John Kosturos (00:00)
Welcome everybody to another episode of the Data Exchange Podcast. Today we've got a very special guest Rohini Kasturi, the CEO of HG Insights. How are you, Rohini?
Absolutely, we're excited to have you. know, HG Insights, in my opinion, is one of the most important companies in the market when it comes to technographic intelligence, market understanding, and helping companies move up market with precision. Maybe ⁓ to start for listeners who may not know you yet, can you share a bit about your background and what ultimately drew you to lead HG Insights?
Rohini Kasturi (00:42)
Sure, so my background, I was a customer of HG prior to joining HG like a year and a half back. So was at a public company called SolarWinds as their chief product and technology officer. Of course, I've held senior roles in several private equity companies, public companies, and even a founder-led startup as well. So I've been in Silicon Valley the last 25 years, being associated with technology companies.
businesses all the time. So yeah, it's a great time to be at HG. And what really drove me here is, if you look at HG, HG has very unique insights that can help good market teams achieve revenue growth. And when I looked at that, the deep insights and intelligence that the company has, and the market timing around data and AI.
and the shift that businesses are doing to really embrace data and AI to drive their revenue growth and transformation. That's what I felt the market timing for HG is so right, and that's what drove me to HG Java.
John Kosturos (01:42)
Very cool. You know, how do you define HG Insights mission today and how has that mission evolved since you took the helm?
Rohini Kasturi (01:51)
Yeah, so our mission really is how do we help customers drive revenue growth through our intelligence. So that's really the one-liner. And for us, the revenue growth is in three dimensions. It's driving top-line growth, achieving customer retention. The third one is operational efficiency. And everything that we do
revolve around this and that's why if you look at our solutions they're all called revenue growth solutions whether it's called revenue growth intelligence platform, revenue growth intelligence fabric and even what we are putting together in terms of revenue growth intelligence solutions is all around how do you create your own growth playbook and how do you move your revenue growth from X million dollars of ARR to 2X and 3X based on data and AI.
John Kosturos (02:38)
Very cool. What do you think most companies still misunderstand when it comes to truly understanding their market and where real buying intent exists?
Rohini Kasturi (02:49)
So if you look at, see a lot of companies when they're building their growth plans, it's based on historical financial projections. It's based on some sets of wins and losses or based on the high level market sizing that they get from analysts, right? But the reality is you have an ideal customer profile. You have some wins, you have some losses.
You want to take that and really calculate your TAM total addressable market, SAM serviceable addressable market, and also SOM, which is what can you obtain based on your product strategy, go-to-market strategy, and how do you really map your ICP to your SOM makes it very interesting, right? So if you're able to do that, then you can get a very prescriptive set of customer lists that you can target. And that is what is the missing part, right? People are operating at a high level.
keep trying to get customers and your conversion rate suffer, your cost of sales is high, your cost of marketing is high, all these are the challenges.
John Kosturos (03:47)
Yeah, I agree. And companies are still investing so much into this whole ABM strategy. And in some cases, like we're talking investing hundreds of thousands or millions of dollars to break into a single account. And I think, you know,
Making sure you break down that ICP, TAM SAM all the way to SOM to help you drive those target account decisions is really what decides whether you're going to succeed or fail. Where do technographics fit into that picture and why are they often misunderstood or underutilized?
Rohini Kasturi (04:23)
So if you take ABM for example, that you mentioned, it's all about precision targeting. If you want to precisely target the customers, you need to know your ICP. Based on your ICP, you need to get your SOM list, or serviceable obtainable list, market list, which is a list of accounts. Then you need to be able to know what is your white space, what is your competition. If you are not aware of that,
then it gets challening. So this is where technographics come into picture because we're using technographics. You'll know what products or technologies that are being used by customers. So you can look at the white space. can start going into each account and say What products do they use? What technologies do they use? So this is very important. That's one aspect that we provide as part of revenue growth intelligence. The second two parts that we also are very differentiating is the spend intelligence. One is about knowing your right
space of your competition. But then when you go into the account, you want to know how much do they spend.
Like for example, let's say you're selling a firewall. How much do they spend on security today? How much do they spend on firewalls today? Having the precise insights at a product level or a technology level is very important. And the third one is intake. Hey, are these buyers, are these prospects, are they in market today? Are they trying to look for something to buy? Because if you know that, then you can precisely target who is in the market today and how much do they spend? What products do they use?
and completely map it with a very precise message using ABM
John Kosturos (05:51)
Very cool. Yeah, I mean, HG is often described as having the most accurate technographic intelligence in the market. I'm interested, like, what fundamental differences, what fundamentally differentiates your data from what else is out there?
Rohini Kasturi (06:08)
So a couple of things. One is we have been curating this data from 50 odd sources over 15 years now. So one thing that differentiates in our technographics is really the time series. we have, for example, you go to Sysco's website. You want to know their products? It's available on their website, right? They list down their products. But if you want to know the firewalls that are still in the market that are not end of life
that distributors, partners in different parts of the world are using, you need a lot of historical information. So we have robust technographics that we have built over 15 years. So we have technographics with time series data that makes it very differentiating. The second part is we have behind the firewall signaling information that uniquely helps us make sure our technographics is high quality. And then what we did is we took the technographics mode that we have
we applied to various elements in our intelligence fabric. We applied that to thermographics to make sure thermographics are differentiated. We applied that to intent. We get the top of the funnel intent from various providers that are out there. But we apply the context to that intent, which is called contextual intent. Now with the TrustRadius acquisition we did, we had the buyer intent. We again applied the technographics
John Kosturos (07:14)
Hmm.
Rohini Kasturi (07:21)
context to that to make the contextual intent. So even the spend intelligence uses the technographic. So all of it
uses the moat that we have, but applies to thermographics, applies to technographs series, applies to spend intelligence, applies to intent, applies to the partner intelligence, et cetera, that makes our revenue growth intelligence fabric very unique and compelling. That's why we keep hearing, you are high quality in the depth and the matching and so on.
John Kosturos (07:46)
Very cool.
So would it be safe to say that other vendors in this technographic market, like number one, the time series, but you guys also have spend, I know some metrics around cloud.
like cloud spend, which cloud, et cetera. And then now layering intent, some type of intent onto that technology footprint. And then overall, the accuracy sounds like where you're leading.
Rohini Kasturi (08:20)
So one thing that we did this year, John, is what we announced in July is called the Revenue Growth Intelligence Fabric. It's a combination of our thermographics that we curate, high quality thermographics that we curate on a real time basis. There's technographics, then we have spend intelligence, was an acquisition that we did a few years back. Then we have intent, three types of intent. Top of the funnel intent, middle of the funnel intent, where we map context. Then we have the buyer intent, which is more bottom of the funnel intent.
Then we have partner intelligence, contract intelligence. What we did is we took all these intelligences that we have, they spread data intelligence sources, aggregated at an account level. So if you take a prospect like SAP, we have intelligence, all these types of intelligence aggregated at an account level. So it's easy to go into SAP and say, OK, for SAP, I need all types of intelligence. And then using this, also built a buying group intellegence
of the functional area intelligence, which is not only talking about SAP, we have intelligence about SAP, but we now have intelligence at a group level. The next step we doing is mapping contacts, so it becomes easy for a salesperson or a marketeer to know, I know.
which group in SAP is using what products and how much are they spending. But by the way, are they in market? So who is that person? Right? Having that contact helps us really navigate this extremely well. And helps go-to-market teams navigate it extremely well.
John Kosturos (09:45)
Very cool. Yeah, I know that you guys have kind of two delivery mechanisms. One is a pretty robust platform that you have many users on. And then there's another offering, which is more of these data deliveries that are more of the raw data files. And I'm wondering, you know,
Is the data science community and data warehousing community kind of a strong customer segment for you? Is that where you see a lot of your customers getting major value is when they take those raw data assets and start to analyze them in their big data environments?
Rohini Kasturi (10:28)
So we do have, call it broadly, two categories of customers. One category of customers that say, give me your intelligence, your data, right, for X number of companies based on ICP, but I want various types of intelligence. They take that, which is third party data, they combine it with their first party data
and other sources that they have and they do their own automation. So all these are data science teams and large scale companies who have, they have resources to actually mine the data, creating their own agents and solutions. That's one cohort. The second cohort is they don't have huge data teams or analytics teams, it's smaller teams usually less than a billion dollar companies.
because they are very lean in terms of overall resourcing. For them, they need a ready-made platform. For them, we have a SaaS platform. It's an agentic platform. So when you log into the agentic platform, there are three agentic apps we provide. It addresses roughly 10 different use cases. For example, Market Analyzer agentic app helps you take your ICP, win losses, and create your time-sand song.
And then there is a rev-up studio which is you can do propensity modeling, scoring, writer best fit models, territory planning, account planning, etc. Then there is a sales agentic cap where the sales agentic cap is taking the entire third party intelligence
and for all the accounts that salesperson has, it provides intelligence from the entire third party, but we also have a first party, second party integrations framework, right? So it connects with Salesforce, HubSpot, etc, etc, outreach and many other tools. It takes the intelligence, combines it with the third party, and it shows up in the account brief. So it's very dynamic, updated from first party sources, second party sources like LinkedIn, and then the third party sources like what we have.
all aggregated at an account level providing that intelligence to sales teams. But then it can also do, let's say you are given a lead by marketing, it can do scoring, can do best fit modeling, prioritization, everything, and it can provide you account rates, and it connects to outreach. So you can start even nurturing that through the sales agent decap, right? So that's the readymade solution we have for growth customers, or less than a billion dollar customers.
John Kosturos (12:40)
Are a lot of those kind of agentic features new? A lot of that sounds very new to me. I mean, I've been very close to the HG platform for many years and even some of that stuff sounds new to me. So tell us a little bit about that.
Rohini Kasturi (12:55)
Yeah,
no fantastic question. So I know you've been associated with HG for a long time. So HG had a platform, or has that platform still, which was basically.
doing a lot of market research kind of use case where you can go dig deeper into the intelligence that we have. Then we created a market analyzer, copilot agentic app in July, we launched that. And then we did an acquisition in August, which is MadKudu. MadKudu was an agentic platform, it has a sales agentic app and a reboks agentic app. What we did now effectively last month is we brought all of these three agentic apps into one common platform.
When you log in, it's one single sign-on, one experience. But there are three different agentic apps, but all powered by our
Revenue Growth Intellegence Fabric which is intelligence for 20 million companies across all these dimensions. Now the fabric is suddenly enriched, the platform is suddenly enriched with the fabric. But also the platform has all these first part and second part integrations with 30-odd integrations. So that is something that we have pretty much gotten ready with all the integration of MadKudu in HG platforms. And we'll be announcing that again in January, very shortly.
John Kosturos (14:07)
Very cool. know, I've been through a lot of acquisitions in my day and congratulations to you guys for such a quick integration. I've seen even acquisitions I was part of 10, 20 years ago still not be integrated into a full product suite. So that's ⁓ super exciting and you guys must be doing really efficient work over there. We'll get back to the acquisitions in a moment as part of our talk track here.
But if you have to be very clear, where does HG deserve to win versus competitors? And what are the use cases or customer types you feel the strongest kind of fit into that?
Rohini Kasturi (14:47)
See, HG is the company, I would say, for the agent world Especially if you want to build agentic apps, agentic apps are only good if your data is good. You need high quality data.
But you need different dimensions of data to guide your sales teams, your marketing teams. Sometimes you need to spend intelligence. The reason you need to intelligence is, let's say you're doing a competitive takeout. And let's say you have a customer, a prospect is using a product that is very similar to yours. You want to know how much do they spend and what are they doing?
where in they in the buying cycle or using cycle etc etc so you need time series similarly hey how long have they been using right so you need various types of dimensions of data to really guide the marketing and sales effectively and you need all those dimensions to prioritize otherwise you might be getting into an account which is five year old account
customer is deeply enriched with so much footprint and you might be trying to displace that account, might be a very hard thing, might take time, because especially we have no intention to displace it. The sales is much harder in that case than displacing something where they're ready to displace. So having all these types of intelligence is going to be key. That is where, that's one of the excitements for me joining HG, like this differentiating data and...
And the company has been doing ML for a while, even before I joined, machine learning for a while. But then we realized there a lot of AI tools that will help curate our data much faster, so we can get to 100 million accounts of coverage in the next nine months. That's a compelling story, right? So for the agent world, so we are the revenue growth intelligence for the agent world. And that's where we deserve to win, and we deserve to be positioned. And more importantly, if you're a CEO or a CXO, thinking about revenue growth,
CRO, CMO thinking about revenue growth. You need revenue growth intelligence, And you need to know what's my growth strategy, what's my retention strategy, what's my efficiency strategy. And that's kind of where we fall right in the horizon job.
John Kosturos (16:47)
Very cool. I think that kind of leads me into the next topic, is kind of partnerships, OEM resellers kind of world where, you know, it's not just brands.
building internal AI agentic use cases that it sounds like you guys want to enable, but also brands that are building agentic solutions that are gonna service thousands or hundreds of thousands or millions of customers. And kind of historically, HG was more cautious about OEM and resell models. What has kind of changed in your thinking?
along that line.
Rohini Kasturi (17:26)
So I think a few things have changed the thinking along that line. One and first and foremost is what we realize is the opportunity that the company has is much, much bigger than what we have captured. We're sitting on a huge stand ourselves. But then how do we capture that faster? That's one. The second is customers are trying to build applications, agents, solutions, whatever you want to call them.
If they are doing it, they need the best in class intelligence. And they are going to do it in different form factors. They are going to do it in ecosystems. They are going to leverage some platforms. They are going to build their own agents internally. We have all combinations that exist from the customer side. When we look at that, and the third element is how much capture did we do as of today from our brand, from our own internal sales effort. When we look at our context and what's our opportunity,
There is a huge gap. We have a huge opportunity to grow this business and that's where we said, hey, how do we maximize this potential because there are various types of agentic workflows getting created. For example, Clay has created a beautiful platform for agentic workflows, but the underpinning is the data and intelligence. So we are a premium partner to Clay. So if customers are building agentic workflows for marketing and marketing or DevOps is building it, but they need the best in class intelligence
And there are other go-to-market platforms doing something very different. But the intelligence is going to be key because the intelligence types that they use is very different from the intelligence types that are being used by other personas or platforms. So we do have like 10, 15 varieties of data that we have that we brought together. So given that flexibility that we have and given that we can power a lot of use cases.
That's what changed our strategy to say, know what, okay, how do we now make sure the distribution gets effective? How do we support customers to solve different use cases for different platforms and different ecosystems? And that is what is driving our decision to be present through partners, OEMs, and other mechanisms or ecosystems and so on.
John Kosturos (19:29)
Yeah, and that doesn't mean you guys are still not going to be very cautious about what partners you're looking to work with. So with that being said, when you think about partnerships, what matters most in that alignment to make sure that somebody's right for HG to partner with?
Rohini Kasturi (19:48)
It's truly the end customer value and what is the joint solution. Like what is it one plus one that's equal to three, right? You know, if we see that clearly, you know, that a customer end customer problem can be solved. We have best in class, you know, intelligence or agents to provide, but the customer or the partner has other complimentary nature things to provide. When we put this integration together, it shortens the time to value for the customer. Right it brings significant value for the customer.
That is what is gratifying result we need because we want to solve customers problems and that's the crux of any solution, any partnership. As you know this, we've been around the block for years now.
John Kosturos (20:29)
Yeah, very cool. And From your perspective, what role does do like curated marketplaces like SpringDB's data exchange or Snowflake's data marketplace play in helping HG reach more of these partnership or upmarket buyers?
Rohini Kasturi (20:47)
Yeah, so I would summarize it into my favorite term, speed scale economics. Right? speed because it's for customer. if a company like SpringDB is curated, right? know, doing a lot of pre-work for a customer. That's a curated data set. It saves a lot of time, right? That speed in terms of operationalizing the data. The second one is scale. Hey, if I'm trying to build an agentic recap or I'm trying to roll some solution out to my DevOps marketing
or sales. I need something that can help scale. I need coverage for more companies, more types of intelligence. The third one is economics. If you're able to solve it and the team that is specialized in a particular set of problems, they know how to solve it, then it always brings economics as well to the end customer. So all of this is what is driving our engagement, our partnership.
John Kosturos (21:39)
Very
cool. Maybe we can switch this into a little bit of a tactical conversation now about the data assets. On the firmographic coverage, is the database a domestic or global data set?
Rohini Kasturi (21:54)
It's a global data set. So the key here is, so we have these sources that we procure, like I said, for over 15 years. What happened historically is we only mined technographics out of that data set that we got. Now the same data sets are useful to mine thermographics, to mine other types of intelligence as well, like for example, skills as well. And you can mine more types too.
We started using a big company, Firmographics, QVS for a long time. What we realized is that the Firmographics weren't getting curated on a real-time basis. Now what happens is when you combine that with the Firmographics that are, Technographics that are getting cleansed and updated real-time basis, then overall, then customer problem isn't getting solved. So we went into starting curating our own Firmographics from all the sources we have.
Now we have created a whole call it an AI data platform underneath that is curating all of this at scale. Now we want to significantly improve the coverage. And especially with AI tools, you're able to do a lot of problems like data matching, quality, et cetera. can solve them at scale. So that's why we are able
John Kosturos (23:02)
in.
Rohini Kasturi (23:05)
to solve this at scale. But again, everything that we have is a global data coverage. It covers in all the regions, MEI, APAC, et cetera. Absolutely.
John Kosturos (23:14)
Very cool. And do you have a hierarchy structure for businesses in your taxonomy?
Rohini Kasturi (23:21)
Yeah, 100%. Hierarchy says something that the company had for a while, but this is something we have never advertised as a capability or a feature. So I think that's something that definitely exists. And the next thing we're also doing is mapping or making our taxonomy best in class. By mapping to industry leading taxonomies, so customers have flexibility to pick our taxonomy versus other vendors' taxonomy. That also changes the game because that gives
customers with choice because how they do it, what they do it, and know, et cetera.
John Kosturos (23:54)
Yeah, I built the whole business on allowing users to segment whatever taxonomy they wanted from their data sources into their own desired taxonomy. And so that's definitely a big one there. And if you guys can get businesses to standardize on that taxonomy, it can be game changing. On the firmographic side, let's just...
Forget the technographics for a second. Do you know about how many attributes you have in the taxonomy that cover businesses? OK. OK. What about technologies? Do you know about how many technologies that HG is tracking today?
Rohini Kasturi (24:34)
So we are progressing towards 64,000 products is what we are progressing towards very shortly. And we want to cover at least 100,000 B2B companies while we apply all the other sub-entities and become even 150,000. So we want to provide the widest coverage in terms of overall data coverage.
We want to be best in class and coverage, quality and taxonomy, and then multi-dimensional. So the data family that we have is our differentiating mode because of all these sources of data and data types that we have in place.
John Kosturos (25:10)
Thank you.
⁓ And did I hear you mention something about contacts and skills? Is there something that you want to talk about there?
Rohini Kasturi (25:21)
Yes, so we have done a partnership with a couple of contact providers, which do enriched contacts or waterfall enriched contacts, because they curate at scale globally. That way what happens is when a customer is looking for contacts for a particular prospect at a group level or a company level, they want contacts
So that is something that we have included in our platform effectively right now. It's part of our platform, it powers our sales agent app So the sales team have contacts, access to the contacts. The next thing is we're including that contacts into the fabric itself. So that will be available very shortly. Yes.
John Kosturos (26:04)
Well, that's some big news. I think that's
great. That's big news.
Rohini Kasturi (26:08)
Yes, and then adding the skills data, scrubbing it to the same sources we have is part of our plans as well. ⁓
John Kosturos (26:17)
Yeah. I mean, it's like, Hey,
if this company's been using S3 for 15 years and they have, you know, out of their 30 cloud infrastructure people, 29 of them have three different AWS S3 or developer skills. You're probably going to have a hard time displacing that technology. Right.
And the other way around, if they've been using it for five years, of their 10 developers, five of them have expertise in a competing platform, pick up the phone and call those guys right away. Very cool.
We're almost done here, but I have just a few more questions. HG did make ⁓ two very strategic acquisitions. We mentioned a little bit earlier, MadKudu and TrustRadius What was the core thesis behind bringing those companies together under HG?
Rohini Kasturi (27:09)
So the whole thesis behind MadKudu is MadKudu went into customers, they built agentic apps, like I said, the sales agentic app and the robots agentic app, and when they reached out to customers and they started working with a of enterprise customers, they said, hey, can you make, power those apps through HG's data? So they went and built on top of HG's data, okay, and they did a partnership with us and they built all the agentic apps on that, and then it became very natural
for us as we built our market analyzer AgenticCap and we were looking at how do we expand it to multi-persona, then we found Madkudu. Madkudu already built AgenteCap on our HG's data and it was very slam dunk acquisition because we were able to bring it and we said, hey great, it's already ready to go on day one, the day we acquired because they integrated the HG's data and intelligence. So that's what drove it, but also the fact that we can
address multiple personas in an enterprise. sales is something that we weren't able to serve in the past with our prior platform and similarly revops. Revops was using all our data sources heavily but there was no app where you can do propensity modeling and scoring and so on. So we were able to solve that very quickly through MadKudu acquisition. The TrustRadius acquisition is basically, you look at it, TrustRadius has a lot of unique data about the buyer.
They have always been focused on B2B buyers. They have developed a robust taxonomy. They have coverage for 64,000 products. They have reduced for 64,000 products in many companies. And what we realized was that buyer intent...
is going to be critical because what happens is when customers go to a TrustRadius website, they're doing reviews. TrustRadius had something called long form reviews, or it has long form reviews. It's not like a one-liner review that, hey, product is good or SpringDB is good. But it will detail out the lot of pros and cons about the company, the products, et cetera. So there's a lot of deep intelligence about various products, which buyers tend to come in, they do research, it's very useful.
for the research, it was meant to be a research site. They can compare products, they can do deep competitive analysis between two companies or two products. what TrustRadius did was, one is this buyer reviews led to buyer intent, especially as they came in to compare products and depending on how much time they spent, they could determine where are they in the intent, because the more time they spend, let's say they're spending, they would do some AI automation sequencing with the buyer
and then say, are you really in the market looking for such a product? And then the next created a product called IDL, Intent Driven Leads. Intent Driven Lead is basically now you know that this buyer has been researching this product. They go do automation sequence. They go work with the managing agencies and actually do some outbound sequencing to actually make a lead out of it. And that would become an Intent Driven Lead. And what we saw was in our due diligence that ⁓
70 % of the buyers who are in market convert in three months or less. So we said that ties exactly to the sales scope. Imagine the sales teams besides giving them an agent app you give them the leads as well that are going to convert in three months. There's nothing better that you can provide, right? So it's basically giving a sales team the leads that are going to convert.
John Kosturos (30:27)
Yeah.
Rohini Kasturi (30:30)
three months and giving them all the tools that will help them close the deals. we said okay that Nirvana was good. So we took that and the buyer intent is something that we didn't exist, that is not existed in our fabric.
and the data intelligence corpus that TrustRadius has is very unique and complimentary. So we said, okay, it makes sense to bring that together, right? So now what we're doing is with the acquisitions and the mergers, or the integrations we've done, the buyer intent is now included in the fabric. They're including all the reviews also into the fabric, but we making the reviews to revenue, how do you convert reviews to revenue? It's a use case on the platform. So when you log into the platform,
the SaaS platform, you can, as a vendor, you can start looking at the reviews and you can see how to convert those reviews into revenue because by doing brand marketing, customer marketing, you're leveraging those reviews. So there are a lot of interesting use cases that are differentiating and compelling, and that's what drove the acquisition of TrustRadius.
John Kosturos (31:30)
Very cool.
when we say HG Insights has intent data, you're not like...
most of the other companies going and outsourcing third party intent from one of the handful of B2B intent players, you actually have intent data from a trusted review site that gives you everything from top of funnel, like who's scratching the surface of doing the research to, who's at the end of the funnel ready to buy in the next 90 days?
Rohini Kasturi (32:02)
We do have our own, call it the secret sauce intent, which is the buyer intent, intent about the buyers who are in market right now. That's the very bottom of the funnel intent. The second is we also partner with intent providers. We get the top of the funnel intent, and we layer in context, which is a technographics context. We call it contextual intent, because we know that a company X is looking for a firewall.
But when you layer in the technographics that they have, we know who is in market and it's a contextual intent. So we aggregate all of this at an account level. That's why it's an intent aggregation solution and it's a contextual intent along with buyer intent.
John Kosturos (32:41)
Very cool. So.
Rohini Kasturi (32:42)
That looks very, very
unique.
John Kosturos (32:43)
So really any company who has an ABM strategy that's sewing technology solutions, this should be a piece of that strategy.
Rohini Kasturi (32:54)
100%, 100%, because there is very, very differentiating, compelling data sets that bring revenue growth into life. And that's what we are excited about.
John Kosturos (33:05)
Very cool. Just finishing up, you know, looking ahead two or three years, how do you want people to describe HG Insights?
Rohini Kasturi (33:14)
I want the customers to describe HG as a core underpinning for our revenue growth. We were able to build our growth playbook on the revenue growth playbook using HG's intelligence so we know if your growing revenue from 100 million to 500 million to 3 billion
We were able to create a growth strategy or a growth playbook using all the types of intelligence they have. And based on that, I have a real playbook that is guiding my strategy teams, my DevOps teams, my sales teams, my marketing teams. So they're all orchestrating revenue growth. And that's how we're able to grow revenue, right? And we want to be that revenue growth intelligence solution for the agent equal. And that's what we're excited about.
John Kosturos (33:50)
Very cool. Where can listeners follow HG's journey or learn more about working with your team?
Rohini Kasturi (33:56)
best place is our website, HGinsights.com. Of course, we do have updates regularly on LinkedIn and other places, but always look forward to hearing feedback from customers, partners in this process, and always sharing and learning.
John Kosturos (34:12)
Well Rohini, this has been an incredibly insightful conversation. Thanks for sharing the vision behind HG and how you're thinking about the future of data partnerships and market intelligence. And to our listeners, thank you for tuning into the Data Exchange Podcast. We'll see you in the next episode.
Alright.
Welcome everybody to another episode of the Data Exchange Podcast. Today we've got a very special guest Rohini Kasturi, the CEO of HG Insights. How are you, Rohini?
Absolutely, we're excited to have you. know, HG Insights, in my opinion, is one of the most important companies in the market when it comes to technographic intelligence, market understanding, and helping companies move up market with precision. Maybe ⁓ to start for listeners who may not know you yet, can you share a bit about your background and what ultimately drew you to lead HG Insights?
Rohini Kasturi (00:42)
Sure, so my background, I was a customer of HG prior to joining HG like a year and a half back. So was at a public company called SolarWinds as their chief product and technology officer. Of course, I've held senior roles in several private equity companies, public companies, and even a founder-led startup as well. So I've been in Silicon Valley the last 25 years, being associated with technology companies.
businesses all the time. So yeah, it's a great time to be at HG. And what really drove me here is, if you look at HG, HG has very unique insights that can help good market teams achieve revenue growth. And when I looked at that, the deep insights and intelligence that the company has, and the market timing around data and AI.
and the shift that businesses are doing to really embrace data and AI to drive their revenue growth and transformation. That's what I felt the market timing for HG is so right, and that's what drove me to HG Java.
John Kosturos (01:42)
Very cool. You know, how do you define HG Insights mission today and how has that mission evolved since you took the helm?
Rohini Kasturi (01:51)
Yeah, so our mission really is how do we help customers drive revenue growth through our intelligence. So that's really the one-liner. And for us, the revenue growth is in three dimensions. It's driving top-line growth, achieving customer retention. The third one is operational efficiency. And everything that we do
revolve around this and that's why if you look at our solutions they're all called revenue growth solutions whether it's called revenue growth intelligence platform, revenue growth intelligence fabric and even what we are putting together in terms of revenue growth intelligence solutions is all around how do you create your own growth playbook and how do you move your revenue growth from X million dollars of ARR to 2X and 3X based on data and AI.
John Kosturos (02:38)
Very cool. What do you think most companies still misunderstand when it comes to truly understanding their market and where real buying intent exists?
Rohini Kasturi (02:49)
So if you look at, see a lot of companies when they're building their growth plans, it's based on historical financial projections. It's based on some sets of wins and losses or based on the high level market sizing that they get from analysts, right? But the reality is you have an ideal customer profile. You have some wins, you have some losses.
You want to take that and really calculate your TAM total addressable market, SAM serviceable addressable market, and also SOM, which is what can you obtain based on your product strategy, go-to-market strategy, and how do you really map your ICP to your SOM makes it very interesting, right? So if you're able to do that, then you can get a very prescriptive set of customer lists that you can target. And that is what is the missing part, right? People are operating at a high level.
keep trying to get customers and your conversion rate suffer, your cost of sales is high, your cost of marketing is high, all these are the challenges.
John Kosturos (03:47)
Yeah, I agree. And companies are still investing so much into this whole ABM strategy. And in some cases, like we're talking investing hundreds of thousands or millions of dollars to break into a single account. And I think, you know,
Making sure you break down that ICP, TAM SAM all the way to SOM to help you drive those target account decisions is really what decides whether you're going to succeed or fail. Where do technographics fit into that picture and why are they often misunderstood or underutilized?
Rohini Kasturi (04:23)
So if you take ABM for example, that you mentioned, it's all about precision targeting. If you want to precisely target the customers, you need to know your ICP. Based on your ICP, you need to get your SOM list, or serviceable obtainable list, market list, which is a list of accounts. Then you need to be able to know what is your white space, what is your competition. If you are not aware of that,
then it gets challening. So this is where technographics come into picture because we're using technographics. You'll know what products or technologies that are being used by customers. So you can look at the white space. can start going into each account and say What products do they use? What technologies do they use? So this is very important. That's one aspect that we provide as part of revenue growth intelligence. The second two parts that we also are very differentiating is the spend intelligence. One is about knowing your right
space of your competition. But then when you go into the account, you want to know how much do they spend.
Like for example, let's say you're selling a firewall. How much do they spend on security today? How much do they spend on firewalls today? Having the precise insights at a product level or a technology level is very important. And the third one is intake. Hey, are these buyers, are these prospects, are they in market today? Are they trying to look for something to buy? Because if you know that, then you can precisely target who is in the market today and how much do they spend? What products do they use?
and completely map it with a very precise message using ABM
John Kosturos (05:51)
Very cool. Yeah, I mean, HG is often described as having the most accurate technographic intelligence in the market. I'm interested, like, what fundamental differences, what fundamentally differentiates your data from what else is out there?
Rohini Kasturi (06:08)
So a couple of things. One is we have been curating this data from 50 odd sources over 15 years now. So one thing that differentiates in our technographics is really the time series. we have, for example, you go to Sysco's website. You want to know their products? It's available on their website, right? They list down their products. But if you want to know the firewalls that are still in the market that are not end of life
that distributors, partners in different parts of the world are using, you need a lot of historical information. So we have robust technographics that we have built over 15 years. So we have technographics with time series data that makes it very differentiating. The second part is we have behind the firewall signaling information that uniquely helps us make sure our technographics is high quality. And then what we did is we took the technographics mode that we have
we applied to various elements in our intelligence fabric. We applied that to thermographics to make sure thermographics are differentiated. We applied that to intent. We get the top of the funnel intent from various providers that are out there. But we apply the context to that intent, which is called contextual intent. Now with the TrustRadius acquisition we did, we had the buyer intent. We again applied the technographics
John Kosturos (07:14)
Hmm.
Rohini Kasturi (07:21)
context to that to make the contextual intent. So even the spend intelligence uses the technographic. So all of it
uses the moat that we have, but applies to thermographics, applies to technographs series, applies to spend intelligence, applies to intent, applies to the partner intelligence, et cetera, that makes our revenue growth intelligence fabric very unique and compelling. That's why we keep hearing, you are high quality in the depth and the matching and so on.
John Kosturos (07:46)
Very cool.
So would it be safe to say that other vendors in this technographic market, like number one, the time series, but you guys also have spend, I know some metrics around cloud.
like cloud spend, which cloud, et cetera. And then now layering intent, some type of intent onto that technology footprint. And then overall, the accuracy sounds like where you're leading.
Rohini Kasturi (08:20)
So one thing that we did this year, John, is what we announced in July is called the Revenue Growth Intelligence Fabric. It's a combination of our thermographics that we curate, high quality thermographics that we curate on a real time basis. There's technographics, then we have spend intelligence, was an acquisition that we did a few years back. Then we have intent, three types of intent. Top of the funnel intent, middle of the funnel intent, where we map context. Then we have the buyer intent, which is more bottom of the funnel intent.
Then we have partner intelligence, contract intelligence. What we did is we took all these intelligences that we have, they spread data intelligence sources, aggregated at an account level. So if you take a prospect like SAP, we have intelligence, all these types of intelligence aggregated at an account level. So it's easy to go into SAP and say, OK, for SAP, I need all types of intelligence. And then using this, also built a buying group intellegence
of the functional area intelligence, which is not only talking about SAP, we have intelligence about SAP, but we now have intelligence at a group level. The next step we doing is mapping contacts, so it becomes easy for a salesperson or a marketeer to know, I know.
which group in SAP is using what products and how much are they spending. But by the way, are they in market? So who is that person? Right? Having that contact helps us really navigate this extremely well. And helps go-to-market teams navigate it extremely well.
John Kosturos (09:45)
Very cool. Yeah, I know that you guys have kind of two delivery mechanisms. One is a pretty robust platform that you have many users on. And then there's another offering, which is more of these data deliveries that are more of the raw data files. And I'm wondering, you know,
Is the data science community and data warehousing community kind of a strong customer segment for you? Is that where you see a lot of your customers getting major value is when they take those raw data assets and start to analyze them in their big data environments?
Rohini Kasturi (10:28)
So we do have, call it broadly, two categories of customers. One category of customers that say, give me your intelligence, your data, right, for X number of companies based on ICP, but I want various types of intelligence. They take that, which is third party data, they combine it with their first party data
and other sources that they have and they do their own automation. So all these are data science teams and large scale companies who have, they have resources to actually mine the data, creating their own agents and solutions. That's one cohort. The second cohort is they don't have huge data teams or analytics teams, it's smaller teams usually less than a billion dollar companies.
because they are very lean in terms of overall resourcing. For them, they need a ready-made platform. For them, we have a SaaS platform. It's an agentic platform. So when you log into the agentic platform, there are three agentic apps we provide. It addresses roughly 10 different use cases. For example, Market Analyzer agentic app helps you take your ICP, win losses, and create your time-sand song.
And then there is a rev-up studio which is you can do propensity modeling, scoring, writer best fit models, territory planning, account planning, etc. Then there is a sales agentic cap where the sales agentic cap is taking the entire third party intelligence
and for all the accounts that salesperson has, it provides intelligence from the entire third party, but we also have a first party, second party integrations framework, right? So it connects with Salesforce, HubSpot, etc, etc, outreach and many other tools. It takes the intelligence, combines it with the third party, and it shows up in the account brief. So it's very dynamic, updated from first party sources, second party sources like LinkedIn, and then the third party sources like what we have.
all aggregated at an account level providing that intelligence to sales teams. But then it can also do, let's say you are given a lead by marketing, it can do scoring, can do best fit modeling, prioritization, everything, and it can provide you account rates, and it connects to outreach. So you can start even nurturing that through the sales agent decap, right? So that's the readymade solution we have for growth customers, or less than a billion dollar customers.
John Kosturos (12:40)
Are a lot of those kind of agentic features new? A lot of that sounds very new to me. I mean, I've been very close to the HG platform for many years and even some of that stuff sounds new to me. So tell us a little bit about that.
Rohini Kasturi (12:55)
Yeah,
no fantastic question. So I know you've been associated with HG for a long time. So HG had a platform, or has that platform still, which was basically.
doing a lot of market research kind of use case where you can go dig deeper into the intelligence that we have. Then we created a market analyzer, copilot agentic app in July, we launched that. And then we did an acquisition in August, which is MadKudu. MadKudu was an agentic platform, it has a sales agentic app and a reboks agentic app. What we did now effectively last month is we brought all of these three agentic apps into one common platform.
When you log in, it's one single sign-on, one experience. But there are three different agentic apps, but all powered by our
Revenue Growth Intellegence Fabric which is intelligence for 20 million companies across all these dimensions. Now the fabric is suddenly enriched, the platform is suddenly enriched with the fabric. But also the platform has all these first part and second part integrations with 30-odd integrations. So that is something that we have pretty much gotten ready with all the integration of MadKudu in HG platforms. And we'll be announcing that again in January, very shortly.
John Kosturos (14:07)
Very cool. know, I've been through a lot of acquisitions in my day and congratulations to you guys for such a quick integration. I've seen even acquisitions I was part of 10, 20 years ago still not be integrated into a full product suite. So that's ⁓ super exciting and you guys must be doing really efficient work over there. We'll get back to the acquisitions in a moment as part of our talk track here.
But if you have to be very clear, where does HG deserve to win versus competitors? And what are the use cases or customer types you feel the strongest kind of fit into that?
Rohini Kasturi (14:47)
See, HG is the company, I would say, for the agent world Especially if you want to build agentic apps, agentic apps are only good if your data is good. You need high quality data.
But you need different dimensions of data to guide your sales teams, your marketing teams. Sometimes you need to spend intelligence. The reason you need to intelligence is, let's say you're doing a competitive takeout. And let's say you have a customer, a prospect is using a product that is very similar to yours. You want to know how much do they spend and what are they doing?
where in they in the buying cycle or using cycle etc etc so you need time series similarly hey how long have they been using right so you need various types of dimensions of data to really guide the marketing and sales effectively and you need all those dimensions to prioritize otherwise you might be getting into an account which is five year old account
customer is deeply enriched with so much footprint and you might be trying to displace that account, might be a very hard thing, might take time, because especially we have no intention to displace it. The sales is much harder in that case than displacing something where they're ready to displace. So having all these types of intelligence is going to be key. That is where, that's one of the excitements for me joining HG, like this differentiating data and...
And the company has been doing ML for a while, even before I joined, machine learning for a while. But then we realized there a lot of AI tools that will help curate our data much faster, so we can get to 100 million accounts of coverage in the next nine months. That's a compelling story, right? So for the agent world, so we are the revenue growth intelligence for the agent world. And that's where we deserve to win, and we deserve to be positioned. And more importantly, if you're a CEO or a CXO, thinking about revenue growth,
CRO, CMO thinking about revenue growth. You need revenue growth intelligence, And you need to know what's my growth strategy, what's my retention strategy, what's my efficiency strategy. And that's kind of where we fall right in the horizon job.
John Kosturos (16:47)
Very cool. I think that kind of leads me into the next topic, is kind of partnerships, OEM resellers kind of world where, you know, it's not just brands.
building internal AI agentic use cases that it sounds like you guys want to enable, but also brands that are building agentic solutions that are gonna service thousands or hundreds of thousands or millions of customers. And kind of historically, HG was more cautious about OEM and resell models. What has kind of changed in your thinking?
along that line.
Rohini Kasturi (17:26)
So I think a few things have changed the thinking along that line. One and first and foremost is what we realize is the opportunity that the company has is much, much bigger than what we have captured. We're sitting on a huge stand ourselves. But then how do we capture that faster? That's one. The second is customers are trying to build applications, agents, solutions, whatever you want to call them.
If they are doing it, they need the best in class intelligence. And they are going to do it in different form factors. They are going to do it in ecosystems. They are going to leverage some platforms. They are going to build their own agents internally. We have all combinations that exist from the customer side. When we look at that, and the third element is how much capture did we do as of today from our brand, from our own internal sales effort. When we look at our context and what's our opportunity,
There is a huge gap. We have a huge opportunity to grow this business and that's where we said, hey, how do we maximize this potential because there are various types of agentic workflows getting created. For example, Clay has created a beautiful platform for agentic workflows, but the underpinning is the data and intelligence. So we are a premium partner to Clay. So if customers are building agentic workflows for marketing and marketing or DevOps is building it, but they need the best in class intelligence
And there are other go-to-market platforms doing something very different. But the intelligence is going to be key because the intelligence types that they use is very different from the intelligence types that are being used by other personas or platforms. So we do have like 10, 15 varieties of data that we have that we brought together. So given that flexibility that we have and given that we can power a lot of use cases.
That's what changed our strategy to say, know what, okay, how do we now make sure the distribution gets effective? How do we support customers to solve different use cases for different platforms and different ecosystems? And that is what is driving our decision to be present through partners, OEMs, and other mechanisms or ecosystems and so on.
John Kosturos (19:29)
Yeah, and that doesn't mean you guys are still not going to be very cautious about what partners you're looking to work with. So with that being said, when you think about partnerships, what matters most in that alignment to make sure that somebody's right for HG to partner with?
Rohini Kasturi (19:48)
It's truly the end customer value and what is the joint solution. Like what is it one plus one that's equal to three, right? You know, if we see that clearly, you know, that a customer end customer problem can be solved. We have best in class, you know, intelligence or agents to provide, but the customer or the partner has other complimentary nature things to provide. When we put this integration together, it shortens the time to value for the customer. Right it brings significant value for the customer.
That is what is gratifying result we need because we want to solve customers problems and that's the crux of any solution, any partnership. As you know this, we've been around the block for years now.
John Kosturos (20:29)
Yeah, very cool. And From your perspective, what role does do like curated marketplaces like SpringDB's data exchange or Snowflake's data marketplace play in helping HG reach more of these partnership or upmarket buyers?
Rohini Kasturi (20:47)
Yeah, so I would summarize it into my favorite term, speed scale economics. Right? speed because it's for customer. if a company like SpringDB is curated, right? know, doing a lot of pre-work for a customer. That's a curated data set. It saves a lot of time, right? That speed in terms of operationalizing the data. The second one is scale. Hey, if I'm trying to build an agentic recap or I'm trying to roll some solution out to my DevOps marketing
or sales. I need something that can help scale. I need coverage for more companies, more types of intelligence. The third one is economics. If you're able to solve it and the team that is specialized in a particular set of problems, they know how to solve it, then it always brings economics as well to the end customer. So all of this is what is driving our engagement, our partnership.
John Kosturos (21:39)
Very
cool. Maybe we can switch this into a little bit of a tactical conversation now about the data assets. On the firmographic coverage, is the database a domestic or global data set?
Rohini Kasturi (21:54)
It's a global data set. So the key here is, so we have these sources that we procure, like I said, for over 15 years. What happened historically is we only mined technographics out of that data set that we got. Now the same data sets are useful to mine thermographics, to mine other types of intelligence as well, like for example, skills as well. And you can mine more types too.
We started using a big company, Firmographics, QVS for a long time. What we realized is that the Firmographics weren't getting curated on a real-time basis. Now what happens is when you combine that with the Firmographics that are, Technographics that are getting cleansed and updated real-time basis, then overall, then customer problem isn't getting solved. So we went into starting curating our own Firmographics from all the sources we have.
Now we have created a whole call it an AI data platform underneath that is curating all of this at scale. Now we want to significantly improve the coverage. And especially with AI tools, you're able to do a lot of problems like data matching, quality, et cetera. can solve them at scale. So that's why we are able
John Kosturos (23:02)
in.
Rohini Kasturi (23:05)
to solve this at scale. But again, everything that we have is a global data coverage. It covers in all the regions, MEI, APAC, et cetera. Absolutely.
John Kosturos (23:14)
Very cool. And do you have a hierarchy structure for businesses in your taxonomy?
Rohini Kasturi (23:21)
Yeah, 100%. Hierarchy says something that the company had for a while, but this is something we have never advertised as a capability or a feature. So I think that's something that definitely exists. And the next thing we're also doing is mapping or making our taxonomy best in class. By mapping to industry leading taxonomies, so customers have flexibility to pick our taxonomy versus other vendors' taxonomy. That also changes the game because that gives
customers with choice because how they do it, what they do it, and know, et cetera.
John Kosturos (23:54)
Yeah, I built the whole business on allowing users to segment whatever taxonomy they wanted from their data sources into their own desired taxonomy. And so that's definitely a big one there. And if you guys can get businesses to standardize on that taxonomy, it can be game changing. On the firmographic side, let's just...
Forget the technographics for a second. Do you know about how many attributes you have in the taxonomy that cover businesses? OK. OK. What about technologies? Do you know about how many technologies that HG is tracking today?
Rohini Kasturi (24:34)
So we are progressing towards 64,000 products is what we are progressing towards very shortly. And we want to cover at least 100,000 B2B companies while we apply all the other sub-entities and become even 150,000. So we want to provide the widest coverage in terms of overall data coverage.
We want to be best in class and coverage, quality and taxonomy, and then multi-dimensional. So the data family that we have is our differentiating mode because of all these sources of data and data types that we have in place.
John Kosturos (25:10)
Thank you.
⁓ And did I hear you mention something about contacts and skills? Is there something that you want to talk about there?
Rohini Kasturi (25:21)
Yes, so we have done a partnership with a couple of contact providers, which do enriched contacts or waterfall enriched contacts, because they curate at scale globally. That way what happens is when a customer is looking for contacts for a particular prospect at a group level or a company level, they want contacts
So that is something that we have included in our platform effectively right now. It's part of our platform, it powers our sales agent app So the sales team have contacts, access to the contacts. The next thing is we're including that contacts into the fabric itself. So that will be available very shortly. Yes.
John Kosturos (26:04)
Well, that's some big news. I think that's
great. That's big news.
Rohini Kasturi (26:08)
Yes, and then adding the skills data, scrubbing it to the same sources we have is part of our plans as well. ⁓
John Kosturos (26:17)
Yeah. I mean, it's like, Hey,
if this company's been using S3 for 15 years and they have, you know, out of their 30 cloud infrastructure people, 29 of them have three different AWS S3 or developer skills. You're probably going to have a hard time displacing that technology. Right.
And the other way around, if they've been using it for five years, of their 10 developers, five of them have expertise in a competing platform, pick up the phone and call those guys right away. Very cool.
We're almost done here, but I have just a few more questions. HG did make ⁓ two very strategic acquisitions. We mentioned a little bit earlier, MadKudu and TrustRadius What was the core thesis behind bringing those companies together under HG?
Rohini Kasturi (27:09)
So the whole thesis behind MadKudu is MadKudu went into customers, they built agentic apps, like I said, the sales agentic app and the robots agentic app, and when they reached out to customers and they started working with a of enterprise customers, they said, hey, can you make, power those apps through HG's data? So they went and built on top of HG's data, okay, and they did a partnership with us and they built all the agentic apps on that, and then it became very natural
for us as we built our market analyzer AgenticCap and we were looking at how do we expand it to multi-persona, then we found Madkudu. Madkudu already built AgenteCap on our HG's data and it was very slam dunk acquisition because we were able to bring it and we said, hey great, it's already ready to go on day one, the day we acquired because they integrated the HG's data and intelligence. So that's what drove it, but also the fact that we can
address multiple personas in an enterprise. sales is something that we weren't able to serve in the past with our prior platform and similarly revops. Revops was using all our data sources heavily but there was no app where you can do propensity modeling and scoring and so on. So we were able to solve that very quickly through MadKudu acquisition. The TrustRadius acquisition is basically, you look at it, TrustRadius has a lot of unique data about the buyer.
They have always been focused on B2B buyers. They have developed a robust taxonomy. They have coverage for 64,000 products. They have reduced for 64,000 products in many companies. And what we realized was that buyer intent...
is going to be critical because what happens is when customers go to a TrustRadius website, they're doing reviews. TrustRadius had something called long form reviews, or it has long form reviews. It's not like a one-liner review that, hey, product is good or SpringDB is good. But it will detail out the lot of pros and cons about the company, the products, et cetera. So there's a lot of deep intelligence about various products, which buyers tend to come in, they do research, it's very useful.
for the research, it was meant to be a research site. They can compare products, they can do deep competitive analysis between two companies or two products. what TrustRadius did was, one is this buyer reviews led to buyer intent, especially as they came in to compare products and depending on how much time they spent, they could determine where are they in the intent, because the more time they spend, let's say they're spending, they would do some AI automation sequencing with the buyer
and then say, are you really in the market looking for such a product? And then the next created a product called IDL, Intent Driven Leads. Intent Driven Lead is basically now you know that this buyer has been researching this product. They go do automation sequence. They go work with the managing agencies and actually do some outbound sequencing to actually make a lead out of it. And that would become an Intent Driven Lead. And what we saw was in our due diligence that ⁓
70 % of the buyers who are in market convert in three months or less. So we said that ties exactly to the sales scope. Imagine the sales teams besides giving them an agent app you give them the leads as well that are going to convert in three months. There's nothing better that you can provide, right? So it's basically giving a sales team the leads that are going to convert.
John Kosturos (30:27)
Yeah.
Rohini Kasturi (30:30)
three months and giving them all the tools that will help them close the deals. we said okay that Nirvana was good. So we took that and the buyer intent is something that we didn't exist, that is not existed in our fabric.
and the data intelligence corpus that TrustRadius has is very unique and complimentary. So we said, okay, it makes sense to bring that together, right? So now what we're doing is with the acquisitions and the mergers, or the integrations we've done, the buyer intent is now included in the fabric. They're including all the reviews also into the fabric, but we making the reviews to revenue, how do you convert reviews to revenue? It's a use case on the platform. So when you log into the platform,
the SaaS platform, you can, as a vendor, you can start looking at the reviews and you can see how to convert those reviews into revenue because by doing brand marketing, customer marketing, you're leveraging those reviews. So there are a lot of interesting use cases that are differentiating and compelling, and that's what drove the acquisition of TrustRadius.
John Kosturos (31:30)
Very cool.
when we say HG Insights has intent data, you're not like...
most of the other companies going and outsourcing third party intent from one of the handful of B2B intent players, you actually have intent data from a trusted review site that gives you everything from top of funnel, like who's scratching the surface of doing the research to, who's at the end of the funnel ready to buy in the next 90 days?
Rohini Kasturi (32:02)
We do have our own, call it the secret sauce intent, which is the buyer intent, intent about the buyers who are in market right now. That's the very bottom of the funnel intent. The second is we also partner with intent providers. We get the top of the funnel intent, and we layer in context, which is a technographics context. We call it contextual intent, because we know that a company X is looking for a firewall.
But when you layer in the technographics that they have, we know who is in market and it's a contextual intent. So we aggregate all of this at an account level. That's why it's an intent aggregation solution and it's a contextual intent along with buyer intent.
John Kosturos (32:41)
Very cool. So.
Rohini Kasturi (32:42)
That looks very, very
unique.
John Kosturos (32:43)
So really any company who has an ABM strategy that's sewing technology solutions, this should be a piece of that strategy.
Rohini Kasturi (32:54)
100%, 100%, because there is very, very differentiating, compelling data sets that bring revenue growth into life. And that's what we are excited about.
John Kosturos (33:05)
Very cool. Just finishing up, you know, looking ahead two or three years, how do you want people to describe HG Insights?
Rohini Kasturi (33:14)
I want the customers to describe HG as a core underpinning for our revenue growth. We were able to build our growth playbook on the revenue growth playbook using HG's intelligence so we know if your growing revenue from 100 million to 500 million to 3 billion
We were able to create a growth strategy or a growth playbook using all the types of intelligence they have. And based on that, I have a real playbook that is guiding my strategy teams, my DevOps teams, my sales teams, my marketing teams. So they're all orchestrating revenue growth. And that's how we're able to grow revenue, right? And we want to be that revenue growth intelligence solution for the agent equal. And that's what we're excited about.
John Kosturos (33:50)
Very cool. Where can listeners follow HG's journey or learn more about working with your team?
Rohini Kasturi (33:56)
best place is our website, HGinsights.com. Of course, we do have updates regularly on LinkedIn and other places, but always look forward to hearing feedback from customers, partners in this process, and always sharing and learning.
John Kosturos (34:12)
Well Rohini, this has been an incredibly insightful conversation. Thanks for sharing the vision behind HG and how you're thinking about the future of data partnerships and market intelligence. And to our listeners, thank you for tuning into the Data Exchange Podcast. We'll see you in the next episode.
Alright.
