[0:00:00] All right, let's get into it. Welcome, everyone, and thank you for joining us. Today, we're going to be discussing an issue that is near and dear to my little marketing heart, and we're going to go inside the data engine that is ironically powering modern marketing teams these days. So with that, I am Em Wingrove. I'm the Chief Marketing Officer over at Aptitude Eight, We're a leading consulting firm on all things AI and HubSpot. We're one of the biggest and top partners in the HubSpot ecosystem.

[0:00:29] I'm also a career marketer. I've been navigating these fun but sometimes choppy waters of change. And since we're getting into that change and the tools that we use to navigate those changes like HubSpot, um i've invited two perfectly relevant folks to join me in this conversation uh one of my clients a gtm engineering guru uh lexi and then someone who builds product for well my favorite product hubspot kelly lexi kelly could you introduce yourselves yeah sure i don't i don't know if i would go so far as saying go-to-market engineering guru but thank you i appreciate that um Hi, everyone.

[0:01:05] I'm Lexi Kelsey. I'm Senior Director of Growth Marketing at NTT Data Business Solutions. We're a large global SAP partner. I oversee specifically our U.S. growth marketing team. So for us, that means business development, events, and marketing operations. So today's topic is extremely relevant to my day-to-day because so much of my role is is spent figuring out how we turn marketing strategy, data systems into actual execution and results for the business, right?

[0:01:41] So I'm excited to join the conversation and share kind of from the practitioner side of it. And I'm Kelly Yuen. I'm on the product team at HubSpot. I oversee Data Studio, the tool where HubSpot customers prep and shape their data before putting it to work. It's a really fun role to be in right now, especially as AI is changing what's possible, both on the product side and the go-to-market side. And I'm so excited to be in this conversation with you both.

[0:02:14] Awesome. Well, we're going to be getting into it today together. We'll be talking about marketing ops and the shift, like what is it and what is GTM engineering and what does that actually look like in real life? And maybe some common mistakes that we're seeing out in the field. We'll talk about the stack, not just the tech stack, but the full sort of operating stack. And then Lexi and Kelly each are going to show us some real life use cases and demos to apply these new concepts to real life. All right, so I'm going to set the stage here a little bit because execution is an interesting topic for me.

[0:02:49] When I was an early marketer, execution was the most fun part, of course. Everyone would try to jump right to the content, jump right into the fun pieces of getting a campaign out the door. But these days it's a little harder. I remember we would just buy everyone would buy lists, marketers would go buy a list, or you'd build a list inside of your own CRM. And then you take that list and it was almost immediately actionable. You'd validate it. Maybe do a little bit of cleaning. You send sales emails, send marketing emails, maybe get your sales reps to call behind it, maybe target some ads at it.

[0:03:21] And honestly, that worked really well for a very long time for me and other marketers, too. I know. But a list these days or even if you're buying it or pulling it together from your own database, it's just a drop in the bucket when it comes to everything else we have access to. intent data, all the activity data that's coming through. You've got product data, demographic, firmographic, technographic. And then you have these new AI smart properties and other data agent tools that can go scrape the web and give you data that you didn't already have.

[0:03:53] So it's a lot, right? And on top of that, and maybe because of that, the Like email approach doesn't just work anymore. You can't cold email people. You have to show them you know them in ways that we have never even dreamed, right? You have to have so much hyper personalization. Obviously, all of that requires data. So yeah, the email bit is not going to work if you don't have the data bit figured out. So it feels like these days it is an all hands on deck effort on the marketing team to action data. We're all wearing these marketing ops hats.

[0:04:23] And it was interesting because while we were prepping for this webinar, Lexi had done some research and found some really credible data to back this up. We are not exaggerating. This is not marketing speak. There is real data around it. Lexi, what did you find? Yeah. Well, so I think it's up on the screen, one of those stats, right? So the Gartner stat there, I think is pretty telling. Marketing teams are only using about forty nine percent of their MarTech capabilities. And another report that I found was that data integration is the biggest stack management challenge for teams.

[0:04:58] So to me, those points are the same issue. And kind of what Em was talking about is we don't necessarily need more tools. We need the data, the tools, and the processes that we already have work better together. What about you, Kelly? You feeling it on the product side? Yeah, absolutely. We're hearing very similar things from our customers where the problem isn't the lack of data. It's the opposite.

[0:05:27] It's that there's too much all over the place. You have your CRM records, your product usage signals, your website behavior, event lists. And when reps are finally ready to reach out to the right person at the right moment, they're still waiting or it's based off of incomplete data or they're pulling that manual list. Again, that data exists, it's just the gap between the data existing and it being useful in the moment that it matters. It's somewhere, everyone knows it, but it's a matter of being able to act on it in the moment.

[0:06:00] Well, with that, I've got a little poll. Because they want to figure out where is everybody at in terms of their ability to actually leverage and action their data today. So on a scale of one to four, one being very poor, four being excellent, how would you rate your team's ability to leverage or action data? I'm not seeing the scores come through.

[0:06:34] Oh, I see some good. Good is pretty good, but we've also got some additional data around that. I think Lexi also found this. Seventy-five percent of marketers are saying that they're not actually fully leveraging. the data that they have available to them. Seventy five percent. That's most of us. It's the vast majority of us. And I think that that speaks extra volumes right now because there is so much data available.

[0:07:07] Like if you had to ask this question ten years ago, I think most marketers would feel pretty confident and comfortable in their ability to leverage data. But there's just a lot now. So this, you know, clearly tells that same story. And this is looks like it's from E. Clark's, not Gartner. And the previous one was Gartner. So very interesting there. All right, so where does this leave us, right? There's this new term that has emerged, GTM engineering, and it's effectively building the systems that are required, that are necessary to design, automate, and manage your go-to-market operations.

[0:07:43] So I think about things like what audiences are we talking to? Why are we talking to them? What are we actually saying to them? What information do we have about them? Is their organization showing signs of growth on the internet? They're hiring for C-level people and they're hiring different teams, right? What other things can we use to target and personalize so, so specifically and at scale, that's really GTM engineering. It's not just like, hey, we have this idea of a theme and we're just going to run with it.

[0:08:14] It really, really starts with the audience. And what used to look like just a quick data import and maybe some bad handoffs now looks like a huge role. Like the expansion of marketing is massive and into agentic automation and all of the systems first thinking, because I think the ones who are winning and that are going to win in the future are thinking like systematically, like in the form of systems. And I don't mean systems like a tool. I really mean the people, the process, the tool, all of it coming together. Obviously, Lexi, you've been on the front lines for this shift.

[0:08:46] Talk to us a little bit about your experience and the whole GTM engineering emerging. Yeah. Yeah. I mean, I've definitely seen this shift and I think that the, the, presence of the role now of go-to-market engineering is, um, kind of the accumulation of, of that shift. Um, and it's really resonated with me. I think, um, the old model, like you're saying is very much like, well, like the slide is showing that marketing ops cleans things up after the fact, um, you know, a campaign would run and event list would come in leads would be created.

[0:09:17] Um, and then ops would have to go back and fix the data, clean up the handoff, um, figure out why something didn't score if you do have scoring implemented or route correctly, right? And I think that worked-ish, you know, when the motion was simpler, but now we can't do that, right? We have too many signals, too many systems, and too much data, right, and too many handoffs, right, to treat ops as the cleanup clue at the end.

[0:09:47] And I think that really, to me, that's the go-to-market engineering shift and where it starts to really make sense. It's not just about making marketing ops more technical. It's about designing the motion upfront. So we have to think things through, like, what signal are we capturing? You know, what data do we need to add? How should we score it? Who should own it? Where does an actual human need to step in? So instead of running a campaign and then asking ops to clean it up later, we're designing the system around any potential campaign from the beginning, right, that we can add.

[0:10:22] And that's what's scalable. So I think that's the real shift is marketing ops moves from cleanup mode into true design mode and they stay in that mode. And then the rest of marketing has to move with it because everyone's work now feeds that system. Kelly, what about you? Are you seeing anything similar with customers in HubSpot trying to wrap their arms around this new concept? Yeah, absolutely. I mean, the data is there, right? We talked about that, like the issue is an access to data.

[0:10:53] It's realizing, you know, when you're trying to use it, that it's not wired together or set up for you to act on it automatically. And we're seeing this a ton that The teams that are pulling ahead aren't necessarily the ones with more data or bigger budget. They're the ones who've built a system where once that signal fires and something happens, you have all of that done for you. It's all connected and you don't need to call on someone else to make sense of it or help you connect all the dots.

[0:11:27] And when we think about what that actually looks like in practice, if you're systems-minded, you know that everything is predicated on something that's happening before it. And there's always something that's happening after it, like contingencies, dependencies, which matter a ton if we're talking about building automated or agentic systems. But no system is completely without humans. maybe one day, I hope I'm not here for it, but there's got to be some level of human oversight, right? Like they're critical to, humans are, how we market, sell, service, delight, and really connect, right?

[0:11:59] But it also isn't tools alone or humans alone either. Like they all kind of have to come together. Lexi, how did you and your team face this shift and sort of get it into practice? Yeah, I mean, it's still a shift for us. It's a big mindset shift, I think. So... I think what we really had to do is stop thinking about automation and system of just like, how do we make things faster and start asking like what needs to be true before the next step should happen. Um, so a lead cannot be scored well, if the data is bad, um, it cannot be routed correctly.

[0:12:34] If ownership rules are unclear, or if you like the data and the system is just not marked properly with the right owner, right. Business development or sales can't act on it. Well, if they do not have the right context, um, So for us, the new practice became mapping that out more intentionally. What is the signal? What data do we need to trust it? What context should we add? What should happen automatically? And then when, again, when does an actual human need to step in?

[0:13:03] Because people are still critical to the process. The goal is not to remove them. The goal is to stop making people manually connect all those dots, right? So that's the shift that we've been trying to make. It's less like, you know, one-off campaign cleanup or one-off process or program cleanup, and just more of like this always on system design. And I have to say, like, it's, you know, we've been working on it for, I mean, probably for the last couple of years, really in earnest the last year, and it's still not finished.

[0:13:34] And I don't think it'll ever be finished. Like we're still optimizing scoring. We're still adding signals, adding workflows, triggers, building better actions for those triggers. But the goal is a system that keeps getting smarter over time and that we just keep building on. And it's not a bad thing that it's never over. It's never over. No, not at all. Everything's changing all of the time. And I also want to call out, thinking about what comes before what and that whole sort of systematic build out the design, what you just said, isn't it interesting?

[0:14:09] I've done this with my team. We leave those meetings and we're like, wow. One, we recognized a gap that we would have literally never thought of before because we're mapping it out and designing it intentionally and informed by the data we already have. And we walk away from those meetings and we're like, OK, cool. I feel like we didn't leave anything off. We didn't forget anything. And we learned something new. It just feels like taking the blinders off a little bit. It's just changing the way you think and plan, right?

[0:14:38] Agreed. Yeah. And it's so, it's actually, I love it. I, you know, I nerd out on this stuff. I think it's so fun. It's like, it's like opening up your, your mind to like a whole other way of, of thinking and how things can be ran. And I think like one example that we had, right, was we were, we were going through all of these changes with, with your team, you know, aptitude eight, um, to our system. And what kind of came out of it with me and one of my other marketing leaders, she looks after branded digital, was we're like, we see this gap in terms of human presence between her team and my team, because my team's more bottom of funnel and hers is more top of funnel.

[0:15:15] And we're like, we need somebody that's kind of helping connect those dots, right? So it also created like a new role for us that we put somebody else in. So it's just, it can the, you know, it's endless of what, what can happen from all of these changes. So. It's an exciting time to be a marketer. All righty. All right. So at Aptitude Eight, we work with hundreds of customers, right? And they're all either working towards this new world, they're in this new world, they're prepping for this new world.

[0:15:48] And when they come to us for help, these are the areas, the key areas that we see over and over again, where slippage happens, if not slippage, then actual damage or mistakes that are happening. I will say the first two, in my opinion, are the scariest. They carry the most risk. And Kelly, that seems like it has a lot of overlap in terms of the product, in terms of HubSpot and a CRM. What are you seeing across HubSpot customers when it comes to some of these unfortunate mistakes? But we're all learning and it's okay. Yeah, we're seeing that a lot of, most often teams are trying to activate before they trust their data.

[0:16:23] They'll set up workflows, build their scoring models, they'll wire everything up. And then when you get to looking at the output, you're not feeling confident in the list or the data. And once that happens, the whole process or project stalls. The step that gets skipped is doing that upfront work to shape your data so you actually feel confident in it. What does this field mean? Does it match that? Is this record actually who I think it is?

[0:16:54] It's really important to spend that time on that upfront setup to really ensure that everything else downstream is actually used and is based off of trustworthy data. And I will say it's hard in HubSpot because one, there are just so many features and you guys are constantly coming out with new updates. So it's hard to just want to hold yourself back. Like if you're a CRM admin and you have access to some of these, whether it be like data agents I personally have a hard time not just like running in head first and doing something.

[0:17:27] And even I know some of our data might not be the best, but I think it's also common for it's common for there to be bad data. Almost all of us have it. But I think it's also common for folks to be a little overly confident or to just not see some of the gaps that exist. Right. And then they go and push the button, pull the trigger, and they've automated something bad at scale. Right. um that is definitely scary so um what about um the like modeling their data and not like i don't know if you guys have seen this before we have worked with a lot of customers where they model the data but they don't actually map it to things that fire so they've got like okay cool these are going to be the associations this is going to be like how the data model itself will work but then there's nothing that's really triggering based off of that So you've done some work that's not paying off.

[0:18:15] And then I feel like the tools, like you mentioned that earlier, getting more tools isn't always the answer. Oh, no. Yeah. And I'm the first one to admit that that's like probably my biggest mistake. I do wanna touch on data hygiene too, but the more tools is not the answer. And I learned that, I wish I could say that I learned it once, but I had to learn it multiple times to really to hit home. This last year, we really had to stop with adding more tools in because, and really focus on like the foundation.

[0:18:51] And I was like, we need to get everything in HubSpot, what we currently have, working well, automated, we have the best data all the time, as much as we can, right? Nothing's perfect. But, um, and, and I was like, if we need another tool, it needs to be for a really great reason. Um, once we get past this like foundational project, um, but yeah, tool, the adding in a tool doesn't always solve everything. Um, and I think part of that too, is, is going back to the data hygiene.

[0:19:21] I was like, look, like we've got to stop everything and, and just, unfortunately, it's not the glamorous side, but just focus on, on the data quality. Um, and our system was not in a good spot at all. Um, and it's still an ongoing and ongoing battle, but we had to focus on, you know, D duplication and, um, you know, making sure that the records were being enriched with the right data. Um, right. And so, because I think my, my concern in what happens and what's happened to me in the past is that like, as soon as somebody like the BDR team, right.

[0:19:55] Um, If they stop trusting the system, they will. There's like no going back from that. They'll never trust it again. So it's super, super important. So, yeah, no tools are always the answer. Focus on data hygiene from the start. If you don't have good data, then nothing else matters. And shout out HubSpot because the data quality tools are pretty slick. Like back in the day, I would say like, oh, you might need to go get an in-cycle or something like that. I would say today, the Data Studio, like the whole data quality set of tools, it's pretty strong.

[0:20:26] Yeah, you guys are adding on quite a bit. So it's nice to see how this works. And there's more coming. The team working on it is doing a ton. Amazing. all right so now we turn to tooling um because like i said people and processes alone won't cut it and we know the tools alone won't cut it kelly talk to us a little bit yeah so what data studio within hubspot is really designed to do is give you a place where you can handle all three of these layers you can combine shape your data so it means something you can enrich it with ai or data agent and then activate it directly into segments, workflows, or sync it to your CRM.

[0:21:11] And the thing that I always come back to is that the quality of everything downstream depends on that work you do up front, right? We were just talking about like all of the setup. If your data is messy and untrustworthy while it's going in, a better tool isn't going to fix that. It just moves the mess faster elsewhere. Data Studio is really where you can do that foundational work so that activation actually lands. Yeah.

[0:21:42] I would say too that as you think about a CRM, a tool that's going to push a lot of this stuff forward, especially one like HubSpot, it's got tons of AI features. You've got the data quality tools. Then I know, Alexi, I think you're going to cover some things like on Clay. Um, but ultimately it has to like the people, right? Like even if you were to say, okay, we're going to use clay now. Well, do you need to get certified in it? I don't know. Do you need to have some experience? And if you've never used it before, then you're thinking about all of the time that's going towards those things.

[0:22:12] And, you know, it just, the people, the process and the tools really, really do have to come together. And it has to be intentional. Like what you see on this slide of like why we're breaking it up into these parts. Um, which is interesting because, um, HubSpot has this slide or this graphic that they've been using for a good bit, and it's to describe and define an agentic customer platform. I don't know, Kelly, if you want to speak to this, but I'm trying to take the last slide and map it to what HubSpot already has because it works.

[0:22:42] Leveraging context to enrich and build out your data so you can coordinate with humans to unify it and then get it all activated. Yeah, yeah. I mean, it's all coming back to that trustworthy source of data and using it as context and letting that flow all the way through. It really makes a difference and it helps create this really intelligent loop of feedback and of context that's being built throughout. It also makes me feel a little, I don't know, maybe validated as a marketer to see this visualized because it does at least highlight that where we're doing work is a lot deeper than most think.

[0:23:17] Like, oh, like I said, just pull the list. Oh, just import it. No, no, no. There's a lot of orchestration going on here in this, just in this one graphic. And that would be even for like one campaign, one function. Now scale that out and all your programs and all of your campaigns. And it's a lot of work, right? It's a lot of orchestration, so. Let's pop into the tool and see what Kelly and her team have been cooking up. Awesome. So as I mentioned before, Data Studio is basically this data playground.

[0:23:50] You can bring data in and connect all of it, shape and prep it and enrich it, all before using it throughout your GTM process. So we talked about this earlier. The data exists somewhere. It's just a matter of bringing it together, being able to trust it, and taking action on it. And Data Studio is really where a lot of this can start. So not only can you pull in your HubSpot objects such as contacts, companies, and deals, but you can also bring in external data. When you bring in this data, the one thing I do want to call out is that it's not immediately syncing to the CRM.

[0:24:25] So you don't need to worry about your CRM getting cluttered with all these random fields and properties. Your data is in this staging layer until you decide when it's ready and where it should go, where it should be used. When I say external data, that could be anything from a Google Sheet, from a conference that you recently attended. It could be a connection to a warehouse like Snowflake or BigQuery or a third party sync from apps like Stripe or Shopify.

[0:24:58] We know that everyone's tech stack looks super different, and so we're really prioritizing making it so easy to bring in data from anywhere. And this is where all of that scattered data starts to become one central source of truth. And once your data is in, it's time to prep and shape it within the Data Studio Builder. So I'm going to open up a data set that I have already made. And a data set is basically a table where you define what data comes in, how you want to structure it and what it means.

[0:25:31] So here, for an example, I have my HubSpot contacts with a Google sheet that I have from a recent conference that I attended. The builder, this experience, the screen that you're seeing, is essentially that working layer. It's where you do the work up front, and then this is what everything else downstream then depends on. I can shape my data from here, and that's really where the magic happens. So I can rename fields if I need. I can move things around.

[0:26:03] I can write custom formulas. to derive new fields. I can filter down my data as well to really fine tune and narrow down exactly who I want to see in this case. And really the goal is simple. It's helping you get a version of your data that you can actually trust. And speaking of trust, I think I mentioned this, we are investing a ton in our data quality tools so that you really know that you're working off of clean data.

[0:26:37] In addition to shaping, so filtering down, adding formulas, creating conditional columns, you can also enrich your data set. So you can use Graze Intelligence or company and contact enrichment to add in additional columns of enrichment data. You can also leverage data agent to add in a column. So from here, you can describe what exactly you're trying to do, or you can use one of our out of the box prompts.

[0:27:08] You can bring in firmographic data. You can look up intense signals. You can create an AI generated score. There's so much you can do without having to leave the tool and without having to stitch together a bunch of separate workflows. In addition to adding in net new enriched columns, we're also working on, this is a little sneak peek, we're working on the ability to fill in the gaps with this enrichment data. So instead of just adding in a net new column, let's say I was missing a handful of the country codes that I needed.

[0:27:43] I could then, instead of creating a net new column, just fill in the gaps of that missing data. And once my data set is good to go, and once this foundation is really solid, and I'm ready to activate it. And this is where that full circle moment happens. So you have your data and now you can immediately take action. So I can use it in a report, I can use it in a workflow, a segment, I can push it to this CRM if I want that, or I can export it as well.

[0:28:19] The data doesn't just sit here, it gets used in your workflows and across your teams. Some of our customers are bringing in data from a source like Stripe to report on their financials. Others are bringing in engagement signals to create custom segments. Others are creating personalized email campaigns and using smart columns to create that personalized email draft. The opportunities are endless. And really we want to, we're continuing to invest in making sure that all of our users, all of our customers have this one trusted single source of truth to power your GTM processes.

[0:29:30] Oh, goodness, guys, I think I was on mute for all of that. What I was saying in short was that Datasets does a really, really good job of cross-object analysis. So like at Aptitude Eight, we've got projects that are the engagement for our client, the client account, the contacts within it, billings, like all of it. And so we use Datasets internally to pull all of that stuff together to make sense of it. And it just really, really performs well. And obviously the spreadsheets and other data sources are very, very cool. But even your own data, I would have otherwise have to export it, put it into multiple spreadsheets, do lookups and all the other fun or not so fun stuff.

[0:30:08] All righty. I think we are. going to head back into the deck. Yes. Yeah. And what Lexi's going to talk to us about is sort of bringing those layers into her real life. So I will hand the mic over to you, Lexi. Yeah, thanks, Em. And thanks, Kelly, for that. I think I've already touched on a lot of like what we've experienced at NTT Data. So I'll go through that fairly quickly and then get on to kind of just it's a really simple but I think powerful example and one that we all as marketers have run into and run into on a regular basis using the Data Studio.

[0:30:45] So giving an example of how we've used that of what Kelly just showed. So, you know, I've touched on our kind of story a little bit here, but again, I'll just reiterate, it wasn't that we didn't have tools or we didn't have data. We had HubSpot. We have an external sales CRM. We have event data. We have enrichment tools like ZoomInfo and Clay, as well as obviously HubSpot Breeze. And so we had a lot of pieces, but the problem was that, you know, they weren't clean enough, data hygiene was a problem, or connected enough for any of the team to act on anything in any meaningful way.

[0:31:23] So at the same time too, we've talked about the market is changing. Simpler campaigns, cold outbound lists and generic follow-up, it's not enough anymore. Buyers are doing more research before they engage. AI is changing how people search and evaluate vendors. So we needed a system to do more than capture a lead and pass it on. For example, we needed it to help us understand the signal at context, prioritize, and then trigger the next step.

[0:31:56] So the work with aptitude eight helped us approach this more systematically. We did start, well, we started with a large project and then decided, you know what, one thing at a time, right? That was a lesson that we learned. So we started really with the foundation from there, which was data quality, de-duplication in just overall cleaner records, cleaner data. Then we built into enrichment. And for us, it was, I'm actually really excited about this, but we have a three-tier enrichment strategy.

[0:32:25] So it goes through HubSpot first, every record gets enriched by HubSpot Breeze. Then it triggers getting enriched by Zoom Info, both at the contact and the company level. And then for deeper enrichment and more kind of custom signals and data points, it triggers Clay enrichment from there. So that was really fun to build. And then from there, we've got lead scoring that we've set up, lifecycle stages. And of course, the human side of it, clearer ownership and who does what.

[0:32:57] So, you know, the result is HubSpot has become more actionable for the team. Like I said, it's still a work in progress, but we do have cleaner data, clearer scoring, better handoffs, and, you know, a stronger foundation for scaling beyond like one-off campaigns. And so the example that I'm going to show, as I mentioned, is, you know, it's a small sort of subset of a process, but it's a very common one. Um, it's for, you know, event leads that we get in.

[0:33:27] Um, so it's a very, it's a good example. It's a smaller version of that, that same shift taking kind of a signal, them attending an event, adding context, turning it into action. Um, so I am going to move on to that. Let's see. There we go. That's moving. Correct. Okay. Okay, so Kelly just walked through the Data Studio. So I'm going to use that. We're going to use an event list to run through the Data Studio and see how the pieces can work directly inside HubSpot with this example.

[0:34:02] So your team, obviously, I'm sure everyone's had this experience. You come back from an event, you have an attendee list, and it depends on each event of what data you actually get from that. You might have names, titles, companies, maybe, if you're lucky, e-mails. But then for us, it's really where the real work starts. Who are these people? Do we already know them? Are they even a good fit? Should sales follow up or should marketing nurture them more?

[0:34:32] Are any of them tied to an existing opportunity? Some might actually already be customers, right? And I think that that's where a lot of teams, maybe not get stuck, but they're like, that's a lot of work and it takes a lot of manual processes to get that data. So you have the data, but it's raw, it's incomplete. It's not always clear what should happen next. So the goal is to take this raw list, enrich it using any sort of enrichment tool. For this example, I'm going to use clay. I know a lot of people do have that.

[0:35:02] We have it as well. Match it against what HubSpot already knows, apply some logic, and then turn it into something the team can actually action. That's what I'm going to walk through now. Here's your list, your raw list. Right away, this is where Clay comes in. There's already a Google Sheet integration for both Clay and HubSpot Data Studio. We are now trying to fill in the gaps. As you saw on this, emails, we don't have any of the company data, location, et cetera.

[0:35:36] Um, so we've put that list into clay and we are filling those gaps. Um, so we're reaching things like email company size industry, where the company is public or private tech stack, which I'll come on to in a second and any other company level signals that are relevant to, to your business specifically for us. Um, One of those data points is tech stock, actually, and specifically ERP. So what we can layer in now is this extra data point, and it helps give us a tighter kind of follow-up and fit score for us.

[0:36:14] So if we can understand for us what ERP system that company might be running, it gives us a stronger signal and helps us understand and prioritize what of these companies are relevant and what contacts are relevant. From there, as you can see, so we've added those rows. So tech stack finds all of their tech stack. Then we've got an enrichment that it pulls out the ERP systems. From there, we can bring back this enriched data directly into the sheet.

[0:36:50] So now you can see the green is what we originally had from the event. The yellow is... what we've now enriched from clay. And again, this is all through a Google sheet. So now, um, the question is, um, what does HubSpot already know about these people or about these accounts? Um, because that of course changes how we should treat them. So we're matching that list back into HubSpot here. Um, so put the list into HubSpot. We can see where there's overlap. Some people may have maybe contacts, um, some may be connected to deals.

[0:37:24] Oops, sorry. Here we go. Um, and, um, that matters because we don't, you know, if we have an existing relationship, like a totally net new lead, um, or, you know, have that customer relationship, then we were going to, um, manage them differently. Um, so we're right now we're segmenting existing contacts, um, for people that we already know, we can create a segment and handle them differently.

[0:37:55] Like I said, I really like this portion of it, right? So what's great about Data Studio is that you can add additional fields. And so for this one, we're doing an AI fit score. So in this example, we're using AI to look at public information, things like company website, press releases, other available information, and score how AI-forward that company appears to be. So it's not the only signal, obviously, but it gives us a more data point to help prioritize.

[0:38:32] So we've got the fit scores here. Now we're adding score here. Yep. So that field is basically accumulation of a bunch of things. So we've taken the score from the AI engagement, looked at all of the other data points that we've gotten here and essentially given it a score, which then of course, like I said, helps us prioritize. Now from here, we're choosing where we want to export these contacts.

[0:39:18] And then from there, we can action it. The important part is that this data doesn't just stay in the spreadsheet. Once the data is clean, enriched, matched, scored, we sync it right back to HubSpot. But we don't put that bad data directly into HubSpot. And that's what I think is great, one of the great things about Data Studio. And so we are only sending through what data we want and this fully enriched data with now a fit score that helps us prioritize from there. So I think to tie that all together, and talk about the layers that Kelly and Emily talked about earlier, you can see how they show up in that example.

[0:39:56] The enrichment layer fills the gap. The modeling layer is where HubSpot helps us bring the data together, match it against what we already know, validate it, dedupe it, Apply some logic. Um, and then the activation layer is where it becomes useful. So once we get it into the system, you know, that's where we can sync it to the CRM, create segments, trigger workflows, and then route it to the right people. Um, so the value is not in any one set by itself, but it's like how those pieces all work together.

[0:40:28] Wonderful, wonderful, wonderful. You know, it's interesting, like it was a demo, right? Like where you're going through the slides, and obviously, it would take a little bit longer than a few minutes to do that. But when you compare like the actual production execution time of what you just showed to what you would have had to do without those things, that what you the amount of time it would take and the quality of what you'd get, yeah, a lot longer, and you'd have less quality. Yeah, I mean, it literally goes from hours to minutes. I mean, you know, probably thirty minutes or something to do something like that.

[0:41:02] Whereas it used to take us hours before. And the quality too is better. Even the admin side of it, like, okay, you have this process now, you could use AI to help you write an SOP for it. Then you can, you know, whatever you hire someone for your team, they have it. It's already baked. That's that context layer, right? It's not just the data that you have and the people, but it's also like, what are the steps in the documentation? And like, what do we do as a company? And I think that all helps like with that activation, right? And with executing against it. So actioning data is now the job for marketers.

[0:41:34] It's a much more data heavy and systems heavy job. uh role i will also say i feel this like weird splitting and this is a conversation for another day but i feel this weird split of like quant marketers like demand marketers and brand um right like it's feeling like i don't know about you guys like i feel like i'm getting pulled like how am i supposed to do brand and do all of this data work it's a different world and i'm excited to see what that really means for marketers like i don't know if you just heard the um more and more chief brand officer.

[0:42:04] So will it end up splitting companies? I don't know. But I do feel the need. It's so urgent for marketers to be able to get their arms around data and with tools like HubSpot and Clay and honestly leaning on each other, it's really helpful. And we've got to leverage what we have. And that means being systems minded. It means that you should probably model things before you activate them. You should test and yeah, model them out and earn the automation that you build. You can't do it all right. Start with one thing, build one thing at a time, even if it's your own individual workflow, your own, you know, this is what I do for my piece.

[0:42:40] Start with that. Then, then, you know, scale it out to, okay, this is how we do events. And then you have it all sort of, building bit by bit instead of trying to take a big bite off of more than you can chew. It ends up being pretty risky. So the world is different. I'm excited for it. I'm excited to have friends and folks like you guys in my life to lean on. And we should be sharing knowledge as much as possible and sharing our wins. And like, I appreciate you, Lexi, saying like, yeah, I tried that, tried that once again, and I had to learn a couple of times.

[0:43:11] We all have, and that's how we're going to help each other, right? We've got to live vicariously and share knowledge. Let's see if we've got any questions in the chat. Let me pop over and see if we've got anything. How did you get your stakeholders and other teams to buy into the new processes? That's a good question.

[0:43:45] I think, you know, what not to do is try and sell them on this whole new go-to-market engineering role and world, right? Or a big systems project. I would start with a business pain that they already feel. That's, I think, what's going to build you momentum. So pick like one visible use case. Maybe it's like a... a poor handoff between marketing and sales.

[0:44:15] We always have those issues, right. Or, um, you know, bad data for a certain segment, um, and clear routing, whatever it might be, um, then show how fixing the system around that problem can create a better outcome. Um, I think that once stakeholders see that, that one of their pains being solved, um, they can see the like before and after that's when they start really understanding like, okay, we can do this for every other process or pain that we have, right.

[0:44:46] Rather than trying to get them onto one big transformation project. It's a good question though, because it kind of highlights the need for stakeholder involvement and understanding to be a part of it all because it's like, it's an orchestration and it's not just happening in the marketing team alone. I guess other departments as well. Yeah. Awesome. Great question. Is there a feature you wish Data Hub had right now or anything that you're excited to see in the future?

[0:45:17] Kelly, what is brewing? I'm really excited about our continued investment in allowing our customers to really treat Data Hub and HubSpot as that central source of truth. We're investing a ton in making it really easy to bring in all your data, no matter where it lives, clean it, prep it, shape it, and use that external data to power all of your other So whether that you're creating a segment for a campaign, whether you're passing off the list to sales, or if you're creating an automated workflow, I think we're investing a ton in creating a really streamlined process to bring in that data and use it.

[0:46:09] I feel really good about that. So I'm excited to share more when we have more specifics. Exciting. I've got a dream of connecting multiple HubSpot portals too. If that's on the roadmap, I don't know. I think about like partners, you know, being able to share some CRM things across. But anyways, dream for another day. We are a small team and already stretched pretty thin. Where would you recommend as a good starting point to get our data under control?

[0:46:42] Let's see. I would say it depends on how much data you have and how bad off it is. But if you don't have a lot of capacity and you do have a good bit of data, I would definitely recommend working with a partner or a consultant even. But just having someone with fresh eyes you know, open hands that can actually do some work and execute. Um, but also that will help you strategize because it's, it's, if you jump in and try to do it yourself and it's too complex, um, and you don't have enough hands, you're going to get started on it and you're either going to get confused or stuck, or maybe both.

[0:47:21] You also, you know, make a mistake. Um, and you know, you didn't see something and now you've incorporated it into some automations or you've copied a property across a bunch of places. And so you definitely have to be careful. And I would say that it's almost like when you're working on your own little project for so long, you become not numb to it, but like you don't have the same sensitivities as others. So bringing in fresh eyes, you know, definitely helpful. and obviously aptitude eight would be happy to help uh if you're running into capacity issues or even you just you know we don't have the resources on staff to be able to do it uh let us know we would be happy to help and then there's another one in the chat from mary um about books i don't have any nancy drew books but i wish i did honestly the ones that are over here by the way they're they're not even books they're storage little magnet things that open Mine are books, but I don't have Nancy Drew, unfortunately.

[0:48:22] I'm sorry to disappoint. No, but we are pretty good investigators of what's going on in our CRMs. I will say that. You know, when you get a lead or something that looks a little off and you're like, hold on, let me dig in. Yes. I love doing that. I love doing that, right? I'm happy you said that. I know our demand gen manager, Jordan, she's probably listening and like, Yes. Yes. Awesome. Okay.

[0:48:51] Any other questions from the chat? There's no other questions then I will. Thank you very much for coming. We appreciate the engagement and chat. Hit us up if you guys have. We're all on LinkedIn. If you guys have any questions or want to connect, we'd love to connect with you. We'll also be doing another webinar around Data Hub.

[0:49:20] We'll be doing that at the HubSpot user group on the thirtieth. So we'll probably follow up to everybody that came today, get you access to the RegLink and all that good stuff so we can keep the conversation going. Thank you, Kelly. Thank you, Lexi. Thank you everybody for coming. Appreciate you all. Thanks, everyone. Thank you.