Our CEO, Elizabeth Cholawsky, recently spent a week on the MarTech Podcast talking about how B2B sales and marketing teams can use technology intelligence to grow their business faster. The result is a 5-episode series on tech intelligence covering the following topics:
- Episode 1: What is Technology Intelligence and Why Does it Matter
- Episode 2: Tech Intelligence Use Cases from Strategy to Execution
- Episode 3: How Tech Intelligence Aligns Sales and Marketing Teams
- Episode 4: Making Tech Intelligence Actionable
- Episode 5: The Future of Tech Intelligence
The episodes are short (10-15 minutes), but they cover a wide range of topics, use cases, and customer examples geared toward helping you understand what tech intelligence is and how it can help you grow your business in 2020.
Episode 1 Summary
In this episode, learn:
- Why it’s important to know what IT products your customers have installed, what they spend on those products and what the terms of the contract are
- How companies are using technology intelligence for strategic planning and campaign execution
- Different approaches to gathering technology intelligence information
- How HG Insights got into the technology intelligence business and how we ensure our insights are comprehensive and accurate
Benjamin: Welcome to tech intelligence week on the MarTech podcast. This week we’re doing a technical deep dive into one of the most powerful and underutilized data sources for B2B marketers, technology intelligence. Each day this week we’re going to publish an episode that discusses how you can understand, identify, and reach your customers based on the software that they use to run their business.
With us today is Elizabeth Cholawsky, who is the CEO of HG Insights, which is a sponsor of the MarTech podcast and a platform that uses advanced data science methodologies to process billions of unstructured digital documents to produce the world’s best technology installation information, IT span, and contract intelligence to accelerate their sales, marketing, and strategy efforts.
Okay. Here’s the first installment of Technology Intelligence Week where Elizabeth and I discuss what technology intelligence is and why does it matter for B2B brands. Elizabeth, welcome back to the MarTech podcast.
Elizabeth: Thank you, Ben.
Benjamin: Very excited to have you here. You came on an episode of our career day segment. We talked a lot about your career working in government, in technology building systems, and now we’re going to get into a little bit about the company that you run. Tell us a little bit about HG Insights. And more importantly, tell us a little bit about the industry that you work in. What is HG Insights and what is technology intelligence?
Elizabeth: So HG Insights, we’re based in Santa Barbara, we’re about 80 people, and we’ve been producing information about a company’s technology for a little over nine years now. So what we do is we can tell you, for any company in the world basically, what their technology profile is. And as you mentioned, it’s technology installations, and that’s hardware and software. It’s technology spend, and the contracts that come and go about that. That gets sold into companies that are trying to market their products, their technical products, to any company in the world. So our markets are in the sales and marketing automation space and also the market planning and market analysis space.
Benjamin: Okay. So I have some questions about how what you do is possible. You understand what hardware and software a brand uses. What laptop am I using? I’m just kidding.
Elizabeth: I was just in the process of looking up your company, and I would have been able to tell you.
Benjamin: Well that’s amazing, but also interesting to hear about how that’s actually possible. Tell me about the technology behind technology intelligence. How do you know what software services and hardware a brand uses?
Elizabeth: So at the core we’re a big data company. Let me describe the process. At the beginning of the month, we start with over 13 billion unstructured digital documents and over the course of the month we process those documents through machine learning, natural language processing, rule-based algorithms, and some AI techniques to, at the end of the month, determine the company, the technology, and then we model also spend over that and the contracts that they have. And we do that, and you can imagine, take a case study, that’s an easy one on a website.
You can easily get a customer and the technology they’re using, but there’s much deeper information in that multitude of documents that we have. Companies post job descriptions and job postings. People have their resumes out there, that’s a rich source. There’s contracts that have to be posted throughout the world for different kinds of technology spend. And then there’s just a wealth of information throughout the internet. All that is a very sophisticated process of big data and data mining and analysis to get to the usable information that we then give to our clients.
Benjamin: So whether it’s documents published for public consumption, whether it is some sort of a signature used on a contract, or there’s other signals that will inform you that are unstructured, that you’re able to comb through and use machine learning to get a sense of what each individual brands are using from a technology perspective, this is, as you said, a big data play. So talk to me about why tech intelligence is important. Why does this matter to marketing teams?
Elizabeth: I’ll just say it’s not just marketing teams, it’s, what we’re seeing, is throughout the organization almost every functional group in a company these days needs technology intelligence. It’s important because at the heart, every company is a technology company now. You think about Coca-Cola and we wouldn’t normally think of them as a technology company, but if you’re Cisco, they’re a technology company to you and they’re using a huge number of different products. And it’s extremely beneficial to Cisco to know what Coca-Cola is using and what competitive products they’re using.
It’s important to know that information because you need to know market sizing. We can help with that. You need to know what your ideal customer looks like, you need to know that. The sales teams need to know what their territory’s look like and how big they are. You need to prioritize your leads, you can do very sophisticated prioritization with technology intelligence and you can just have a much richer conversation with a prospect when you know their profile.
Benjamin: I think one of the things that’s interesting to me about technology intelligence, obviously if you’re Cisco, you’re a big B2B enterprise brand. It is very important to know what tech stack somebody has because we want to go and understand what you can and cannot sell them.
I think the other thing that technology intelligence is great for is understanding the technological sophistication of the person or company that you’re talking to. If someone is using Mailchimp, a sponsor of the MarTech podcast, and Squarespace for their web hosting, and GoDaddy, these are all products that I use here for the MarTech podcast, then you have a pretty good signal that this is a small to medium size business, probably a sole proprietor.
If somebody is using Salesforce, and Marketo, and Adobe, and larger ticket items, you have a good signal that it’s an enterprise company, probably more decision makers. One of the questions for me is there is the idea of understanding technology intelligence. How and why did HG Insights get into this business, and to decide to develop the technology to understand other brands technology?
Elizabeth: So the market need has been there in a nascent form for many, many years. 20 years ago, people still wanted to know kind of where an IBM mainframe was installed. But the way that you got that information 20, 25 years ago is you had an army of people that were calling into companies worldwide saying, “Hey, can you tell me what kind of mainframe you use?” We can imagine that the scale we’re at today with technology, that kind of manual calling person to person data gathering just doesn’t work anymore.
So our founder, Craig Harris, actually looked at the problem and determined that you could assess the same kind of information by looking at the, as you’d call it, the signals that get left behind by companies. There are many forms, I mentioned before, and he started experimenting with how you could, with very, very high accuracy, pull the information out of free form documents to get the same kind of information that was previously just done on a person to person basis. So the form, because the need was there. And then the idea really came from Craig and how he knew that we could mind free form documents.
Benjamin: So essentially the need to understand the tech stack of a business is something that’s not new. Even probably going back to the pre-internet days, you had to figure out who used which dishwasher, if you’re a dishwasher repair man, to be able to understand what your addressable market is. Now with everything being digitized, right?
Then the internet revolution or whatever we want to call it, we have the ability to process large amounts of data and be able to understand, at scale, what technology people are using. Talk to me about why what HG Insights does is special. I’m assuming that there are other people that are doing technology intelligence. What separates HG Insights and your approach, in terms of the data collection, analysis, and also with the end output is?
Elizabeth: It’s easy to get a very lightweight view and a very external view of what a company might use. Websites are public and there are some products that you can look at a website and tell right away that they’re using, for example, the Zuora product that is doing e-commerce processing. There’s generally some sort of pixel or HTML on the website that you can tell, that’s a small fraction of the kind of technology intelligence that we provide. Obviously, hardware is completely masked from that, and the vast majority of software products are also not exhibited on the web. So we’re different from most other providers in that we go behind the firewall with all these documents and can really tell the internals of a company from how we’re processing the document.
And you can say, “Okay, so why can’t other companies do that”? We built up over a long time, over nine years, the ability to understand which documents are most important. So we’ve got a corpus [of documents], and basically prioritization of sets of information that are more important than others. But even more than that, the kind of intellectual property that we have around the rule-based learning, and AI, and natural language processing that we use to get to the set of information that we sell at the end of each month has been developed over nine years. And that’s really hard to duplicate very quickly. It’s very sophisticated.
And the last thing I’ll say is we have developed it over nine years and we’ve got a longitudinal view that is just not an existence anywhere anymore, because those documents aren’t around forever, but we’ve got the sets of information that we can show technology changes over time, say at a company or in a market segment, that’s really hard to duplicate, if not impossible, because the raw materials aren’t there anymore.
Benjamin: So one of the things, whenever we’re talking about a big data science platform, and when you’re analyzing billions of pieces of data that I get concerned about and I’ve done projects where we’re looking at large volumes of details working at eBay and e-commerce, doing lead generation as a consultant, and of the things that I always am concerned about when I hear, “Hey, we’ve got this really rich piece of AI and machine learning,” is I understand the process, I understand that machines have the ability to consume large amounts of data, and most of the time that leads to accuracy in theory but not always in reality. How do you ensure that the data that you’re processing at HG Insights, when you’re looking at 13 billion pieces of data on a regular basis, is actually accurate when you’re selling that data to a consumer?
Elizabeth: That is a big concern at HG Insights. So we pride ourselves on when we send our data and the applications out to our customers that they are high accuracy beyond what anybody else can produce. So we start with people that know data science. So we’ve got data analysts on staff that really understand the right way of applying an AI technique, or natural language processing, or machine learning, and work every single month to improve those kinds of algorithms that we’re using. So that’s one side of it.
The other is that we put a lot of effort into validating the information that we have that’s the core of what we give to our customers. We have a team and we use offshore resources in India to validate, every single month, a subset of what we’re giving out. So they will take the subset and look at what we’re saying about a company, and then use research techniques. And it can involve actually contacting the company to make sure that we’ve got the right match of information. So we spend a lot of resources and a lot of effort and focus on that process.
Benjamin: I think the big thing that is my takeaway here is that with data science becoming more sophisticated, the ability to process an incredible amount of data across very broad sources is possible now, and that leads to lots of results that can be very finite. But actually using human interaction is still a very important part of the process to make sure that you’re actually delivering quality data. Something that HG Insights does well.
I’ve worked as a marketing consultant independently for about four years, and one of the most common projects that I work on with B2B brands is doing lead generation. And the hardest part about doing accurate lead generation is understanding what the data sources you have at your disposal. And it’s one of the reasons why I’m so excited to talk to you more about HG Insights, about technology intelligence, is not only understanding how technology intelligence can be used for lead generation, but some of the other ways it can be used and applied on a regular basis.
So that’s what we’re going to get into for the rest of the week, is talking about what are some of the practical applications of technology intelligence, how customers are using it, how they’re building it into their operations, and also a little bit about the future of technology intelligence. Elizabeth, we’ve got a lot of ground to cover. Thanks for being our guest today.
Elizabeth: Thank you very much, Ben.
Benjamin: All right, and that wraps up this episode of the MarTech podcast. Thanks to Elizabeth Cholawsky for joining us. If you’d like to hear more about Elizabeth’s insights into how to use technology intelligence, we’re going to publish an episode every day this week, so hit the subscribe button in your podcast app and check back with us tomorrow morning when we discuss what questions technology intelligence customers have.
If you can’t wait until our next episode and you’d like to get in touch with Elizabeth, you can find a link to her LinkedIn profile in our show notes. You can also find a link to HG Insights’ LinkedIn profile, as well. You can contact her on Twitter. Her handle is E. Cholawsky, E-C-H-O-L-A-W-S-K-Y, or you can visit her company’s website, which is hginsights.com.