Agentic GTM Execution with ZoomInfo

Julia Nimchinski:
And now it’s time for a demo showcase. First, we’ve got Snek Aquiletti, VP of Product, ZoomInfo, and we’re gonna walk through a Gentec GTM in action. Sneg, how are you doing? Welcome to the show.

Sneh Kakileti:
Hey, Julia. Thanks for having me, everyone. Great to be here.

Julia Nimchinski:
Awesome! What’s in your agenda class?

Sneh Kakileti:
All right. Well, we’re gonna talk a little bit today about, contacts graphs. I’m sure that’s been top of many people’s minds today, but we’re gonna focus it specifically on B2B sales and marketing. I’d love to highlight some of the pain points on how ZoomInfo is helping bring data and AI to frontline sales and marketing teams, how they’re able to use this information to really revolutionize the way they go to market, and hopefully, let’s build a campaign while we’re at it.

Julia Nimchinski:
Awesome, let’s do it.

Sneh Kakileti:
Awesome. Okay, so maybe just a little bit of a preamble for those that, aren’t familiar with ZoomInfo. You know, we here at ZoomInfo have spent basically the better part of 20 years helping our customers think about how to use data to go to market. And so, what we did was we built a proprietary database. We profiled all the businesses around the globe, the business professionals within those organizations, and then the employees, and then what we call signals. And it’s all the dynamic changes, patterns that happen within a company, within that professional landscape, that help indicate that they have a need for a particular product or service. And so over the course of our, you know, organization’s time, we basically wrap that all into applications that we serve to a seller, or to a marketer, so that they can go and tap into that information that we’ve built out. And so I spend a lot of my time thinking about, you know, where AI is actually going to be transformative in B2B sales and marketing. And if you want proof that that opportunity is massive, look no further than the life of a frontline seller, or an account executive. And, you know, I’ll unpack that for a second. Just think about what our expectations are of them. You know, each AE has to deeply understand their own products. Their competitive landscape, their own roadmaps. They need to manage their customers, they need to build out those perfect pitches, they need to handle objections as they arise for the organization. They need to manage all those different tasks that they do as a frontline sales rep. But, they not only need to know their own organization, but they need to know the customer, the prospect. They need to understand what they sell, who do they sell to? Who are their competitors? What’s going on in their market and their landscape. How are they being impacted by the macroeconomic environment? What are the actual pain points that are going on within that organization and goals that they have that This AE is supposed to connect to their products and services. So, you think about that, and it’s just… that’s just one account. An AE manages dozens, in some cases hundreds, of different accounts where they need to have almost PhD-level information about every one of these organizations. It just simply doesn’t scale. And so now you go and, you know, that’s not entirely profound, we’ve seen the life of a seller. I think one thing that’s interesting is we’ve thrown technology at this problem, and so if you go and now look at the average, you know, AE’s, sales stack. They say that enterprise seller is using, on average, about 34 different tools. And so, it’s really interesting. We did a day-in-the-life study here at ZoomInfo, where we basically did a time-lapse video of, you know, the 8-10 hour day of a seller, and what you’ll see is staggering. It’s basically, it’s basically them non-stop contact switching between applications, information gathering, managing tasks, and so on and so forth. It’s like they’re human APIs that are basically swivel chairing between these, all of these different tools and applications. And so, in a way, the tech stack that we introduced to help them has actually exasperated the problem. Okay, so with that, I think the premise of the problem that we’re looking to solve is that, look, go-to-market data, customer data is everywhere. We’ve only, like I said, exasperated that. And so, the tools and the ability for go-to-market teams to be able to access that is just simply not at their fingertips that… that should be. And so that’s the… that’s the essence of our product, and I will… Share a quick walkthrough. Okay. So, you should be seeing a slide. I’m gonna do one super quick slide, and then we’ll jump into Proct. So when we think about our solution, we basically break it down into three layers. The first is we attack that underlying data foundation. We unify that data set. And what that means is we bring together all of the different first- and third-party data sources that inform a business about their market, their prospects, and their customers. These are systems like your CRM, your marketing automation tool, your data warehouses, and then third-party systems like Zoom Infos. And, and other providers. The next layer, now that we’ve got that unified sort of foundation, is where the architecture is happening. It’s where you actually designed your go-to-market. What this really looks like is your strategies designed in code? What are the audiences and segments that you need to build? What are the automations and workflows that you need to actually build on top of that AI-ready data foundation? And then lastly, and this is a very big part of, sort of, this puzzle, is that last mile activation. That’s where the, you know, every sort of sales and marketing touch has to ultimately run through a frontline seller, or through a marketing channel, display, social, email, etc. And so, the sort of left-to-left flow on this is bring the data sets together. Design your go-to-market plays, and then activate them through your sales and marketing channels here. GoToMarket Studio is one of the first products that we’ve built, in, sort of, the AI platform that we’ve designed, and I wanted to take this group through that. And so, I mentioned context before. You know, we’ve had, like I said, we’ve built this business around that proprietary database. But one thing that we’re really expanding on when it comes to leveraging AI against that data set, we need to really understand who our customers are. what they sell, who they sell to, how they go to market. And so, that’s where, sort of, the AI context graph in ZoomInfo plays a tremendous role. So here, I just, I obviously am using ZoomInfo as the example, and so I can come in, I can see all the different information about this business. what their description is, what is the elevator pitch, different strategic priorities, what products and services do they have? Who do they sell those products and services? What are the pain points that are solved by this? Who do we sell those products and services to? What does our ideal customer actually look like? who are the competitors within our landscape? And then all of this different information that curates and personalizes different signals about these organizations. So, in our case, it’s, you know, what are the actual intent topics that are all relevant for a particular company? in our world, what are all the different places where, you know, in this case, domains, what are all the different channels where our prospects are actually coming and reading about ZoomInfo? And then lastly, I’ll even call out how we go to market via, you know, vis-a-vis sales routing. So, how do we define territories? What does an account owner actually mean? How do we define account teams? So that’s the sort of AI context graph, and then the other side of it is the go-to-market data model. And so in the data model, what we’ve done is really gone a step beyond what CRM is doing, and so we have objects here that look and sound a lot like CRM objects, but they’re all structured in a way that our AI, AI-ready. The premise of these is actually that we’re building out a data schema that AI can make sense of. What does it mean for an account type to be a customer? What does it mean if we had content interactions on our website or our web forms? Providing that context to the system so that it actually understands what’s important and what each of these attributes actually mean. And so again, that’s all the foundational layer. If I put it into action, I think the best way to illustrate it is in the build of an audience for a campaign. So, in my particular case, I’m going to go through a really, really simple use case here, where I want to go and run a cross-sell motion. So, I want to go build a list of customers who are in the fintech space industry, and I want to enrich these with all the different information that I think is relevant for our products and services, and it helped me build a curated audience for a campaign here. Okay, so I actually… I’ll just go through how I built this. You know, when you create a new audience in ZoomInfo, you can go and hit the, sort of, the object that you want. In my case, I’m looking for companies, and I can go query across all of these different data sources. In my particular case, I’m going to go query ZoomInfo, because I want, all the different companies that I may or may not already know. So, in my case, I just searched for financial software. That’s an industry here. And then, just for the illustrative purposes, I’ll just narrow it into Boston as a city. Okay, I can go and pull that data set in. Let’s, give this a view here. Okay, awesome. So, that sheet, it went and run. It’s gonna go and pull in all those different targets. Now, these are the lists of, sort of, that firmographic breakdown that I had. Where now this gets really valuable is not only do I have a list of these organizations and, the basic firmographic details, so, like, shape and size, their employee counts, where they’re located, I can now actually go and do enrichment across that entire context graph of information that we have about these companies. And so I can ask questions. in AI, I’ll get to that. The other things I can do is actually roll up counts of professionals. I sell a product for, typically, marketers. I want to know how many demand gen or growth specialists are in that organization. I can simply click this button, enter that criteria, and roll up the count of those professionals. I can see which one of these companies are researching intent topics, or which one of them raised a round of funding. And so on and so forth. If we look at the, sort of. full taxonomy of datasets that we have here. It’s everything from those company insights, contact insights, first-party data, and more. So I went and prioritized the set here. The first thing I wanted to do was, okay, great, I’ve got these list of fintech companies in Boston. I want to go see which one of them are already in my CRM. So I went and enriched it here with all the CRM details. So you can see this little chip here represents the full payload from my Salesforce instance. The next thing I wanted to do is add funding details, and so I want to know everyone… any one of these companies that actually raised a round, what the recent round was, what that funding amount was, and what, what round series that was, specifically. I can go even further. I can actually layer in job posts. I want to know if any one of these companies are actively hiring in the departments that I care about. In my case, it’s marketing. So I went and rolled up the count of open jobs within the marketing department. And then lastly, what I wanted to highlight is the value of AI within this context. Because ZoomInfo not only is using our third-party data, but we’re integrated into that first-party context, including conversations and meetings, we’re able to get really, really unique attributes, such as, you know, who in the buying committees within the organization. So I can see all the different people within this company that are in my buying group. Again, contextually relevant for my organization. Who are the important contacts? And so these are actually each of the contacts that we’ve engaged with, the context about how we’ve been working with them, and the role that they play within our relationship. I can see, again, a full synopsis here of all the different recent conversations, and this is all just specific pre-canned attributes that I’ve generated. And while that’s helpful, I can… I can continue and go on and on. There’s different pain points and motivations that happen here as well, so what are all the different use cases that this particular company has for ZoomInfo? And then this is actually information that they’ve discussed with us, so things like. data quality. They have a real inconsistency with, different insurance company profiles throughout their organization. They’ve got fragmented data that they’re looking to use ZoomInfo for. They have a very reactive sales approach, and this is exactly what they’ve told us, actually, on calls. So I would consider that first-party data. And then lastly, I created this, quick prompt here. I’ll just show what it was that ran across this, but I just wanted to create an account plan to introduce GoToMarket Studio to this customer. And because the system knows what GoToMarket Studio is, it’s the application I’m showcasing here, it’s able to take all of that as context about the value props, the pain points, and how we target it, and then layer that against all of the information we know about this business. Third and first party, and actually build a detailed account plan for how a sales rep should actually introduce our new product. Building the business case, which key stakeholders to engage with, the timing and approach, is this a good time considering where they are in their contract life cycle, so on and so forth. Lastly, the data shouldn’t live… live and die in this spreadsheet here. Well, what’s very important is that we’re now able to actually go and activate, now that I have a prioritized list with a clear-cut action plan, I can go put this in front of marketing channels or directly into my sales tools, where my sellers work out of. That was my very abbreviated pitch on GoToMarket Studio. Julia, how’d I do?

Julia Nimchinski:
This is awesome, Snee. The question that just comes to mind, we typically see ideal scenarios, so this is, like, the ideal picture, how it should look like, but the questions that tend to come up in the community, are all around, how do you get to this point? How hard is it?

Sneh Kakileti:
Yeah, it’s a great question, and I’ll take the… I’ll take the angle of basically the data here. I think in my sort of, like, ideal scenario that I went through is, is actually that heavy lift of pulling together all the different data sources. That, I think, is the hardest part of getting to this point. You know, where most of go-to-market, you know, teams struggle with is data, like I said, is scattered all over the place. That’s a data exercise, and, you know, it’s really heavy lifting on, ultimately, entity resolution, bringing together all the data sets. what’s great about ZoomInfo is we’ve basically built out the world’s largest B2B master data management system. That’s how we’ve built out our database, that we built. And so we’ve made all of those same services available for connecting to your different data sources and resolving those entities across. So, I think that’s where most of that heavy lifting comes from, really.

Julia Nimchinski:
Hey, since you’re in product, just curious, what excites you the most? What’s next for 2026 and ZoomInfo? Like, what are you allowed to share?

Sneh Kakileti:
Yeah, yeah, I love that question. I mean, I’m… I don’t think I’ve ever been as bullish about, our space right now, and it’s really exciting to see how, the different patterns and paradigms that we’re able to expand on, and how our users are able to interact with data. I think the big… functionality that I’m most excited about is going to be being able to take these types of flows and build agentic orchestration against them. And so everything I just did left to right was very sort of, you know, form-entered. It needed my context and awareness, and as a user, had to kind of build that out. I’m very excited to go one step further and turn this into autonomous motions, where The system actually understands and learns from our customers, their go-to-market motions, and is actually able to build these types of, you know, campaigns and audiences on its own.

Julia Nimchinski:
Awesome, thank you so much, and where should our people go? Message you directly, message James Roth?

Sneh Kakileti:
Yeah, absolutely. So connect with James and myself on LinkedIn, but if you want to learn more, please come check us out. Our website is zoominfo.com, and we’d love to talk to you.

Julia Nimchinski:
Awesome demo. Thanks again.