top of it where you can do analysis. So you can get those answers. That’s fundamentally what it does. Then you can say this is also back to the story from Trustpilot because this data set, once you have it, of course, it’s great for answering questions. But usually the next thing that happens is that people say, look, oh, but can I get all the ones that are like this? Like you have all this data. So can I get a list of everybody that’s you know, not in my sales pipeline, but they fit our core segment and they’re in the US and they’re actually looking at our website. Can I get that list so I can send it to LinkedIn and do some ads for them? So we also have like what I call like a big activation product where you can use the data to build audiences and send to ad platforms or even say, track the engagement on target accounts, send them to sales if they engage. So a lot of what you’d see in, in, in ABM platforms as well.
Lars: Yeah, I mean, I think it falls into this kind of category of customer data platforms, although I don’t really like that category because it’s so broad. It’s so many tools there. One of the things that products in your category do is they tend to be focused on attribution data. And that’s becoming a little bit controversial because obviously attribution relates to effectively touching the customer at some point on the journey. it doesn’t necessarily mean that touch was positive. It could actually be negative. But overall, yeah, the customers going through and buying. So I mean, what’s your view on attribution versus, you know, running tests where you try and measure whether you can generate incremental sales by activating marketing?
Mike: Yeah, I mean, incremental lift and that type of statistical analysis is super exciting. So we don’t do that. It doesn’t mean that we don’t think you should do it. It just means that for our segment of customers, which I would define as, say, mid-market to small enterprise, true lift analysis is you need a certain size, you need a certain budget. You need a lot of things before you can actually do this. I would say, ideally, even if you’re buying consultants or products for sort of statistical lift analysis, you probably also should have a data scientist on your team that can help you understand the results. I think statistical lift analysis is exciting, but it will answer certain things, but there’s also a lot of things you can’t do with it. So we focus on, say, attribution is great for saying, I’m running campaigns in a bunch of different ad platforms. Now compare them against each other. So it might not be that the individual number is the truth, but They are apples to apples. You can compare them. So if one apple is bigger than another apple, it is actually bigger. It doesn’t tell you necessarily that the apple is big enough. So I think attribution data is, I would say, all the digital trackable events that you can associate with the customer. And you should, no matter what, even if you don’t believe in the attribution part of a product like ours, you should still collect that data, have that data model, because you still want to send an audience to LinkedIn of all your ICP customers that viewed your documentation last week, right? So I think it’s the collection of data and the focus on data and the dedication to tracking as much as possible and having a strong, robust data platform, as a modern marketer, you need to do that, even if you’re not going to communicate, let’s say, multi-touch attribution to your board or whatever, which is maybe where multi-touch attribution sometimes plays in. It’s like to manage reporting, you’re going to say, look, marketing contributed 25% of pipeline last month. I also think that even in those cases where it might be like, OK, but was it actually 25 percent? Well, maybe it wasn’t. But if the following quarter you did 22 percent, then it’s still less than 25, which means that, you know, maybe you should change what you’re doing.
Lars: I think that’s a brilliant answer, actually. I think it’s really important to understand that when you look at attribution numbers, and as you say, you can run Google and LinkedIn, they both claim to have done 100k, you’ve only sold 150, so something’s wrong. But actually, if next month Google claims to have done 200k and your sales have gone up, actually Google’s got better. It doesn’t necessarily mean 200 is the right number, but it definitely shows the trend. So I love that answer.
Mike: Yeah. And then I think like when you then layer on something where you can sort of actually aggregate all the data. So now you’ll never have the situation where you get like 100 from Google, 100 from Meta, 100 from LinkedIn and 200 from actual sales, because it will actually always be sort of a meaningful number, that makes it even better. But I think the focus on dedication to data is super important. I recall people, they go like, I don’t really believe in attribution. What about, you know, somebody’s steak dinners or meeting people randomly in the supermarket? Like, that’s a catch. I’m like, that’s true. But if you throw out the idea of data and the importance of data in marketing, then you’re positioning yourself very poorly to win, right? Because let’s say, of course, everybody is doing AI at the moment. So if you are applying AI to something, what do you apply it to? Well, you’re going to apply it to data. So if you don’t have data, you’re not going to be able to leverage AI for anything. So if you don’t have something, let’s say, that can provide the context of your customer to an AI, well, it’s not going to work, right? Then you’re just going to get super generic output that doesn’t really do anything for you.
Lars: Absolutely makes sense. I mean, I think one of the other challenges with data that people in B2B often have, and as you’re a B2B specialist, it’s great to ask you this, is what about the long buying cycles in B2B? It’s often hard to use data when perhaps somebody is not going to actually buy for months or even years.
Mike: Yeah. And I think that just goes back to the importance of a robust data platform where that is what’s going to solve this. Basically, the way a product like ours works is that maybe the spine of it is actually the CRM data. And then you’re building everything from the different other sources. You’re attaching that in a way to that. So at the end of it, you end up with a sort of a data structure, which is, here’s all my accounts. Here are all the contacts. Here’s everything that we know that these contacts did. Here are all the business results associated with each company. And once you basically just, every time you know something, you put it into data model. That’s the goal, right? LinkedIn tells you that somebody from this company engaged with your ad. Well, OK, then you attach it to that account in the data model and then, you know, OK, somebody from that account actually engaged with my ad last month. And if somebody attends a conference, you make sure that, oh, this person who was also in my CRM actually attended the conference. So it is like, It’s the robustness of the data that enables you to then look at the account over a long, long, long period and go like, yeah, it’s great. We won this contract and the sales cycle was 60 days. you know what, I can actually look back and I can see that somebody from our marketing team talked to them on this conference 12 months ago, or they were actually in this webinar we ran eight months ago. So the dedication to building the most robust data model you can, But then, so what’s technologically feasible and legal? I think those are the two sort of components you need to look at. There’s also like legality. In data collection, there’s a ton of things you can’t do. So you can’t do that. There’s a lot of things that you might want to do, but it’s illegal, so you can’t do it. There’s a lot of things that are hard to do, but you should try to do them anyway.
Lars: I mean, that’s interesting. I think a lot of people understand a lot of the legal issues, particularly around GDPR. But what about the technical issues? I mean, how complicated is it to build this data model that has all these different sources of data? I mean, are you going to have to allocate teams of IT pros to actually do this?
Mike: Our product aside, that’s what the product does. So we are a solution for it. So then you don’t need that. But building this yourself, I think it’s super tough as a company. It does require, I would say, a data team. So it requires one or two data engineers, data scientists, and some data analysts. So you need a bunch of people. So one thing is building it, which is time-consuming and takes, I don’t know, when we meet customers that sort of have done it and then give up or have done it and then are scrapping it, it will typically take you like a year or something like that for a team to get to something that’s meaningful. But then comes the problem that now you have these smart data people and they were super excited about building the data model because building stuff is exciting for engineers. Like engineers love building stuff. That’s what engineering is about. Now what? Now you have this thing and it’s running, but you need to maintain it. So you actually need to retain a team of data people to maintain your model. And that can be super hard, right? Because, you know, is it exciting enough? Like the first build was exciting. So a lot of people, then people leave. and then you’re stuck with this data model that nobody can maintain. And then like after 18 months or 12 months, the new VP, like a CRO walks through the door and says, hey, I really love that we’re using Dynamics for sales, but I only work in Salesforce. So we’re gonna rip out Dynamics and use Salesforce. And then you’re kind of like, oh, who’s gonna fix the data model? Or like more like this, probably like a new VP demand chain joins and says, Yeah, I mean, I know you’re really happy about Pardot, but I only use Marketo, so we’re going to switch. The same problem, right? Unless you are a massive, like I would say, like if you are like true enterprise, you’re sort of stepping into 50,000, 100,000 people, then I think this is a kind of project that might make sense for you to take on. But if you are sort of mid-market or small enterprise, I don’t think it’s worthwhile taking on. Or at least a product like ours offers at least the robust foundation. And then if you want some data people to build other stuff on top of it, it’s great for that, right? Because you get the data model in a data warehouse where you can continue working on it. So it’s not like you’re locked down to what we did. But at least we did all the boring stuff and the stuff that you don’t want to retain people for. But yeah, I mean, back at Trustpilot, we built this ourselves for Trustpilot. And we got somewhere in six months, but it was not done, right? It was starting to be something. We could have continued maybe 12 more months and then we would have been done. And then But then we were gone and we created the same problem for Trustpilot. Like, now what? What about the model last built? Who’s going to maintain that? There was no answer, right?
Lars: That makes sense. And it’s really, you know, nice to hear that you can actually get up and running without complex engineering projects to integrate everything. I mean, it’s quite interesting. So I look at your business, well, you’ve basically got a freemium model, which is interesting, because your first price point is almost $1,000 a month. So do people really get, you know, valuable use from the product, trying it for free, and then switch? Does that work well for you?
Mike: No, no. I think in that sense, like who runs truly successful freemium models like Miro, like the type of products where you have like individual contributors picking up a product and then you have sort of building up a team inside of an enterprise. And then the enterprise reps reach out and say, look, you’ve got 200 people using this product. Don’t you do contracts? Don’t you have a legal team? Don’t you want like enterprise level support? So I think we’re not that type of go-to-market. I think what works well for us is that we can spin up a trial for anybody and sort of demonstrate that they can get to a very complex and robust data model in less than two weeks and see maybe not that it is done, but at least get to a point where they can believe that, OK, we could work a couple of more months on this or maybe two weeks or whatever time it takes, and then we would be there. So I think that works very well for us. For some customers in the sort of like we have customers sort of ranging from, like I said, we have two enterprises as well. So we do have those like 50,000, 60,000 person companies as well. Our sort of sweet spot segment, I would define a sort of small enterprise mid market. So maybe 200 to 5,000 people types of companies and Some of those companies can actually self-service into a great product, and they can set up the trial and make it work on their own. Most can’t. So we do, in that sense, a more traditional sales and CS motion for them and help people. The free product, I think, offers a couple of advantages. So one advantage for us is, you know, let’s say we initiate a trial with someone and they like the product, but they’re not going to buy it out. Or they’re not convinced or maybe it’s not the right timing or the person making decision gets fired. Happens a lot in marketing, unfortunately. And then, you know, they can’t make a decision. Now we have a place to sort of put the customer. They can stay in that free product. They do get some benefits from it. We’re not sort of disconnecting that data. So they will continue to build up data and their data platform. So if they come back, they actually have a product that works. So that’s a part of it that does work for us. And then the product has like the free part has sort of some, a few features that are quite exciting, which means that it can be exciting to be in that product for a mid-market customer, not sort of the small enterprise. For this type of product where you’re integrating a lot of data, no enterprise is going to just connect the CRM to Dreamdata without having the legal team look at it, right? So, yeah, I think it’s not a freemium model in that sense. I would call it product-led in the sense that we lead with the product and we let people try the product before they buy. So that is product-led, but it’s not like Miro or let’s say Dropbox or like the classical ones you bring up as product-led.
Lars: I mean, that’s fascinating. I love the thought that if people stop paying, they can still keep accumulating data. So in effect, your product becomes more valuable to them. Yes. And is hopefully an easier sell. I love that idea. Yeah. Thank you for saying that. I mean, I think one of the questions also probably, you know, you’ve talked about the typical size of companies, but in B2B, I think people have very different marketing budgets. So, you know, it’s always a question for marketers, how much do I spend on analyzing performance versus how much do I spend on actually running the campaigns? So typically, as I mentioned, you’re looking at a price starting from about $1,000 a month. Where do you see customers in terms of their ad spend to get sufficient benefit that they can actually get a positive ROI from Dreamdata?
Mike: I think getting a positive ROI is very easy. But I think the big question when you’re building a business is like, what’s the ROI relative to other projects I can do? And most people will be looking for something that’s paying back, I don’t know, three, four or five times or something like that. So to get to those levels, you would want to see your ad spend Let’s say you’re doing an ad spend of 20,000 euros a month. That’s sort of a very small mid-market ad spend. That would be like a 100-person company or maybe a little bit less. How much can you then pay for a product like ours and have a reasonable return? Well, you can maybe pay 2,000 a month, so 10% of that, and then you’ll still have a good return. It depends a bit, but if you’re advertising on LinkedIn and Google, there are some sort of very fast returns in just being able to feed back data to those two platforms and connect that with sort of their AI. And that drives some immediate benefits. And that pays back more than the price itself. And then you do optimizations on the rest of campaigns and typically, unfortunately, fortunately for us, you can probably cancel somewhere between 25 and 75 percent of your ad spend and see no impact on your pipeline performance. That’s sort of the unfortunate truth. But that’s a hardcore ROI there. It’s not even about sort of buying more of your best campaigns. It’s not about whether this super good campaign is better than that super good campaign. It’s more about like those five or four campaigns down here that actually have never touched anything in your sales pipeline. So there is no way that they can have had any impact. So just cancel them. I would say like ad budget starting at maybe 20K monthly and up. Of course, like most of our customers would be a lot higher than that. I would say that the thousand euro a month starting price is for a very like we’re sort of like 50 percent, 75 percent company there. I get company of say, if you are a software company of maybe four or five hundred people, hopefully you’re doing around one hundred million in AR, so annual revenue. your paid budget should be in the 10 million, let’s just say a million a month range, right? Then you will have a super nice return on a product like ours if you’re not doing anything in this space before.
Lars: I find also that comment about 25% to 70% of campaigns can be cancelled, that goes back to that famous quote that I think everybody in marketing has claimed to have invented, which is, half my budget in advertising is wasted, I just don’t know which half. Well, you’re telling people which half, which is an amazing saving.
Mike: Yeah. And I think it’s like sometimes people get over-focused on sort of like, what’s the good stuff? But hey, let’s just look at all the complete crap that’s out there and just get rid of that, right? You clean that out and that will in itself drive a huge sort of efficiency in a marketing budget. You’ll see someone say, this is actually really working well for us. And of course, some of what’s working really well, maybe it’s impossible to do more, but some of it will have scaling potential. And then you can reinvest there and, of course, see more ROI then.
Lars: Yeah. I mean, it’s fascinating. I feel like I could talk to you for ages on this, Lars. And I think it’s really interesting that despite the fact we have a lot more advertising data, we don’t necessarily have the insight as to what’s working. And I think a lot of marketers can confuse data for insight.
Mike: I agree, totally agree. And I think a lot of people are sort of blindsided by, they think of B2C e-commerce. B2C e-commerce It’s extremely data-laden that go to market. But a lot of B2C e-commerce, like think of Amazon. What is Amazon? Amazon is a logistics and marketing company. That’s it. So that’s all they do. So they have like half of their team is doing logistics. The other half is doing marketing. So they have a lot of data people, but that’s not the case in B2B, right? So in B2B, there is a lot of efficiencies to be found in, I would say like 98% of companies can go out and find gigantic efficiencies in their paid budgets and in general in their marketing budgets.
Lars: I mean, that’s great. That’s a little bit scary, but also very inspiring, I think. I think it’s great.
Mike: It means that most people listening to the podcast can go out and generate gigantic efficiencies for their company, which I think is great. Who doesn’t want to be the hero who does that?
Lars: That’s awesome. Lars, this has been amazing, really interesting discussion about data, which you don’t often say. So it’s great to have had this chat. I’m very aware that, you know, you’re busy. So we normally like to ask just a couple of questions before people finish in the interview. And the first one is, what’s the best marketing advice that has ever been given to you?
Mike: So I think best marketing advice would I would say is about is around focus. It also sort of goes very much back to like founding a company experience, like the importance of focusing, zeroing in on an ICP. And I would say once you have focused, then discovery, you can focus even more and then even more. So go from we started by saying, oh, we’re B2B, B2B tech, B2B tech, some geographies, B2B. But you zero in and in the end, you end up with something with a lot of focus and that drives efficiency and makes the work much easier. That’s great. I think that’s really good advice.
Lars: The other thing we’d like to know is if you’re talking to someone who is just starting their career, looking to build a marketing career, what advice would you give them to have a successful career?
Mike: I mean, I could give the advice I was given, but I think the other piece of advice I could give would be to sort of acknowledge that marketing today is heavily sort of engineering and data influenced. So I would say either pick up some data and engineering skills yourself, or make sure that on the desk next to you, there is somebody with those skills so you can leverage that in your work.
Lars: I think that’s great advice. And as an ex-engineer, I’d certainly support that. So I love that. Thank you so much, Lars. This has been fascinating. I’m sure people want to learn more about how they can save half their advertising budget. So if people want to learn about Dreamdata, where’s the best place to go?
Mike: I mean, the website, try the free product is one option. You can always talk to sales. They’re very happy to do that. Connect will be on LinkedIn. I’m happy to chat on LinkedIn as well, for sure.
Lars: That’s amazing and very kind. Lars, thank you so much. It’s been fascinating. Thanks for being a guest on Marketing B2B Technology. You’re welcome. It’s been a true pleasure. Thanks so much for listening to Marketing B2B Tech. We hope you enjoyed the episode. And if you did, please make sure you subscribe on iTunes or on your favorite podcast application. If you’d like to know more, please visit our website at napierb2b.com or contact me directly on LinkedIn.
