Enhancing a B2B MarTech stack with data with Kevin Tate, CMO at Clearbit

Harness data to boost your B2B tech marketing through personalisation, speed and scale. 

On this episode of the FINITE Podcast, you’ll learn how to implement a data tool into your MarTech stack, and you’ll gain a wider understanding of the ‘big data’ landscape and how it impacts B2B tech marketers. 

Alex spoke to Kevin Tate, CMO at Clearbit, a leading marketing data engine for B2B companies. Kevin has extensive experience in the world of data as he shares his views on its current role and where it’s headed. 

This episode covers: 

• How can harnessing data help B2B tech companies grow and scale?
• How can data be used for speed on a website?  
• How should marketers go about integrating a data platform?
• How does Clearbit help marketers wrangle their data?
• Is there a risk of there being too much data?
• How should marketers approach data tools and platform adoption? 
• What does the future of data marketing look like? 

Listen to the full episode here:

And check out more of the FINITE B2B marketing podcast here!

Full Transcript

Alex (00:07):

Hello everyone and welcome back to the FINITE Podcast. Today I’m talking with Kevin Tate, the Chief Marketing Officer at Clearbit. Clearbit is a data business, the marketing data engine for all customer interactions, helping B2B marketers to understand and identify and personalised marketing and sales interactions. 

And we’re going to be talking all about data, how Kevin sees big data playing a role in modern day B2B marketing, how marketers can incorporate data into their marketing and what the future of data and marketing might hold. So I hope you enjoy.

 

FINITE (00:39):

The FINITE community is kindly supported by the marketing practice, a global integrated B2B marketing agency that brings together all the skills you need to design and run account-based marketing demand generation channel and customer marketing programs. Head to themarketingpractice.com to learn more.

 

Alex (00:58):

Hello Kevin, and welcome to the FINITE Podcast. Thank you for joining me.

 

Kevin (01:01):

Hey Alex. Thanks for having me.

 

Alex (01:03):

Looking forward to talking. We are talking all things MarTech data, MarTech stacks, all that stuff that I love. Before we get there, I will let you tell us a little bit about yourself and your role to CMO at Clearbit and everything that’s come before.

 

Kevin (01:20):

Sure. So today I’m CMO at Clearbit, lead our team that we organise into the content, demand team and then the corporate and the product marketing teams. And I’ve been running marketing for about 15 years. It’s not where I started my career, but have been in various marketing or chief revenue officer roles for about 15 years now.

 

Alex (01:41):

Nice. And tell us a bit about the current role at Clearbit. You talked just then about the kind of different team structures that you’ve got, but how big is the marketing function? And tell us a bit more about those teams.

 

About Kevin and Clearbit

Kevin (01:53):

We’re close to 15, 20 overall. We’re a growth stage company. So we’ve really focused a lot on the content and the demand gen motion. And if you know Clearbit at all, we started about four or five years ago and have enjoyed a lot of awareness and a strong reputation with marketing practitioners. And I think part of that comes from being early and at the right time with some very strong growth marketing tools. 

So the ability to understand who’s coming to your site and personalised content or awareness or acquisition campaigns to them. And especially the ability to move data around the stack and make it as rich as possible. So as a customer of Clearbit, you have the most context you can about who you’re interacting with. 

So that was very good timing in 2015, 2016, when Clearbit got started and since then have have grown largely through reputation and through content and through the demand team’s efforts. More recently, I joined about four months ago to bring the other two legs of the stool, the product marketing and positioning and category strategy piece to that. And then more of the corporate marketing. And so we’re talking more about Clearbit and where we fit into the stack and where we’re headed and that sort of thing.

 

Alex (03:16):

Cool. Sounds exciting. I think we’re going to cover off a bit more about that when we progress through this conversation. So I guess as a starting point, we’re talking about how data really enhances that MarTech stack, which is exactly what you guys do in terms of providing that data to power as your website says the entire business. Why don’t we set the scene by talking about how data has evolved to really power what B2B tech marketers do these days?

 

Kevin (03:42):

Yeah, that’s a good and big question. So for context, I realised, and thinking about this topic, I’ve spent pretty much all my career working on data in some way. So when I got out of school in the mid nineties and dove into the internet, I was a programmer and a database administrator actually for Informix, which I don’t know if any of your podcasts listeners remember Informix. 

But it was one of the first web centric database solutions. And so I had to think a lot about, especially in those days, what data are we collecting? How are we going to organise that data? And the strategy for organising and storing and retrieving the data was all driven by what are you gonna do with it? What’s the why behind this data? 

And I think that’s an interesting approach to get into question of how that dynamic has changed with the advent of big data and different ways of managing and storing and retrieving data. But that the ability to apply data to the business is something I’ve gotten to work on first in e-commerce and then human capital management and in social media. and then business intelligence, then the internet of things. 

And now at Clearbit, I get to work on kind of the meadow version of that working on big data in marketing, not just as a consumer of it and the buyer of it, but also as the folks providing the solution. So all that is to say it’s changed a lot. And I think the data is now a pervasive part of every company’s marketing stack and figuring out how to put it to work most effectively and most efficiently is an interesting mix of a business and a technical challenge.

 

Alex (05:28):

Interesting. And I think that more broadly points to a challenge that many marketers have. Is that a marketing role now straddles both business and marketing outcomes and quite technical ones often. And marketing as a discipline is becoming more technical in different respects. 

So it’s an interesting time and interesting challenge I think for marketers to embrace big data. Big data as a term is a big term and can mean so many different things depending on the vertical in the space that you’re in.

 

Kevin (05:58):

Yeah. I think you’re right about the trajectory of marketing roles in the role and becoming more technical. It’s interesting. If we go back maybe 10 years, maybe a little more, there was the sort of the advent of testing. And so the marketing, especially online marketing, how do we AB test? How do we optimise? How do we do multi-variant testing had the sort of maturation of the ad platforms who were able to not just test, but also optimise automatically based on outcomes? 

And so I think for a lot of marketers that was a sort of technical Renaissance around the role. And now I think we’re seeing a similar one around data. And so the same way we had to figure out how to use testing and measurement on the front end. Now, how do we think about putting data to work? Data about our customers, our prospects, our business, to work in the operations of the whole go-to-market team, marketing sales customer success?

 

How can harnessing data help B2B tech companies grow and scale? 

Alex (07:00):

Yep. Makes sense. What about this ability to help B2B tech companies scale? Because I think data is the challenge that we’ve just talked about and in a lot of cases, the solution. Are there ways in which you see, and this can be specific to Clearbit, because I think this is obviously focused on what you do, but for a marketer, how can they embrace the type of data that you provide to really help drive marketing and drive scale?

 

Kevin (07:27):

It’s really interesting to see how that plays out in different stage companies and different go to market motions. So just to explore that landscape for a minute, we’ve been very fortunate to work with a lot of rapidly growing product-led growth, PLG companies. 

So companies that offer, typically some freemium version of their product that you experience and you get some value from, and the sales motion is showing enough value that you then pay for it and start to bring more people into the group in your organisation. And that in that type of motion, this type of data and insight about those freemium customers or the prospects in your funnel is extremely helpful in showing you where to focus because they’re huge numbers. 

So you might have a hundred thousand people a week or a month that are coming to experience the product and not all are going to be the best fit for what you have to offer or be in a position to buy it today. And figuring out who is in that tens or hundreds of thousands of people and what signals are they giving you about the type of company they’re in or the type of role they’re in, or the type of actions they’re taking. That might be a place to spend your time. 

It turns out to be really helpful for companies who are going through that kind of growth motion. And then on the flip side, you have companies that are pursue much more traditional enterprise sales motions, right? And say ABM account based marketing or account based management is certainly a piece of that too. And they want data for a related, but slightly different reason. 

Every contact they have could be high value and high potential and so they want as much context as possible for if I’m talking to a salesperson or as a marketer. If I’m talking to a director at one of my target companies in my ideal customer profile, please give me all the context I can get to help me make that a valuable conversation for them. And so broadly it depends on what motion a company has and what stage, but in both cases, the context that data can provide is as a huge advantage.

 

Alex (09:46):

And I should say that I think your website is probably my favourite B2B tech company website of all the websites out there. Particularly you’ve got better personalisation, but your reveal page in particular, I think is so great. I think anyone should head to Clearbit.com/reveal because the way that it personalises based on, well, I’ll let people go and discover it for themselves. But website screenshots and all kinds of stuff, it’s pretty cool and pretty clever. I think it kind of sells itself in it. It gives a pretty good idea of how the product works.

 

Kevin (10:17):

Thank you. I appreciate that. And it’s interesting because personalisation is really hard. And I remember trying to create personalised e-commerce and websites way back in like 96/97 even. And in some ways the challenge hasn’t changed. You can get inputs about things that would give you clues, is this a small company or a big company that’s coming to my website? But finding a way to cost-effectively tailor that content at scale can be very daunting. Personalisation gets complicated very quickly. 

So one of the things that we see our customers and other companies do, I think is very effective, is keep it simple. So being able to say, if you’re a small company, I’m going to show you small company logos and small company case studies. If you’re a really big company, I’m going to show you different ones. And that ends up making a big difference in how a visitor or prospect connects with the content. And that level of fidelity or complexity is often just the right place to start.

 

How can data be used for speed on a website? 

Alex (11:28):

What about the speed side of things? Because we’re talking about using data on the fly really rapidly to personalise digital experiences of website. In this case, the speed in general being key within using this type of data and making informed decisions. Maybe you can talk a bit about that side of things?

 

Kevin (11:45):

Yeah, it’s huge. In fact, when I first got to Clearbit, one of the things I did was go back and read a lot of customer interviews that we had taken place and some customer research over the last year or so. And if I was to word cloud, what I saw in those 50, 60 interviews, speed would be the biggest word and not so much speed in a less than half second latency round trip from the database kind of way. That’s important, but the speed was realised through competitive advantage. 

So especially companies that are in highly competitive markets offering a product that is sort of easy to jump into. They’ll say things like if someone comes to my site and starts filling out the form to get a free trial or get a demo or learn more, I want to understand if they’re a top prospect while they’re filling out the form. And I want to pop up a chat window to engage them in conversation before they’re done right before they fill out the form, because once they fill out the form, they’re gonna leave. They’re gonna go to whoever’s next on the list of search rankings, and they’re going to fill out the same form. 

So it’s the level of engagement and the speed with which we’re looking to capture customer’s attention is really important these days. So that’s a big part of what we try to do with the data platform is make the relevant context, the relevant data available right there. It’s literally available to the form. Not, let me go call a database and see if I can get something out of an online data store and figure out like, here you go. And I think that ends up being a big factor in helping these companies be more competitive in speed.

 

FINITE (13:30):

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How should marketers go about integrating a data platform?

Alex (13:50):

I think it’s all very well getting the data quickly or data being at the fingertips. But when it comes to market is actually using it to enhance what they do in terms of marketing automation, CRM website, other things, how can they go about that? 

Because I think the common challenges we talked about are more technical ones. Integrations, do you need a marketing ops person, engineer, team of developers? How much of this is plug and play and you just click a few buttons and you’re up and running? Versus months of development life cycles and launches and QA before anything happens?

 

Kevin (14:27):

Well, this is exactly the market opportunity we see, frankly. So going back to Clearbit’s start four or five years ago, we’re very fortunate to work with data scientists and growth engineers who are alchemists of a sort. They were figuring out how do we get the most important data up? 

Where we can use it and how do we create API based plugins to pretty much any system that needs it? And they’re using Clearbit to stitch all that together. And we have learnt from them and they told us, here’s the kind of data availability I need, and these are the links and how the API can function to be fast and most portable. And so we learned a ton from those alchemists, if you will.

 

Kevin (15:14):

And what we’ve built into the platform is a way of achieving that, that doesn’t require that you have data scientists and growth engineers in order to do it. So it looks much more like what marketers are used to creating scenarios in, or creating sequences in, or driving personalisation with. And I think that’s a lot of fun, trying to figure out how do you get this lightning in a bottle in a way that the whole market can take advantage of? So we do.

 

How does Clearbit help marketers wrangle their data?

Alex (15:47):

And I guess a big challenge is again, just the landscape of different products being used, right? So there’s CRMs and marketing automation, all these different tools, where do you see a more pure data platform? If that’s a fair description of what Clearbit is, fitting into that landscape. 

Because I know things like HubSpot have bits of firmographics and there’s lead for that. There’s all kinds of tools that have different bits of what you do maybe even built on Clearbit I’m not sure. You might say that some of them are actually using you guys, but how does Clearbit fit into that landscape? And is it one or the other? Is Clearbit like this magic layer that sits on top of and alongside everything else?

 

Kevin (16:23):

I like the magic layer. Yes, in most cases we’re an ‘and’ to those systems, and nearly any system that you can think of that’s on the CRM or marketing automation or a website engagement or sales conversational selling side, we work with all of them. And the role we play is typically the provider of the right data at the right time and the right format. 

And to your point, the right format is often what’s so important in understanding how does that say, chat window and chat bot, how does it need to receive that context? So it can make decisions on the fly about that customer interaction. So we’ve learned a lot about how to integrate with those systems. 

You’re right in that a lot of them, all those systems have to do some version of understanding customers or creating segments or making data available to the customer touchpoint. But what we’re finding is that there’s a common set of things that MarTech teams want to do with the data that are frankly best done in one place. 

So a lot of it comes down to the combining of the data, the refining of that data into audiences or segments, and then the application of that data through an API or a call to another system. If you’re doing that part over and over and over and over and over in the 30 different systems all across your go to market stack, that there’s a big opportunity to bring that into a single system. That’s just really good at that part. And that’s the role we tend to play in these stacks.

 

Alex (18:10):

It makes sense. And what about enhancing a customer platform? I think we were talking about more marketing front end type stuff, but data has its part there as well.

 

Kevin (18:21):

We work with a lot of CDPs as well. I think there’s an interesting trend that we like a lot, which is often our intersection with something like a customer data platform. And that’s this reverse ETL trend, right? So ETL being the extraction, translation, loading of data, typically into a warehouse or into a data store or data lake. 

And now companies are more focused, I think on reverse ETL, how do we get it out where we can do something with it and how do we get it out in a format that’s ready to run, old enough to remember data cubes coming out of these BI systems. That’s not what we’re talking about. We’re talking about something that feels like a real time stream. 

So that’s often where we are working with and alongside a customer data platform, is how do we take that intelligence and make it available in a reverse ETL time and rich context for whatever system needs it?

 

Alex (19:28):

Do you think there’s a risk of there being too much data? A bit of an aside and a slightly philosophical question, but we have the debate in the B2B world all the time around data performance marketing versus brand marketing. We all know that it’s not one of the other, you’ve got to do both. But is there a risk of data overload on all fronts?

 

Is there a risk of there being too much data?

Kevin (19:49):

I think it’s a really good question. And going back to my comment at the beginning about being a database programmer early on in those days. You had to think a lot about what are you putting in the database? How much is too much, how are you going to get it back out? Everything started with a bunch of table maps. 

I think in the era of big data and near infinite cloud computing, there’s been a shift, the pendulums swung more to just grab it all, throw it in the data lake, the data warehouse, and we’ll sort it out later and we’ll probably use AI to do it. And I think there’s a lot of truth to that, but the sorting out still has to happen. 

And so you’ve still got to figure out of the things I have collected and the things I can can enrich to make more contextual, what matters and how do I put it to work? And at least for now that requires some strategy and also to your point some resource. And so I think the point at which you’ve got too much data, is the point at which you’re not able to actually understand it and refine it and put it to work in a way that’s efficient enough. 

And I think actually that it goes back to this trend around this idea of reverse ETL, that is something people are talking about a lot because it is a problem. And I think the problem in a lot of ways, it comes back to so much data and perhaps a little less forethought about what went in in the beginning. You have to think now on the back end, what do I need to get out and how can I do that?

 

Alex (21:24):

Yeah. Interesting. I hadn’t thought about it that way, but I guess it’s akin to someone these days with Google drive or Dropbox. And I remember the days where it felt quite expensive to upgrade to a bit more space and now it’s like your iPhone fills up and you collect like less than a dollar or something and suddenly hundreds of gigabytes more unlock. It’s like we got lazy as a result of just not being limited by capacity, I guess.

 

Kevin (21:48):

Well there are huge opportunities for innovation there too. So in our world, we use things like machine learning and computer attributes to make sense of a very large set of global data. The ability to bring context to really any company with a website, that’s a lot of data. But we can’t bring the whole thing every time somebody hits a webpage. So how do we make sure just the right amount, is there in the right format for that fast interaction?

 

How should marketers approach data tools and platform adoption? 

Alex (22:22):

We’ve talked a lot about the technology side of things. For any marketer that’s approaching working on their kind of MarTech stack as such, looking at new tools, taking advantage of more data, any tips or thoughts about how they go about approaching that kind of thing? 

I’m always of the view that people and process come first and technology comes second. And I think a lot of marketers fall into the trap of thinking tools and products first and then realising that they don’t have to the team or the processes to actually get the most out of them. But I don’t know what your perspective is and not just relation to products like Clearbit, but more broadly in terms of making the best use of marketing technology products.

 

Kevin (23:00):

Well, I think you’re exactly right. People in process should be the lead. And ultimately the measurability is what’s going to provide a sense of ROI and figure out whether or not this is worth doing and worth continuing. And that should be measured on the scales of people and process, right? 

So is it helping the team be more effective, helping us be more efficient? Are we seeing our growth rates increase? It’s cliche, but I think generally starting small and starting within a very defined area is typically the best recipe. When we talk with clients about areas to apply data in a measurable way, we tend to break it up into three: acquisition conversion and then operations. Acquisition and conversion map to sort of top of funnel and bottom of funnel. 

And so you can think about things related to campaigns or personalisation or form capture or lead routing. And then the operation side is often about systems and people and processes in the area that is increasingly talked about as rev ops, right? So how do we understand what’s happening across the funnel and across the value chain for our customers and how do we report that in a way that lets us run the business? 

Having rich real-time data can make all of those things better, but it will be most effective. And I think you’ll have the most effective change management if you apply the data in a specific and thoughtful way to those in each specific system. So I think there’s a lot of benefit to doing some planning upfront and creating something that feels more like a roadmap. 

 

Alex (24:40):

Yeah. Makes sense. I think it’s so common for us to have a marketing team that’s got some super expensive, shiny marketing automation tool that I feel like you have a race car and you don’t have the driver or the fuel, or you just have something that’s kind of sat in the garage and not being used. It’s a common scenario, unfortunately.

 

Kevin (25:00):

Interesting too. Cause that’s something we think about a lot here because we’re not coming in usually to a company that doesn’t have anything. They’ve probably got a lot of stuff. They’ve probably got 10/15 systems that, to your point, are handling different parts of this and maybe under utilised. 

And so usually where Clearbit comes in this, let’s see how data could make the systems you have more effective. And let’s see how we can get them to work together in a way that’s more coordinated, more effective for your customers. Which is a little different than, throw out whatever’s in the garage and here’s a shiny new thing and this will be better. 

And so to your question a little while ago, we are this often this sort of ‘and’, this thing that joins the stack to turbocharge it. But it’s also a fun place to be. We spend a lot of time mapping out the MarTech stack with our customers and figuring out where can we apply data in a way that’s going to make all this stuff work better.

 

What does the future of data marketing look like? 

Alex (25:55):

Cool. Maybe we should wrap up by looking ahead and looking into the future of big data’s role within the B2B marketing world. What are you excited about?

 

Kevin (26:08):

So I think companies are learning a ton right now about how to put data to work. What data matters to me? How do I get it where it needs to be? And I think a part of the MarTech stack is going to be this data activation layer, and then that’s different than having a data warehouse. And it’s different than having a bunch of integrations. This is a place in the stack where data is combined and refined and sort of ready for action. 

So I think we’re seeing the evolution of that data activation layer. Now I think as that becomes more commonplace, where things will get super interesting is being able to make that context available everywhere it’s needed, right? So the level of intelligence and even a predictive nature of the B2B digital funnel will start to accelerate dramatically. And in a lot of ways, that’s the shift I think that companies like Clearbit are participating in right now. 

B2B buying has gone almost entirely digital and certainly accelerated by the last couple of years. And that has meant a lot of change in how companies engage buyers and how they help buyers along the journey. But the fact that it is digital and that’s happening through these digital customer touch points means that it can be measured and optimised, not unlike the way we saw optimisations in ad platforms or commerce platforms. 

So once we have this data activation capability, I think we’ll see some really interesting developments in AI and machine learning and the optimisation of a funnel that really better represents how customers are buying, rather than some of the more tried and true models of awareness and interest in decision. Which are truisms, but probably don’t apply to individual customers in the way that we’ll be able to really understand it in the not too distant future.

 

Alex (28:17):

Awesome. I think we’ve covered some pretty interesting stuff there for anyone that’s, I mean which marketer is not thinking about data in some form or another. These days, particularly, maybe with the last 18 months and with everything digital just speeding up. 

So there’s some food for thought there and some marketers will no doubt be heading to Clearbit website for a look around at some of the personalisation. Cause it’s very cool. And I think some good ideas in there. I think I might be inclined to try and steal or maybe implement some of them myself, but that’s for another day. But thank you, Kevin. It’s been great talking with you.

 

Kevin (28:50):

Thanks so much, Alex.

 

FINITE (28:52):

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