New ways to identify new market segments

Every company that looks to enter a new market segment faces the same struggles: understanding the true total addressable market (TAM) and identifying the target accounts most likely to engage. There’s never been a handbook for this, no way to ensure that a company with strong market/product fit in one segment will meet any success in another.

So revenue leaders rely on certain methods that, on the surface, make intuitive sense. But what we propose at Rev is to move beyond “intuition” and put data science behind these decisions. We want B2B companies to enter new market segments with the highest probability of landing new customers.

Our whole thesis is that if you can understand the underlying dynamics of how companies operate, you can identify your true ideal customers, even if they belong to entirely unexpected industries—thereby overcoming the common challenges revenue leaders face when entering new market segments.

 

The old ways are based on assumptions

The traditional methods for moving into a new market might work sometimes—the same way a broken clock does. The strategies themselves, however, are follies.

These are some of the more common less-than-successful practices we see, all based on assumptions made with the best information typically available.

 

Folly #1: Adjacent markets make for strong prospects.

We have a lot of sympathy for this assumption: if your current market segment is successful, an adjacent one will be too. Why not? The two markets look similar and have similar needs. You won’t have to tweak your messaging or your marketing much. The new customers might even be familiar with your product already.

Just because your product fits the apple vendor market, though, doesn’t mean it will necessarily fit the orange vendor market. The way those two business types operate under the surface differ in a lot of ways. Assuming they are both ideal target markets is based on the assumption that all fruit vendors are alike, when in reality they are… well… apples and oranges. 

It’s not as easy as we think it is to identify a new market and build a go-to-market program around it. Targeting adjacent markets is grounded in the assumption that lookalikes are also act-alikes. Companies fail when they pivot to adjacent markets without understanding their underlying behaviors and practices.

 

Folly #2: Rely on firmographics to build ICPs.

Oftentimes, when entering a new market, a company will pull a list of all the potential customers with certain firmographics: they fit a particular vertical, within a particular range of headcount or revenue, and so on. Then the company makes the leap that they’ve identified their total addressable market based on these firmographic parameters.

Within that strategy, we also see companies build their TAL based on names they recognize. Familiarity as a substitute for fit.

However, a target account list based on firmographic parameters is shallow. Just because a company matches certain (relatively superficial) qualities does not mean they are likely to become actual customers: firmographics tell you next to nothing about how a company behaves, or how they actually compare to your existing best customers.

If your ICP is shallow, so is your strategy. And firmographics by nature do not dig deep into a target company. Building a target list this way is like thinking you can find your soulmate by looking for five particular traits, when really so much more than that has to add up.

 

Folly #3: Fail faster in targeting first accounts.

All of us who have worked with startup companies know the notion of failing faster. The flaw with that mentality in breaching new markets is that the failing only matters if it’s in the right direction—and all too often, because of the previous assumptions, the first target accounts are the wrong ones.

This is the first-mile problem: if your first ten accounts fail because you targeted them for the wrong reasons, you just walked a mile in the wrong direction. The market might still be a fine fit! But these prospects were not.

Walking in the right direction, where you can still learn from failing fast, starts with high confidence in the target account list. Which can only happen when you are not polluting yourself with bad data from the start.

 

New ways are faster and better informed

So how do we start with good data? How do we start with an informed sales strategy instead of best-guess assumptions?

At Rev, we use modeling techniques based on company behavior to enhance the probability of understanding target markets and getting things right, right away.

 

Data that digs deeper than firmographics

The B2C world has figured out that demographics don’t do enough to understand customers, so it has developed psychographics. The B2B world lacks that same sort of insight, so we developed exegraphics.

Exegraphics, in short, are pieces of information or characteristics that convey how a company executes its mission. Our AI-driven modeling technique looks at everything from company messaging to hiring boards and employee resumes to understand how a company functions, how it changes over time, and how it compares to its peers—and it examines millions of data points to build a clear picture of what makes your best customers your best.

(For a more in-depth exploration of exegraphics, read What are exegraphics?)

When you understand the exegraphics of your best customers, you’re better able to create a true, working ICP—one that goes beyond firmographics. We call it an aiCP, and its dynamic nature empowers you to always know the changing attitudes and behaviors of the companies that are “fit and ready” to hear from you. (Read more about how we build aiCPs and how they work here.)

These concepts are a lot to take in, but they are like Moneyball for B2B sales teams entering new markets: once you’re free from targeting only lookalikes, you can discover entirely new market segments that behave very similarly to your ICP, regardless of industry.

 

Meaningful prioritization of target accounts

The old way to decide who to call first usually comes down to familiarity or even gut feel. The stacking of exegraphics results in what we call a “Rev Score”—the higher the score, the stronger the likelihood of a successful fit.

Ranking the entire TAM by Rev Score results, essentially, in a tailor-made prioritization list, so you know you’re walking that first mile in the right direction.

Now, you can still customize that list for your first forays into the market segment. If you’re taking a SWAT-team approach, sending in a small number of sales reps without extensive marketing, you might want to identify, for example, a specific function in these organizations that will resonate with your value proposition. Prioritization gives us that starting point, so we have the ability to pinpoint companies that improve our probability of success—and enable us to fail forward, when we fail.

 

Deep roster of target accounts

The AI-driven modeling technique doesn’t just give you the best prospects: it gives you a lot of them—and if it doesn’t, you know that a particular market segment isn’t actually very promising.

A team of humans can easily enough build a target account list of a few dozen companies. It will take a bit of time to comb through their press releases, research their team and evaluate their market. And that list would likely have some really good fits.

But you need more than a handful of companies in a new market segment—you need hundreds or thousands. There’s no way humans can go through that evaluation process manually in any effective way. And the list will change with time, too—exegraphics evolve, your existing customer base shifts, and companies undergo internal changes, all of which will affect Rev Scores and the prioritization of your list.

The best targets this month might not be your best targets next month, and you need to stay on top of who your best prospects really are, right now, out of all the potentials in the segment.

 

Go to market quicker than ever

All these new ways of entering a new market segment boil down to having much more rigor about your process. No longer do you have to throw spaghetti at the wall to see what sticks: you know you’re sending reps after the prospects most likely to bite.

That means you can have confidence in a more surgical strategy. You can develop early messaging and simple collateral right from the start. A few good pieces is all you need to start testing the market. Is engagement happening? Can your reps actually close a deal? If not, what needs to shift? You’re not stuck bringing in the marketing team and waiting for that engine to turn out materials before you can get started.

This whole approach speeds up the information-gathering period and helps you assess, quickly and definitively, whether a new market segment is actually viable. You should be able to manage this in 3-6 months, not the more typical 12-18 months that companies need to determine market fit.

Not only are you making sales that much sooner—you’re getting your product into that new market faster, which can be particularly powerful with new adopters, and you are more likely beating your competitors to the new market too.

Momentum matters in B2B sales. Momentum early on makes even more of a difference. By breaking out of the old ways of testing new markets, and getting precise with the new ways, you’ll experience success in segments you never imagined entering.

If you’re considering expanding into a new market, make exegraphics part of your strategy. Let us show you the exegraphics behind your best customers.

 

What are exegraphics?

Straight up, the B2B world does not get measured and tracked the same as the B2C world. Years ago, consumer sales vendors used to track just demographics. Today, they know a ridiculous amount beyond your age, your gender or your location: the large data attractors (think Amazon or Google) can predict what you’ll want to buy, before you know it yourself.

There is no such thing in the B2B world. No giant data attractor for companies that want to know what other companies need. No way to predict who the most likely, or most ideal, customers for a product or service will be.

So at Rev we decided to construct that information ourselves. A deep picture about companies—what they produce, how they behave, how they work—helps our customers to identify other potential clients much like the way the e-commerce giants can target you as an individual consumer.

Anything you could want to know about a company, we call an exegraphic: a piece of information or a characteristic that conveys how a company executes its mission.

Let’s take a look at what exactly exegraphics are, and how they help companies like yours find more and better prospects, faster.

 

Exegraphics capture how a company projects itself to the world.

Exegraphics require us to look at companies in two major ways. The first is, what does a company say about itself? All companies make promises (both explicit and implicit) to their market about what their goods and services can accomplish, how they conduct themselves, and what stands them apart from their competition.

This external facet captures both functional language and messaging: both what a company says and how it says it. Essentially, our models take in however a business communicates its value to the public. We generally capture this information on company websites and social media platforms, as well as other similar sources.

The difference between collecting firmographics and collecting exegraphics is their depth. Just as B2C companies have moved from demographics to psychographics, B2B needs to shift from firmographics to exegraphics. 

No large or successful B2C company would be satisfied with making decisions based merely on demographics anymore. Psychographics simply tell a better story about where a consumer is positioned. Exegraphics do the same thing for B2B companies: they extend beyond firmographics not only to tell you the industry a company is in, but also to show you how it positions itself—and how they operate—within that industry.

 

Exegraphics puzzle out how a company functions on the inside.

The second major way exegraphics require us to look at a company is, how does it function on the inside? This one is less immediately apparent. Most companies don’t publish how large their legal team is, or how fast their software department is scaling. And these things are not always discernible from external projections.

Think of it like this: two companies can be in the same field, offering competing services. One does it with an army of people, and the other does it with five AI engineers and a bunch of robots. One of these companies might be your ideal candidate, and the other a worthless lead.

Exegraphics get to the difference by looking inside. Our model turns to professional networking sites and similar platforms to build a sense of who works at a company—what are their functions? their responsibilities? their skills? their professional backgrounds? It also looks at job postings to determine what the company is asking for in potential employees.

Whereas the first way exegraphics look at companies focuses on a company’s position in its industry and the value it offers to its market, this one focuses on the functions of people within the company, and how those functions are built, sized and prioritized.

 

Exegraphics figure out these things millions of times to place companies among their peers.

A human being with time and the internet could do that work for a single company. In a day, a team might be able to assemble information for a hundred. But to be of any significant use, you need to do it for the thousands upon thousands of companies in existence. Rev uses a mix of powerful AI technologies to undertake the sense-making by doing what a human would do, millions of times over: read websites, resumes, and job postings to create a clear picture of any characteristic of what a company does and how they do it—then compiling how individual prospects compare to their aggregated peers.

What industry a company is in is a firmographic. So is the fraction of people at that company who are in the legal function. Now, the model looks at the peers—same industry, same basic size—and evaluates if the legal team is about what you’d expect, way bigger or way smaller. And bam! You have an exegraphic that helps you understand that potential customer’s position.

Context matters for any exegraphic. You have to know what peer group you’re comparing to in order for exegraphics to matter. For example, you’d expect a large software team at a software company. But at a dog-walking service, one developer means that company is probably a software giant compared to its peers. Same for other considerations—slow growth in one industry might be massive growth in another. Or, one company scaling when others in its industry are contracting might offer you valuable insights into their behavior.

 

Exegraphics account for change over time.

Comparing peers tells you the relative bench strength of a company in distinct functional areas. Exegraphics can also account for how those strengths have changed and are changing.

Let’s say that you’re selling cloud-based accounting solutions. You’ve identified a bunch of growing prospects. Great—growth is one such exegraphic. Now you can identify the ones whose accounting departments are actually shrinking. These accountants might be overwhelmed; maybe they need new solutions, or maybe it’s hard to hire good accountants in their area and they need something to change.

Now you’ve identified clients who not only fit your audience, but who also have a reasonably demonstrable need for your services.

In this way, you can think of any exegraphic having a characteristic called speed or velocity. How quickly does it change, and in what direction? On an individual level, change is constantly happening. Companies hire and fire people all the time, and workers move between jobs. But looking at groups over time, exegraphics can identify trends within a single company and relate them to shifts across an industry.

Exegraphics can also identify sudden changes. We’ve developed new data sources around funding, for example. Companies that close a round of venture capital have loads more money in the bank overnight. But those kinds of changes don’t happen that often; sudden hiring changes do, however, especially in scaling companies. 

For instance, an emerging Acme Inc. is probably growing their marketing team right now. They’ll have a lot of new hires, and the average tenure will be pretty low. That would be good to know if you wanted to sell them your marketing solution—Acme finally has people who can use this stuff, and marketing is clearly a growing priority for them. And if it’s a priority, they might spend some money to do it right.

That’s the premise and the value of examining change through exegraphics.

 

Exegraphics stack multiple factors to identify top target accounts.

Each single exegraphic can feel pretty powerful to hold. You’ve just identified how many software engineers a whole bunch of companies have. You’ve even identified which ones are growing most rapidly. It’s like magic.

It’s also not a full solution. Individual exegraphics are really handy for getting your first grasp of the concept. But relying on isolated exegraphics will always have limitations. Instead, you want to stack several of them together to get a more multi-dimensional identification of top target accounts.

For starters: not only do you want a team with a need for your product—you want one with the funding to buy what you’re offering.

It turns out humans are really bad at considering multiple factors at once, combining them in a way that makes sense. Humans want to make rules. In or out. Yes or no. What our exegraphic models do instead is weigh the evidence and determine what is worth more points to your query, and what’s worth less, to deliver a more nuanced, inclusive understanding of how companies operate.

 

Exegraphics deliver you companies that look (and act) like your very best customers.

You’ve already got your best customers. The ones you wish every new customer could be like. But you may not know all the reasons why these are your best customers. What are their defining characteristics?

After you feed our Sales Development Platform the Seed List of your very best existing customers, it creates an aiCP (essentially, a lookalike model) to tap into, say, fifty or sixty exegraphic characteristics of those companies and figures out what traits they share. You don’t need to know what the data patterns are—the AI will identify them.

Then the model finds other companies whose combinations of distinctive exegraphics are comparable to your best customers. Essentially, it solves your give-me-more-like-these problem, a million times closer to instantaneously than a human team could manage, and in complex ways that humans simply can’t.

 

Exegraphics let you drive.

Rev’s AI doesn’t think like a human being. It creates and evaluates exegraphics with a points-based system. It also doesn’t make all the final decisions for you: rather, the model translates its points-based system into terms we humans can understand—industries, functions, growth rates, things like that. Once the model finds your lookalikes, you can define and apply lenses to whittle down the prospect list with your own considerations in mind.

To aid in that process, Rev’s exegraphic data and lookalike technology also lets you know which exegraphics are the most defining characteristics of your best-fit accounts. Once you have your list of more-like-these, you can use both exegraphics and traditional firmographics to sort or filter target lists to fit your sales and marketing goals and processes.

Just as an example—perhaps you want to focus on potential customers within a certain region. You can filter the results by geography. Or, you want to see which lookalikes are at a particular size. Easy.

The possibilities really are limitless. If you can dream up something you’d like to know about companies, our tool can take a credible shot at it for you. It’s a unique and valuable offering in the world of B2B commerce, where for too long we’ve been driving in the dark.

Want to see how exegraphics work? Schedule a demo, and we’ll show you our 500+ exegraphic lenses and show you how you can create your own.