Eric Hanselman

Welcome to Next in Tech, an S&P Global Market Intelligence podcast where the world of emerging tech lives. I'm your host, Eric Hanselman, Chief Analyst for Research at S&P Global Market Intelligence.

And today, we're going to be talking about the science of sorting out total available markets and what it means and what the importance and impacts are of them. And with me to discuss it is Greg Zwakman, Director of Market and Competitive Intelligence for S&P Global Market Intelligence. Greg, welcome to the podcast.

Greg Zwakman

Thanks, Eric. Pleasure to be here.

Eric Hanselman

Great to have you on. We kick around a whole range of different ideas about potential and a lot of the things that are out there. And maybe, well, to start, just a little bit of background on your role, what is the position that market and competitive intelligence has? And what are you typically involved with? Just for a little background for our listeners.

Greg Zwakman

Yes, absolutely. Thanks, Eric. So what my team does is we maintain a syndicated product portfolio that looks at market sizing of the different, again, tech segments that we're tracking. We build these analysis with an overall bottom-up. So we're looking at every individual vendor, so we have the forecast by segment. We have the different vendors, what they're doing, what their actual true portfolio is like. But we also have a mandate where we do a lot of custom work for clients.

A lot of custom engagements, and the primary custom engagement that we do is addressable market analysis. The TAM and SAM. So we're involved in both the syndicated side with an overall kind of 451 Research and also with like a consulting, helping companies understand what their market opportunity is.

Eric Hanselman

And it's a range of different data types that you're working with. Leika Kawasaki has been on talking about some of the energy perspectives that he's put together, also part of your team. When you start thinking about total available market, well, to start off with, again, for our listeners who aren't familiar with it, we kick those terms around a lot.

Looking at what are the opportunities for a business in terms of the size of the market they're looking at. And I guess what's important and impactful about thinking about a total addressable market and what that really means?

Greg Zwakman

Yes, I think you basically summed it up well. What is the overall opportunity? Like a TAM-SAM analysis helps businesses again define and quantify what the revenue opportunity is for the products and services that they're bringing to market. And as the name suggests, addressable market. Now it's again, different varietals here. You have the total addressable market, which is everything, all-in. All company sizes, verticals, regions, geos.

Yes, there are certain standouts where that kind of analysis will stick, but in my humble opinion, typically, it's not right. So we focus more on what we call segment addressable market, which is focused more on what's actually achievable, what can a company or even a market, what can they actually serve? And that's where we ring-fence our analysis.

So it's important to look at it from the overall kind of perspective of what's the opportunity for what we're bringing to market all-in, what can we expect, but also what can you achieve. So you want to have something realistic. So that's really kind of something that we really focus on here.

Eric Hanselman

Yes, going from the universe and stars down to really what's our solar system and what can we actually reach within the solar system.

Greg Zwakman

Yes, it's more what are our dreams versus what's reality. It's -- and those can be a whole range of kind of litany of issues that companies really do need to work through, so people want to see a huge number. And they want to pound the table and they want to see something that's amazing. But you have to really -- and this is what we do with clients, is we work with and understand what they're bringing to market, how they would do that? What overall kind of capital do they have to get that product or service in the market?

What are they going to target and the overall kind of potential return from that investment has to follow along with where they could possibly do that. So that's, again, critical when you're looking at this kind of analysis.

Eric Hanselman

Yes. Really sort of getting to the nuts and bolts of not just who is that broad universe of everyone you could sell to, but practically in terms of the capabilities you've got, how can you get there? So that really covers the practical aspects of what a business can do. But how do they get put to work? Well, what are the things that people are actually doing with this kind of market sizing? And how are they leveraging them?

Greg Zwakman

It really boils down to once you have a product or service, you have your market defined. Is there a market opportunity large enough to justify the investment. And you can look at that from two angles, two different lenses. From the supply side, that being the company who is supplying that service, offering that product and also from investors. If you're backing up doing again due diligence, is it a good investment opportunity.

And again, we're talking a lot about new market entry use cases. But these different analyses, they should or they should be present throughout virtually every stage of a company's development or product or services development. Like I've been a part of massive corporations. I've been a part of consulting firms. I've been a banker. I worked at a three-person start-up. I worked at a mid-tier, again, research firm. Of course, before we were acquired.

At all of those different, again, locations and companies, an opportunity analysis was part of my mandate. An opportunity analysis was something that we were actively working on. So it's really a use case and, again, requirement almost for companies across the spectrum from startups to the largest companies on earth to understand the opportunity of the markets and the market segments that they're into, of course, for different use cases.

Eric Hanselman

Well, but you really called out the important part here, which is that this is what ought to be happening before you're making business decisions. This is something where the whole point is, we're going to go spend effort, time, money, cash, whatever it happens to be, to go do a thing, are we going to get a decent return on it? And the first step in figuring that out is what's the market for it? What could you sell into?

Greg Zwakman

Yes, exactly. Exactly. So you look at the different, again, types of organizations. And I touched on this a little bit already from where I've been. But you can see from companies and how they utilize this kind of analysis. It is pretty flexible.

If you think of the actual use case of putting this type of data together, if you look at kind of startup landscape, do with a lot of those companies, and you can guess what one of the most important kind of use cases for the opportunity analysis would be is capital raise campaigns, where are they're going to get their next round from. We have done some DevOps, some, again, business development projects for some of the smaller companies. It was primarily around the capital raise campaigns.

How are they going to secure their next round of funding? Here, again, demonstrate not only again for them, looking if it's a good market to go into, but we could demonstrate and portray what the company is doing and the opportunity it's facing as an overall good investment. I talked about this again before. The flip side, PE companies, is it a good investment? Is it a sound investment? Again, due diligence. So you can see the different use cases for different company types where this should be playing a part in their overall decision-making.

Investment banks, again, due diligence, equity research. We've been in S-1 filings, showing the overall opportunity of new investments. I mean the list does go on and on. And then once you move up the tier of the larger organizations, it's almost the use case gets broader. It gets more nuanced. If you're looking at portfolio analysis, what is your product portfolio? Where did you double down, budgeting decisions. So again, the majority of the projects that we're doing, Eric, are with management teams and product teams.

They're fleshing out their budgets for the upcoming year or whatever the time frame, they overall kind of set their budgets, but -- and this is part of the process. So many projects we've done have an overall opportunity analysis. Added on top of that is, again, the bottom-up market sizing of how penetrated is this market.

Then you look at your kind of competitive landscape of who has market share within this market? Who are the leaders? And then portfolio analysis of what are they doing differently? Who's best positioned to penetrate this market and capture most of that opportunity? And what does the company need to do to be in that position themselves.

And I know I rattled off a lot of new things there, so maybe those are topics for future podcast episodes, but again, this is like a demonstration that -- but this is something that definitely common, but typically used as part of an overall decision-making process. And it's a critical piece, but yet a piece.

Eric Hanselman

Well, both internally and externally, it's something where you're both hopefully internally making those decisions upfront and then also using that to justify externally to investors in whatever form they happen to take.

The challenge, of course, that we always see is that the complexity of bringing together the various aspects that you were just identifying, which is, it's not just what's the total set of people you can sell this to? It's who else is trying to sell or what are the nuances, the direct applicability? And my guess is a whole bunch more challenges beyond that.

Greg Zwakman

Yes, Eric, when you look at again some of the challenges out there. And I think a lot of the challenges relate to interpretation and confidence, and that's broadly. If you look at the overall kind of methodology, the process that's actually employed and trying to flesh out the opportunity analysis for markets, they're all over the map. It's just some are based on high-level assumptions of total IT spending. Some are carving out spending from a certain subsegment, what is against security?

How much is that going to MDR, for instance. Like that sort of an analysis as some companies extrapolate from existing books. I mean, literally, the list can go on and on. But more often than not, one thing that I found that is the most common is that there's a lack of visibility into the process that they took, the methodology that was employed. How the models were built, what assumptions were used and where they came from. And [ God forbid, ] you put metrics out there that underpin and support the analysis.

So in short, there are a lot of TAM summaries out there, but not so much in terms of data and assumptions that help defend the analysis. Now Eric, as you know, I used to be a banker back in a former life. I saw a lot of opportunity analysis and I saw a lot of them that were supported by a narrative at best. Here's a giant market opportunity, and this is the reason why this is the best thing that ever happened to the human race.

Eric Hanselman

And by narrative, I will point out that another word for narrative is story.

Greg Zwakman

Story. Thank you. Thank you very much.

Eric Hanselman

There is a good story behind it. You were being kind there, but...

Greg Zwakman

I continue to see a lot of that today. A lot of what we see today is just this big number and vague descriptions of what was done and then a story of why it's great. And I think the primary challenge is the availability of granular data. And that data is really kind of two major kind of brackets if we look at the way I see it, how do you quantify the target population? How do you get the metrics around the number of potential customers that you have? So that's again, one.

And then how do you get the assumptions to back up the analysis. We'll talk about this in a bit, but the most important thing are the assumptions to spend. How do you get that? How do you back those up? Now if you want to tick both of those boxes, then you have a TAM-SAM process. A defensible TAM-SAM process that is often expensive and pretty time consuming.

So I think that's the challenge is people want to get a big number out there. They want to explain themselves. And oftentimes, a lot of the nitty-gritty detail, that's expensive and tedious and hard to do, gets overlooked, and it gets replaced by a big number and a narrative or a story.

Eric Hanselman

We're pretty sure that this is what the number looks like. So yes, because if you look at -- if you add up all of the total IT spending, if you look at what the major public companies are bringing in revenue, well, you just what, maybe double or triple that, and we're good?

Greg Zwakman

Yes, there's a lot of kind of triangulation, not so much in terms of validating assumptions, but a lot in terms of does this make sense relative to some other big number? Or does this make sense relative to a publicly, again, available figure and then they run with it. Because that's something that can kind of "validate" the number that they have.

But if you want to break down an approach, if you want to break down a structured methodical way to structure the analysis, then you have to look at how do we populate with all this data that's difficult to get, oftentimes expensive to get, but something that's going to build something where you can validate from the bottom up, see where the overall opportunity is going to hit the market? In what areas, what locations, verticals, company size, and that can support the number. That's difficult to do. That's the challenge.

Eric Hanselman

So how do you overcome that? And I guess this gets to the nuts and bolts of what really is the process of 451 Research in terms of how you really put this together?

Greg Zwakman

Yes, absolutely. So what we've done is we've built a dataset, and kind of methodology. I'm going to unpack all this later. So that it does strip a lot of the time and cost out of the process. So I think one of the big hurdles is budget, let's be honest. These are, again, expensive projects, time frame and other thing. So we've -- from the team I have, again, Leika Kawasaki primarily of building a TAM model that we can reuse and kind of redeploy in different projects.

And then what we have to voice of the enterprise in terms of the spend and adoption assumptions, we can leverage that data that's "off the shelf." So that's kind of the overarching theme of kind of what we're doing. Now to get into the process and the details of what we would do? There's a lot of different components. I'm not sure how many. We'll just name them off and then we'll count them after we're done. So market definition from segment addressable scope. That's number one. Then you look at the target population model. We'll talk about that model in a bit.

Creation and validation of what I call the assumption tree, which is the adoption and spend, not just one number, but at each individual layer within the population data. We're going to unpack all this in a minute. And then we've got how we do the final vetting. Not only do you get a number that you feel comfortable with, but how do you really kick the tires on this analysis? And then how do you plate the analysis? Are you going to shift plating the meal? How do you present? What do you present? What do you showcase so that you can support it?

So if we start from the beginning, something that gets often overlooked and not a whole lot of time spent on, is defining your market, the market scope. What does this product or service do? And is it -- does it mimic an existing? Is it a combination of several different existing markets that you can constitute it, those could be? Is it something mixed with consulting or a managed services layer? You have to define what the market is, not just name it, but you look at the use cases.

And I'm going to bring up use cases quite a bit throughout this discussion, but you have to look at what does it actually do? Why do people use it? What are the overall itches that you're going to scratch with this. So we look at it more of not just say, oh, this is a version of just throwing in like an endpoint security, we're saying this does blank and blank and blank. This is for this kind of a use case.

It focuses on cost reduction, revenue growth, risk mitigation. How does it do that? Where does it get deployed? Who's the person making that purchase decision? So we really unpack that market, in terms of what it does, who uses it, who pays for it? So once you get that wrestle to the ground, you can start.

Eric Hanselman

I mean if this is going to be the next biggest thing since sliced bread, then you need to make sure that, in fact, that you're finding markets where people are actually going to be using bread, is it just sandwiches? Are they going to be for breakfast? Is there toast? All these kinds of things that you got to be able to assign that, again, in a lot of these assessments that we typically see out in the wild have, I guess, shall we say, broad interpretations of use cases.

Greg Zwakman

Yes, I would say, vague. And so that's something that -- and you'll see how we'll use that for several different tiers within this process, really unpacking what those use cases are. So let's just say we have the market defined. We feel comfortable and we're happy with that. Then we look at our segment addressable market. And this is where we define in terms of -- based on those use cases, what is this overall SAM that we're going to be looking at?

And I told you, look at the company. If you're looking at an individual company or a market, you have to look at what can be addressed. Like we've only done one analysis, and we've done dozens of these over the years, where we've included enterprises, what I call enterprise is even with 1 to 10 employees. And that's because the client asked for it because they were going after the very low end of the SMB space. But typically, Eric, we don't really -- a lot of the companies that we work with don't cater to that world.

Is there a one-off? Absolutely. They're not designing enterprise software, enterprise security that's going to be really again deployed by companies at that tier. Maybe even again, 10 to 100. It's just -- we have to step back and say, based on those use cases, what is happening, where do we see this overall company or this market gaining traction. So we'll look at those use cases and say by vertical markets. If we don't have any real, again, compelling use case, if no one's eating bread within hospitality, they don't say we're going to sell them a bunch of bread.

So it's that kind of an overall exercise. So if you select the company categories and the regions even, again, some basic countries, it's just not feasible. They can't get there for, again, whatever reasons. If you select the company categories and the regions, that a company or market can actually address. You're starting with a far more defensible approach and, therefore, a more defensible outcome of what that company can do.

So again, whatever the company is using this for, if it's for internal planning, you're talking to management, you're talking to investors, you're talking to whoever, it's realistic. And you can point that out. This is -- we can go after everything on this piece of paper, and we can showcase how we can do it. So from there, we're looking at the target population, right, in model development. Once we know the market and we know the segments we're going to go after is how do we quantify the population sets.

Now that's where Leika Kawasaki and a lot of the work that she's done, like us, again, collectively as a team have, again, put this together where it's looking at enterprise datasets. Now that we're owned by S&P, we have access to a very extensive portfolio of enterprise datasets. So we've collectively built a model that quantifies the population of target enterprises broken up by region, individual countries. There's 61 individual countries and for each country, there's 18 vertical markets in 7 company size categories.

So you could say, "Hey, Greg, you just basically locked your team in an office for a couple of years and built a model, that's a need." But it's important. Because if something like that gives us the overall kind of flexibility where we can quickly ring-fence what an overall addressable market would look like the segments. We're not in APAC, we'll take on APAC. It's an easy thing to do. We can also do scenarios. We'll talk about scenarios later on, but this model really does help.

And it's something that's prebuilt, a model that we can basically deploy across almost every use case when you're doing an opportunity analysis. Again, there's no time involved. There's no kind of cost involved. But the thing I love about this model is it really enables us to, again, develop opportunity assessments with visibility into each layer within enterprises and validate assumptions. It's got a built in, again, validation mechanism, if you will, when I get to -- and I'll kind of get to that when we talk about the vetting phase.

Eric Hanselman

We can really dig in to see in more detail.

Greg Zwakman

Yes, you can dig in and you can drill into what are the assumptions for specific segments, company sizes, verticals, countries, how they again relate to other, how they again relate to other data points that you have, so you can really be granular when you're vetting these different assumptions.

Eric Hanselman

Just to point out also the point that you're making, this is enterprise data as opposed to consumer level data, which is a whole art and/or science in doing of itself.

Greg Zwakman

Absolutely. This would be enterprises. So again, consumer data, yes, probably talking to different groups within S&P, but what we're looking at is enterprise business. So that would be the number of enterprises, the businesses that you're selling to. That's actually a good point to point out. And then if you go down to really the most important thing in this, I'm saying that most important thing a lot here, but all of this is important. But the assumption tree, what we call the assumption tree.

So we don't use what's aggregate spending in aggregate, the adoption estimates. We have an assumption tree where we're looking at all of those metrics sprinkled by company size, by vertical, by region, by country. So we can validate where we have points where we can validate at that level. So it's a pretty big tree. And really, you step back, and this kind of goes back to, again, defining the use cases for these markets. Why do we expect companies to invest?

If you step back and, say, why is someone going to do this, cost reduction, revenue growth, risk mitigation. We look at the use cases by vertical, by company size. We've already done that. But we also have to double-click into something that I call is, use case density. I make up a lot of terms here today, Eric, but use case density.

Eric Hanselman

This is what analysts are all about.

Greg Zwakman

So we do. That what we do.

Eric Hanselman

If you can't chuck in a new eulogism every now and then, our day is not complete.

Greg Zwakman

Absolutely. So for instance, identity verification, fraud prevention, important everywhere. But if you were to look at use cases for, let's say, finance and insurance versus hospitality and leisure, you argue it's a more dense use case for finance and insurance, important everywhere, but we'll look to only not identify what those use cases are across verticals and company sizes, but quantify them.

Say, this is really going to hit home. This is something where we're going to see a lot of spending. Maybe we see a lot of spending on similar or different like in products and services. So that's where it's going to be very helpful for us to really inform that massive assumption tree that we use to validate at different points and then we can waterfall assumptions from there. Also, we have to take in again why there wouldn't be adoption. What would be barriers to this. Is it too expensive?

That's where you hit the low end of some of these different offerings where the low end of the spectrum, it's just way too expensive. The use case doesn't justify them spending a material amount of their aggregate IT spending on this product or service. Therefore, either take it out of the analysis or have it be a very low bar in terms of that being something that they can address. So for there, some projects, we do employ custom surveys. We still do that.

And these are projects that are a couple of months long. They'll have a survey. It's very in-depth. So we'll have -- where we'll -- again, the adoption spend case is identified, but we also have something called Voice of the Enterprise. And Eric, I know you've had podcasts focusing on Voice of the Enterprise.

Eric Hanselman

Absolutely.

Greg Zwakman

Again, regular, again, listeners to the Eric Hanselman show will understand what we mean by Voice of the Enterprise, and that's, as we all know is a survey instrument where we have a lot of this data off the shelf. For the vast majority of markets that we're tracking, we know adoption. We know spending. So we can use that as the metrics for this analysis.

And this is part of what I talked about before of cutting the time and the cost of these projects down because not only do we have the model that we leverage done and ready to go, but we can have some of the -- probably the most time-consuming and expensive pieces of these analysis that we can pull off the shelf. And it's not sale off the shelf.

Eric Hanselman

Yes, it's already there. We've got the data. We've already done the research, the legwork is complete, off we go.

Greg Zwakman

Absolutely. And again, not to mention the SMEs. Eric, you are one. So the experts in these markets, they're not going to step back and set all these different metrics for us. But again, the role they play, right, and you know this is to be the smartest person in the different markets that they're tracking. They're talking to companies, they're talking to their customers. They're getting a lot of -- it might be anecdotal feedback, but it's valuable. So all these projects that we do, we have an SME that's working on it with us.

So we have that extra layer of market insight, where we look at, again, defining those use cases. It's so valuable to leverage those SMEs who has that deep level understanding and knowledge to help define these metrics that we look at. If people want to look at something that we recently put out on network APIs, that's something where, again, [ Rahul Hestanan ] and I, he was the SME, and we went deep into that market, deep into the use cases, deep into the vertical market.

So if you're looking for an example, go on and take a look at that one. But it's really, really valuable to have the SMEs. We shouldn't discount their importance in this overall process. And then adoption and spend assumptions for comparable technologies. APIs, we did that. There are similar APIs in the market right now that we could validate. We didn't use it directly. We could validate rough ranges of what would be reasonable spending for companies across different sectors.

We can validate that from percent of IT spending, percent of revenue, spending per employee. So those metrics we can pull and analysts can help us validate, so that's something that we would do using different comp companies -- or the comp companies, comp markets.

Eric Hanselman

Yes. And the big value there is that you've now got an objective eye that's taking a look at what that impact is to be able to really take a look at without any of the hopes, dreams and desires that you might get with an internal analysis, something that's actually looking in with maybe a little more seasoned perspective.

Greg Zwakman

Absolutely. And that really kind of brings us to the next step of the process, the final vetting. So I talked about how this model has sort of a self-validating mechanism in it. Leika, when she built this, we have for all the company tiers, size, vertical, country and region, we've got the IT spending, aggregate IT spending for those different carriers. So -- and we also have revenue, we also have employees.

So of the data points that we have across Voice to the Enterprise, if we do something custom or going to be -- like you say, the SME, that outside expert looking in, can validate if we're talking about a brand-new market. Eric, we're not going to assume it's going to be 25% of IT spending. It's just -- unless it is the best thing that ever happened to world of technology.

Eric Hanselman

But Greg, the idea is huge. This is going to be awesome.

Greg Zwakman

That's going back to the narrative angle. No. So if we look at it and say this is something that really comes into where we see from survey data or where we see comparable offerings, where what's just flat out reasonable. If you're looking at something brand new that's not going to move the needle too much, don't assume it's going to be 10% of the company's IT spending. It's not, again, realistic. So that's the first layer that we do. And then we'll look at when the rubber hits the road, like what are our assumptions for spending by company, by different size and vertical and country and region. So would we think that a company whose 250 to 100 employees is going to spend $2 million? No, that's crazy. So that's not something we're just going to go with. We're going to bring that down and we're going to validate.

We're going to adjust that number to bring it down to reality and something that we can defend. So those are the two primary that we do. Data that we have in terms of actual adoption, SMEs, and then we'll use the model to help validate to ensure that we don't have something running off the rails that really makes the overall analysis, again, ridiculous.

Eric Hanselman

It's being able to pull together all these perspectives, that's the big thing.

Greg Zwakman

Yes, absolutely.

Eric Hanselman

In terms of areas, I mean, you mentioned API markets. Are there specific areas that the team has been focusing on? I mean I keep thinking about things like generative AI. But the topic, of course, we've got -- we have to have an obligatory Gen AI mention in every podcast episode. So...

Greg Zwakman

Well, you're going to love this. It's going to be the worst answer you've had at any of your podcasts and make me the worst podcast guest ever. But we can't comment on project in motion, just put it that way. So we can't really go about Gen AI, so I don't lie...

Eric Hanselman

I can neither confirm nor deny whether or not we're actually doing any research on generative AI.

Greg Zwakman

That answer is, "Oh my God, yes." But it is...

Eric Hanselman

So realistically, we've heard worse on Next in Tech.

Greg Zwakman

Good to know. Good to know. But it is more than just an extension of AI. Let's put it that way. Like there's -- and again, the use case metric, that's where -- again, you look at this as a quantitative output. The chart graph looks fancy, but it's really a market study. It's a lot of qualitative analysis that goes into it. And this is where something that is so big and so out there. And so -- well, I don't think anyone is saying that they understand it, but there is a lot of, again, discovery that needs to take place and that's how we'll treat every single project.

Even if you think that's buttoned up and kind of wrestled to the ground and everyone is doing this, everyone knows this, you still need to unpack and look at, again, the details behind the market. The use cases. Why people are going to spend and then we quantify it and that's the quanti. So when you get to the end result, all of that -- everything we've basically talked about until now, the drum roll please, if you will.

What do you showcase? How do you play it, if you're a chef. So not the high-level number of the narrative. I don't want to do that because then you just hear a deadline because Eric would have hung up on me. But detail is behind the opportunities.

Eric Hanselman

But the number is big. It's big.

Greg Zwakman

It's big and it's the greatest thing ever. We're just going to leave it at that. So -- and that's just a high-level number. Let's call it seeing a $20 billion opportunity, but details around region, top countries, vertical market and company sizes. And just like the vetting phase where we look at and, say, do those make sense based on the use case metrics that we've identified and that over kind of use case density analysis. Does that make sense that the finance and insurance is 50% of this market?

Yes, it does because there's 25 use cases and it's the most important thing that they could ever imagine doing. That's, again, exaggeration, of course, but that's kind of thing that you present in the analysis you show. So you can see that $20 billion number. How do you get there in this big murky world? First and foremost, what's included and what isn't?

Eric Hanselman

It's not a matter of just what is the market? It's what are the pieces, what's the granular perspective that helps you understand not only how do you fund it or what the big numbers are, but what's direction?

Greg Zwakman

Absolutely.

Eric Hanselman

To your point, region, specific users and use cases. How do you target? Where do you invest? How do you deploy first? If you're looking for a minimum viable product, who do you target first? All these kinds of things get rolled into the requirement to be able to get that specific.

Greg Zwakman

Yes. And then to take it one step further is to really -- this is what I call showing our work. So we'll again present the assumptions. So we talked about vetting the assumptions about spending by company size, by tier, if you look at it by region or by vertical market. So what are our assumptions. If we basically say that companies between 50 and 100 employees, they might spend a couple of thousand bucks. That is reasonable. That's something that, okay, that's not completely off the rails.

It's something we can understand and appreciate. Got survey data back that supports that. We've got SMEs are saying, that's what I'm hearing about all the different customers. I talk about at that range. That's a number we can "validate." Same thing up the food chain. So big companies, largest companies in the planet, maybe they will spend a couple of million bucks, if it's that important. It's a tiny fraction of their IT spending. So looking at that $20 billion, our number, and what we're used to seeing is that $20 billion number is great and it's huge.

You take our word for it. So here's that $20 billion number to, Hey, this is how we got there. And this is why in these different areas, why it's important for this vertical, why it's important for this geo? Why it's not going to happen in this country versus that country. Okay, now I understand how we're more about how we're getting to that $20 billion. Now you're looking at, okay, well, these are the breakdowns of where we see spending. First of all, how many enterprises are actually going to be adopting.

If you say it's going to be 50 million enterprises, good luck. So unless I'm sure there are some things that have that many users, but typically no. So if you're looking at this and saying, that's a reasonable number. That's based on our book, that's based on what we know about our aggregate, again, competitive landscape, that's something that's reasonable. And then if you look at spending by different company size.

Okay, that's something that's reasonable that we have seen before for similar types of technologies, you can see what we're building out. We're building on that overall kind of confidence in the number. And again, whatever use case, if you need to understand that market, you'll be able to see how we go from all these inputs into this model. And what it boils down to in terms of the key assumptions of spending by company, by vertical, by country, how that rolls up from that level and then the rationale of why it's there, by vertical, all looking at all those use cases.

And then you can look at that big number and say it's more than just a number and a story. It's a number and a whole process laid out and the overall, again, details to backup that number. And most importantly, the data points that will add up and will make sense to get to that number. So that's our overall kind of intention. And we, again, plate that analysis. And it's -- what we've done, and I've kind of touched on this as we've talked about it.

But what we really wanted to create is because, again, we've done -- we've been doing this for the last couple of decades. We've done dozens of these projects, but like the big six-figure -- and again, we still do those, don't get me wrong. But now we have the ability to cater to the lower end. And when I say lower end, I want to say quicker turnaround, lower budgets. That's what we talk about the lower end, where you're looking at. I don't want to do something in for six figures in two months in a custom survey, I want something measured in weeks.

And I want to spend tens of thousands on something. And I want to talk through analysts and understand it, still get the level of granularity that we're talking about in terms of supporting that analysis, but I don't have the time or the budget to do it. Now that's something that we can do, and that's something that we are doing. But again, it's looking at from this overall -- from the first sentence to now, I think we're looking at -- we need to open up the overall kind of kimono here.

We need to show what is the process, what is the methodology that we're employing? This is how we built our model and what's in it. These are the assumptions that we're using, and this is where they came from. And here are the metrics and the data that underpins and supports the analysis. So that's what we're looking to provide.

Eric Hanselman

And it seems like something where this really is the product of those many years of building out that back-end data. And now, of course, with the addition of the S&P data as part of this, that now can make that faster turn, quicker analysis, all that much more possible. And it sounds like we've gotten to a useful place.

Greg Zwakman

Yes, absolutely. You summed it up much better than I can, for sure.

Eric Hanselman

This has been great, Greg. And thinking about this, I will point our listeners to the show notes to a set of both some of the data, some recent perspectives on this and some of the background on this. But it is, as we were talking at the beginning, something that whether or not it's internal projects, funding a company or whatever, winds up being so critical to what should be the reasonable objective decision-making parts of making any decision, especially in our area in tech, where all this really is all the much more critical, given the concerns about investment.

Greg Zwakman

Excellent. Eric, thank you so much for having me.

Eric Hanselman

Well, it has been great to have you on. We -- there's so many more things we could talk about, but we are at time. As you said, there are a bunch of different topics we could dig into, but this is it for this episode of Next in Tech. Thanks, Greg.

Greg Zwakman

Absolutely. Thanks again.

Eric Hanselman

And thanks to our audience for staying with us! I also want to thank our production team, including Sophie Carr, Feranmi Adeoshun, and Kyra Smith on the marketing and events teams and our agency partner, One Nine Nine.

Join us for our next episode, where we're going to be going to the Broadcom VMware Explore Conference and reporting with the analyst team, who is going to be there to get the perspectives of what's taking place at the conference itself. I hope you'll join us then because there is always something Next in Tech.