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.

Getting started: A roadmap for RevOps

Most companies are new to revenue operations, or RevOps. It’s a relatively new practice that’s only started to get traction in the last decade. And the reason RevOps has gained so much momentum is because it can dramatically—and positively—impact an organization. The downside? Growth via RevOps can be slow to start. 

If you’re moving into a RevOps role, you might be feeling both scared and invigorated. There’s a lot of unknown territory—and a lot to explore. So, to help you get started and navigate the field, we put together this guide.


A brief history of RevOps

According to predictions by Gartner, “75% of the highest growth companies in the world will deploy a RevOps model by 2025.”

While searches for revenue operations have picked up speed in the last few years, it seems like a job function that’s here to stay.

How B2B sees the customer has changed over the years. For a while, most demand funnels focused heavily on customer attraction but left current customers wanting more. RevOps understands that companies must combine marketing, sales, customer success and finance to build a revenue-driven company.

Since more companies have found value in getting and retaining customers, revenue-driven job titles like Chief Revenue Officers and Revenue Operations Managers have become more common.

RevOps has a place in several areas of a company’s strategy. Revenue leaders find ways to maximize money coming into the company, so they often have a place in go-to-market strategy, customer acquisition and customer retention. It’s important to realize that revenue leaders are strategic people. Revenue leaders need room for big-picture thinking, auditing and roadblock removal. They are vital forces in helping all departments thrive—and stay aligned.


Getting started with RevOps: Auditing strategies to grow revenue

When companies begin investing in revenue operations, the first step is an in-depth audit of current processes and procedures. RevOps is typically charged with accelerating what’s already there. Companies tend to invest in Chief Revenue Officers or Revenue Operations Managers after having a solid marketing and sales foundation.

As you enter your role as a CRO or RevOps Manager, one of your first tasks will likely be an audit to see what you have to work with. RevOps depends on people, processes and technology to work effectively. Give yourself some slack here. Your work can be eye-opening, especially if you are a new employee with an outsider’s perspective.



First, get to know the people you have on your team. You should meet one-on-one with everyone handling GTM, marketing and sales. And because customer retention directly impacts revenue, RevOps leaders should also meet with customer success team members.

From this audit, determine:

  • Where there may be unnecessary siloes or breakdowns between different teams
  • What dynamics look like within teams and how things can be mended
  • Employee strengths and weaknesses and how to maximize individual performance

After you understand these different dynamics, you’ll be able to recommend specific tweaks an organization can make to improve team dynamics and company performance.



Companies need strict processes to increase revenue. If your organization is stuck reinventing the wheel every time you need to submit an RFP or onboard a new client, you’ll create more work for everyone. So, audit current strategies to determine where:

  • Great processes exist
  • Processes need to be updated
  • Processes are nonexistent

After this audit, you’ll have a clear plan for how your team can save time and money by implementing templates or step-by-step instructions.



Spending on information and business technology in the United States is expected to reach $2 billion in 2022. As businesses grow, there can be many redundancies and gaps in how money is spent on technology. These challenges can make companies fall behind their competitors or waste hours of employee productivity. As a RevOps leader, you’re there to:

  • Audit where technology spending is going
  • Look into what different technologies offer to identify overlap across teams
  • Identify technology gaps based on what competitors are using by examining sites like BuiltWith and G2
  • Find new and existing technologies to create the perfect tech stack
  • Help negotiate contracts with these technology companies to maximize revenue

RevOps doesn’t have to be the most tech-savvy person in the room to do this work. You can (and should) partner with your organization’s IT department and respective teams to determine what the organization needs to move forward.


Building the perfect plan: Activating and operationalizing RevOps

Once you’ve completed the audit, it’s time to move to the next phase of the RevOps process. Your company must do the work to make the most of revenue operations. Activities like setting goals, reporting, holding employees accountable and building pipelines can all be great ways to activate and operationalize RevOps. The title of Chief Revenue Officer is more than a fun title. RevOps is tasked with producing more revenue for a business.



One of the first things you’ll want to do as a RevOps leader is set goals for your position.

Some goals will be harder to define, like ones related to breaking down departmental silos. For these goals, it’s essential to recognize the qualitative nature of these objectives. You might need to rely on pulse surveys, one-on-one conversations or team feedback to understand if silos are breaking down.

Other goals are directly tied to numbers. For example, you might be charged with growing the company’s pipeline by $X or accounts. You should be able to easily use your company’s CRM to track when you’ve hit a revenue goal.

No matter what goals RevOps is given, you must ensure they are relevant and achievable. Revenue operations isn’t a fixer coming in to create short-term change. Growing revenue can take months or years. Companies have to be willing to invest in future growth and be patient.



If you’re dealing with departmental silos, you’re likely having to navigate different reporting mechanisms. While teams need to have their own language, companies need to be able to bring data together to succeed. If there are multiple reporting mechanisms, a company has numerous sources of “truth.” Organizations that want to grow revenue need a streamlined reporting process.

First, it’s important to designate one place as the go-to place for company truths. Most companies use their CRM is typically where things get updated and tracked for reporting purposes. Next, think about how you integrate tools that departments use. Integrations can ensure people still use their loved tools while making reporting and cross-departmental knowledge easy.



Next, it’s vital to clarify accountability. As a RevOps leader, you are responsible for a lot of the work driving revenue, but you aren’t the only accountable partner. Departments across the organization have a role to play in increasing revenue. If RevOps is the only one championing these changes, nothing will happen.

RevOps leaders need to create a pact with other departments. In this accountability pact, it’s crucial to outline:

  • What RevOps will do or experiment with to increase revenue.
  • How RevOps leaders will share duties or responsibilities with other departments.
  • The level of action RevOps leaders expect from department managers and individual contributors.

Clearly defining duties and responsibilities will keep everyone on the same page. As a RevOps leader, clarity will make your job easier.


Pipeline growth

Above all else, RevOps leaders need to increase revenue. One effective way to do this is pipeline growth. You need to help bring new, high-quality leads into the funnel. Unfortunately, companies can grow without creating consistent processes for pipeline growth. Some teams might be relying too heavily on inbound or outbound leads. Some teams may be going through transitions and a lack of knowledge transfer. RevOps comes into these situations to operationalize pipeline growth and make it more consistent.

At Rev, we help RevOps leaders optimize their company’s pipeline with consistent, high-quality leads. Our AI-powered Sales Development Platform builds prioritized target account lists for every outbound function. Our platform analyzes a seed list of your best customers and, from there, creates an aICP (an AI-generated customer profile) that gives you and your team rich insight into what makes your best customers your best. We call these insights exegraphics, and they surface the deep signals like whether a company invests heavily in customer care, is on a hiring spree for more sales professionals or uses a specific technology. Our platform then finds other organizations with those same characteristics. 


Territory and pipeline management

Last, you must focus on territory and pipeline management. As a RevOps leader, you are responsible for thinking outside the box to create larger opportunities for your organization. This work can be challenging, but you don’t have to do it alone. With Rev’s Sales Development Platform, you know you’ll be able to manage pipelines and territories effectively. 

One of the biggest challenges for RevOps leaders is identifying new market opportunities. It’s easy to be swayed by a new industry’s TAM when you’re researching a new market. When you use our Sales Development Platform, you can dig deeper to see which accounts in that market make sense for your organization. This strategy allows you to spend time, energy and resources on markets that will produce results. (No more hoping it all just works out.)

Once you are ready to move forward with a new segment, we also make it easy for you to route targets to the right internal teams. First, we determine bullseye targets that are perfect matches and ready to hear your sales pitch. You can route these targets directly to sales and make their jobs simple. We can also find matches that might need a bit more convincing. You can route those to marketing for more nurturing until they are ready to move to sales.


Build a promising RevOps foundation

Revenue operations is a strategic function poised to help companies create more consistency. Getting started in RevOps often starts with auditing the business as it stands. After laying the foundation, you can start creating and addressing business goals and growing the company. Building revenue takes time, but it will be easier now that you have the roadmap to success.


Looking for ways to predictively grow your pipeline? Schedule a demo to see how AI can help.

The problem with B2B data (and the solution your team is waiting for)

B2B buying behavior involves multiple stakeholders, evolving buying criteria and an elongated consideration cycle. The sales and marketing tech stack only increases this complexity, so it seems counterintuitive to say B2B companies need another source of data to add to the mix. 

Unless such a source introduces an entirely new way of thinking about prospects—a revolutionary avenue to get your solution in front of new accounts that look and act like your best customers. 

To make the case for a new approach, let’s first look at the B2B data being used today to understand current limitations. 


The truth about relationship data

Businesses have relationships with customers and vendors. People have relationships with each other and companies, both as buyers and sellers. People we don’t know but respect influence our behavior, including our buying behavior. What could be more predictive? 

Unfortunately, when it comes to relationship data you might as well be throwing darts, blindfolded. Using relationships for targeting is the fuzziest realm of them all. Where’s the scale?


The truth about titles

People are more than their titles. They have varying levels of influence and competence, experience, tenure with their companies and so on. These things matter. Moreover, the meaning of a title depends upon the context—the size of the company, the company’s core activities, the titles of others on the management team.

Additionally, there is no universal standard for what a title means. In the tech sector, “business development” in a title can mean an entry level salesperson qualifying inbound leads and/or prospecting on behalf of other sales people, or it can describe a very senior level executive developing strategic alliances with huge companies. There are many examples like this.

Next, people grant themselves fancy titles to create all manner of perceptions. LinkedIn is famous for this. Self-aggrandizement is a core benefit of a resume database like LinkedIn.

Similarly, banks are notorious for making lots of people a vice president or at least an assistant vice president rather than giving them better compensation. Many companies use this strategy.

As if that weren’t bad enough—and it is—you also have the problem of decay. The dynamics of the marketplace result in people starting new jobs, receiving promotions and being laid off. 


The truth about firmographics

Most sales leaders use firmographics (especially employment or revenue) to allocate sales resources and route marketing leads. Sales and marketing rely on this data to identify account based marketing targets. Firmographics as the primary filter eliminate more accounts than all other targeting mechanisms. However, scrutiny of firmographic data from any of the usual suspects in the B2B world reveals much of the information gleaned is out of date and inaccurate.


Why firmographics fail


1. The self-classification problem

The US government and many data compilers and publishers like LinkedIn ask businesses to self-classify. So do most B2B publishers and social media giants. Unfortunately, those who self-classify have no training to make such assignments. Moreover, companies will often make selections based on how they want others to perceive them. This approach is very common with directories, where additional “lines of business” equate to additional opportunities to sell products or services.


2. The innovation problem

The North American Industry Classification System (NAICS) with its thousand-plus six-digit codes, provides the illusion of accuracy, even for well known companies. Industries like the tech sector create business models more rapidly than government bureaucracies can update the classification systems. The NAICS can’t easily accommodate emerging business models, while dynamic industries like the tech sector are often the most lucrative targets for many companies precisely because of this characteristic of innovation.


3. The conflation problem

Those who assign industry codes to a business often confuse what a company does with who they serve. For example, software companies that serve the healthcare industry get classified as healthcare firms rather than as software companies. Of course, it’s useful to know the markets a company serves, but blurring markets served with activity performed is not helpful.


4. The line-of-business problem

Even small businesses diversify their lines of business. IT services firms often build software products, for example. Gas stations have fast food restaurants quite frequently. Car washes sell gas, in many cases. Accounting firms provide consulting, configuration, and management of financial systems, offer human resources services, merger and acquisition services and other lines of business. These scenarios are common, as businesses constantly experiment with different models as a means of appealing to customers, getting a larger share of the wallet and responding to competitive pressures and industry regulations.


5. The degree problem

In each of these cases, the question is not simply whether a company is part of an industry but rather to what degree. For example, if you find that a company needs to target software companies generating at least $20 million in revenue, what do you do with a $50 million company getting part of its revenue from software? You don’t know if 10% or 95% of the revenue is coming from software rather than another line of business. This problem is widespread in the B2B ecosystem.


6. The descriptiveness problem

Despite the seeming granularity of the NAICS system, you can’t distinguish very simple criteria. For example, the code for software publishers, 511210, does not distinguish between firms selling to consumers (like gaming software) and those selling to businesses, or both. You can’t tell the degree to which companies sell products rather than services. You also can’t tell where in the supply chain the revenue comes from. For example, to what degree does the company warehouse and pick, pack and ship its products, use ecommerce, indirect channels, retail outlets, and/or direct sales via field or inside reps? These characteristics are simply unavailable in industry codes. 


The truth about intent

To be sure, intent data should be one of many signals marketers use to identify when companies are in-market. Other signals might include a job posting, a leadership change, a round of funding, new legislation, good or bad press, just to name a few.


Intent data you already use

If you have a marketing automation platform, like Marketo, Oracle Eloqua, HubSpot or Pardot, then you are probably using intent data. Intent data can include the email opens and clicks of your customers and prospects, the pages your customers visit on your website, the webinars they register for and attend, the e-books they download, their social likes and shares, and similar behavioral activity. You are using this intent data, among other things, as an input to your lead scoring model.


Third-party intent data you can buy

Third-party intent data vendors have arrangements with numerous B2B publisher sites to license this same behavioral data on those sites, including page views, email opens, white paper downloads, site search strings and so on. You can select a topic area and get that data at the account/domain level, not the contact level. The basic idea is that, if an account is more active around a topic, chances are they are in-market for the solution. Sounds reasonable, but it’s still a guessing game. An expensive one.

Search data is another type of intent data. Google Ads lets you bid for clicks, one search at a time. The Google Ads advantage is speed and relevance. As soon as someone enters a search string, advertisements can pop up. And those ads are specific to the keywords used in the search.

Unlike third-party intent data vendors, Google tries to tailor the search to the individual typing in the search string. Marketers can try to gain first page ranking through search engine optimization or pay for clicks through Google Ads, Bing Ads or other paid features in search engines.

The weakness of Google Ads in search is that you must bid for top positioning, and it’s pretty expensive, especially when you do the downstream math. Just multiply the cost per click by the small percentage of people who fill out your lead form and multiply that number by the small number of people who convert into a customer. Acquisition costs are hefty. That’s OK if you’re selling a solution with a $100k+ lifetime value. It’s not so great for most B2B scenarios.


The problem(s) with intent

Intent data can be a helpful input for inbound marketing efforts, but it still doesn’t tell you the full story or help you with your outbound sales strategies. Here’s what you need to keep in mind. 


Problem #1: Bad fit

None of the intent data vendors or the publishers, social media companies or search engine companies want you to ask yourself whether the people and the companies they work for are a good fit for your product or service. Let’s say someone really is in the market for a solution. Does that mean your solution is the right one for that individual? Of course not. If you are marketing an enterprise CRM, for example, and you reach a small business who needs a simple CRM, chances are there is not a good fit between the intent and your solution. In that case, your enterprise CRM sales person will likely waste time talking to that prospect. Worse, the sales person might convince the small business owner to buy the enterprise solution. Before long, you have a very unhappy and possibly unprofitable customer, one who is posting negative comments on social forums and review sites and consuming lots of sales and support time.


Problem #2: Decay

Intent data has a short shelf life. If you don’t act on that behavior quickly, you’ll often lose the opportunity to do so.

Google has built a $95B search business by understanding the need for speed. You do a search. You see results in sub-seconds. You don’t come back in a few minutes, a few hours, tomorrow or next week. If that were the case, Google would probably be a small company.

That’s the first big problem with third-party intent data. The lack of immediacy. Do you know what blog posts or articles you read last week? Referencing something a prospect came across a week ago (or longer) is not likely to be effective.

Still, intent data can help you figure out areas of interest so you can be more relevant in future interactions. In theory, intent data can potentially reveal depth of interest, helping you with lead scoring. However, interests change. And fast. 


Problem #3: Invisibility of individuals

Unlike Google, intent data vendors don’t tell you who the person or people are who have interest in the topic. Instead, you just know the account has an interest. Now, knowing the account has value, but not nearly the value it would have if you knew who the people were that a topic is engaging. You are left with reaching out to people who may not have expressed the interest at the account.


Problem #4: No intention

First, not all content within a topic has predictive value. You’ve probably read about lots of topics and never purchased anything related.


Problem #5: No influence or authority

IBM has about 280k employees. Most of them, no matter what they read, have absolutely no influence on any purchase above $5k. The bigger the purchase, the lower the number of people at IBM who get to vote on what IBM buys.

Traditional media, social media, search engines and demand-side platform companies often don’t want you to spend much time thinking about that reality, but you should. You’re paying to reach that audience, regardless of whether they influence the purchase of your product.


Problem #6: Late to the party

For many products and services, it’s crucial to talk to the customer early enough to help shape the buying vision. Get in too late, and you are often responding to an RFP your competitor helped craft, one that highlights your weaknesses relative to areas of advantage for your competitor. Obviously, there is intent. You just got there too late.

All these problems with intent data should not give you the idea that intent data has no value. It does, but it is only one type of signal in an ocean of signals that lets you know which companies are in-market and a good fit. It makes sense, then, to use a broad set of signals, not just one.


B2B data re-imagined: Using exegraphic data

Data, to be effective in driving sales, must not be a snapshot in time but a network of ever-changing people, ideas and companies. Mere firmographic models are too inaccurate and simplistic.

AI can predict who will buy, their buying capacity and buying longevity, not just who will respond. It can paint you a picture of how your prospect executes their mission: Are they hiring/growing a certain way? Do they have early adopters on their team? What real-time changes does the data reveal? The right data enables identification of your ideal customers above the funnel—before they’ve shown intent. The data you need is called exegraphic data, and it’s your solution to chasing down the wrong leads at the wrong time. 


In short, exegraphics make everyone on your team smarter, faster, by providing access to the deep signals that reveal what makes your best customers your best—so you can find others that look and act just like them. An AI-driven B2B marketing system with no complicated tech stack integrations saves time, money and effort moving your customers through your sales funnel.

Make your investment in AI count by using the right data. Get a demo.

Where to invest to solve sales and marketing misalignment

Good news first: Advancements in B2B sales have a catalytic effect on humanity. 

Who would’ve thought? Improving the ability of buyers and sellers of complex solutions to find each other more efficiently makes the world a better place. Whether your company is researching a cure for a disease, improving education or developing answers to environmental crises, products born from advancements in digital data will propel you to your moonshot better, faster and cheaper.

Think about how innovations from recent memory have already been used to impact your company’s bottom mission (and money): the introduction and embrace of social media strategy, account-based marketing, automation, retargeting, attribution models and funnel metrics…the list goes on.

With all these levers in place, why are even the best B2B companies still wasting massive amounts of money trying to find customers? 

The bad news: Despite massive leaps forward in tools and processes, we’ve stalled at the most critical points: sales/marketing alignment, conversion and closing deals. Let’s explore the state of B2B customer acquisition.


The sad state of B2B customer acquisition 

In one study, conversion benchmarks for B2B companies doing paid search and display ads (stats based on 14,197 US WordStream customers) saw:

  • 2.41% click-through rate—the percentage of users who clicked on a paid search ad. In other words, 97.59% of people didn’t click. For display ads, the click-through rate is much worse, just 0.46%. That is, 99.54% of people didn’t click the ad. No wonder they call it banner blindness.
  • 3.04% conversion rate—the percent of those clicking a paid search ad, sharing their identity and converting into a lead. That is, 96.96% of the visitors to your landing page leave without telling you who they are.
  • 1.54% closed-won deals on MQLs. You would think, with all the upstream filtering, that the closing rate on marketing leads would be much higher, but it’s not. For that reason, the best companies use lead scoring to reduce the number of unqualified marketing leads sales receive. The best companies also have a dedicated team of inside sales people who just follow up and qualify scored marketing leads and then set up meetings for sales people.

What does that effort tell you about the quality of top-of-the-funnel marketing leads? It’s not a pretty picture.

Conversion rates are low and sales/marketing alignment remains a top challenge

Today, marketing sources 15-25% of the leads that sales needs to hit quota. In addition, the larger the organization, the lower the percentage of closed-won business.

As a result, salespeople spend 20% of their time prospecting, according to CSO Insights. If you multiply the sales budget by the 20% figure allocated by sales to prospecting, the dollar figure often surpasses the entire marketing budget. It’s not just the time investment, however. Time spent looking for prospecting robs sales teams of revenue capacity, so the problem of lead conversion has significant lost-revenue implications.

Additional benchmarks shed light on this problem. According to Salesforce, 12% of all leads convert into opportunities (higher than the Forrester MQL benchmark above). A Marketo study found that just 22% of sales qualified leads convert into closed-won business. Very few leads become sales opportunities or even sales qualified.

When you break out the prospecting function to a dedicated team who qualifies marketing leads and prospects on behalf of salespeople, the conversion rate picture doesn’t get that much better. A report by The Bridge Group reveals reps make an average of 40 dials per day, 10.6 attempts to reach a prospect and have 4.4 quality conversations per day. For teams delivering meetings, that activity level results in 19 meetings set per month, with 8.8 converting into opportunities. For teams providing sales reps with opportunities (i.e., which are more qualified than meetings), the monthly average was 12.5 per month, with 7.5 converting. That’s 1200+ phone calls to get 10 sales opportunities each month, per rep.


The impact of low yields on lead generation

The problem you’re facing is reduced sales productivity, even if you have a prospecting team and low returns on marketing investments.

This problem doesn’t just exist in old-school industries like heavy manufacturing and wholesaling. Peek at those darlings of innovation: the SaaS industry. Cross-functional resource allocation highlights just how much sales and marketing inefficiency is costing B2B customers and shareholders in that sector.

In a study by SaaS Capital, the percent of revenue allocated to functions like engineering, cost of goods, general and administrative, and customer support/success declines as a company grows, but not sales and marketing. 

“Sales and marketing do not scale. Spending in these areas is at least 30% across all revenue levels.”


Industry leaders cite many factors for this poor performance. Sales methodology vendors believe improved sales training will make a big difference. Messaging consultants believe better messaging will win the day. Brand advocates believe a great brand advertising program will steal the spotlight. Sales and marketing technology vendors believe new technology is the answer.

These and other explanations clearly have merit in certain contexts. Anecdotes of success for these narrow remedies won’t solve the core problem. Identifying and dealing with root causes will.


The thought experiment

Everyone has their own idea about which levers to pull, and they all want to be the one to pull it. Efforts are spent in brand, messaging, sales training, etc., but with little or no impact. 

What if the lever is in the customer’s hand all along? 

Maybe it’s not about getting the right message into the right channel. Maybe it’s not about asking the right question at the right time. Maybe it’s not about name-recognition. Maybe it’s not about more technology. Of course, those things matter, but low conversion rates suggest something more fundamental is wrong.

What if these low conversion rates have more to do with the customer situation than your message, your sales and marketing channels, your brand recall, your sales skills, or your sales and marketing technologies? What if targeting could more precisely identify these situations?

There are many reasons most of the market won’t buy a B2B product or service. Some recently invested in an alternative solution. Others have a solution in place that is working sufficiently. Some can’t find what they need. Others tried a similar solution that didn’t work out and so are cautious. Others still have more pressing priorities, based upon their current situation. Many lack the resources (money, management attention, capabilities) to make the solution work. Most don’t even have the authority to buy. No message or cadence, regardless of the method of contact, will change the reality on the ground. As a result, many look but very few buy.

Between targeting the wrong accounts and soliciting those with no voice in decisions, is it any wonder conversion rates are low, and sales and marketing alignment is still a top challenge? The implication of this sober view of the conversion funnel is that something is fundamentally wrong with the ability of sales and marketing to target the right accounts.

Better targeting, then, is the absolute-must application for investment in artificial intelligence in your coming quarters.


The solution: Invest in AI for the right data 

To transform data into actions, leading companies are using machine learning, predictive analytics, natural language processing and other techniques enabled by artificial intelligence.

Most of us experience the benefits of artificial intelligence in our own shopping experiences, such as when we get a ride with Uber, find a movie on Netflix or book a room on Airbnb. The success of B2B giants like Facebook and Google, however, should be a wakeup call to every B2B CEO in the world. Effective use of big data and data science is far more predictive of financial viability than many traditional balance sheet and operating income line items.

The long and short of it: AI can produce better targeting, the immediate solution to your bottlenecks in B2B sales. 

Here’s how it works. 

Mining the right data using AI produces what are called “lookalikes.” Consider these the hyper-focused prospects just ready to be introduced to your B2B sales funnel. Finding your next best customer is made simple through sophisticated pattern matching that prioritizes the right data at the right time, making closing deals that much easier. It’s seeing who’s a good person to purchase before they even know to show intent.

As Joël Le Bon, Ph.D., says in the foreword to AI for Sales, “In sales, time kills deals. In modern sales AI kills time!” We would add it dramatically reduces the overwhelming efforts to further align sales and marketing and makes the whole machine run smoothly. 

To see how investing AI can improve your entire funnel, set up a demo.