Fit Beats Intent: The New Rule of B2B Demand
A direct exploration of one of Adzact’s strongest points of view: not every in-market account is worth pursuing, and not every high-fit account is currently showing intent. The episode contrasts short-term buying signals with deeper structural fit.
Core questions:
Why can high-intent accounts still be bad commercial targets?
What makes an account fundamentally “good fit” before it shows buying signals?
How should B2B teams combine fit, intent, and timing in their go-to-market strategy?
Guests
• Saralee Katz, Demand Generation Manager at Adyen
• Tom Kay, Sr. Digital Marketing Manager at AudioEye
Transcript
Joaquin Dominguez (00:00)
Welcome to another episode of B2B Marketing Futures. For years, B2B teams have been told to chase intent signals. Who is searching? Who is researching? Who is showing signs that they might be in market? And of course intent matters, timing matters, but the problem is that intent alone doesn't tell you whether an account is actually worth your effort. A company can show intent and still be a poor commercial fit.
They might be too small, too complex, in the wrong market, missing the right use case, or unlikely to convert into a high-value customer. So today we are going to explore a different starting point. What makes an account fundamentally a good fit before it ever shows buying signals? And we will talk about this with Tom and Sarah Lee. And I would love
You to introduce yours yourselves. Maybe Sarah Lee, would you like to give a sentence on yourself? Yes, of course. Thank you for having me, first of all. Very excited to be here. I am Sarah Lee. I am based at the Stockholm office at AGM. We are a financial technology platform, and I work as a demand generation manager and have been here for almost six years now. And
I'm working on a daily basis with trying to figure out this whole thing. So yeah, excited to be here. Thank you so much. Welcome. Tom. Yes, I'm Tom. I am the senior digital marketing manager at Audio I. We are a digital accessibility compliance platform, essentially helping companies be combined with different
accessibility laws in North America and in the EU. My day to day is managing paid acquisition. So investing allocating our budget towards channels that bring revenue. To start, I want to ground the conversation in the first principle here. Because a lot of targeting still begins with fairly basic filters like company size, industry, geography, dub title
maybe a few technographic or figmographic signals, but in reality, fit is usually much more nuanced than that. So let's start there. What makes an account a generally good fit before it ever s ever shows buying intent? I think for us it's really dependent on where we are focusing at the the current time. We identify our focus verticals.
In the beginning of the year. And then we based off of those focus verticals create target account lists. And we have multiple requirements for these target account lists that we kind of need to follow for it to be worth our time and energy to go after these accounts. So there's a lot that needs to be perfectly aligned. Let's say it's a revenue product fit.
how mature they are, just their tech stack and their tech case are also important factors. and other things such as locations and just their willingness to actually implement such a change in their company as well. So there's a lot of things that needs to be aligned for it to be a fit for us. And and
You mentioned some things that in my mind are easy to understand, like revenue, the tech stack, right? That's zero or one, or the location. You could say, yeah, this industry in certain city. But you mentioned some some nuances here and you would love to to know like how you can measure perg fit or the willingness to actually make and to change. Is it possible to actually observe these things?
paper or when you are planning your campaigns before you start actually? Before we start, that's that's a challenge. I don't think we'll ever manage to do that, but we do have a lot of accounts that we have been after for a while that we've had conversations with, that we are, following up with years after we've lost an opportunity or had conversations with them. So we know where they are in their like stage. That makes sense.
And if they are taking in new people or if they are moving to new markets or similar stuff like that, which can affect if they are a fit right now and they weren't before, for example. Like that knowledge that could be on salespeople's head so valuable, right? And the challenge then becomes how you translate that into something that is organized and
Yeah. You can act on right. And you can't do that. It just has to be you just has you just have to communicate with the sales managers a lot more. But if you align on those target accounts and you have conversations about them and structured meetings and follow ups about them, it's still possible to track all of that and find the right timing as well of when to reach out. What about you, Tom?
We know that targeting is one of the most important parts of paid media. So with your experience in that regard, how do you think about targeting and fit before an account shows in 10? Yeah, absolutely. I think Sarah Lee made some interesting points, especially on some of the formal graphics. But the underlying question I always look at is how big of a problem are we solving for that account? Two companies, they can look identical.
on paper, right? And that answering that question either sways them to, hey, this is worth spend or it's not worth spend. And especially if that problem is tied to revenue and we could solve that makes a really good fit. And I think from there, once you've identified those types of accounts, then you could start looking down at maybe there's certain size of companies that fit more towards that. Or
certain job titles that work at these companies or revenue base or et cetera. So that underlying question really helps solve the other kind of factors you're looking at to really identify what a good fit is and identifies the ICP that you're gonna target. So how much of this comes from data and how much comes from human judgment in in your opinion? I think a lot of it comes from data.
One of the things I think we're privileged asked here at Audio I is that we are not a new company. We're not a one-year-old startup where you lack data and it's hard and you really have to use more data and benchmarks that you find online or any research you do in the market. But for us, we have twenty years worth of data. And for me, even when before I joined Audio I, there was tons of data for me to work off of.
And find that data and really find what's working, what isn't within the data. It took a lot of that toll away from me from making my own judgments. And I think that's something that marketers face a lot, especially when you join younger companies, where you kind of have to assume and you have to take risks and you have to spend money to to answer those questions. And okay, so
Let's bring intent into the picture now because intent data is valuable because it can show timing, right? It can suggest that an account is researching a category, comparing vendors, becoming more active around any problem. But one of the themes from things that you're discussing now is it apparently it only becomes useful when it's applied to the right account. Otherwise teams can spend
time prioritizing accounts that look active but are not actually likely to convert or accounts that create noise for sales rather than real opportunities, right? So I would love to hear like why can high intent accounts still be poor commercial targets? Have you seen examples where intent look strong but the accounts were not a good fit? Yeah, ⁓ I think as where
A lot of marketers can be tricked by intent because intent doesn't necessarily mean good fit. Those are two different things, right? Intent and fit. So you could see from your dashboards, hey, people are engaging, they're downloading the content, they're filling out forms, leak volume is good, and it's showing intent. But then when you actually look to the data and you start connecting that intent into
revenue that there is no pipeline, right? But then that tells you the fit is not good. So that's a good example where high intent could look good, but it doesn't necessarily hit your bottom numbers. I think that's where high intent can be bad, right? Because it's reflecting the wrong signals too if you're not looking at full funnel data. And another example I would say high intent could be
could be a bad target, more specific to paid media, is that high intent audiences tend to be small, which makes it difficult to advertise on ad channels because either the ad platform doesn't allow you to advertise because it's too small and you gotta go more broad. And it's also more difficult to segment and personalize if you're working with a smaller audience. Yeah. We have that exact issue in the Nordics because we do only so
We've just started working with looking at intents more closely. and what I do is that I do it the other way around. I identify the accounts that I want to look at intent at, if that makes sense. So with the target accounts that we have, I only monitor intent, because that's what's valuable for me to look at. The problem is that they're so small and niched. We have
around 40 accounts in each focus vertical, which makes it very hard to get correct data, but also to monitor that and to see the spikes and to see the highlights and the lowlights of that data set, if that makes sense. That's an issue that we have here. And I guess it's what I hear from you, Tom, it's maybe a common issue. ⁓ but we go more in depth into the accounts and
What I try to do is that I try to combine the data that I see within our system and third party data and the data that we can see from LinkedIn, for example, on contact level and combine all of that data to see if there's any changes in their intent and to see if there's higher intent, for example, and if it's time to reach out to them. But that's a really hard task to get all of that data into one and to make it.
actionable insights. So yeah. Especially if if you are going after big accounts, right? W many people complain that third-party intent especially is really hard to or to get real insights from it if it says someone at Apple in Cupertina or someone at Amazon in in in Seattle is researching for your account, right? Yeah. Okay, yeah, then how can I use this data and
Cross-referencing that is really hard, right? It's very hard. But it's also you kind of want to see what they did before. You kind of want to see follow the changes because it could be that they have done that prior as well and that there's not really a change in their behavior. So I think it's also both looking at intent, but also seeing if there's something that has changed recently. and as you say, it's hard to trust that data. You kind of want to confirm it with other data sets as well.
to make sure that you're thinking straight, basically. Yeah. And and and and what you mentioned about being focused on on on the 40 accounts that you should target because your strategy is telling you that you should go after those verticals and for these accounts, these verticals. Then you put all your effort in analyzing the right data. Otherwise this could be even more complicated if you start bringing
third party intent data into and trying to analyze that and com and cross referencing that with your first party data. So but if you know the accounts that you want to target, right? And if yeah then you Yeah, everything becomes whatever you do after that is is way more effective, in my opinion. Yeah. Yeah, you just you keep a little bit of a you keep a closer eye on them and it's you know it more by heart what they're doing. But it's the way we
work nowadays because the market is super saturated. We don't we have a lot of great clients already and we don't have that much to go after anymore or at least it's much harder to find. It's really niched and highly personalized for those accounts. And same with intent that we try to follow that very specifically for those specific accounts. And because a good fit account might not be ready to buy today, but
may still be worth educating and warming up and building familiarity with over time, right? And a high-intent account may be worth immediate sales attention, but only if their underlying fit is strong enough. And this is where the strategy becomes more interesting. It's not just target the accounts showing intent. It's deciding when to create demand, when to capture the demand, when to personalize
And when to involve sales. So I would love to hear like how should teams bring all this together in in a practical way? How should B2B teams combine fit, intent, timing? And on top of that, personalization, how do you engage with accounts and how do you involve sales teams in this process? Yeah, I think you touched on it regarding combining some of those together. I like to
kind of create a matrix of if it's high intent, high fit, that's high priority, right? That's where we're gonna spend dollars on, that's where we're gonna spend our energy on. And that's somewhere that we will invest in personalization, right? Because there are benefits to it. Versus if it's the opposite, less spend or maybe no spend at all. Maybe no personalization. So the priority is lower. And that's the way I kind of break it down. ⁓ I think for timing is an interesting one because there's
Two different types of timing, right? There's behavior, whereas is that person at the right stage right now to buy? Are they at the research stage or are they actually looking to buy? And there's also event-based timing. For instance, in the compliance world, if there's a new regulation or a new deadline that's coming up, that will affect their timing. So it could flip a bad fit to a good fit based off that new event.
So combining all three or four, even you just have to really decide and what I think helps is defining if this plus this equals what do you do? And ha break that down for everything and that that will really help you prioritize of where you spend money and your energy on.
Yeah, I think it's really important that you have those meetings with sales and also the SCRs and just align on the focus that you have. The target accounts, everyone should know of them. Everyone should be aligned and and everyone should know what kind of campaigns that you're doing towards them. And you should have just a structure for it. And you should have follow-up meetings where you go through all of the KPIs and look at if you manage to convert any of those target accounts.
And that should be an ongoing just recurring of meetings of events. And ⁓ I think that's probably the biggest challenge to get that to work. But if you manage to do that, I think you're doing great. It's I think that's the hardest part. But it should definitely be your commercial organization should definitely be aligned on what you're going after. And it shouldn't be spread out like a you shouldn't necessarily have people going after things on the side.
where you don't know of it and it's not included in your marketing campaign. I know that's gonna happen either way. I'm not saying it's such a bad thing, but it's my ideal world. Everything should just be aligned and visible and transparent for everyone and we should have the same GPIs so that we feel like it's worth putting our time and effort and energy on the same things. Yeah. But at the same time so many teams fail at that, right? There's one of the actually one of the our classic topics that we discuss in
Our podcast is sales and marketing alignment, and I hear a lot of teams struggling to just setting the right KPIs and and not having the same KPIs for sales and marketing. And then how can you expect that teams be aligned, right? So yeah. That's definitely a a challenge in marketing. Yeah. And and personalization, I think that naturally brings us to AI because
A lot of the AI conversation in marketing has focus on content production, more ads, more emails, more landing pages, more variations. But one of the more interesting use cases is upstream for content in the targeting and segmentation itself. AI can help analyze historical deals, identify patterns across one and lost accounts, summari summarize those meetings that
you were describing sorally the account context, ⁓ build audience segments, and create different messaging angles for different buyer groups. But there is also a risk if teams give AI too much control without clear strategy or human judgment, they may simply automate bad assumptions, in my opinion. So I I would love to hear what role can AI
play in improving targeting and segmentation, in your opinion. AI. I I'm sure people are not sick of talking about AI yet, but we'll talk about it. I think honestly, I think as marketers, we're very spoiled right now with AI and data. And I say this because I remember 10 years ago when I was running programs and managing a marketing team.
I would always love to dive into the data and buy signals and find what's working for our buyer is. Because we data was never a problem. We always had data. The problem was always we don't have the time to go through all the data to get all the answers that we need for the team. And I think that's where AI plays a huge role right now, where it solves that problem. Now we have the data, but now we don't necessarily need the time because AI can jump in and do something that would take
A week of going through data and putting it together and putting into slides now takes maybe a day or two. And that's extremely powerful, I think, because now when it comes to segmentation, when it comes to finding what's a good fit, we could tell AI, hey, go into our CRN, scrape all of our close one deals, who's involved in those conversations, job titles, company, revenue.
All right, connect it to a different platform, enrich those leads, give me all the data, and then by the end of it, you have a full scope of who is actually buying your product down to revenue, down to job title, down to function. And then from there, you could then use AI to actually work on that messaging and the position for the specific groups of people. So you could segment and personalize messaging and positioning
V fairly quickly again. Something that marketers had to do manually and now can be done really quickly. And I think both of those, it's super powerful for marketers. And to be honest, I don't think there's an excuse to not segment anymore because of AI. There's so many workflows and manual things that we used to do that now are just taking us a couple of hours. I do think it's hard to be completely I don't really trust everything or
I'm very critical of the information that I get, not if it comes from our own system, as you're saying. If it goes into our CRM and just scrapes the information, that's one thing. I've definitely used it for prospecting, finding good target accounts. And that is very the quality of those are not the best. I think that's still a very big challenge. Other than that, I've only used AI to be more efficient in my own working.
processes. I do create gems for like all of our target accounts, not our main ones, just to not repeat the same work that I've done before. ⁓ but I still think that I'm really a rookie in all of this. I have so much more to learn, but there's yeah, and there's so much you can do. But for me it just helps not repeating the same work and having a place to go to find information of our target accounts, for example, and creating those personalized
the personalized content for them becomes much more easy when I have everything gathered in one place. So yeah, it's it helps me be more effective and and it has helped me to some degree to find good accounts. But also I'm super critical of it 'cause the quality sometimes is really low. So it's sometimes it's more work 'cause you have to vet all the accounts, for example, or confirm that they are good accounts. So it's
⁓ yeah. I think in terms of being critical is very important. AI to this day makes a lot of mistakes. Sometimes it could do complex things really well, but then it fails on the simplest things, which I find funny, right? It's like a super genius computer that could solve all these formulas and complex business problems, but it can't tell you the what the data is today, right? Being critical and over
analyzing everything that AI gives you is definitely important. Look over everything. But also what you mentioned about sometimes it takes more work working with AI. And I found that true as well because I find that you really have to micromanage AI. That you can't just okay, I trust you, you gotta get your work done. It's no, you gotta micromanage it to the point where
You end up asking yourself, okay, this is taking much longer than if I just did it myself, right? So those two points definitely when you're using AI, just have to be critical and also keep track of time because maybe it's better just to do it manually. Yeah, definitely. It's been way too often that it has actually taken me more time, for example, because it doesn't really get what I want to say, for example. And coming back to today's topic about how we define
fit and why intent can be useful but also misleading, how to combine fit with timing and personalization, and also how AI can help teams segment and target more intelligently. But before we wrap up, I'd love to ask each of you for one final thought for marketers who are still relying on heavily on data, what advice would you give them about building a stronger fit first approach?
to demand generation. What is one practical change B2P teams should make if they want to stop chasing intent and start prioritizing the right accounts? I think it's just talk to the sales managers more. For me that has helped a lot. I think it helps you to understand as you mentioned as well, Tom, the real challenges of the prospect that you're going after. Just
be in the sales meetings with the sales managers and keep doing that because the changes and the needs of the prospects will change. So it's important to stay on top of that. but for me that has been the biggest asset to actually learning what the prospects need and what could be a fit. And there's so many times where I think something is a fit and it's actually really not because of very
small nuances that the sales managers know about that I don't necessarily know about. So I think for me that has been the biggest asset. Just talk to the sales managers. Just go ahead, join the meetings. I would say lack of time is no longer excuse. I think marketers in the past, sidelin or personalization or looking at high intent because of time. It takes a lot of time, it takes a lot of effort.
For let's launch this campaign without doing any of that, right? It's all about let's get it out. I don't think that's the case now. Like I mentioned, I think AI can play a huge role in doing all of those things. So saying that we don't have enough time to do those things because we want to get the campaign out. No, you have the time now, you have the tools. So leverage them and run the campaigns in a better and smarter way.
Thank you both. This was a great conversation. My opinion the key takeaways for me is that intent is not the enemy, but it cannot be the foundation. Intent tells you who might be active, but feed tells you who is actually worth investing in. For B2B teams under pressure to create pipeline efficiently, that matters more than ever. Thank you again, Tom.
And Sara Lee for joining us. And thank you to everyone listening to this episode of B2 Marketing Futures. We'll see you next time.
