How AI is changing B2B Marketing

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How AI & ABM Are Improving B2B Marketing



Artificial Intelligence as Applied to List Building



Artificial Intelligence as Applied to Programmatic Advertising



Guiding Principles on Artificial Intelligence for B2B Lead Generation



How the LeadCrunch B2B Artificial Intelligence Model is Set Up

Podcast Transcript:

[0:00:08.2] ANNOUNCER: Live from the city with the most perfect weather ever, San Diego, California, all the way to the gleaming shores of Jacksonville, Florida, it’s the Green & Greene Show. Here are your hosts, Dave Green and Jonathan Greene, goofing off instead of working, while unlocking the mysteries of demand gen. The Green & Greene Show is brought to you by LeadCrunch, which creates B2B look-alike audiences.


[0:00:33.0] DG: Jonathan, I told you we had to edit out that goofing off part.

[0:00:38.3] JG: Not going to happen. 

[0:00:42.1] OH: What do I pay you guys for?

[0:00:44.7] JG: Oh, God. Straighten up, Dave. Straighten up. The boss is on this time. We have to act like we actually know what we’re doing here.

[0:00:51.1] DG: Oh, boy.

[0:00:54.8] OH: Hope somebody does.

Introducing Olin Hyde, Co-Founder of LeadCrunch

[0:00:57.6] JG: Well, listen. It’s the Green & Greene Show, if you haven’t guessed. I’m big Greene, he’s little Green down there, at the bottom. Today, we’ve got Olin Hyde, President, Co-founder of LeadCrunch. He’s doing big things. He’s looking good doing it. Welcome on the show there, sir.

[0:01:12.2] OH: Thanks. I’m a little confused. You guys couldn’t get another guest today? How did I end up here?

[0:01:19.7] DG: We need more material, man.

[0:01:22.0] JG: These slots are highly coveted, but we figured we’d carve one out for you, since you write the checks.

[0:01:27.9] OH: My third-grade teacher told me, “Empty cans make the most noise.” It’s going to be a loud show.

[0:01:35.8] JG: Right on. What we want to do today is get a state of the industry of B2B demand generation from you, because you’re well-positioned to know these things and have a perspective on it. We’re going to just jump right into that, talking about B2B demand generation as a whole. As I look at the industry today, probably the overarching thing that jumps out at me is that there are a few things that are not great about B2B demand generation. Targeting is one of them. It’s probably the biggest one as far as we’re concerned. How did we get into a situation where B2B targeting sucks so hard? How do we find ourselves in this position?

Why B2B Targeting Is So Terrible

[0:02:17.0] OH: Well, you nailed it Jonathan. It is awful. B2B targeting is absolutely awful. There are a lot of reasons for it. First, very few marketing technology solutions have focused on targeting. Most of them focus on channels, like how to get better social engagement, how to get your e-mail to be more effective, or how to get whatever channel to work. There hasn’t been a whole lot of evolution around targeting. The few tools that do exist on targeting really are limited because they look at the world the same way as everybody else does. That’s through firmographics or intent. Does everyone know what firmographics are?

[0:02:56.4] JG: I would venture a guess that everyone does not know what firmographics are.

What’s Not Working: Firmographics, NAICS Codes, & Intent Data 

[0:02:59.1] OH: Firmographics are things like industry, headcount, revenue. You can think of these things as descriptions of the company you can look up in a database. Almost all these descriptions are fundamentally flawed. Take industry for example. What industry is Apple in? Are they a software company? A hardware company? A music publisher or retailer? They’re all of that and a lot more. What industry do you put them in if you’re a government bureaucrat coming up with NAICS or SIC codes?

When those codes were developed, Apple was only a computer company. The whole idea of having these online marketplaces didn’t even exist when those things came up. Firmographics are going to be flawed just because they were developed a long time ago. Companies are complex, and every company does more than one thing.

The other way a lot of companies do targeting is with intent. This is flawed because intent is saying, “Okay, these are the people who are searching for you on the web, so they must be interested in you.” What if they’re searching for you because they’ve already looked at your competitor? The competitor has already set the frame for how they’re going to view you. You’re already starting from second place or beyond in intent marketing. I think that’s fundamentally why targeting is bad. There hasn’t been a lot of evolution in it.

It’s also bad because it’s really hard to do. There’s no silver bullet. There’s a lot to consider. People like to think about companies or industries or products or people, and it’s all that and a lot more. On top of all of that, even if you can figure all of that out, you might not have any influence at all on a given company’s ability to make a purchase decision. I know that when my board of directors comes and tells me, “Olin, you’re going to buy X,” I get hammered for X. If you’re selling Y, it ain’t going to get bought.

I think there are a lot of reasons targeting sucks. Fundamentally, it’s because everybody’s looking at the world the same way through a distorted lens of trying to fit complex data into a very simple model.

[0:05:24.8] JG: What do you think about that, Dave?

[0:05:28.0] DG: Yeah.

[0:05:31.6] OH: You don’t have to agree with me. I think it’s more interesting if we have a debate. I would love to hear other people’s perspective on this. Don’t just agree with me. I mean, is that true?

[0:05:43.3] DG: Intent data is one of those things where I do think, to your point, everyone has seen it as some new magic bullet. Just because somebody’s reading an article, you have no idea if they’re a good fit for your company or they have any role in the purchase decision. It’s one little tiny faint signal. To hang your hat on that, I think, is maybe crazy. 

Also, I was talking to the head of our data science team the other day and we have a huge database here. I think it’s like 200 million contact names in the US or some crazy number. He went in and was analyzing all the different titles, because that’s the other method people use to target. They have titles. There were 250,000 different titles. When you put “IT Director”, guess what? You’re probably not going to get the guys you want. You’re going to get some of them.

[0:06:44.5] JG: I’m pretty sure my title on LinkedIn right now is Marketing Ninja or something like that.

[0:06:49.4] OH: Yeah, Demi-God. I think you’re part God, part human, something like that.

[0:06:55.0] JG: By the way, I hear Apple has a really nice doggie daycare as well. I’m not sure that’s how they should be segmented, but “Software company” definitely doesn’t capture everything that they do.

[0:07:05.5] OH: I walked by Dave’s office earlier. You brought your puppy in today, Dave?

[0:07:09.3] DG: I did. Hey Mish. He wants to pick up my camera.

[0:07:14.4] JG: Mish is older than half of the client success teams. I’ll throw that down to you.

Anyway, we’ve got a status quo and the status quo is that things are marginally crappy, like Dave’s first marriage. No, I’m just kidding. What are the core problems with the status quo and B2B targeting? What’s going on underneath the surface there?

The Limits of Cognitive Load and Fighting the B2B Status Quo 

[0:07:41.9] OH: Status quo almost always sucks. I don’t think anybody says, “Wow, the status quo is awesome,” particularly if you’re working at a startup. I start startups because I don’t like the status quo. The status quo of targeting is bad because you have to find out exactly why a company is a good target. That’s what targeting is all about. Why are they a good fit for you? “Why” is not something you can just look up in a database, as we were just talking about with firmographics.

Firmographics is something I love to beat up on, because we spent more than two years fixing that problem. Forty percent of all firmographic descriptions are flat-out wrong. We did a really interesting study where we had a panel of people go on websites and compare the firmographic identification of the NAICS and SIC codes. The most recent information were from take a really great database. I like DiscoverOrg. I think they’re great database. ZoomInfo is great database. There are some good databases out there.

Even the best of the best are all pulling from basically the same data sources. They’re saying the industry is wrong 40% of the time. We only get it right about 60% of the time. We came up with a way of looking at industry as a group of features. If you can think of this in math, you’d call it a vector. To what degree is Apple a software company? To what degree is Apple a retailer and everything else they do? When we looked at this, the error rate was about 15%.

Going from an error rate of 40% to 15%. Fifteen percent sounds really high, but when you combine that with other things, you can actually get to high levels of precision. The problem with the status quo is they’re thinking about things in a very structured, linear way. We live in a world that’s very complex. The human brain, the way we think, if I can geek out on this a little bit, is we all have something called bounded rationality. The human brain can only keep track of about seven things in working memory. Think of that as seven variables. You can’t think of a thousand things at one time.

The degree to which you fill those seven available slots to think about is called your cognitive load. A marketer has a lot going on in their day. For them to think about the thousands of attributes of the company, why they would be a good fit, and think about all the other things they’ve got to do in the day, there’s not room for that to happen. Your status quo does not account for this limited capacity of the human brain, the incredible complexity of the world around us, and the fact that we are surrounded by data that is factually wrong. That’s a pretty bad place to start, and that’s the status quo.

[0:10:46.3] JG: That’s why robots are dope, because they can do all kinds of crazy calculations at one time.

[0:10:50.7] OH: Right. It’s why Google is dope. Imagine if we went back 40 years ago and said, “Someday, you are just going to a little place where you can type in a question and get magical answers to almost anything you can think about.” You’d think that’s science fiction. The good news is that we have the data science and the technology to not only change the status quo, but replace it with a better status quo, which allows people to really deeply understand why companies are good fits, why they should be targeted, and how to engage them.

[0:11:27.1] JG: Yeah, that’s crazy if you think about it from a tech perspective. I was alive when there were rotary phones and you had to go stretch that long cord across the room and under the door to your bedroom and shut it, so you could do the angsty teenage thing. I’m sure you guys pretty much did the same thing with clay tablets when you were young.

[0:11:48.7] OH: Like cuneiform. Yeah. We’re actually still fluent in cuneiform and Aramaic.

Massive Improvements: From a 40% Error Rate to 15%

[0:11:54.6] JG: Look, we’ve glossed over the point for a second, but this is important. You said you took the error rate from 40% down to 15%. That’s a lot of waste in marketing budget. If you start looking at the universe that you’re targeting with your advertising and 40% of it is BS, and you can take that 40% to 15%, I don’t know if people really understand the compounding effect of that impact on the bottom line, but your waste is being reduced by a lot—roughly 60%.

[0:12:27.4] OH: Marketing waste is the tip of the iceberg. The data is changing constantly. Twenty-five percent of Americans change jobs every year. Human knowledge doubles every four years. The data accuracy for contact information, for example, decays by about 3% to 4% per month. As you pointed out earlier, there’s no standardization for titles. There are 250,000 titles and approximately 160 million people in America with a job. That means there’s probably an average of about 160 or so people per title. That’s just insane. The world is not that complex for there to be that many different jobs, but it just shows how difficult it is to find the signal for whom to sell to.

[0:13:21.1] JG: Yeah. It’s not just human knowledge that doubles every four years, human stupidity does as well. It’s true. The terrible analysis of that knowledge and the terrible application of it by people who really don’t know what they’re doing floods that channel and every data source. It’s not just for the human brain to find connections in data. It’s also to sift through all the BS that isn’t right, or that isn’t measured. Robots are just way better suited to doing that than we are with mass applications of linear algebra and stuff like that.

[0:13:56.8] OH: The robot actually makes our jobs a lot more interesting, because we can use the human brain for what it’s really great at, creativity and building meaningful relationships and the human side of it. The inspiration for starting this company was really around how we can use artificial intelligence to make the human life better. That’s a pretty cool thing to come to work to do every day. Hopefully, we’re doing that for marketers.

[0:14:26.2] DG: I felt you really nailed it right at the outset. I’d just like to touch on this idea of “why”. “Why is somebody a good potential customer for you?” is a question that I think marketers need to spend a lot more time and think more deeply about. Part of that is trying to get the right data to even know who the heck you should be targeting. That “why” question is one that, because they’re so overwhelmed with minutia, is sometimes very difficult to just step back and ask the more important questions about why. Why did they respond, and why should I go after this target versus that target?

[0:15:06.9] OH: Yeah. Why is a really fun thing to work on. That actually allows you to have that meaningful conversation, where you’re immediately relevant. You get to the endpoint a lot faster, which, financially, is far more efficient. From an emotional standpoint, I think we all want to be relevant. People love to buy; they hate to be sold. That really gets down to the why thing for marketers to understand their customers more deeply. That can really only be done at scale with artificial intelligence.

[0:15:45.2] JG: Yeah. There are a lot of people who heard you say that and they’re rolling their eyes right now, and I’m going to tell you why, and then I’m going to give you an opportunity to cement what you just said because I think you’re right. This is not the first time that B2B marketing land has heard that a tech solution is going to change everything.

For all the tech advancement that we have—ABM, advanced segmentation, artificial intelligence, CRMs, digital retargeting, all the iterations that marketing has gone through in the B2B space for the last 20 years—we ostensibly are no better in terms of ROI than we were when we were buying single-page ads in whatever magazine. Tech is the do-all-and-be-all and the answer to all these questions. Why hasn’t ROI skyrocketed for B2B marketers in the time since the tech revolution began?

[0:16:42.8] OH: Yes, you’re right. Actually, ROI has gone down. The reality is ROI sucks for a very simple reason. Targeting technology has not improved in decades. What we’ve done is scaled channels while conversion rates drop. The noise floor is deafening, and that’s because channels make money by selling volume. Conveniently, marketers are addicted to volume. Audience volume, traffic volume, lead volume, you name a volume, if you’re selling more of it, marketers want to buy it.

This addiction comes from the horrible conversion rates. I guess it’s what’s called a feedback loop, which just keeps getting worse and worse and worse the more technology you add on to it. That’s why the ROI sucks. We can go through a specific example. One of my favorites is display ads. Holy shit. Only about half a percent seeing your display ad clicked on. That’s why the marketers call it banner blindness. I think it’s more accurate to call it banner pollution, because you’re showing ads to people who really don’t care.

The story gets worse. Most of those clicks are going to bounce because even the ones that click don’t see that you’re relevant. For sake of argument, let’s say everyone who clicks sees irrelevance. That’s not true, but of the ones who do click, only about 2% of them share their identity. You’re left with 98% having no idea who they are. Ninety-eight percent of your 0.5% are anonymous. By the time you factor that in, let’s say half of those are relevant, now you’re down to a 0.003% of your ads, or about one in 300,000, have any value to you at all. You’re buying 300,000 to get one. If you’re really good, 1% of those will close. You need to buy three million ads to get one converted customer. That’s why you do paid search, and that’s why Google’s worth a trillion dollars.

[0:19:10.2] DG: I think if we could just get it down to maybe a billion ads, or something.

[0:19:19.2] OH: These numbers are like what you’d expect from the spawning of salmon or something. 

[0:19:29.9] DG: This is one of those pieces of data that, when the aliens are looking down at us, they’re thinking, “Yeah, no real intelligent life here on Earth. Let’s keep going.”

[0:19:39.5] OH: It reminds me of mayflies, fly-fishing on rivers in upstate Michigan. There are billions of them. They’re all going to die, right?

[0:19:50.4] JG: We might as well be a bear sticking our face in the stream is basically what you’re saying.

What ABM Could Signal For the Future of B2B Marketing 

[0:19:54.8] DG: One area where I think there’s a huge opportunity, and I think it was all with the right intent, is the whole movement with ABM, Account-Based Marketing. Finally, it occurred to someone that not all the leads have equal value, after throwing over tons of sludge that the salespeople hate. Somebody said, “Hey, you know what? There’s a smaller set of accounts that actually buy a lot and maybe we should put more resources on those.”

Yet, they use the same tired approach. What’s your opinion of which companies we should go after and things like that, as opposed to using all of this modern technology to really have a more dynamic ABM list based upon a lot of different factors that would guarantee more revenue for you in the long run? I think that is a huge opportunity for marketers into the future, getting a lot more sophisticated about ABM targeting and using artificial intelligence to help with that rather than mere opinion.

[0:20:55.6] OH: Yeah, I think ABM is just the beginning of a long evolution. We’ve gone from what’s segmentation-based on firmographics, which is crazy, to segmentation-based, like let’s take a universe 10-X and just focus on 10% of it and focus our efforts where we think it’s most likely to generate return. That’s called ABM.

The evolution beyond that thinking that it’s going to convert ABM version one is the next version of ABM, where you take a more scientific approach. That’s the big why. Why is that company on your ABM list? A good reason is company fit, supply-chain fit, that our solution is relevant. Currently, most of those ABM lists are generated because marketing goes over to the sales department and says, “Hey, guys. Who do you want to target?” Salespeople are better at knowing that than almost anybody, but it’s still not science. I think there’s still room to improve on ABM.

[0:21:57.9] JG: To make it really concise and clear and encapsulated in a nutshell, ABM is a great advancement in terms of thinking about how to approach the problem, but the way it’s done is subjective. It’s a supposition-based marketing. We’re almost saying that the answer to that is going to be intelligence-based marketing, in the sense that there’s an artificial intelligence curating who those targets ought to be. What is that curation based on? How does the artificial intelligence know who the right people to target are?

[0:22:32.2] OH: Well, it’s going to be different for every company. You need to have a way of looking at every unique company in their context and understanding, “Okay, how does this data apply to this particular company?” I think the easy way to describe this is to look at what the algorithms did to baseball. Baseball’s sitting on all the statistical information and suddenly, someone has the idea of, “Hey, let’s create a model to use this data to select and recruit and develop a baseball team.”

That’s much the way ABM is going to evolve. You’ve got a lot of data on the company, but people aren’t squeezing that data to get all the value from it. That’s the way you use the AI to get the right list. 

Nuts & Bolts: How AI Improves B2B Targeting

Now there are a lot of ways to do it. I would argue that the algorithm is always going to improve and getting into exactly how the algorithm works is probably really boring. I would probably want to bring in our chief scientists to talk about it. Basically, you can think of it as if you can pick out the shape of something, that’s called features. Think of it as pattern recognition. The pattern for every company is going to be unique for every company.

If you give me a list of 50 of your best customers, Jonathan, and Dave gives me a list of 50 of his best customers, they’re not going to be exactly the same list. Certainly, your two websites are going to be unique. I can look at the similarities and differences between each of your lists of 50 and your website. Now I can start to infer linguistically how similar your customers are to you and to each other. What are the keywords? What are the ways that they are similar and different? That’s just one of many cuts. There’s the cut on linguistics. There’s the cut on supply chain. There’s the cut on people. What type of people are working for you versus working for Dave?

This process goes on. Of course, machines never get tired and their math is perfect. Eventually, they spit out a model that we can use to measure how good the model is. The way we measure that is by looking at how accurately we can recall the 50 that you gave us.

[0:24:53.5] JG: The bottom line is that basically, the problem with ABM as it sits right now, as humans, we’re trying to model what we can’t possibly necessarily understand in terms of complexity of relationships between which campaigns are turning out well, which companies we’re working with are going well. There’s such nuance to what makes them successful relationships that we can never possibly unpack it with human understanding. 

You turn the robot loose on it, then all of a sudden, it turns it into a mathematical equation and extrapolates it to a data set that we couldn’t possibly consider. Suddenly, you’ve got a really cool list of look-alike targets based on people you’re already having success with. That sounds fancy as hell.

[0:25:38.5] OH: It took a long time to build. It wasn’t easy to build, that’s for sure. I sure hope it works.

[0:25:47.0] JG: What do you think, Dave?

[0:25:50.2] DG: I think that’s all for today, folks. I think we have totally crystallized the biggest problem that demand gen marketers face today: the enormous amount of wasted advertising they do. We’re here to help fix that.

[0:26:03.8] JG: Yeah, right on. Last word, Olin. You got anything you want to say to the market at large, all the beautiful people?

[0:26:11.4] OH: Yeah, I really hope that better targeting develops better relationships. I think that’s what we’re all in this for. I’d like to see a world where people are spending less money than what it takes to buy three million ads to get one customer.

[0:26:30.4] JG: All right, play the music. We’re done. It’s been the Greene & Greene Show. 

[0:26:30.6] OH: Thanks.


[0:26:39.8] ANNOUNCER: Thank you for tuning in to the Green & Greene Show by LeadCrunch. Green & Greene think differently about B2B and are starting a movement to transform demand gen. If you have ideas for topics or would like to be a guest, send an e-mail to If you’d like to find more customers, visit our website to talk to one of our demand gen guides,

[0:00:07.3] ANNOUNCER: Live from the city with the most perfect weather ever, San Diego, California, all the way to the gleaming shores of Jacksonville, Florida, it’s the Green & Greene Show. Here are your hosts, Dave Green and Jonathan Greene, goofing off instead of working, while unlocking the mysteries of demand gen. The Green & Greene show is brought to you by LeadCrunch, which creates B2B look-alike audiences.


[0:00:32.9] JG: I see we finally got an accurate intro.

[0:00:37.1] DG: We’re getting into this whole honesty thing. I thought, “Might as well come clean.”

[0:00:43.1] JG: Yeah. I’m over here, like I got my toenails painted this weekend, if you believe it. I’m just admiring my nail polish instead of working.

[0:00:51.7] DG: That is so good, man. I haven’t gotten into painting my toenails yet. I can see that I’m just missing out in life and I’ve got to get into these finer things.

[0:01:03.3] JG: It all started when my daughters were young. They used to paint my toenails for me. Now I just take the whole family for pedicures.

[0:01:13.8] DG: Well, I think we’re supposed to talk about demand gen, B2B-type stuff. We should probably get cracking before you’re not doing anything.

[0:01:22.9] JG: I’m sure audience has already found something else to watch. Last week, we talked about artificial intelligence as applied to programmatic advertising and discussed some of the ways we’re leveraging that in our core business and some of the things we think are possible. That’s not exactly the end of the story, is it? I mean, we also think we can apply this to list building and other ABM pursuits. Do you want to talk about that?

What Your SDRs Are Really Doing All Day 

[0:01:49.6] DG: Yeah. If you ever go watch sales reps, like SDRs for example, you’ll notice that they spend a huge amount of time screwing around. It’s not that they intend to screw around, it’s just the way it works out, because what do they do? They go on LinkedIn, and they munge around for a long time. Then they go on the client website and they dink around on that. Then they go into ZoomInfo, or Discover, or whatever data tool, or tools they have to try to figure out who to talk to and try to get some contact information for them, in order to send them some pretty irrelevant e-mail. That is the playbook du jour for probably 60%, 70% of SDRs today, and a heck of a lot of the actual sales reps.

[0:02:42.0] JG: It’s funny. I’d like to actually run an experiment, because most of the SDRs that I know make about five Facebook comments to every one I make. I have this theory they sit there and argue on Facebook all day.

[0:02:56.3] DG: Yeah. There are probably a bunch of them watching this show right now thinking it’s a strategy for trying to figure out how to sell you something. There are a whole bunch of marcom sales guys watching this show right now thinking, “Okay, what’s the angle here that I can use to send a pithy e-mail?”

Well, one of the things I think people ought to be thinking about is do you really want to have your $100,000 or $200,000 resource screw around all day? I think maybe not. Maybe that’s not the best use of limited resources that are hard to scale. I’m just spit-balling here, but maybe you should actually give them a list that’s already cleaned up, and it’s the right people and the right companies. What do you think?

[0:03:44.9] JG: You’re getting crazy. I don’t know if I could follow you with that. 

Why Contact Data Is So Dirty

[0:03:53.0] DG: For the uninitiated, you get a resource like Zoom, and I’m not trying to throw shade at those guys, but contact data is just incredibly hard to keep clean. That’s just the reality. Zoom and DiscoverOrg and Inside View and all these different providers, they really do a very good job, all in all. It’s never perfect and you can spend a lot of time, and then you find someone, and you go send them an e-mail and it bounces, or you dial the phone number and it doesn’t work.

After you put all that effort in, it’s pretty disheartening. If you’re a sales guy, it’s worse, because sales people are often given very broad parameters. Your job is to call any of the companies in your patch that have 500 to 2,500 employees, or…, or…, or…, right? Within that, of course, there are fantastic prospects and there are terrible prospects that are a bad fit, completely unlikely to buy. The very worst thing that happens is you get some interest and, because it’s hard to get interest, you hold on to those guys and you take them all the way through the closing. You overcome all of their objections and they sign the contract and you get your commission. Then you’re slammed with phone calls from an angry person who hates your guts, because you’ve sold them something that didn’t really work for them. 

That’s even worse. That’s demoralizing. You don’t really want to get back on the saddle and do that again. I think the better thing is, “Hey, let’s go look at the characteristics of your best customers. Let’s go figure out who it is that’s involved in the kinds of decisions of the products you sell. Let’s go find companies like that. Let’s go find the people. Let’s go validate the phone numbers and the e-mail addresses and make sure that they’re still there,” because I don’t know if you knew this, but on LinkedIn, I’ve noticed that when people get fired, they don’t tell you right away. They make it look like they’re still employed at that same company. There’s a lot of that. There are also fake LinkedIn profiles. Instead of making your sales people try to sort all that out, do it for them so that they can do what you actually hired them to do, which is… mmm… sell something.

[0:06:22.3] JG: If only it were that simple, dude.

[0:06:24.6] DG: I know. I’m over simplifying by a lot, by a whole heck of a lot.

[0:06:30.3] JG: No, I’m kidding. It is that simple. I mean, the technology is emerging. We own a fair bit of it, actually, that can make this happen. Do you want to explain how that works?

[0:06:43.6] DG: Before we go there, let me tell you about what I think is the ideal use case here. ABM has rightly become the rage. Everybody’s doing ABM, except for the people who don’t know what it is. I’m not sure who those people are. It’s more pervasive than Donald Trump.

ABM & An Integrated Marketing Strategy

With ABM, one thing you can do is generate leads. Here’s a list of accounts. Let’s go introduce our whitepaper or e-book to them. A small percentage of them will raise their hand, and you can opt them in. You can do more, right? You can do display advertising to all those same people. That might make your lead generation efforts more productive because of brand awareness and tying an integrated message in and things like that, tricks that you do for LeadCrunch.

The third thing is that’s still not good enough. You know, these are your $100,000 or quarter-million-dollar or a million-dollar accounts. You have an SDR team or a sales rep banging on the phone, trying to get into the account as well, in addition to all those other things that you might be doing with digital or direct mail or whatever your marketing strategy is to create awareness and to spark some interest. Why not put all those things together in a single package and really kill it? That, to me, is the kind of thing that people ought to be thinking about with this, so they have a much more integrated approach. Cleaning up that list for the sales guys is part of the deal.

[0:08:23.9] JG: Yeah. The important thing about that integrated approach is that, when you begin to think of your entire market as a cohort, instead of onesie, twosie leads, it fundamentally changes the way you approach marketing. You might even find that you do it in phases, instead of trying to do everything at once and having a whole funnel. Obviously, a whole funnel needs to be built, but instead of trying to curate the whole funnel at once, you find that you try to move the cohort.

You start with a couple of months of really top-of-funnel, familiarization messaging, and then you move to conversion messaging over time. I think the efficacy of those things increases as a result of having done that.

[0:09:05.3] DG: They do. I’ll just give you a personal proof point. Long ago and far away, I worked for a company with a huge brand, really well-known. I didn’t realize at the time what a huge benefit it was to be able to call up and everybody already knew the company and had respect for it. Then I went to a little agency after that and no one wanted to give me the time of day. I was the same guy. I was actually a little smarter. Forget it. I didn’t have that brand behind me.

Building a recognition and awareness helps sales enormously. It’s hard to measure the benefit of that, but I think it also has a catalytic effect on the actual lead generation the marketing is doing as well. If people already know and trust the brand, it really does help.

[0:09:52.8] JG: Sure. Yeah, but the key, I think, is a world-class segmentation, or being able to arrive at that list of people in an intelligent way. I think that’s where most marketers fail. We’re very lucky that you happen to have a ton of experience with that. We’re able to layer together a whole bunch of different data sources and arrive at a well-curated list. I think that a lot of people perhaps don’t have that level of savvy, or they’re not ready to step up to that level of spin, because that’s an expensive proposition as well. I think we can help with that.

The Not-In-A-Million-Years Correlation

[0:10:28.2] DG: I was just talking to one of our rock-star clients over at Oracle Bronto, a guy named Bryce Roberts. Bryce, if you’re listening, sorry for telling the dark secrets that you shared with me, but I thought it was really instructive. We had found a correlation for Bryce, that he said, “Hey, I wouldn’t have seen this correlation in terms of targeting in a million years. I never would have thought of it.” I think that’s one of the powers of data science. You have all this data out there and there are signals that are really germane and they’re usually specific to your company and your particular product, and you need to figure out who those folks are.

By the way, it’s not how many employees or what industry you’re in. Those are maybe fence posts that you want to have in, but they’re not really going to get you to where you want to be. It’s usually much more nuanced than that. I think I was giving the example before: we use our own look-alike audience. The look-alike audience, among other things, will go out and look at a website and essentially look at keywords that seem to be common between two companies.

In our case, they found that if one of our prospects has the word “Gartner” on it, the big market research company, they tend to be a good prospect for us. We do content syndication. People are licensing Gartner whitepapers. There’s a good chance they’re trying to get that up, be on their website. They’re a good target for us. That’s the segmentation or filtering that I think is simply unavailable on the market, unless you’re using some look-alike engine.

[0:12:10.5] JG: Yeah. I used to use that similar targeting in the B2C base extensively. If I was selling surfer’s rash guards and I couldn’t figure out how to grow the market anymore, I’d try to think about crossover audiences. It turns out that Brazilian jiu-jitsu fighters wear surfer’s rash guards, so there’s a crossover there.

There’s no good way to do that in the B2B space. It’s much more difficult to cross-reference mentally. The AI, for whatever reason, has a knack for going in and ripping those things out for you and making them apparent. After the fact, you’re usually like, “Well, duh.” You never could have arrived at that conclusion by reverse engineering it.

Beer & Potato Chips: Insights from B2C Marketing

[0:12:50.6] DG: Yeah, that’s absolutely right. I think that consumer marketing in this regard is so far ahead of B2B. You just need to walk into your local convenience store. They’ve got things arranged per that crossover idea. The beer and the potato chips are in one place and the milk and the butter and the eggs are in another, because you’re usually not going in and getting beer and butter, right? They’ve learned that, so they put things together that are likely to relate to you, cross-selling and upselling. This is the same thing. That’s all segmentation really is if you get a little bit more elite in B2B.

[0:13:28.0] JG: Yeah, but you could have never observed those connections in the B2B space with your natural eye, because it takes 10 million different websites and natural language processing and higher algebraic linear analysis of the various points to even arrive at an idea of what these various crossovers could be. The AI does it efficiently and effectively and it’s been beneficial for a lot of our customers.

[0:13:52.8] DG: There you go again, man, using these big words, “algebraic” and stuff.

[0:13:56.4] JG: Yeah, it’s scary man. It really is.

[0:13:58.6] DG: I have no idea.

[0:14:01.6] JG: After Mark Russo watched our show, we’re busting out the approach records right now. Just let the robots do it, people. Trust me.

[0:14:11.4] DG: Well, great show, Jonathan. Thanks so much. By the way, if you noticed and you probably didn’t. It’s pretty hurtful if you didn’t, but we have a new jingle. It’s because I am in San Diego. I don’t have my beautiful San Diego backdrop yet, but I do want everyone to know, because I’m sure you’re on the edge of your microphone or audio device. We will have a San Diego backdrop shortly. It really is an awesome place here, man. I’m so glad I’m not living in Houston, Texas anymore. No offense to all the people there, but I’ll take San Diego any day.

[0:14:49.8] JG: Rub it in, rub it in.

[0:14:52.2] DG: You’re in Jacksonville. That’s a good spot. There’s nothing wrong with that.

[0:14:55.0] JG: Ain’t that far. It rains every day at 3:00 here. It never rains in San Diego. Every time it rains in San Diego, people lose their mind. Yeah. Anyway. Play the music, that’s a wrap. It’s been the Green & Greene Show.


[0:15:14.4] ANNOUNCER: Thank you for tuning in to the Green & Greene Show by LeadCrunch. Green & Greene think differently about B2B are starting a movement to transform demand gen. If you have ideas for topics or would like to be a guest, send an e-mail to If you’d like to find more customers, visit our website to talk to one of our demand gen guides,

[0:00:05.1] ANNOUNCER: Live from deep in the heart of Galveston, Texas all the way to the gleaming shores of Jacksonville, Florida, it’s the Green & Greene Show. Here are your hosts, Dave Green and Jonathan Greene, ready to unlock the mysteries of scaling demand gen. The Green & Greene show is brought to you by LeadCrunch, which has reimagined how to find B2B customers at scale.


Dave Comes Clean With the Audience

[0:00:23.8] JG: Dave Green.

[0:00:26.8] DG: You know, we have been dishonest with our audience, Jonathan. I am no longer in Galveston.

[0:00:36.0] JG: I know.

[0:00:37.0] DG: I am now in beautiful San Diego, and all you people in the other parts of the world can eat your hearts out because you don’t have the best weather ever.

[0:00:45.7] JG: It is. It’s like I get off the plane in San Diego and my body automatically feels better. No humidity, it’s ridiculous.

[0:00:55.2] DG: I have actually started to age backwards now. It’s incredible.

[0:01:01.6] JG: We’re going to have to have new intro music made. I kind of want to get a hypeman, too like a hip hop: “The Green & Greene show, what? Yo!” Something like that. Have a little something for the kids, I guess.

[0:01:15.7] DG: Yeah.

AI in Programmatic Advertising & Rad Results

[0:01:17.0] JG: Anyway, we’re going to talk about programmatic advertising today, and specifically, we’re going to talk about artificial intelligence in programmatic advertising. We have done some experiments and the results are pretty rad, so we might want to just go ahead and throw that out there. What do you think?

[0:01:35.8] DG: Absolutely. Just to clarify, you know, AI has been used in programmatic in order to do bidding. This is using AI to do targeting, which I think is way more important. 

[0:01:50.6] JG: I think it’s way cooler anyway. Everybody has this problem. Unless you’re using a very specific ABM list, you sit down at your computer terminal to do top-of-funnel programmatic display advertising for awareness campaigns the top of funnel and the targeting options that are available to you are somewhat uninspiring. 

With your general stuff that most people use, like the Bombora segmentations and stuff like that, you have general programmatic data. This is, as far as I know, the first real true artificial intelligence application of targeting for top-of-funnel programmatic, which is how it’s conceptually meant to work.

Instead of doing all that segmentation and stuff, you would just bring a list of your best customers and we would feed that list into our artificial intelligence machine, and it would go out and find the data, algorithmic commonalities between those companies that you had success with already.

Then it applies that to a large data set, in this case, the universe of B2B businesses and in North America, not necessarily B2B which is businesses in North America. It compares that data set of businesses in North America to the algorithmic pattern that you developed from your best customer list, and it returns a list of people to whom you should market based on those you’ve already had success with. That’s pretty freaking cool if you think about it.

How Gartner Helped Us Key in on our Real Target Audience

 [0:03:34.5] DG: Not to get too technical because, you know, part of my audience is in California, where they might not really get it but… my point is that, to build lookalike model is when you put your best customers in there. They’re doing kind of what a sales person would do. They’re going out and looking at sites and looking for keywords that are unusual which seem to be indicative and part of this profile of customers.

What’s close? We do this for ourselves and did it on this experience and found that, if the customer had the word “Gartner”, for example, the technology giant, that tells you what kind of CRM to get and stuff like that, it was a good indicator. Why? They’re licensing content probably from Gartner and they tend to be good prospects for us.

I don’t think that even our best sales guys would have necessarily come to that kind of a nuanced conclusion, and every company is going to be different. That probably has nothing to do with what’s important to you, but there probably is a key to all of those sides from which a really good sales person could say, “Yeah, these guys look like they’re a good prospect.” That’s sort of the essence of that modeling.

We have other kinds of models, too, but I just thought, in this case, that was the type of model that was used. I thought it was so different than, “Give me all the people that are this size in these industries.” You know, that’s like trying to cut bread with a river rock. It’s just not very precise. 

On top of that, this was an internal experiment, and Jonathan, if you didn’t know this, it’s like he’s not just a black belt, but there’s some darker color after that in digital. I was thinking, “Well, this is going to buy us a test.” It’s got to be done for the average Joe. Jonathan was way ahead. He actually outsourced it to an agency that did all the creative and didn’t put all the Jonathan magic to it and just to make it like what everybody else would get. 

Jonathan, do you want to walk people through the experiment and what the results were?

[0:06:40.0] JG: You make a fair point not to espouse the idea that I’m like some sort of magenta belt or something, I don’t know.

[0:06:48.5] DG: You are. 

Jonathan Runs an Advertising Experiment

[0:06:49.9] JG: I don’t know what the next color would be, but anyway, the point is that I’m good at digital, so if I just ran my creative against somebody else’s creative, I would win nine times out of 10. I’ve been doing it for so long, so I didn’t do that. I set the test up and I held static the creative. I had the creative display banner ads generated by an agency so they were identical. Roughly identical number of impressions, that’s really hard to hold static, but we held static budget. We held static bids, everything that we needed to do.

This is a head-to-head test between two targeting segments which is what we wanted to get at. I took the LeadCrunch-generated artificial intelligence look-a-like audience segment for our business, for LeadCrunch. I took our best customer list and submitted that to the AI. The AI spit out a list of lookalikes to go after and then I used LiveRamp to append cookie and device ID to that and hung it in TradeDesk. Then we ran a head-to-head against a typical top-of-funnel targeting segment, like the Bombora segment that we use, which is essentially a segment of advertising and marketing executives in North America.

This is what a lot of people do with their top-of-funnel demand generation. This is a proxy for what most marketers would do right. I ran that for 30 days and came up with the results. 

… and Shares the Results of Using a Lookalike Audience 

We ended up getting a 285% lift over the control with that particular experiment, which is tremendous. I mean, that’s three times the click-through rate in terms of the click-through rate. That’s everything to do with targeting. The creative was identical between both targeting segments. That initial experiment was very successful. We’re very excited about potential here. That’s what we did.

Define Best Customer 

[0:08:59.6] DG: Yup. You know, just so people know, best customers is kind of an amorphous term, and you can actually be very precise with your definition of a best customer. These could be best customers for a product in your product line. These could be big spenders. They could be from a segment of the market that you want to go after.

However you want to define best customers, we can use the same approach and find people to look like that side of best customers. I think the possibilities with this are really spectacular, and I’m pretty excited. I think you’re just getting started. There’s a whole series of tests you’re going to be doing around this same framework over the next several months.

[0:09:47.5] JG: Yeah, absolutely. You can categorize marketers into buckets, in a sense. At the low end of the spectrum, you have people who are sort of upstarts and would grab a general targeting Bombora-style segment and run that. They don’t really know what else to do, so that’s the initial test. If that’s you, if you’re just getting started in programmatic, come to us. Our targeting is going to revolutionize what you’re doing. 

More sophisticated people who have the skills to go out and curate an ABM audience, for instance, are using traditional programmatic-plus layered data, like maybe DiscoverOrg or something. I want to test against those audiences next and see what the list looks like for our artificial intelligence against an ABM list. 

I suspect we will still get a lift. I don’t suspect it will be 300%, but it may be 150% or something in that range in terms of improved click-through rate, even over ABM list. If that happens, obviously, we’ll be very excited to go to market with that.

[0:10:57.4] DG: Incredible story, Jonathan, incredible success. Is there anything else you think the audience might want to know? I have one thing, but I’ll defer to you.

Real Talk on Artificial Intelligence for B2B 

[0:11:11.1] JG: Listen, not all artificial intelligence is created equal. A lot of people are throwing the word around, you see it on just about every B2B services website now. If somebody is claiming that they have artificial intelligence, you need to research what they mean. Predictive analytics and what we’re doing are not the same thing.

We have devised a completely different way of analyzing businesses that includes but does not hinge upon programmatic data. This is like an entirely new way of looking at things, and it’s not been done. Our artificial intelligence really is intelligent in a way that most people cannot approach.

I think it’s an important point of differentiation.

[0:11:59.0] DG: Yeah. By the way, I think the word “artificial” is the operative word in some of the artificial intelligence claims and I agree. I think you have to really peel the onion on that and find out exactly what they mean and how they’re doing it. 

You know, they may have some wonderful application of artificial intelligence, but I think it has become such a buzzword, it’s being grafted onto everything, whether it applies or not. Even small services business that don’t have the data scientists or a programmer on staff are going to be using artificial intelligence now, I just saw one yesterday.

The thing that you’ve done, for anybody who is trying to do programmatic advertising, I thought was really interesting, Jonathan. I wonder if you could just take two minutes, but it’s the idea that, often, you’re not driving somebody to a landing page with a lead capture form. 

That’s not the objective, but you’re trying to educate them a little and cooking them and following them with other ads that ultimately get them to convert. Can you talk a little bit about that philosophy? I thought it was really fascinating the way you were doing that, and I suspect other people might find some value in learning about the thinking behind it.

Top of Funnel vs. Middle of Funnel 

[0:13:20.3] JG: Yeah. From a strategy perspective, people spend an awful lot of money at the extreme top of funnel, trying to get a lead capture, which if you think about it, is really more of a top-middle of the funnel position activity. I didn’t think of lead capture as more of a middle-of-the-funnel activity.

Top of funnel, all I want to do from my strategic perspective is to begin to familiarize people with my brand and how my product operates, what the value proposition of the brand is and what our specific operates are. I think that, especially when you’re launching a new or a revolutionary product, people are not quite ready. The value is not there yet for them to exchange their data for whatever it is that you have behind your lead gate. 

The only way to move them to the point where there is going to be an appropriate value exchange is to begin by having a conversation, by curating information that’s interesting and moving.  There’s no burden to it, so people can engage when they learn about your brand and then, look, when people click through my programmatic ads, they land on a story. It’s a page called “A Tale of Three Marketers”. It’s illustrated, it looks like a cartoon, basically, but it’s about this marketer named Jen who is having a very specific problem that all marketers can relate to. You hit this landing page and you get engrossed in the story and you find yourself clicking through.

The hero of the story ends up being the artificial intelligence that we have available and what it could do for Jen. Her problems are like, “I can’t prove my ROI,” or, “My targeting sucks,” or just, “My sales team hates my leads.” 

[0:15:07.0] DG: Hey, Jonathan, there is no one in the audience who has experienced those kinds of problems.

[0:15:12.4] JG: Yeah, right. I bet you every one of them has at least one of those problems right now. I know I do, and everybody does as well. 

Anyway, the point is I’m familiarizing. I’m telling a story. I’m engaging people rather than converting them. When you get to the point of conversion, I don’t know what everybody here pays for brand leads, somebody who has decision-making authority and who is well-indoctrinated on the brand and who is ready to buy, but by the time I’m done with my nurture process right now, it’s costing me $72.

I’m looking at my Facebook, $72 to capture brand leads and give them to the sales team. That’s fantastic in the B2B space. Most people are probably paying a multiple of that, I imagine. That’s because of this top-of-funnel-like nurturing engaging strategy.

[0:16:01.0] DG: You do have the people who are fooled by volume and by very inexpensive leads because that’s what they can measure, the cost per lead, and that’s how they think they’re extracting value from the spend. 

If and when they’re able to close the loop, not in every case but in most cases, sales quickly learns that those aren’t worth following up with and there’s no ROI whatsoever. It’s actually even more negative because you wasted sales capacity which is very expensive on something that doesn’t convert.

[0:16:36.8] JG: Yeah, I should clarify those $72 leads are people who are actually saying, “Let me talk to sales.”

Why Conversation Leads Are a Truly Valuable Metric 

[0:16:44.0] DG: Yeah, we have a nomenclature here we use. Nomenclature is a big word for a phrase. We used to distinguish between leads that really just introduce themselves and are willing to give us some information about themselves in exchange for a white paper or whatever, and then we get people who we believe are ready to talk to sales.

That’s a much smaller subset, we call those ones conversation leads because that kind of describes, in our mind, what people do. I know there’s all the Sirius Decisions lingo out there about NQLs and SQLs, but I always liked language, and this comes up with something that’s easier to remember and descriptive of what it does for these stages.

[0:17:33.8] JG: Yup. Best to just keep it simple. I tested this targeting technology at the extreme top of funnel for that sort of awareness campaign. I suspect that, when we get to the middle of funnel with the same technology, we’re going to find that the conversion rates in terms of lead capture are higher as well. 

It makes sense. Think of it this way. You can start with the Atlantic Ocean, trying to catch a specific kind of fish or you can start with a stock pond. We’re starting with the stock pond, so it’s much easier to catch the fish that you want to catch.

[0:18:08.0] DG: Yeah, there’s also another benefit. If sales people have to go find people who will buy it from them, their bias is towards people who will buy, not whether the customer is going to be a good fit. Yeah, there are really professional sales people who will, in fact, steer people in another direction when it’s not a good fit, but a lot of sales people don’t.

The downstream problem with that is that your customer service department or customer success team is getting a lot of grief from the customer because they’re unhappy with the decision they’ve made. It’s hard to really build a robust business if you’re leaking customers out the back end, and so you’re doing something to try to find out, “What can I do to replicate more customers that tend to be happy with us?”

That’s a really under-appreciated part of demand generation. Rather than, as you said, trying to boil the ocean, really getting very focused and very targeted, I’ve always found that, in a lot of aspects of life, focus matters a lot. Whether it’s getting your product portfolio focused, the way that Apple did when Steve Jobs came back and took more than a hundred products and reduced it to three or four, or targeting your audience in a more precise way, focus really does help with efficiency.

[0:19:45.6] JG: Absolutely. That’s it, you know. That’s what we wanted to talk about today, but I wonder if anybody has any questions. There are a few people on, you go and drop them. We’ll give it a minute or two. I guess while we’re doing that, Dave, parting thoughts? Anything else?

[0:20:02.2] DG: Here’s a question. They’re asking why we don’t wear hats.

[0:20:07.0] JG: Man, listen, I make this look good.

[0:20:12.4] DG: A polished man.

[0:20:14.2] JG: No. Look, I got the sides tightened up not long ago. Beard looking robust, it’s fluffy but soft. Feel this, it’s like… 

[0:20:23.4] DG: I’ll take your word for it.

[0:20:28.3] JG: It’s fantastic, I don’t need a hat.

[0:20:30.3] DG: All right folks, thanks so much.

[0:20:32.5] JG: All right, no questions, we’re done. It’s been The Green & Greene Show. Get yourself some top-of-funnel programmatic advertising mojo. Give us a call; we’d be happy to help you. 

It’s been real, we’ll talk to you next time.

[0:20:46.5] DG: Bye.

When I first dove into this domain, it was a head-scratcher. Why hadn’t more progress been made? A lot of companies with a lot of funding had been trying to apply AI machine learning in this area, yet no one had really hit the ball out of the park. There was no clear winner. There was no one who had made us say, “Okay. They’re really on to the right core of the solution.”

What I think happened was that because the data was in such bad shape and because there’s so much complexity to deal with, you have to know about all the companies and all the people who work at all the companies and how they interact with each other. It’s much easier for someone trying to solve those issues to say, “Look. I can’t deal with this complexity. I’m going to have to pick a piece where I can see some clarity.”

It’s one reason you see a lot of companies in the intent space. “Hey, let’s just figure out who’s raising their hand, who’s giving me signals that they need to buy something in this particular category.”

The problem with that is it’s only applicable to maybe a couple of percent of the cases of deciding about what to buy and when. Even if you have a great signal, it’s not great all the time. In fact, it’s usually great just a couple percent of the time. There’s no free lunch here. This problem is going to require pasting together lots of tiny little pieces, and so far, nobody seems to have the formula for doing that.

When I looked at the problem, that’s what I saw, and I just said, “Look. We’re going to have to do our best. We may not get enough of these little pieces pulled together, but if we don’t try, we’re not going to get the solution we want.” My approach is different. It’s to face this complexity head on. I know I’m not going to be able to solve it perfectly, but I’m going to take the best shot I can at putting all these pieces together and making a reasonable picture out of it to get a sense of why companies do what they do. To our surprise, we’ve got a small but very powerful team of scientists here. We’ve been able to actually show that, by doing that, we can create performance that others can’t.

If we talk about an entity in the B2B ecosystem—let me draw a little bit of that here. We have a bunch of companies. I’ll make companies circles, and we have a bunch of people who work at these companies. I can’t draw them all, there are millions and millions of the companies and hundreds of millions of people who work there, and not every one of these triangles is an employee. Some might be investors. Some might just be consumers of products or services. We’re going to connect these guys. There are lots of connections, different types.

One of the most important is which companies sell to which other companies. I might sell something to this guy, and he might sell something back to me. This guy might sell things to this guy, but he actually is operating as a channel and he’s reselling those to these guys. There’re a lot of buying-and-selling relationships going on. Those are very important in the ecosystem. But there’re also people who work at companies, people who used to work at companies. He used to work there. He also used to work here. This guy actually consults for two companies. These guys work here, and this guy is his friend. There’s a connection there, and he knows this guy, too.

It’s a complicated picture, with lots of companies and lots of people. There are more entities, things like ideas that flow in this ecosystem. Relationships are critical, but the nodes are important, too. For every one of these nodes, I need to know more about this company. What do I know about Company C1?

In the traditional framework, I would know, like I said, their industry, their headcount, their revenue, things like that. There might be a few more, but it gets really thin pretty quickly. It turns out that’s not enough, and it’s also not enough to know an individual’s job title at this company, right? I need to go way deeper.

For a company, I need to know a lot more things. For an individual, I need to know a lot more things. The way we’ve chosen to do that is the concept of a vector. It’s just like the vectors you learned about in physics, such as the forces acting on a rock, the gravity downward, and maybe the force of throwing it upward that you imparted on it when you threw it. There’re vectors associated with the direction and magnitude of those forces.

It’s the same thing here. A vector is really just a list of numbers, and the key point that I want to get across is numbers and continuous values versus what we have in the old data repositories of our companies, which are very categorical. What industry category are you? What headcount bin are you in?

We took something that’s naturally a number and we turned it into a category. Why did we do that? I don’t know. Maybe it was convenient, but it’s not particularly useful when we’re really trying to dig in and understand companies.

Our decision was that everything is a number, and it’s typically a real number, meaning it didn’t have any value. If I’m going to understand this company, I’m going to want to understand its industry, headcount, revenue, growth. Maybe I want to score for its sophistication and marketing, so marketing sophistication score … right? This goes on and on. Hundreds and hundreds of characteristics I might want to know about companies.

I can even make these things categorical. There might be 100 different industry codes, right? Typically, things like NAICS and SIC have thousands, tens of thousands of codes. We chose not to do that. We made it much simpler. For every one of these different categories that are in industry, we actually have a real number: A .9 in agriculture. A .1 in mining. Zero in construction … Lots of different things for industry.

Headcount. There is some sort of measure of what my headcount is relative to all other companies, say, in my category. Revenue; same thing, everything becomes a real number. Growth; I’m growing exceptionally fast, so I’m going to get a .95 in growth. My marketing sophistication is low, so I’m going to get a 0.1 there.

When we squash all of these out, we end up a big list of numbers. That’s all a vector is. It’s just a list of numbers, and the useful thing about having a list of numbers is you can do a lot of comparisons from one company. This is the vector for company C1, but I have a whole bunch more companies up here, millions of them. If I want to compare them to each other, I have a lot more flexibility when every one of them is represented by exactly the same structure for a list of numbers.

Their numbers are different, but the structure is the same. If this has 75 real numbers in it, every one of these guys does, I can do comparisons of all of it or pieces of it to answer questions like, “Hey, if I give you company C1, can you go find me out of all the millions of companies, the ones that have industry, [so these first few codes here, these first few real numbers], the most similar to this guy?” That’s a mathematical question. There’s a body of mathematics, essentially linear algebra, that says, “Go find me the closest numbers to this guy.” That math is blazingly fast. I don’t have to do a bunch of database queries to make it work.

It’s really, really a convenient way to find what’s similar and what’s not, and that is a deeply valuable thing to do many times over for different questions that we might want to answer, and it has superior performance to just querying a database and trying to draw some sort of a box and say, “Well, if your headcount is between here and here and your industry is either this one or that one, those are the types of queries that get done today.” This is a far more capable way of doing that, and it’s a much more precise way to characterize what this company really is.

We do that for individuals, too. What are their skills? What sort of career path have they taken? What’s their education? Those kinds of things. Everything in this diagram gets a vector, and we can use those numbers to ask and answer a lot of questions that are very valuable to us.


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