Activating an Intent Strategy to Build Lead Flow and Revenue

As B2B Marketers, we are more and more reliant on data to help shape our customer journeys. Activating site and contact-based intent data present an unrivaled opportunity to deliver 1:1 relevance at scale. The ability to predict buyer behavior allows marketing and sales teams to customize and adapt top-of-funnel programs, improve lead scoring/nurturing, prioritize outreach, and provide account intelligence to sales teams.

What you’ll learn:

• How is intent data sourced and what are the different kinds of intent data?
• The powerful combination of site AND contact based Intent Data
• Demo of LeadSift contact-based intent data
• Aspirational/novel use cases of intent data
• Activation of Intent Data using managed programmatic and lead gen

Presented By: Tukan Das CEO, LeadSift and Alex Lukashov VP, DaaS Sales Anteriad.

See the specific chapters below:

00:00 – Introductions

03:11 – Elements of a Successful ABM Strategy

05:23 – What is Intent Data & Where Does it Come From?

10:30 – Anteriad’s Intent Data

14:11 – Anteriad’s Intent Output Examples

19:07 – Who is LeadSift?

24:23 – Combining Different Types of Intent Data & Putting it to Action

26:28 – Audience Question: What are the Metrics You See Using Intent Data?

28:24 – Audience Question: Is There a Way to Measure the Accuracy of Intent Data?

34:10 – Audience Question: How Do You Derive the Intent Topic Clusters?

00:00 – Introductions

Dee Blohm: Hi everybody. Welcome to today’s webinar presented by Merit B2B in featuring LeadSift I’m Dee Blohm SVP of Marketing here at Merit B2B, and we’re glad that you could join us for activating an intense strategy to build lead flow and revenue. A little bit of housekeeping. Our webinar should last just around 35 minutes or so, depending on q and a.

And as far as that Q and A, you’re welcome to post any questions in the Q and A button, in the Q and A questionnaire on the GoToWebinar platform.

So, a little bit about us. We are Merit B2B. Data is at the core of everything that we do. Every client engagement, every model, every transaction is driven by data.

Since 2000, we’ve used this data-first approach to solutions that are built for the multi-channel B2B and technology marketer worldwide. Our clients rely on us for new customer acquisition, ABM and demand gen data management, and customer data platforms. It’s our comprehensive data, proprietary technology, and deep expertise that differentiates us and uniquely qualify us to be your growth-driving partner.

When it comes to business marketing solutions, we power B2B. It’s been almost a year since Merit B2B acquired the company 180byTwo, another data-driven marketing org focused on developing ABM solutions that enable a new level of understanding of your B2B prospects and customers. And it’s really been a perfect fit as we continue to grow and become one Merit B2B.

So, a great acquisition for us last year, and that will lead us right into our topic for today. I’d like to introduce our speakers. We’re proud to feature our special guests Tukan Das, CEO and co-founder of LeadSift as well as Alex Lukashov, VP of Sales here at Merit B2B. Gentlemen, welcome, and thank you.

Tukan Das: Thank you for having us.

Alex Lucashov: Yeah, thanks Dee. So, what we’ll cover today is essentially the webinar is about intent and activating intent different kinds of strategies around it, and using different types of intents to accomplish your ABM goals. So, to start, we’ll talk about typical ABM flow.

We’ll dive into intent to zero in on different types of intent. Talk a little bit about our research-based intent and what that type of intent looks like, and then Tukan is going walk us through our LeadSift intent, and then we together talk about kind combining different types of intent pull a list, and then finally activating integrated campaigns. As Dee has mentioned, we’ll have a short q and a section towards the middle and the course towards the end, so feel free to put the questions as the webinar moves along, and we’ll be happy to answer them as they come in.

03:11 – Elements of a Successful ABM Strategy

Alex Lucashov: So, to start, let’s talk about different elements of successful ABM strategy. It typically all starts with gaining an understanding of your ICP and TAM. Typically, it’s an analytical exercise that, marketing takes on together for sales, whether it’s analyzing existing customers or sourcing information directly from sales to help you figure out who your ideal customer is, and then building out its own addressable market.

Typically, with that, the audience is pretty large and it’s very difficult to effectively reach with the limited marketing dollars that folks have. So, the next step in that is then to focus on targeted clusters that you’re going to reach out to that are typically accomplished by using different types of data.

So, data that you might have in your CRM already, or intent data, technographic data. Any signal that you can pick up on folks to help you figure out and come up with different targeted clusters of accounts. Based on those targeted clusters, you want to then personalize experiences to maximize impact, in terms of the kind of messaging that you’re reaching out to them with, as well as the timing on who you’re going to reach out to at what time. Once you have that figured out, you can deploy integrated campaigns. So ideally, what works best are integrated campaigns. We’re going to talk about different ways to set that up based on the database, and the signals that you have.

And then finally, as you’re running these campaigns, you want to understand the end optimized contain performance based on the response that you’re seeing, the customer interactions, that in turn then flows into your ICP and understand your total addressing market. So, it’s certainly a flow and a purchase cycle that keeps improving with every iteration.

Now, to make that happen, what you need to do is you need to have the right tech in place and of course, the right data. So, what we’re going to focus on today is talking about the targeted cluster and how to come up with them specifically. We’re going to double-click on intent data.

The next slide, please.

05:23 – What is Intent Data & Where Does It Come From?

Alex Lucashov: And before we dive into this, yeah, let’s talk a little bit about what intent data is. Most of you probably have heard of intent data or already are using perhaps different types of intent data. But because of the fact that the market is pretty fragmented, have a lot of different types of intent data, it makes sense to really go over that and ensure, they were all on the same page and we’re talking the same language. So, the first type of intent data that everyone is using is your first-party data. Data. Data is directing marketing, automation or CRM systems, website visits, et cetera. Very high-quality signal. The problem with the first part of data, obviously, is that scale.

There’s very there’s not a lot of it and to, so to sub supplement that and to how we figure out, expand on the same strategy. You can go to individual publishers, so you might have a publisher that’s collecting information within their own ecosystem to help understand different kinds of company activity.

There’s a ton of publicly available information that might be posted on social media, on block posts, and just generally on press releases and articles. Some review sites like G2 or Trust Radius, also collect their type of intent data, which is more like the competitive type of intent data.

Great sources of intent data. The issue with them is not the issue with them, but I’d say one thing, one consideration there is once again, scale you might not have as much of this information available. And then the next layer of 10 data that is for sure the most scalable is publisher networks, right?

So, you have a number of different publisher networks that are collecting data and then and then publisher technology partners, right? That’s another way to another way to scale your intent data as well as any information that’s available from DSPs

Tukan Das: Yeah. If I may add one more thing, Alex, you talked about all the different sources of intent and one thing that I would like to emphasize for the audience here is the reality is, outside of first-party intent where someone is coming to your website and declaring, “Hey, I want to buy your software. I want to see a demo.” which is probably the strongest form of intent, but less than 1% of your total market is actually talking to you at any given time. The rest are doing things you just don’t know. Hence, third-party intent sources exist. Now, and why there are multiple, as you pointed out, like all these different sources of intent, and I’m guessing there are probably other sources that we are going to uncover, or people are going to uncover later on.

And the reason you, you have these different sources of intent is because in a B2B buying journey, it is super complex and people, there is no one source of intent that can confidently tell you, hey, they’re going to buy from you or a solution like yours. Right now, a lot of that is not observable.

It’s not declared, so there is no way you can clearly find it. So as a result, you have to rely on all these different signals or proxies, whatever you want to call them, whether it’s publisher data, public data pub, publisher exchange data, bitstream data, different data sources to then pick up these signals and then tie them together, to make that prediction saying, hey, we are observing all these different things across the web, across different sources. And if I see these happening enough times, then the systems can predict that if enough of these things happen, likely heard of them being in market is greater. And that’s why it’s super important to look at, I would say, multiple sources of intent to come up with this consulate view of who’s in.

Alex Lucashov: Awesome. Yeah,

Dee Blohm: I launched a poll during that and it’s still collecting responses, but I’m glad I left it open because we’ve got about half of our attendees responding and looks like we’ve got about 7% that are only using first party sources. We’ve got 40% using first and one third party source, 20% using first party and multiple third-party sources.

And then we’ve got a good third of our voters saying that they’re using all the intent data that they can get their hands on. So, we’ve definitely got an experienced bunch. Nobody here seems to be new to intent data, so getting a good idea of what’s out there. Where the intent is derived from in that combination of signals is important.

Tukan Das: Yeah, absolutely.

Dee Blohm: Go ahead, Alex.

10:30 – Anteriad’s Intent Data

Alex Lucashov: Yeah. Yeah. Please do. So as a next step, we’re going to talk about Merit B2B intent data and how we go about it. So, we collect our research intent, research-based intent data. So, let’s talk a little bit about it. This type of data is typically captured in specifically how we process what it would look like.

So again, normally. So, we work with a network of B2B publishers and publish technology partners observing about 6 billion monthly signals. And a signal in that world is essentially like an article that is being read on a B2B website. So, from there on, we’re able to tie that article back to the topics that are being.

The company and an actual office location of the company that’s being that’s doing the research and we’re able to do that because of the kind of account link that we have built out here internally. So, then we aggregate the data by topic and office location and then produce a weekly velocity score for the company.

And each topic is actually at each office location of the company. And we’re able to this graph, 6.7 million companies and on about like 6,000 topics and 18 million plus locations in the US and also in UK going international very shortly.

Tukan Das: Alex, quick question for you: we also look at intent data, but obviously the volume, one thing that stood out for me is that volume of signals that you could observe 6 billion, interactions with content. How does that sort of distill down to an end customer, whether they’re running ABM programs or outbound or whatever how that’s a massive volume.

Yeah, no,

Alex Lucashov: That’s a great point. And so, we obviously wouldn’t pass on 6 billion signals to, to anyone.

It’s a lot to process. We have a whole engineering product team that focuses on that. But even so we do process all this information. We tie back then the strength of the signal. But even with that, it’s still 6,000 topics, 6.7 million companies. That’s still a massive amount of data.

So, we will then for individual customers will segment it to only the topics that matter to them. We’ll segment it to a subset of companies that matter to them. And then from there on we also then build out their best practices that applications of data up as well as we walk them through, you know how to actually kind of act on it and make the data actual cause like to your point, Yeah, there’s a lot of different types of data and one of the biggest challenges is how do you actually activate So do you can you go to the next slide and then we’ll talk

exactly

Dee Blohm: Another question there, Alex.

Sorry. Somebody’s asking how we derive intent data at the location level.

Alex Lucashov: Oh yeah so, the way that we’re able to do this is because we’ve built out an account and graph that is essentially a map of companies to IPs. But in addition to just using IP registry data and IP partners, we also incorporate MAID and mobile location data into that.

By using a combination of both, we’re able to more conflict on zero and on, and Kind of where the research activity is actually being like originated from and from there on, we can build that breakout and send buy office location.

Dee Blohm: Okay. And when you say MAID mobile ad id.

14:11 – Anteriad’s Intent Output Examples

Alex Lucashov: Yeah. Yeah. Sorry, the acronym is just so used to them. Cool. Yeah, and I actually, if you go to next slide, we can talk about, we can actually showcase like that what that data looks like and how it’s actually broken out. So, to your point an individual like data point in, in, in here would be essentially your company researching a specific topic with a score.

But that’s not going to be as actionable as let’s say you are working with a customer that works in the automation space, right? And then we’ll create a topic cluster of topics that are related to automation. And then based on that we can build down kind of the strength of the signal, figure out what the signal figure out different action points based on the strength of the signal.

So, in this case, you’ll see Shell and specifically Shell and in Houston, Texas. Or we can go down to the actual zip code of the office location. See that there was like a lot of activity ranging your furnace. 10 topics out of let’s say there’s 16 on the topic cluster, right? And there was research activity over the threshold of 70 on 10 out 16 of the clusters with the average score of 81.

Given you a combined score of eight, 810. Right? That is a very strong signal. Normally we would draw the line somewhere depending on, once again, the customer, the tactic, et cetera, to say, at this point you pass on the directly to a sales team. Or maybe for some of the like weaker signals you’ll the wrong kind, programmatic and pain, et cetera.

But yeah, but that’s, so we essentially like consulting with our clients on the best way to activate the data and build those strategies tailored to that.

Tukan Das: Alex, one question I have is just curiosity. So, in this case, like a client comes to you and they are in sort of the automation.

And they want to identify companies that are showing intent around automation space. And this is the output that they would get.

Alex Lucashov: Yeah. Yeah. That would be one way to consume the data. Yeah. It really depends. So, we can build that up. But that’s like a more of an aggregated view.

You can also see what actual topics they’re interested in and the actual,

Tukan Das: Yeah, I was going to ask – so with regards to topic cluster, this is a new concept for me. Is there a predefined number of topics within a topic cluster that you, see?

Alex Lucashov: So, you can come up with like custom topic clusters. But that’s a great question that we’re literally working with a product team right now to build that.

And we’re predefined topic clusters that are based on just machine learning and kind of insights. Just to ensure that you have some standardization across the board. All right? And then your ha you can have a single topic living within multiple topic clusters. That’s also because there’s going to be a bunch of overlap, right? And then ideally you also have different topics that can be weighed as well, right? So, it really depends on how specific you are because some topics are more important than other topics, right? A lot of exciting stuff on that end coming up.

Tukan Das: Cool.

Dee Blohm: We have a question. Sorry. Somebody is asking how does the Merit B2B data compare to other solutions such as Tech target or Zoom info? I’m assuming we’re talking about intent data here, but I’ll let you answer around.

Alex Lucashov: Yeah, for sure. So, I’ll definitely answer it on my end and then you can also talk about that, or maybe we can repeat that question a little bit further down.

But so, the way tech target is an individual publisher, so if you’re thinking about kind of the grab that we’ve described, we have you three different pillars, right? For sure. They collect their intent data from their network of size and it’s great, but it’s, the scale is somewhat.

And I’d say so for us the biggest difference would be the way that we collect the data the way the proprietary account link technology breaks it out by office location, and then different ways that we can then activate the data across, manage programmatic campaigns or integrated omnichannel campaigns.

And then when it comes to Zoom info, it also would be the source of data. To believe their data comes from the acquisition of click agency which is more on the midstream side. And then also how it’s packaged. So, like them, they typically will sell within their platform as solutions, not necessarily something on more of their overall product.

Dee Blohm: Okay. Thanks Alex. Ready for me to move on Tukan?

Tukan Das: Sure.

19:07 – Who is LeadSift?

Tukan Das: Cool. So, this is my slide now. So high level LeadSift is a contact-level data provider where we help other B2B technology companies identify which accounts. And which contacts within those accounts are in-market, meaning they’re likely to buy their solution or a similar solution in the next 12 months.

That’s what we focus on. So, if you go to the next slide, our goal at leads this to mine every form of public data that’s available to predict buying intent. So, the way we look at it is we are historically, our primary source of data is by looking at public web sources, and from there look at different trigger events or, signals or proxies, whatever you call, and then combine them to predict buying behavior.

We have also recently, hence the webinar. We have also started licensing this very interesting source of data, research-based intent by Merit B2B, where now we can not only have Intent signals from the public web. We now also have this new proprietary stream of data provided by them that provides a 360-degree view for our customers.

What that means is, you can come in for us, a signal of intent could be one of the multiple different things. It could be if someone is researching a specific topic or an intent category could be around automation, or it could be them researching about a competitor. Around that, or a partner, it could be them researching a specific keyword attending an event, all of a sudden calling their team and then talking about what are the roles.

So those are all sources of signals or trigger events we consider predicting buying behavior. And I go back to what I said before. I still think there would be other sources and types of intent data that would go into this model. For example, we also include technographics as a signal.

So, when did this start using this and what are they using based on that we start predicting? So, there are multiple different things that’ll be, that’ll go in but that’s what leads have does. And then we filter the data based on your ICP. Which kind of companies you want to go after, what rules you go after for 30 to 40% of the signals, and this is really one of our biggest differences, is we can, for 30 to 40% of the signals we can tell you it’s not just Pfizer, it’s Jenny Smith, Director of IT out of their Virginia office who was researching about automation amongst many different things.

So, when you are activating whatever, ABM strategies or sales and marketing strategies, you have way more actionability around that. So that’s really what leads it does at a high level. We also are guessing, we also have, if you go to the next slide, Dee we also have a, I think a screenshot.

So, we also took a screenshot. Based on automation, and it’s not by coincidence we coordinated on this, just to give some consistency. So, for example, if you see here, very similar to what Alex was showing, but the big difference is in the trigger column. The second column you’ll notice is, we have different kinds of trigger events.

So, a trigger could be someone researching over to keyword business operations or engaging with a specific vendor, Nitech, or researching about RPA or S or microfocus, attending Gartner’s IT infrastructure vendors, things like that. So that’s, that is the big difference. That’s how the data is provided.

And what you notice is beside every single company, we have a score similar to what Alex was talking about, we also look at different sources of data. We are also integrating now this new research-based intent data to bump up or bump down the score. And we also try to find individuals within them It’s good to know that Fiserv or Cherry bakeries have been researching about Nitech, which is a vendor in this space, but it becomes a lot more interesting if, it’s actually shown their IT ops person who is researching around this.

So that’s really what LeadSift is. This is the data that we provide. Again, the big use case for this is if you go to the next slide, I’ll quickly scan it. There are three broad use cases, email outreach. That’s one of the most common use cases. Sales teams, SDR, figuring out which accounts, who within the accounts to go after with what messaging, add audiences.

A lot of our customers take this and build, add audiences on Facebook, LinkedIn, Google to target them, and then ABM. So, for example, if you’re running an ABM platform like me, b2b, you can build an audience from this and then activate it there. So that’s that. Those are the three broad use cases of LeadSift.

So yeah, so this is interesting. Alex, do you want to start talking about this and then I can jump in?

24:23 – Combining Different Types of Intent Data & Putting it to Action

Alex Lucashov: Yeah. Yes. Sorry. It just takes a second on my end to, for the slide alone. Yeah. So cool. Yeah. So how do you then combine different types of intent data, and how do we actually put it into action, right?

So, this is an example of that. So, in this case, we’re looking at automation. We have this topic class for automation on the Merit B2B side, 60 topics. You have the and 10 triggers, right? And based on the strength of the signal, assuming that you are looking in this gate, in this example of less of those 10,000 accounts based on the strength of the signal, you can basically then build out different flows and different activate different use case against different subsets of the data.

So, you have about 10,000 companies see that with Merit B2B low-end leads at low-level of intent. You have about 6,000 accounts, right? That’s a lot of accounts and, at scale normally the lower touch, the programmatic contains will be a better, your best bet too, to kind of reach this audience with.

Then moving up, we have their Merit B2B medium and reach up. Medium could be like, And or statement really depends on how you can get really complex with this and you that’s obviously just an example, but you can just say Okay let’s look at this, smaller audience, but still relatively large and activate paid social campaigns against it, right?

These people are also being hit with the programmatic not doing as well. Then finally you get, get to email activation, maybe 3000 accounts. Once again, whitling the audience down, reaching out with more direct messaging for them. And then finally we’re the highest signals, where you’re combining.

High and leads to intent signals. You have only about a thousand of these accounts. Let’s pass them on directly to the SDRs and then the medium those folks are getting hit by programmatic, a social email omni channel, which, by the way, we can at, we can help activate you from scratch and run it as a managed service.

26:28 – Audience Question: What are the Metrics You See Using Intent Data?

Dee Blohm: We have a, I have a couple questions here to can, this came in while we were on your slide, but the font is the smallest you’ve ever seen, so it’s very hard to read. Okay. What are some of the metrics that you see lift, that you see using intent data?

Tukan Das: Yeah, that’s a good question.

And it completely depends on the customer to Yeah. Yeah. It completely, my diplomatic answer is it depends on the customer to customer. We have seen anywhere between 20% improvement to 800% improvement in their marketing and sales efforts. But consistently what we see though, and this is something that I’d like to clarify just because…

Whether it’s from LeadSift or Merit B2B or combined or zoom in for whatever. I don’t think third-party intent can, automatically solve your sales pipeline problems and give you crazy results. What we consistently see and that’s something that we can stand by, is let’s. You look at outbound sales motion as one of your executions, like email outreach as a use case.

If you take a hundred people, you reach out to hundreds of them. Five. Let’s say you get five people replying back to you. If you use 10 data from LeadSift, you should at least get a 50% improvement on the positive reply. That’s pretty consistent. So that is the kind of expectation sometimes people expect, if you reach 150 of them will book a meeting and then 20 will close.

I wish that is the case. It would be an outlier, but intent is basically giving you a notional direction into who you want to go after so that, there is a, there’s a lift there. That being said, there are way better results, but then I’m cherry-picking. This is the average that we see.

Okay.

28:24 Audience Question: Is There a Way to Measure the Accuracy of Intent Data?

Dee Blohm: A question that’s attached to that and maybe covered in what you just said is there a way to measure the accuracy of intent data? And then my question is, the multiple sources of intent, are they all contributing to a higher confidence? If all of them are saying the same thing, obviously there’s, we’re contributing to a higher level of confidence, but back to that measure of accuracy.

Tukan Das: Yeah. I’ll answer the second question first. The letter half? Yes. All the different signals contribute to higher confidence scores. And that’s why I’m so excited about this new form of intent because you, through this there’s a lot more activities we have seen. So, we cannot just use a simple counter.

So, we need to incorporate the scale of the activities happening and the acceleration of it in the overall score. So yes, they definitely contribute to it. Every different signal type, trigger type has different importance. So, someone researching about a vendor, it’s typically a lot lower funnel than someone researching about a high-level, broad category or someone researching about a vendor.

And if you know the job title that goes up. So, if you are an automation product and an IT operations person is researching, that’s way more relevant. So, all of that plays a role in the intent city score for prioritization. Now how do you measure the accuracy of intent? I actually think I don’t have an answer for it.

It’s so tricky, right? Because how do you really measure intent? The promise of intent is it’s going to predict who’s in the market. That’s the promise. The action you take is you take this and then you reach out to these people because they have shown intent, and you see how many of them buy from you.

That is the primary method of figuring out intent. Now, the problem there is, you reach out to them, maybe your messaging is off, maybe they don’t want to talk to you, maybe they bought from someone else. Maybe your pricing is off. So, it’s very difficult to predict what’s the accuracy of it. If I gave you a hundred people, did those hundred people, where all of those hundred people in-market to buy it, it is very difficult to get a clear answer.

But I think that’s where we should be striving towards. So, one of the ways we are looking at measuring that, and I actually think as an intent data platform, this is something. We want to address we want to come up with that answer. One way we measure it is when we push the data to Salesforce so we can see the funnel within Salesforce so we can measure off the leads that we pushed.

How many of them became opportunities? That way we can say, Okay, we sent these many became opportunities. That doesn’t necessarily mean the rest were bad, but these are for sure they were in market. Otherwise, they wouldn’t have gotten the opportunity. So that’s how we measure it. It’s not a very scientific way if you do not have a Salesforce integration, we probably wouldn’t know that.

So, a lot depends on the customer, but I think there needs to be. And evaluation like a subjective evaluation done that says, this is the gold standard of measuring the efficacy of intent. I haven’t seen anything done in the industry. I don’t know Alex, if you guys have noticed or not, but I think that needs to happen.

Alex Lucashov: Yeah. So, AB testing for sure is one way to do it right? Everything is the same. You randomized all samples. For the t let’s say email outreach, right? Go out to the same amount of people and then you can essentially see what performs better, right? With the same copy, with the same messaging.

The key is to keep everything the same, and the variable really is intent and you have utilized intent.

Then another way to do it would be if you have a, this is an interesting one to clearly you can go in and talk to a person and say, Okay, talk to a company and be like give us your sales data, right? And then based on that individual sales data, try to go back into a backtest of the data that’s not super scalable.

And it’s and it’s somewhat biased because just based on that one company, A better way to do it potentially would be, it really depends on the accuracy of technographic data. If the Technographic data is super accurate and it is changing, then you can use Technographic data as a truth set to go in a scalable manner.

Yeah. Go in. Backtested. Yeah. But it is certainly a big undertaking and it’s not easy. And it would be interesting to see how they like contributing to different sources, right? Or maybe this is something where you figure out you have three or four different sources; they’re all indicating the same thing.

And that is a true measure of intent, that’s like the ultimate.

Tukan Das: Yeah. The technographics piece is interesting. We have thought about that. That’s something, some experiments we have run to build models. The problem is some of the techs are not observable sometimes, and it’s not accurate for sure that is one of the challenges.

Yeah. But yeah, it is. Yeah. It needs to be answered and we hope, webinar, we’ll have that answer.

Alex Lucashov: In an ideal world, it’s really more of a machine learn like a model, that you build out based on. And then if that will automatically figure out the topic cluster will figure out the actual scores, the way of like different topics, right?

But at this point, that’s a bit science fiction. We’re getting closer to it, right? We’re getting closer to a state where we’re going to be able to do that.

Dee Blohm: So, Alex, I have a question. I’m going to pose it to you about the clusters. I’m looking at the clusters here, somebody is asking how you derive the clusters.

34:10 – Audience Question: How Do You Derive the Intent Topic Clusters?

Alex Lucashov: Yeah, so for now it’s more of a manual process where you work with the client. A lot of times you start off with, let’s say pay per click keywords, right? That kind of it back to our topics. But we’re also building out this kind of like standardized topic cluster as well that are based on different products and what we’re seeing and what we saw work for different, across multiple clients. And that actually, like building out this standardized topic product also gets us closer to that state where you can analyze things analyze these signals in a standardized way across multiple true sets and do some backtesting. Okay.

Dee Blohm: Okay.

That was it for our questions. I want to thank both of you for joining us today and covering all these different types of intent and how they can drive revenue. I do want to Invite all of our attendees today to check out all of our upcoming events and webinars on the events page of Merit, B2B.

And please join us next month as we feature Malachi Threadgill of Forester, and we get you ready for 2022 and how to avoid the four pitfalls of ABM. Thanks again everyone for joining us and to our speakers, Alex and Tukan have a great day.

Tukan Das: Thank you. See you everyone.

Alex Lucashov: Cool. See you.