EP 6 - Revenue Attribution / Build vs Buy

Hi, I'm Michael Hartman.

I'm Naomi Liu.

And I'm Mike Rizzo. And this is OpsCast, a podcast for marketing ops pros and RevOps pros, created by the MoPros, the number one community for marketing operations professionals.

Tune into each episode as we chat with real professionals to help elevate you and your marketing operations career.

Hello everyone, and welcome to OpsCast episode 6. I'm here joined today by my co-host, Naomi Liu. Mike Rizzo, who's normally here, has been unexpectedly detained, so he won't be able to join us today for this exciting conversation. Today we're going to be talking about revenue attribution. Should you build? Should you buy? How should you use it? I think it's a topic that we all probably have to go through and deal with in the marketing ops world.

Yeah.

So today we're going to get started with Ole Delerup, who's the Chief Technology Officer at Dreamdata. He's joining us today from Denmark, so good evening, Ole.

Good evening.

So those of you who are online or joining, please feel free to use the live chat here. If you want to contribute to the conversation, feel free to asked to be brought on air, and we'll try to fit that in as well. But for now, uh, would you please introduce yourself and tell us a little bit about you and Dreamdata?

Yes, of course, and thank you for having me, Michael and Naomi. It's truly a pleasure. Well, so I'm the Chief Technology Officer at Dreamdata and co-founder, and we I mean, really, we got into attribution and both the marketing side of attribution, but we like to kind of look at it holistic. We'll get back to that. But from the same question, like, should we build or buy? And we were working in a company called Trustpilot where we really needed an attribution tool. I came from more the product side of things. So we were building a review platform. At Trustpilot, and we were trying to understand how much money were we generating from all kinds of channels. One thing was all the marketing channels, but someone says he can't hear anything. That's okay. But so is my mic— you guys are hearing me, right?

I can hear you.

I can hear you.

Okay, we'll continue. So we were building this large kind of consumer platform which drew a lot of traffic in, and we're trying to understand how much business revenue are we actually bringing in from that. At the same time, marketing was of course doing all the classical things of both doing content, paid media, all kinds of things, and we were trying to really understand this from a holistic point of view. Of all the activities we're doing to acquire our customers, what works? What doesn't work? What brings in money? What should we kind of invest more in? And so we're sitting in the management team and trying to decide, should we hire more software engineers to work on the consumer platform, or should we hire more marketers to kind of speed up that? Should we do more Google paid? what should we do to acquire more customers, both on the short term and the long term? And we actually did have the data. So we actually had all the tracking data. We had, of course, Salesforce. We used HubSpot. We had all these pieces of data, but we couldn't find a tool that could take our historical data and then just process it and and tell us what to do. And that was basically what we expected to find. We were looking for a tool that would just like, here's our data, and then tell us what to do. So we couldn't find that. I don't know if that was naive. Now looking back, it was maybe, but we were basically naive. And I was, I mean, I dealt with marketing and marketing ops once in a while, but that was far from my primary job. My primary job was to run engineering teams and build infrastructure and those kind things. And so I was a little bit surprised it was so poor. I was also surprised that sales cared so little about it because I was like, I mean, this must be your most important thing for you, like how, which leads are actually good. But at the— yeah, sorry, feel free.

Yeah, so I think, you know, that hits on a— I think it's one of those things about that we've maybe talked about a little bit before this, which is I think there's a little bit of a love-hate relationship across revenue functions for attribution modeling, right? And I think your point about sales maybe not paying attention to it or caring about it as much is one that we probably all experience, but maybe it would be helpful to start with a definition, right? Like, how do you think of revenue attribution, which I think is the term you guys use, versus marketing attribution? And how are they related or different?

So when we say revenue attribution, we mean it from the broadest, most kind of broad definition you could get. Any activity, whatever it is, whether it happens in support, in sales, marketing, in partnerships, wherever the touch is, as long as it helps you bring revenue, it is relevant. The most, at least for me, the most classical kind of way of looking at marketing attribution is more in a silo. We're looking at, I mean, if we take Google Ads or Facebook Ads, they look very precisely at the activities ads bring. kind of do, what activities or ad activities drives revenue for you, which is a silo for me because, well, does my Facebook ads kind of help me drive revenue? They probably do. But will I have journeys that touch both Facebook and my emails or BDRs or my support channels? Probably as well. Well, should Facebook then get all the honor? Probably not. Should it get most of the honor? Well, if I acquired the customer through Facebook, I think that can be discussion whether Facebook should get most of the honor, but it should probably not get all the honor because there's a lot of other activities in there that also matters. And particularly if you want to look at it from a more holistic point of view in a management team, And I'm not talking about the management team as in I'm the head of marketing, but the management team as we are the entire management team of the company and we care about the growth of the entire company. What should we do? Well, if we create more demand from creating more ads on Google and Facebook or wherever we do ads, then that's great. But if our BDRs is actually a crucial path towards bringing in revenue, then we have to scale those as long as well. Or if we have other activities that is actually also important, and so they also do kind of bring in revenue and they are also important. In a B2B world, which is kind of what we focus on the most, it is even more complex because Well, mostly you have more than one person you're trying to hit, and you often need to hit more people and target more personas. That can be like for our own company, we often need to talk with marketing, marketing ops, or sales ops. We need to talk with BI teams. We sometimes need to talk with engineers, and we often need to talk with legal. That's a lot of different personas you need to bring in and they matter, right? I can at least imagine that a deal is close to kind of— if we were tracking whether sale or legal were looking at the legal documents we sent, then we know the deal is close. Like, that's a good indicator that the revenue is closing now.

I have a question for you. I, one of my questions is around, like, socializing this information towards senior leadership team. Do you ever have issues around, you know, how do you present this information in a clear and concise way that's digestible for people who may not necessarily be looking at the data on a day-to-day basis and getting their minds around, you know, you know, it's not one activity to one lead to one opportunity. It's, you know, the whole attribution model and how does that Because, and I'm asking for selfish reasons because I have that.

So I think it all depends, right? If I would kind of, kind of the prediction I believe will soon, like we are moving towards, is that in, if we kind of go 20 years back, sales and marketing were very kind of siloed, and most often sales decided over marketing. At least in a lot of companies, it was like that. And that's kind of how it was, and the CEO was typically also a sales guy. I believe that what is happening is some of the things we've also seen in product and engineering. In the old days, in product engineering, we also had this problem. So project management, we called it, or program management decided what engineers should build. And they built some things that were good and a lot of horrible things, mostly horrible things. Now today, I don't think any modern tech company works like that. Product management and engineers and designers, they work really close together. They sit right next to each other and fight together. I think we'll see that in sales and marketing as well. Sales and marketing will get much, much closer. They'll fight together and they'll work together, and they will much more see and realize that marketing's impact and sales impact, they have to follow each other. It is not either or, it is both, and they both have to be there. The reason why I start saying this is Most of the companies we work with have realized this, and they—I believe strongly that that's the ones that's going to be kind of the leaders of the new world—is those they simply going to make more money. They're going to acquire customers cheaper and faster, and make more money. So, and yeah, so sorry. So this is. Like, how do you convince the management team? Well, first you have to convince them in this and kind of bring them into that, and then we can talk about attribution. So that is the complexity of attribution.

I think Naomi hits on probably the biggest question of all, right? Let's say you think that you need attribution modeling and you're going to use it. How do you use it? How do you present it to people who aren't familiar with all the sausage-making underneath the hood of how it's working? I'm mixing metaphors, sorry. So I just want to make sure I understand. So, you know, I think what I heard was, I think most of us are familiar with marketing attribution really focusing on, you know, customer acquisition related things and how are they tied to ultimately like somewhere in that handoff, whether it's ABM or traditional lead, you know, lead flow where you, you know, say you go from marketing to sales, right? You know, how much of that was marketing impacting? And I think what you're— what you described, it goes all the way through the full customer lifecycle, right, from prospect to, you know, opportunity to customer to recurring customer, right, and involves retention. Is that— did I understand it correctly from that standpoint?

That's absolutely correct, you understood exactly, yeah.

So, you know, one of the things, um, I think that begs then is when you guys are thinking, like, if you're talking to a new client or a potential client, What are you looking for as sort of the minimum data that you need to have available for you to be able to produce something that's actually usable and valuable for the client, whether it's a marketing leader or a CRO? Because it sounds like you could be selling into multiple places, but like the source data is going to be critical, right? So what data do you need?

So we need So usually we pull data in from the CRM system, the marketing automation system, and tracking on their website. If they don't have an online— like companies are not collecting that already, most companies are not, then we have our own tracking script we can install. So that's kind of the sources we kind of pull data from. That is the primary source. We'll also pull from paid media and other things, but that is not so much to kind of be able to build attribution. That is more to be able to talk about return on investment and those kind of things. At a detailed level, which is also important, but that's more a marketing question than the holistic kind of question. So how much do we need? The truth is it depends a little bit on how long your journey is. That's one factor that's very important. If your journey is very long, we need more data. If your journeys are very short, we need less. So most of our customers, they have journeys between 3 and 18 months, I would say. Sales and journeys here, when I say journey, it's not the sales journey, it's the from first touch to actually closing the deal journey. Sales journeys are shorter, at least. So that's the first step we have to— how much do we need? It is everything from a few thousand visitors, maybe 5,000, 10,000 visitors a month, and then some deals. But it doesn't have to be a deal. We do attribution also earlier in the funnel. That could be to a marketing qualified lead or an opportunity created stage, whatever kind of your company has that makes sense for you where you start being able to be predictable. most companies are relatively predictable from opportunity created to deal closed. Yeah, I can see Michael here live on the feed and he said, well, maybe not. It depends, right?

I think sales leaders would probably roll their eyes and say, predictability, maybe, right? I don't know.

And of course, It is as it is, right? It is only as predictable as it can be. But for attribution to actually work and you can be able to say something, you of course have to have a certain amount of deals coming through a certain channel, right? If not, you would want to generalize even more. Like instead of talking about how much did we make on this Google Ad campaign, well, if you don't have much, you would want to talk about how much did we do on Google Ads. in all token, right? Because there's not enough details to optimize on.

Let's switch gears a little bit. I think sourcing data, I get it, right? You've got a tracking cookie and you've got, I think most of us in marketing ops would understand that. I think one of the things that I've always struggled with when I've worked through attribution, I've done both build and buy, so it's kind of like we're getting to that kind of question as well. And one of the challenges I always run into is Data quality, this is kind of a twofold thing, right? Data quality in general, right, and consistency. And then the other would be, you know, especially— sorry to all my sales friends, right— but the discipline by which the salespeople and others are managing. Are they consistently updating opportunity status and expected close dates and things like that? Which kind of— that's why I was kind of shaking my head and sort of rolling my eyes a little bit about that. predictability on funnel rates. So, yeah, how do you have any suggestions or best practices for those of us in the marketing ops world for how we can help, you know, guide, encourage, twist arms with, you know, the rest of the revenue organization to help enable some of this?

So what we generally see, and the best companies, what they do to make this work, is that So a lot of people, some companies think it starts by, let's first get the data quality high and then we can kind of talk about attribution. What we generally see is that the best companies, they do it the other way around. They say, let's start with, it doesn't necessarily have to be attribution, but any kind of data problem, you start with the data and then you start measuring people on the data. So some of the best companies we work with, they use attribution as commission models. So attribution is commission. So it's like, do you want to track it? I think they want to track it, right? So do marketing want to pick a new tool they can't track things in? I don't think they want that tool, right? So they push everyone to make sure it is trackable. So that's one way of doing it. Another way is start presenting it as a leadership team. Show the numbers. Like every week, every month, wherever you kind of have these kind of talks, you show the numbers saying, well, you know what, we have so much attribution to whatever, Google Ads, so that's really good. And we have so much attribution to the BDR team. That's also very good. We're not really seeing Facebook work yet, whatever. And then some of the team might say, yeah, but Facebook works really well. But then it's like, then show me. We are a data-driven company, so if we can't see it, it doesn't work. That doesn't mean you can track everything. I'm not trying to say that, but you are trying to push everyone to think about how can we track the data How can we measure it? And instead of saying, hey, let's first get the perfect data before we start using it, you turn it around and say we are going to get the perfect data by kind of measuring people by it.

So you're building in the incentives to improve data quality over time is what you're talking about, right?

Exactly right. And on the given times, your data will be a little bit wrong. You did something bad in your infrastructure, whatever, you're missing a tool. That's normal. It doesn't mean you can't make decisions. It doesn't mean that everything will be wrong, but it is something you will be aware— you have to be aware of. For me, that's normal days. Like, that's also how we track, I mean, usage in our product. There's glitches. It's normal.

Right. Yeah. So you use it, you kind of get sort of a flywheel effect, I guess, for data quality. I suspect that there's not a lot of people in organizations who've ever been measured on data quality, so that's probably a new one for most of them.

Yeah, so I think data quality is also a tough one because there's not that— like, what is it? Like, how do you measure it? I mean, it's a little bit hard, right? Do you track all users? Is that a way of kind of data quality? I mean, usually the problems we see more often is that— so we see, I would say we see 2 kind of big problems where I don't necessarily have a good solution for it. One is when they have a lot of partners. So they do a lot of partner sales. And that is maybe a large percentage. If it's a small percentage of the business, it's usually not that big a problem. But if it's a large percentage, let's say 80% or 70% of their business, it's hard to push the— like for them, it's hard to push the partner saying, hey, you now have to track all this. That kind of doesn't work, particularly if the partner is Amazon or— like those kind of companies, right? Then it's hard sometimes. And then there's sometimes when you have very powerful sales organizations, and actually the way they work sales is that they have their own personal spreadsheet where they manage their own accounts, and they don't really like to share this data. Right. It's obvious why they don't want to share it, and there's a company culture where They don't want to break that. I don't think it's an attribution problem. It's a management leadership problem, right? But that's the type of problems we are— it is hard to solve.

I had an internal cringe when you said managing accounts and leads and opportunities by spreadsheets. That's always kind of been a bit of a battle.

Yeah, I suspect right now, or we'll listen to the recording, we'll have the same visceral reaction. And then we've all been through it. I know I have. Um, anyway, um, so, you know, um, you know, one of the things, you know, this is kind of the genesis of your company, right, is that you were looking to buy something and you ended up having to build something.

Uh-huh.

So, you know, how do you, you know, do you have a suggestion for, um, you know, customers, clients, people like us, and just even how to like frame out that thought process of should I buy, should I build, what are the trade-offs, and, you know, is there anything that you say you think, maybe this is getting to the data quality and those incentives, but any, like, is there a minimum like prerequisite set of situation, right, whether it's systems, tools, Level of revenue, whatever it is that you think where it makes more sense to go from build to buy or buy to build, you know, from that standpoint.

So the step we always, and I generally recommend all companies to start doing any size, is to map out where do we actually track important things, like not track, but where is the important things in our sales funnels actually happening. And sales funnels here, I mean like marketing and sales, whatever kind of happens. What is important? Like what do we believe is crucial for this to work? Most companies, they have a website that is important. Sometimes they have a lot of emails they send out. Maybe they have BDRs doing LinkedIn outreach and similar things. And then I would sit down and consider how can I track this data so I have the data. Not so Google Analytics has the data, not so whatever kind of, let's say you use HubSpot, HubSpot has the data. HubSpot you can actually use if you're at enterprise tier, so you can actually get the data out in that case. But let's say you are a type of company that can't afford HubSpot Enterprise, then you want to consider what is the— how do I track this data so I have it like in raw format in a database, in a file? So I can actually have the data. That's the first thing you want to make sure of. And this is also the third-party cookie conversation, it's the CDP conversation, customer data platform conversation, uh, companies are having. You want to own your own data.

I'm understanding what you say. That first thing is, the first thing is you're saying don't, don't rely on the— well, I guess I'll call them transactional systems, whether it's marketing automation or CRM. But you're talking about You need to get that out into a place where you can actually do something with the data. Is that kind of what you're saying?

Okay, yes, ensure at least you can do that, right? I mean, whether you do it or like you— but you have to ensure you can get it out, right? So you actually have it and own it and can do something with it, right? Because whether you are building your own or gonna use someone, you need that data. So let's say you start using Beam data. That's great for us. You'll start using us. If you don't have the data, well, then we'll have to start collecting it. We can't do magic. I mean, we can't magically turn out data. So we have to start collecting it. If you already— and the companies that come to us that already have this, we can actually show them attribution model relatively fast because we can just pull in all historical data. And I mean, then it's just doing the magic of the modeling. And that's just very easy for us. And then, like, we're done. But if you don't have that data, you start from scratch. And if your sales journey is 12 months old, well, you know what, you gotta wait 12 months before you have a relatively good answer, right? So that's the first step. And that doesn't matter whether you are buying or building, you need to do that and take very seriously on that. That also includes considering whether you have a lot of offline processes, then consider how can you make some of them digital touches. That could be by trying to encourage salespeople to send their clients or prospects through a signup form so you can get a touch from them. It can be through having better content. It can be through being better in digital marketing, sending out more emails and so on. So you start having these touches. My experience is that as soon as you start getting a little bit more touches, you actually learn that a lot of those kind of people, they were already visiting your website. You just didn't know who they were. So as soon as you get them to register, sign up, you can actually start talking about who they are and whether they already were there, whether your ads worked or they didn't. That's the very first and minimum bar if you want to talk attribution at any more advanced level. This is the minimum bar. Then if you want to build it yourself, So I can easily do a quick version of that.

Well, can I— so I want to interrupt you a little bit here, but you— I think I— again, I want to make sure I'm understanding what you're saying. Were you suggesting that if, if, um, if an organization's got a go-to-market motion that includes offline or not, you know, non-digital, um, channels, that you're suggesting that they pull back from that a little bit and go more digital, at least temporarily, to try to help accelerate the, I guess, the insights that you can get from the—

So it's not like you have to move everything digital. Like, I mean, these days conferences is hard, right? But let's say kind of in a year's time COVID is gone, And we are all back to conferences, right? And that works for you, it worked in the past, so you're gonna continue on that. That's great, that's not bad. But for example, at your booth, right, so maybe instead of having a computer there where you can sign up, you actually do a barcode, and if they use that barcode on their phone, they get whatever, a lottery ticket for something, right? This way you start tracking them on their phone. It can also be that you register them on normal batch scan and then you upload them into Salesforce and campaigns, right? And then that way you get a digital touch. So digital touches doesn't mean like they go to your website That is one way, and like, that is best to— like, if you can get customers to do that, then you can get a tracking footprint on them, and that's great. But getting any digital touch that actually, yes, this guy was touched by the conference, he attended there, and we met with him, we spoke with him, and getting into that Salesforce campaign That you should definitely consider, like, because it's so hard to prove that your content has any value.

Yeah, so, okay, so it's— to me, what I think— I'm glad I asked the question because I think what you're describing is a way to improve the capture of that offline activity by, you know, whether it's through technology or whatever. So I think that's what you're getting at. Naomi, do you have any questions?

Yeah, so I'm curious as to What do you think of how mature the, I guess, the marketing operations and just the industry is in general when it comes to accepting attribution? Do you find that it's still a challenge to educate people on what exactly it is and sell into an organization? Or do you find that generally most people are kind of, okay, we've done all of our modeling, we've done all of our analytics, we've absorbed it all, now we're ready to move on to phase 2 or graduate into better reporting? reporting? And do you find that that's a challenge still, or where do you kind of see the industry?

I would generally say that the industry is coming from the tech business, and having been like working with data and technology all my life, I would be— I'm very surprised in how bad the shape is. I think a lot of businesses, they have very poor kind of ways of tracking and measuring things. Most B2B companies we talk with, even those who are in a good state, they have very, very weak signals from touch to kind of deal. They still talk about opinions or we think or the best sales guys always says this is how it is. Okay, maybe it is. maybe not so much. Maybe we even see B2B companies that don't have any tracking on their website, like not even Google Analytics, like they have nothing. So I would say it's pretty— the average, I would say, is pretty bad.

So would you say it's like a people or a process issue, or is it— it sounds like it's a bit of both, right? It's kind of A little bit of column A, a little bit of column B?

I think it's people management. I think mostly it's a management problem. I think there is a lot in management. I mean, it depends. It's very different from company to company, right? But management want to be data-driven. I think all management teams say that, but I experience that they don't know what to actually do to become data-driven.

This then kind of goes back to my original question, right? How do you kind of bubble up this information and get senior leadership to buy into things like this? Because at the end of the day, it is a spend that they are then looking to hit their P&L, right? I think part of it is, and I kind of, it's a self-serving question too, because it's something that I struggle with, right? Like, how do I serve up the need for attribution software to my senior leadership team and to those that may not be necessarily as close and tied to the data where attention spans may only really be 10 seconds? How do I get them to understand and absorb what attribution is? It's something that I am— still challenged with.

Yeah, I would add, Naomi, that on top of, you know, what you described, I would argue that most senior leaders, um, who have not, you know, not really familiar, are not familiar with this. Um, and in fact, I think I saw, I heard a statistic a couple months ago that if you looked at boards, you know, something less than 10, like, it's like less than 5% of board members have any kind of marketing leadership experience, right? So I, you know, I'm gonna take a shot at answering your question, Naomi, because I have a strong opinion about it, and then we can hear Ulla's as well. But mine is that I still think there's value in attribution modeling. I've done it. I think where I've changed really is actually what you're getting at, which is how do you use it? And I think there's a lot of value in using it to help inform decisions about where do you make your bets, you know, what tactics do you think you want to work on, what ones do you don't. I don't think there's a ton of value taking it up to the C-suite or senior executives that, you know, cross-functionally, simply because of what I just described, right? There's a lack of understanding of what market— how marketing works and what it— you know, how long it takes for marketing activity to actually show in the pipeline. And then second, right, it's just, it's complicated math, even a simple attribution model, right? And so trying to explain that, especially, and I think it especially is harder to use that at that level if you've got a long sales cycle, big ACV, right, you know, business. So that, I mean, my, so my take is, you know, don't use it for executive levels, you know, like this is how we justify marketing spend and investment. But do use it for marketing to make smarter decisions about how you allocate your resources.

Yeah, the challenge then may also come up that how do you then even acquire such technology if you can't justify it to— and I'm just, I'm kind of throwing like—

No, I think it's— yeah, I'm gonna guess that you, you know, just given your position now, right, that you come across this more often than either of us do, so I'd love to hear your perspective.

The companies that come to us, they often end up in a spot where they've been challenged on whether they generate any value. Not like any value, that's usually not the conversation, but it's like they're challenged to either invest more aggressively. spend more money, like we need to grow faster. So you come up with a plan on how to spend more money, but you have to be able to justify that the spend actually gives a return. So that's one way. And another way is that they are more pushed, like, do we actually produce anything in marketing? What's the outcome? So what we do see there is that they don't report, like they don't talk about attribution at management level. That I would agree. I wouldn't either. I would talk maybe about return of investment or which channels kind of give us the best kind of returns. It's more rational which attribution model to work— to use. You'll pick something and you might even also have more than one attribution model you're reporting on depending on what you're trying to kind of look at. But But I think it is interesting to report on some of them to the management level. I would do that at a high level personally. I would expect marketing— if I'm in a management team at Trustpilot, I would expect that marketing comes with a report telling these are the high-level channels we are making money on, this is how much money we're making on each channel. Therefore, I want to spend more time on these and these things the next quarter. I would expect, like, the marketing leader and the management team to do that.

Yeah, yeah.

But I agree, it's not a board level, for example.

Yeah, I say what I say at one point, and then I also know that in my last role, for a variety of reasons, I actually did report marketing attribution-related numbers to— at the senior executive level. And a lot of that was just because I think it was a brand new, like the whole idea of marketing being involved, it was a very sales-driven organization, and so there wasn't historically a lot of marketing involvement with pipeline activity, you know, revenue-generating, lead-generating activity, and that was shifting. And so it was the best proxy I had to show that we, you know, there was an impact, right? Now, when I get into it with the finance People, they're like, well, what's the incremental? And that's where it really gets— this is where it starts to break down, right? How much can you say was actually net new or wouldn't have been created through the way things were running before, right? And that's the unanswerable question in some ways.

So if you have enough data, you can actually answer that. So I mean, I can— that's a more data science kind of conversation, but But you can actually do that. You can do some modeling with Markov chain. Markov chain example, that's a common kind of mathematical model to do attribution on. And what you do there is you basically kind of draw up a graph of all kind of the touchpoints you have. And then what you try to do is you try to simulate when you remove one of the activities. So let's say you remove Google Ads, And then you'll see how much revenue you would actually make. So you can actually simulate that. It requires a certain, I mean, some level of data. So like now I said customers with 5,000, 10,000 visitors, you probably need a little bit more or you need to generalize your kind of touches very much. But it can be done. And I think that's the interesting piece. Is you can actually, like, if you start doing these things, you can actually answer some of those super, super, super hard questions we all want when we sit in the management team. Is if we stopped doing Google Ads, what would happen? This would happen.

Yeah.

If I spent a million more, what would happen? This would happen. And you can actually predict that if you kind of With relatively high precision.

Well, I think we could probably go on forever. Naomi, any burning questions that you have before we—

Yeah, I mean, I feel like this is a topic that could definitely continue or have a second episode, but I think that, you know, it's definitely a lot of food for thought and definitely some really great information. So thank you for joining us on this episode.

Yeah, you're welcome.

Absolutely.

So thank you. Yeah, so again, thank you, Ulla, Naomi. Thanks for joining and adding in. For everyone who's been listening in or who's listening on recording, thank you so much for being part of this as well. Look for more episodes coming up in the future. We've got several of them coming up. There's potential we're changing the format a little bit, but it will still be conversational. It just may not be live. And if you have suggestions for us, please feel free to reach out to me via LinkedIn or whatever. You can always also join the MoPros. So themopros.com is where you can go for that. And you can always kind of keep up with these episodes here. Last but not least, Ola, where can— if people are interested in connecting with you or learning a little more about what you're doing? How can they reach you?

So, so go to dreamdata.io. I mean, if you want to try out the product, we have a free offering and we have of course trials and demo requests if you are more into that. And me, you can also find on LinkedIn, I'm sure.

Terrific. All right, so that's dreamdata.io for those who are interested. Ola, Naomi, thank you. Have an excellent rest of your day and week. Bye everyone.

Thank you.

Bye everyone.