Marketing is quickly evolving – is your team agile enough to navigate the waters of evolving customer expectations, best practices in marketing measurement, and the rise of AI?
Agility requires more than just quick reactions; it demands a proactive understanding of emerging trends and the ability to adapt your strategies, processes, and tech stack accordingly. It’s about building a brand that can continuously learn and evolve.
Today, we’re going to talk about navigating the complexities of modern marketing measurement and the critical role data plays in building an agile brand, especially as AI rapidly transforms the landscape. To help me discuss this topic, I’d like to welcome, Fredrik Skantze, CEO & Co-Founder at Funnel.
About Fredrik Skantze
Fredrik Skantze is the CEO and Co-Founder of Funnel, the marketing intelligence platform offering customers such as Adidas, Sony and Samsung advanced marketing data, analytics and visualization. An alumnus of MIT and Stanford, Fredrik is a serial entrepreneur and co-founded Funnel over ten years ago. In that time, the platform has become a global frontrunner in marketing intelligence, serving some of the world’s best-known brands, e-commerce companies, media agencies, b2b businesses, mobile apps and gaming companies.
Fredrik Skantze on LinkedIn: https://www.linkedin.com/in/fredrikskantze/
Resources
Funnel: https://funnel.io/
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Transcript
Greg Kihlstrom (00:01)
Marketing is quickly evolving. Is your team agile enough to navigate the waters of evolving customer expectations, best practices in marketing measurement, and the rise of AI? Agility requires more than just quick reactions. It demands a proactive understanding of emerging trends and the ability to adapt your strategies, processes, and tech stack accordingly. It’s about building a brand that can continuously learn and evolve.
Today we’re going to talk about navigating the complexities of modern marketing measurement and the critical role that data plays in building an agile brand, especially as AI rapidly transforms the landscape. To help me discuss this topic, I’d like to welcome Frederick Skantze CEO and co-founder at Funnel. Frederick, welcome to the show.
Frederik Skantze (00:46)
Thank you, Greg. Thank you for having me on the show.
Greg Kihlstrom (00:49)
Yeah, looking forward to talking about this with you before we dive into the topics here. Why don’t you give a little background on yourself and your roll it funnel?
Frederik Skantze (00:57)
Absolutely. Yeah. So I’m the CEO and one of the co-founders of Funnel. We’re a leading marketing intelligence platform and marketing intelligence is really the product that marketing buys to improve their marketing effectiveness. ⁓ we’re a software as a service business about just getting up to $70 million in annual recurring revenue growing by about 30%. I have about half our business in the U.S., half in Europe. I’m based out of Stockholm in Sweden. And then we have an office in APAC which is one of our fastest growing markets. My background is I’m Swedish, but spent about 12 years in the US, studied at MIT ⁓ engineering and then business school Stanford, worked in Silicon Valley and enterprise software for five years. And then I was in London for seven years and ⁓ built an e-commerce company. And that’s where I first came across this problem of sort of measuring marketing effectiveness. And sort of at that time, you know, ⁓ really kind of understanding the pinnacle of your marketing. We spend all this money in these channels, but what is the real effect? Which channels are driving what effect? Everybody had a spreadsheet. That’s what we did later. We came to start to solve with Funnel, but that was 10 years later.
Greg Kihlstrom (02:14)
Yeah, yeah, love it. So, yeah, let’s let’s dive in here and going to talk about a few things here. But I want to start with some of the things I teed off in the intro is, you know, AI data and really the future of measurement. And so why don’t we start by talking about some of the challenges of modern marketers? ⁓ Lack of data isn’t usually the issue anymore. Usually there’s so much data that marketers almost don’t know what to do with it and so many sources that it can be hard to prioritize. Can you talk a little bit about how challenges like this have shaped your approach to building funnel?
Frederik Skantze (02:51)
Yeah, absolutely. And solving this data complexity problem is core to what we do. And I think a good way to look at it is that it’s based on the number of data sources that are available to marketing. And that is roughly correlated with the number of marketing products that there are out there. And in 2014, when we started building Funnel, there were about 1,000 marketing products available. For marketing and adjacencies, like know, e-commerce platforms, CRM platforms, and so on. And now there are 13,000 of these 13,000, 3,000 were launched in the last year. So complexity of data is just going through the roof. And the larger platforms are just providing orders and orders of magnitude of more data. So that is absolutely the case. to help narrow that down, the fundamental problem that we help with is this cross channel cross marketing initiative, attribution and sort of measurement and reporting problem. That is sort of harder problem than just looking at silo data. And that is where we fundamental focus at at Fund.
Greg Kihlstrom (04:06)
Yeah. And so certainly AI is transforming a lot of things. You know, we talk about it every episode on this show, of course, in different ways, but it’s also transforming the field of marketing and data analysis. What are some of the areas that you see as exciting opportunities to enhance marketing measurement with AI? And what are some of the potential pitfalls that marketers should be aware of?
Frederik Skantze (04:34)
Yeah, no. I mean, I think it’s really exciting. And I think there’s a lot of really high potential things coming out. I think one of the things I’m most excited about is that, you know, forever data analysis has been the same. You sort of look at the data and then, you know, you sort of create dashboards and use drop downs or SQL to sort of to create graphs. And then you sort of look at your dashboard or you click through your analysis tabs.
But now with generative AI, there is an opportunity and a new interface is emerging for how to interact with your data, which is sort of this chat interface and essentially conversational analytics where you can have a conversation with your data. Now, there are these interfaces starting to emerge. I don’t think anybody has really cracked it yet because it’s a really hard problem. And it’s a hard problem because
For you to be able to have a conversation with your AI, your AI need to understand your data and your data, how it’s structured and set up. And I actually think we’re pretty well set up at Final there because we are in a vertical. We’re in marketing. built all our, we have 700 connectors to pull in data. We’ve built those. We understand all the data. We understand the data model and have the data model out of the box. So we’re pretty well set up there to do this in a vertical rather than some of the horizontal tools.
where you have to do a lot of staging to get this to work. So I think that’s one of the things I’m very excited about. then in data analysis, and then if you talk about measurement, what I would say is, we’re increasingly using machine learning and AI to sort of make measurement better. So one example is, we use long short term memory neural networks, which are one type of recurring neural networks in our measurement technology, they are really good at understanding sequences where the order of the sequence matters. And if you think about like marketing and marketing data, if you have two instances, for example, first in one instance, somebody comes to a website through branded search, and then they come directly to the websites. That’s one thing. In the other one, the sequence is the other way around. They first come through
a direct visit, and then they come through a branded search. In the first case, it’s very clearly the branded search that stood for the conversion. In the second case, actually, they already knew about you somehow differently. It was like a referral or something. And so you have to understand those differences, and you have to make those decisions in split seconds and sequence this information. We use neural networks, for example, to do that. So that’s just one of many examples of how we use AI more widely.
Greg Kihlstrom (07:25)
Yeah, yeah. And so maybe building on that, you know, you’ve talked about triangulation using marketing, mix modeling, multi-touch attribution, and from mentality testing. I’m sure a lot of those listening to this are familiar with some of those terms, if not all of them, but triangulation using them. You know, can you unpack that a bit and explain why this approach offers a more holistic view of of ROI in an admittedly complex media landscape.
Frederik Skantze (07:58)
Yeah, absolutely. And triangulation is really the gold standard using all algorithms essentially available for measurement. And that’s sort of more and more sort of what thought leaders, even like Google and Meta and LinkedIn are sort of pushing for. But it’s important to understand that that is, you know, the most advanced type of measurement. And there is a whole set of range of measurement that you can do. You can start with sort of last click, cross channel attribution, then you can do
rule-based attribution, can do programmatic attribution, which is sort of where you bring in the neural networks and statistics. And then you can kind of layer on incrementality testing or some sort of marketing mix modeling type of techniques into that. But then you come into sort of this golden standard and it requires two things. It requires that you have enough data, that you’re a large enough company and have enough conversions for this to be possible. And it requires you to be reasonably sophisticated as a marketing team to kind of absorb.
absorb this. So really what you do then is, and this is the fundamental premise, that the world isn’t deterministic anymore. Cookies are essentially going away. so you really have a lack of signal and signal loss. And to make up for that, you need to do two things. You need to use all the data that’s available. So be it data from all the different channels and
all your first party data and so on. And then you need to do use algorithms technology and use all the algorithms available. Now, individually, these algorithms, because of signal loss, have challenges. But when you use them together and have them influence each other, they provide a very accurate picture. And you sort run a machine learning framework to sort of coordinate between these models, which is quite sophisticated. But that then
really provides you with a good solid answer. And it also provides you with a very accurate way to model your marketing. So if you take a step back, we sort of got lost a little bit in digital marketing. We basically want to attribute all our marketing to the different channels that we spend money on. very common attribution model is
last non-direct click. So if it’s non-direct, we don’t count it. We instead give the credit to a paying channel. And that sort of means that you’re trying to this month attribute all your sales to one of these channels that you pay money for. But for most brands, if you actually stop marketing, you probably get 70 % of the sales anyway, because you have a really strong brand.
That’s called the baseline. So you need to model your baseline. And then, you know, if you turn on your marketing, initially, even when you buy clicks, it takes a while for that to have effect. That’s called ad stock where it has an effect, but that, it’s over time and you have to model that as well. And then you need to model, you know, not only what it calls, you you, got, you got a hundred conversions and you spend a thousand dollars. That’s $10 per conversion. But if you want to buy.
additional conversions, you’re going to be able to buy them at $10 because there’s saturation in these channels. So need to model incremental CPA and then see where you are on these saturation curves for all your channels to optimally figure out how to balance your budget. It’s pretty sophisticated, but that’s what you get with triangulation. Then you get really good modeling and then you get on top of that, the correct attribution. Like these are the channels that are really driving whatever sales that you get.
Greg Kihlstrom (11:45)
Yeah, I mean, because I’ve you know, I’ve I’ve heard debates on, you know, like media mix modeling versus multi touch attribution. But, know, what you’re saying here is we need to go beyond, you know, one versus the other and and and look a lot more deeply. Is this is this primarily for companies that are either spending a lot a lot on advertising or have a lot of of traffic or.
How big does a company need to be to be able to benefit from this kind of triangulation?
Frederik Skantze (12:17)
You don’t have to be an enterprise. You can be a mid-market company, but you have to have six, seven different media channels that you’re on, and you have to have a good number of transactions to really be on this. they have to ideally be online as well. We can also model store traffic and all these things, but
But there has to be online elements to them as well. But if you’re not quite at that level, then we can of prune it down and simplify it a little bit, have it be a little bit more skewed towards multi-touch attribution, but sort of gradually bring in some of these elements from either running tests or looking at modeling other things than clicks, maybe views.
through marketing mix modeling data science techniques. And so there’s a scale and we can help you at any level of sophistication and size.
Greg Kihlstrom (13:19)
Yeah, yeah. Well, I know there’s a lot of enterprise marketers listening to the show, so I know that a lot of those could really benefit from the full triangulation as well.
One of the other things impacting marketers in addition to the number of channels, the number of data sources is third party cookies. And I know we’ve all been through a bit of a journey with the cookie apocalypse and then it wasn’t happening. And it’s sort of gone back and forth a little bit on exactly what’s happening. But I think deprecation of third party cookies and the
the increasing value of first party data has not changed. mean, that’s still something that is very much constant and growing. That said, deprecation of third party cookies is altering the landscape of marketing measurement. How are you helping your customers navigate this shift and leveraging first party data effectively so that you get the best insights on campaign performance?
Frederik Skantze (14:23)
Yeah, so I think that’s a really good question. And this is really key to what we do. And there are two aspects to this. The first one is sort of the signal loss for you as the advertiser. So, you know, it used to be really that like just by us doing data integration for you and making a business ready and sort of measurement ready data set for you, you can kind of look at that in a table and kind of make decisions based, you know, based on a click based attribution model.
And that is no longer the case. Now you actually need to use data science to sort of make these decisions and you have to use algorithms and increasingly AI to do it. So that is the first thing. And that’s sort of modern measurement and where it’s going. And it is pretty sophisticated and really a hard marketing problem. That’s the first thing. Now, we as an advertiser aren’t the only one who have signal loss. If you think about
If go back seven years, what was it like to be a search marketer or like a marketer on Meta? So your job was to set up campaigns and traffic came to your website and you looked at the conversions you got from each campaign. And then certain campaigns gave you customers that bought more, ⁓ had a higher, longer lifetime value.
you know, maybe higher margin products or lower return rates. And you wanted more of those customers. So what you then did is you went back to the ad platform and you increased your bids on that platform. That was your way to signal to the platform, give me more of those leads. Now that job has gone away. Now, you know, on Google there is performance max, there’s meta advantage. It’s an AI black box. It does this better than any human, but it doesn’t have that signal. It doesn’t actually know what happens on your website because the same cookie blocking happens to you, happens to Meta and Google. So they can’t really see what happens on your website. So now when you have done the attribution and you know which conversions are coming from which channels,
And you also can look at your backend data and see lifetime value and margins. you know what customers you want, how much customers are worth you and what you want more to. You need to in sort of near real time, go back and push that data into these ad platforms and say, here I had these conversions. This is how much I attribute them to you. And this is how much they’re worth to me. If you do that, you can improve your performance.
per platform by something like 20 % is massive, massive increase in performance. So those are the two things we help customers with. And this is modern marketing. is data-driven. It’s really complex. It’s hard. And it’s to do this in real time because you have to set up systems to do it.
Greg Kihlstrom (17:29)
Yeah, yeah. And so I, you know, kind of following on that, I want to talk a little bit about how you’re growing and building funnel and scaling, know, so scaling a product to meet the demands of, you know, you work with multinational corporations like Adidas, Sony and others doing that presents some unique challenges. What have you what have you learned and what learnings has Funnel gained in tailoring your platform and services to these large scale clients.
Frederik Skantze (18:04)
Yeah, absolutely. I mean, we work with a whole spectrum of customers from small e-commerce companies to some of the world’s largest brands. You mentioned some of them. We also do the global marketing reporting for Uber, Samsung, some of the large, really large media networks like Havas Media Group and Publise. So massive, massive amounts of data. So many of these customers have literally tens of thousands of data sources.
And we call it the funnel at scale problem, which is really kind of how do you manage this many data sources at scale? so these are individual accounts they have on those data sources. But some of these customers have between 100 and 200 different connectors they use from us, meaning Google Ads has one connector, Meta has two, and TikTok has three. 150 of those. And they keep changing the versions. keep having data issues. so what we provide is marketing data as a service. It always works. It’s always there. There are so many of these data sources. So every fourth customer that comes to us comes with a data source we’ve never seen before. we have a service level agreement. will, and particularly large enterprise, we have a service level agreement. We will build any, any new platform.
Greg Kihlstrom (19:23)
Yeah, yeah.
Frederik Skantze (19:28)
that they have that we haven’t seen before built within a couple of days. Data as a service, we just provide it, we solve the problem and they don’t have to think about it. And that has really been transformational, especially in how we work with the enterprise.
Greg Kihlstrom (19:41)
Yeah, yeah. A lot of SAS companies struggle with maintaining a balance between, you know, customization and scalability. How have you how do you think about this and how do you approach this so that, you know, you can do what you’re talking about, which is, you know, achieve that that ability to to be flexible while still maintaining that that high quality experience for everybody.
Frederik Skantze (20:07)
Yeah, it’s a great question. you know, I think there’s two things we’ve done. We have, in general, we’ve said we’re going to build one product for all customers. have advertisers as customers, have agencies as customers that use the same product, and it’s going to be a standard product and we don’t make any modifications or customizations to it. And it’s meant that we’ve built a sophisticated product with a lot of flexibility. I would liken it to like Notion.
⁓ So the notion is like Lego for workflows as opposed to like a CRM tool, which is a workflow, but it’s like a really particular workflow. So you never outgrow funnel. is a little more, you have to be a little more sophisticated when you come in and you might need some help, but you never outgrow it. That’s the first thing. And then we do, we break the rule once. There was one thing we do when it comes to customization. And that’s what I talked about, data sources.
because there are so many of them. we will, within SLA, build any data source that you need whenever you need it. We will also build any data destination you need when you need it. So data in, data out, we will figure out for you. But other than that, it’s one platform. It’s like Lego for marketing data. We solve any problem. You never outgrow it. You can start with five data sources and go to 30,000 data sources. We have one customer who added 100,000 Facebook Data sources. ⁓
Greg Kihlstrom (21:34)
Wow, yeah, yeah, love it. Well, as we wrap up here, a couple couple last questions for you. You know, certainly you’re you’re the person to ask this question to, know, where do you see marketing measurement going? You know, in the months to come, certainly there’s been a lot of changes. There’s there’s a lot more to come. You know, what role will.
Marketers, data scientists, measurement tools, and even dashboards play as some of these AI enabled tools continue to grow and the role of marketers shift.
Frederik Skantze (22:06)
Yeah, no, absolutely. So the role of the marketer in AI is shifting. We already talked about how Google and Meta and TikTok’s AI is handling the bidding. Search is going be these gen AI tools going forward. That’s changing massively. We’re seeing less search traffic that you can buy. And then, of course, automated image generation for ads, automated
copy for content marketing, B2B lead gen automated with agents, that’s going to happen. But ultimately, you still have to attribute your marketing spend to what your results are. As I said, that’s a really hard data science, machine learning, AI problem that doesn’t go away. And we’re going to continue to evolve that and continue to solve that. And whether it is a
marketer who does the marketing or an AI that uses it, they still need to know the impact of what they do and relative to the cost.
Greg Kihlstrom (23:12)
Yeah, absolutely. Well, Frederick, thanks so much for joining today. One last question for you before we wrap up. What do you do to stay agile in your role and how do you find a way to do it consistently?
Frederik Skantze (23:24)
Yeah, that’s a great question. I mean, I definitely feel a lot of that right now with the world changing so fast. But at same time, 30 years ago when I did my master’s at MIT, I did it in AI and adaptive systems. It feels a little bit like coming home. But when you do a scale up, one of the most important things you can do is keep learning. The company grows, it changes, you have to learn. The industry changes, technology changes. So you have to keep learning. That is over 20 years of having done this. That is what I do.
Now, the other thing to be agile in my role as a CEO is actually get the company to be agile. That is actually much harder. And when we were 30 people, we were really agile. And as we then grew to like 150, 200 people, we got much slower. And that really worried me. We had this bottom-up culture and it kind of was really hard to make bigger shifts and find big mountains. And so I spent the last sort of two, three years rebuilding the operating model of the company to balance bottom up with top down strategic planning and also sort of setting top priorities for the company and an ability to sort of really get everybody to row in the same direction. And that’s really been transformational. That combination has caused us to be probably twice as fast as we were three years ago. ⁓ So that is really, that’s really been my focus on Agility.
