Sramana Mitra: Tell me a bit more about use case. By the way, there’s another company that used to be called InsideSales that’s now called Xant. They’re working on the same problem.
Jonathan Spier: We know a bunch of these. We know enough to know that we’re unique. In companies, there’s a process that companies follow whether it’s explicit or not. They identify a target market. They identify an ideal customer profile. They turn the teams loose.
By the way, one of the things that attracted me to Rev is the customer list – Zendesk, Adobe, Splunk, Salesforce. It’s a heck of a customer list. There’s a place where the process falls down where a company says, “Here’s our target market.”
Then when it comes to executing, you go to your sales team or marketing for lead gen. They have no idea how to operationalize that. How are they supposed to judge who’s an early adopter? The data that they’re relying on is mostly industry, size of the company, and revenue. There are too many companies that are firmographic twins.
Think about John Deere and Caterpillar. Firmographically, they’re the same. They’re both industrial machinery, both Fortune 100, and roughly have the same number of people. We use AI to build a new data type. We call it exographics. It’s a look inside the company in terms of how they execute to find things like Caterpillar is an earlier adopter than Deere. Caterpillar has a higher B2B focus than Deere.
Sramana Mitra: Where are those signals coming from?
Jonathan Spier: From their own website. People information that we get from LinkedIn. Then it’s proprietary data.
Sramana Mitra: Are you able to bring in LinkedIn data?
Jonathan Spier: Yes.
Sramana Mitra: LinkedIn allows you to do that?
Jonathan Spier: It’s public information.
Sramana Mitra: So you can scrape it?
Jonathan Spier: We don’t. We acquire it from people who do. The chief scientist invented the fraud detection algorithm that FICO uses. We have over 500 lenses. Lens like early adopters. It’s all these different heuristics that AI is better at finding. One of the things they figured out is that companies with a wide age range distribution tend to be more early-adopting than companies with either all senior or all junior. They’ve proven that with extensive studies. That’s one of 19 signals that goes into our early adopter lens.
Sramana Mitra: Is there a company or two that’s using your system the way it’s supposed to be used? I’ll qualify my question. At one point in my career, I worked for a mechanical design software company. I was doing a turnaround. This company had fairly advanced technology, but the customers were not using all that advanced technology. What you’re telling me is that you have all these Ferrari capabilities. My experience with software and industry is that, often, Ferraris get driven like Toyotas.
Jonathan Spier: We have a lot of those customers. The company started with services. I can give you an example. Companies will come to us because they want to run lead generation campaigns using a content syndication approach. If they want to do that in a market they know really well and they built a good ABM, that’s great. They can just do a lead generation campaign.
More often than not, companies come to us saying they want to go after this new market, but they just don’t know who to target. Their list is too small. They have a hundred companies, but they don’t have a thousand they could go after. In the services model, we use the technology for them. We build a model of their ideal customer profile. We work with them to customize their targeting. Based on the exographics they care about, we expand out and give a list back where now they can go after thousands.