Build Target Segmentation Through Your Ideal Customer Profile - John Buten - Revenue Today - Episode # 054

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Sales, marketing, and RevOps. It's sink or swim out there, and yesterday's strategies and tactics won't help you today. This is Revenue Today, and I'm your host, Jared Robin. Join me as we interview revenue leaders in our community to learn what steps we could take right now to help you scale yourself and your company. Revenue Today is sponsored by Rev Genius, and we're on a mission to bring inspiration and creativity to all revenue professionals in the world. Want to shout out our sponsor Demandbase. Demandbase is smarter GTM for B2B brands. They help marketing and sales teams spot the juiciest opportunities earlier and progress them faster by injecting account intelligence into every step of the buyer journey and orchestrating every action. For more information about Demandbase, visit demandbase.com.

We have our traditional model, and when we usually do market sizing or market prioritization, we're thinking in a very traditional mode, and we define a target market segment as, you know, between A and B, and we have an example of that on the left. And this is really important, you know, to do most media buying, to buy lists. This is the way companies source them and want to be able to define them. But it might not always be the most useful. Like when we think about other applications like lead scoring or qualifications, sometimes the ideal customer profile is a different way of thinking about these target segments. So instead of thinking about the boundaries, we think about what's the center point. And, you know, it stands to reason that our target market segment is between A and B, but, you know, the upper right corner of a company with 5,000 employees in 10 locations might not be, you know, really ideal for us. So having a definition that we can use and score and prioritize within that segment can be really, really helpful to operationalize this. The other element of this idea of ideal customer profile is when we do targeting and segmentation, we've been talking a lot about B2B targeting and talking about the number of accounts, because it's almost always a company that's signing the check. And so we're looking at the attributes of the different companies, the number of companies, and that's the way we understand market size. But when we get to an ideal customer profile, it's very easy and helpful to add other attributes as well around potentially the buying center, the department that is the champion for this purchase, or the person, the persona. just the title and say they're BANT, but understanding other elements like their experience, their current software usage, their professional affiliation, or their tolerance for risk. And some of these things we can find out through data, but we can also find out passively through our websites or through qualification questions for downloading assets and materials. And this is when we start transitioning from macro-segmentation into some of the micro-segmentation that some of these digital platforms enable us to do, or passing some of that information, that micro-segmentation information, on to sales to help them be intent-led sellers and understand the buyers better. So I'll wrap up my spiel on finding market segments to drive to growth. As we said, you want to use the data to understand these buyers as markets shift and really try and understand them on the basis that matters to your company, that matters to your buyer based on how their needs differ. And when you do that, you'll find lots of different kinds of opportunities. So one thing we often hear about is companies worry that they're not first mover or that they're late to market for a fast growth market. And we think that's a fallacy that, you know, even— yes, there's these high-growth markets, but it's never too late to find opportunities. So even if there is a period where a market is growing fast, there are a lot— a huge segment of the market that is a laggard, that isn't on the cutting edge, but is waiting, and often waiting because a set of needs haven't been met for them yet. Whether that's compliance, regulatory, or they need to see a specific use case catered to, specific integrations, there's lots of opportunity there. And then as the market continues to grow, those early adopters are going to get more sophisticated in their needs and they're going to have new needs. So there's opportunities for growth all throughout the market spectrum and all throughout the market lifecycle. And lastly, we didn't really talk about— we talked about prioritizing segments, but how many segments should you prioritize? A story I like to talk about here is from the Yankees catcher Yogi Berra, who supposedly, at least, went into a pizza place. And he asked for a pizza. The pizza man said, do you want that in 8 or 12 slices? And Yogi Berra said, you better make it 8 because I'm not that hungry. And of course, the humor is the pizza is the same size. But we all know that cutting the pizza into more slices makes it harder and harder on our organization. And to be really effective with these segments, we want to really dive into them a little bit deeply and be able to create more unique marketing that caters to their specific needs. So think about the appetite and how hungry you are as you come up with your segmentation scheme and try not to bite off more than you can chew. With that, let me turn it over to Iris to talk a little bit more about how to use data to use this segmentation scheme and really drive growth.

Great. Thank you, John. So yes, I'll try to go to the operationalization that you mentioned and talk about how to take those best practices and those ideas to real life and fuel your growth with the right data. So let me start with an example. We see many of our customers using traditional segmentations like John mentioned. It's usually a 2-dimensional segmentation. You saw that John had the same slide just a couple of slides ago. And the reality is that this is limiting what you can do with those segmentations. Because if you're using this, if your real ICP is over here on the right-hand side, those green dots on the right-hand side, if you're using only 2 dimensions, you're missing out. You might be using, looking at this bigger square here, but then that means that your ICP or segment is too broad and you're casting the net too wide and your salespeople are actually wasting time because they're targeting some companies which are not ideal for you, not really in the segment that you were wanting to approach. And if you're looking at the small square over here, then you leave money on the table because here are 2 dots, 2 green dots here that your salespeople are not targeting, not approaching, and they could be your best customer ever. So 2 dimensions is not enough. And really the main question is, why are people not using more dimensions? And Jon touched on some of that before. Why don't people use more data and more attributes to get a better understanding of the segment and be able to understand the segment deeper? And we really see— I'm sorry? Yeah, you need to move to the next slide. We really see 2 problems, and again, John touched on some of that. The first problem is finding the right data, and the second one is actually using that data to get the value out of it and to operationalize it and use it in your systems that help you during the segmentation. Let me drill down on the first one. Finding the right data is not as easy as you would think. For example, I gave some examples before about doctor clinics and restaurants. They have very low footprint. Don't— not always do they have a website. Sometimes all they have is maybe a Facebook or an Instagram account. How do you really know their recent address? During COVID for example, many restaurants shut down and then reopened. If you're selling to restaurants, how do you know what's going on and what's the latest and greatest situation or status of those accounts out there? So you need to be able to find reliable data that provides good coverage of the specific segment that you're looking for. And if that's private companies or small and medium businesses, that's difficult. Private companies compared to, again, big enterprise, public enterprises, they don't have to publish any official reports. So it's even difficult to know what their size is, what's the revenue, what, how many people work there. Yes, there are other ways you can get that, but for private companies, it's very, very difficult. And so the result is that in many cases, people go back to the traditional 2-dimensional size, revenue, location, industry. That's it. And that's weird because to some extent, the world has changed dramatically. And there's almost an explosion of data today. And if, I don't know, 5 years ago, if you were looking for some unique data points like foot traffic, It was very difficult to find someone that could actually provide foot traffic. And today, it's almost the opposite. You have 20 providers that can provide foot traffic, and you need to pick the best one or the best one for your needs. So why is it still that although there's so much data out there, data about consumers, data about commercials, companies, and much, much more, Why is it still that companies are not using all of that data that is available to them? And I think maybe the answer is that it made the problem even bigger because now I have too many choices. Where do I go to find full traffic data or any other data? How do I vet companies or data providers for their coverage of my segment, for the attributes that they provide, for freshness of information, accuracy? That's a big process that you have to go through if you want to work with every single provider here, or even pick 5 or 10 or 20 that you want to work with. So that is kind of a barrier of entry for many companies. So they go back to using just, again, the size and revenue instead of using this wealth of alternative data providers that they have. And maybe the other barrier of entry is that if you do decide to work with 5 or 10 or or 20 data providers, that creates a new problem. And the problem is that you now need to stitch the data together because every data provider provides his data in a different schema, in a different approach. It gets updated recently, and you need to be able to stitch that together into a coherent picture. That's, again, another problem that sometimes prevents many companies from really taking that step and using all of those attributes. They are available now.

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