Inside B2B Data and Sales Enablement with Ajit Thupil, CPO of Bombora

Summary

Marketecture Podcast: Inside B2B Data and Sales Enablement with Ajit Thupil, CPO of Bombora

Host: Ari Paparo
Guest: Ajit Thupil (CPO, Bombora)
Date: September 8, 2025

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Overview

In this episode, Ari Paparo interviews Ajit Thupil, Chief Product Officer of Bombora, to explore the nuances of B2B data, marketing, and sales enablement. They dive deep into how Bombora collects and activates data, the unique challenges and strategies of B2B advertising, the evolving role of identity and AI, and what differentiates Bombora in a crowded data market. The episode is rich in practical examples and industry insights, making it essential listening for anyone interested in B2B marketing technology.

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Key Discussion Points & Insights

1. What is Bombora? (00:25–01:42)

• Background: Founded in 2014–2015, Bombora just celebrated its 10-year anniversary.
• Mission: A laser focus on B2B, serving both sales and marketing teams by delivering actionable data and insights.
• Ajit's summary:

  “Our focus has been steadfast for us through this whole period.” — Ajit Thupil [00:46]

2. The Unique Nature of B2B Marketing (01:43–03:15)

• Complex Sales Cycles: B2B sales involve long, multi-party buying processes.
• Sales and Marketing Alignment:
  • Sales teams use Bombora data to prioritize outreach.
  • Marketing provides ‘air cover’—coordinated messaging across multiple channels during sales engagement.

  “You want the marketing teams to be reaching out to the same folks... making sure as your SDRs are calling, these same folks are seeing your brand show up.” — Ajit Thupil [02:36]

3. Bombora’s Product & Data Activation (03:16–05:00)

• Data-First Approach: Core product is B2B intent data.
• Activation: Data can be used across all major DSPs, social networks, and integrated into sales enablement tools (e.g., Lucia, Cognizant).
• Measurement: Reporting on reach and engagement by account is available.

4. Using Bombora Data in Practice (05:00–06:57)

• Audience Building:
  • Off-the-shelf syndicated taxonomies are available.
  • Custom ABM (Account-Based Marketing) lists: clients can upload their target accounts, select by role, function, seniority, geography, etc.
• Example Use Case:

  “That could be about buying extra IT decision makers in extra large companies that are in the construction industry.” — Ajit Thupil [05:38]

5. Addressing B2B Targeting Nuances (06:58–08:32)

• Problem: Balancing spend between giant companies (e.g., General Motors) and small startups.
• Bombora’s Solution: Granular audience filters (demographics, geography, roles) and account-level frequency capping.

• “You can also add other factors around geography... start adding further granularity that allows people to focus on a certain segment of people.” — Ajit Thupil [07:46]

6. Identity & Reaching Prospects Across Contexts (08:33–10:08)

• On-the-Go Audiences: Can Bombora reach B2B targets outside the office (e.g., Starbucks)?
• Technical Detail: Data is tied to identifiers; offsite activity is included if it can be reliably resolved to the target.
• Exclusions: Some locations (e.g., generic public Wi-Fi) may be screened out for precision.

7. Buyer Sophistication & Market Evolution (10:09–12:54)

• Sophistication Spectrum:
  • Some clients experiment with targeting, frequency, and measurement.
  • Others are highly prescriptive, splitting audiences and messages for granular bidding and measurement.
• CPM Sensitivity: B2B buyers are generally willing to pay a premium for high-value targets.

• “Typically, you’re not as budget conscious as you would be if you’re trying to reach a person in a Starbucks.” — Ajit Thupil [12:48]

8. Inferring Roles and Titles (12:55–13:53)

• Data Collection:
  • Partnerships with data vendors for functional and seniority attributes.
  • First-party content consumption data from over 5,000 publisher partners.

• “We are able to model where we think they fall both in the functional spectrum as well as in the seniority spectrum.” — Ajit Thupil [13:50]

9. AI’s Role in B2B Data and Marketing (13:54–16:48)

• Industry Hype: Many platforms launching AI-driven features.

• Bombora’s Approach:

  • Maintains focus on high-quality data provision for clients and AI/ML models.
  • Will lean in with AI where it aligns with their core value and expertise.

• “You’re not going to be able to train these models on substandard data and expect superlative outcomes.” — Ajit Thupil [15:07]

• Use Cases: AI can assist with dynamic audience qualification, campaign optimization, and frequency capping.

10. Lightning Round (17:59–20:00)

Competitive Advantage

• “Our core data asset, our publisher partnerships... to be able to get that source of truth on what people are researching.” — Ajit Thupil [18:13]

Biggest Challenge

• “Making people realize that B2B is different and what role we have to play...” — Ajit Thupil [18:29]

On Google & Meta Competition

• “They’re kind of closing their walls, not opening their walls. So... I see them having the need to work with parties such as ourselves, less so kind of competing with one of us.” — Ajit Thupil [18:54]

What Animal Would Bombora Be?

• “An owl... They can see in the night as well, so they can see through darkness. So... having that particular skill will be helpful.” — Ajit Thupil [19:43]

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Notable Quotes

• On B2B complexity:

  “Nobody is choosing on their CRM software based on what they saw on TikTok.” — Ajit Thupil [11:23]

• On the role of AI:

  “There is a role that we get to play in terms of what we think is qualifying IDs against that audience.” — Ajit Thupil [17:47]

• On industry trends:

  “Probably every two days I get a link or something that somebody pings me saying about some platform launching something agentic...” — Ajit Thupil [14:15]

• On being like an owl:

  “It’s smart. I think they can see in the night as well, so they can see through darkness.” — Ajit Thupil [19:43]

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Timestamps for Key Segments

• Intro & What is Bombora? (00:10–01:42)
• B2B vs. B2C – Sales & Marketing Alignment (01:43–03:15)
• Product Overview – Data, Activation, Measurement, Enablement (03:16–05:00)
• Audience Creation in B2B (05:00–06:57)
• Handling Targeting Size Disparities (06:58–08:32)
• Identity & Reaching Prospects Everywhere (08:33–10:08)
• B2B Buyer Sophistication & Spend (10:09–12:54)
• How Bombora Learns Roles & Titles (12:55–13:53)
• Where AI Fits in B2B Data (13:54–16:48)
• Lightning Round: Advantage, Challenge, Competitors, Animal Metaphor (17:59–20:00)

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Conclusion

This episode unpacks the distinct needs and strategies of B2B marketing and sales, revealing how Bombora leverages deep data partnerships, publisher integrations, and evolving measurement to give sales and marketing teams an actionable advantage. Ajit Thupil offers candid insight into the data industry’s challenges, the value of specialization, and Bombora’s pragmatic approach to AI, positioning themselves as the ‘owl’ in the B2B data night.

Transcript

[A]

Welcome to marketecture where you can get smart fast with in depth interviews of technology executives. I'm Ari Paparo and I'm joined today by Ajit Thupil who is the chief Product officer for Bombora. Ajit, thank you so much for being here.

[B]

Thanks, Ari. I'm excited to be here.

[A]

Yeah, yeah. So I've worked with Bombora in the past. I'm pretty familiar with it, but maybe some people are not. So tell us, what is Bombora?

[B]

So Bombora, we've been around for a while. Things started in the 2014, 2015 timeframe. We celebrated our 10 year anniversary last year.

[A]

Congrats.

[B]

Thank you. And I've been around for a year and a half so I can't take credit for the entire longevity of having been here. But one thing that has been constant through all of this time is our focus on the B2B space. And we think it's sufficiently different from everything else that is there and sufficiently interesting and wanted to kind of, kind of double click on that particular space serving both the needs of the sales folks as well as the marketing folks. So we have, from our point of view, we want to make sure that we make signals and insights available to both the sales teams and the marketing teams so that they are able to reach the right accounts at the right time with the right messaging. And this focus has been steadfast for us through this whole period.

[A]

Right, that makes sense. So it's interesting you bring up the salesforce. It's not typically the way people think about digital advertising is interfacing with the salesforce. A lot of what you do is oriented around that, around generating leads and moving people in the funnel 100%.

[B]

That's what makes B2B different from B2C. If you think about the complexity of that whole supply chain and how things are bought and sold in the B2B world compared to B2C, there's long sales cycles, there are complex sales cycles, there are multiple parties involved. So making sure that you are able to first, if you're a rep, for example, that comes in on a Monday and has to figure out who I need to call that particular week, it's an expense to make sure that you are and you want to make sure that you're directing that effort towards those right accounts and the right people. And so you want to have a good data asset that allows you to do that and prioritize who you are call in. And at the same time you want to have your marketing teams giving air cover to the sales folks. Because when these guys are calling, these prospects, you want the marketing teams to be reaching out to the same folks, be it through email marketing, be it through programmatic channels, maybe through social, making sure that as these calls are coming through, as your SDRs are calling, you're also being these same folks are seeing your brand show up, the value proposition of your brand show up in front of their eyes.

[A]

Right. And we sort of skipped over this. But the primary product you're offering, I believe is data, right? Or is there activation? Is there reporting? Give us the kind of surface point how your data is used.

[B]

So we are definitely a data first company. So our focus has been on data. And that is something that we continue to lean on, that, that will be a pillar that we will stand on in terms of activation, in terms of other things that we have, we allow for activation using our data through all of the programmatic and social channels that folks are used to, all the big DSPs, all the big social networks. So all of that is available from a measurement standpoint. We also offer folks in certain platforms and it's continuing to grow where we offer capabilities around measurement. So you can come back and see if you have a certain set of accounts that you've targeted, being able to measure that. Did you actually reach those accounts? Who did you reach within those accounts? That's also a capability that we have. And lastly, because we are so focused on data, and we have this whole other thread that's focused on the sales use case, you'll see our data also available through a lot of the sales enablement platforms as well.

[A]

Right, right. Sales enablement. Meaning, like. Meaning what are examples of sales enablement?

[B]

So think about anything that serves contacts. So if you have a platform like Lucia or Cognizant that has contacts, you'll see our data available through those platforms as well.

[A]

Okay, great. Let's talk about the granularity or like how people use the data, because I think this is kind of interesting. Is it if I log into a DSP like the Trade Desk or whatever your data's there are people using it sort of by category. Like I want to reach small businesses or businesses involved in agriculture, or are they using it more like abm, where they give you a list of specific companies that you want to reach.

[B]

As with most of these answers, once prompted, the answer is both of the above, never one of the above. Right. So we serve both sides. We have a syndicated taxonomy that's available off the shelf that you can go into a DSP of your choice and you'd See that taxonomy available and you can go buy. That could be about buying extra IT decision makers in extra large companies that are in the construction industry. That could be, for example, a use case, but also frequently. And we get this all the time. And a large part of our business is geared around this, which is around customer audiences. So we're in very much what you mentioned, a client, a brand, an advertiser, an agency coming in with a list of accounts, layering on top of that any demographic attributes they want. So they might say, here are 10,000 accounts that I like to target. But I would want to layer on, say, functional Group Marketing, functional group it, functional Group finance. I also want to add some level of seniority. I want to add, say, directors and above. And that's the group that I want to target. And I want to target them on the DSP of their choice. That is also something that we enable.

[A]

Right, okay. So there's some nuances to B2B that I want to ask you about because I'm a nerd about this stuff. So the first question is, one of the problems in B2B marketing is that the size of the companies are so different. So if I spread my budget sort of by number of employees, I'm going to reach General Motors a million times more than I'm going to reach a startup. What's your thought on that? And does your product help with that?

[B]

Yeah. So because of the granularity that we provide in terms of how you can design that audience that you want to target, you can kind of get around that? Absolutely. If you're targeting, call it General Motors or an Amazon or a Walmart, you'll end up in a broad basis, you're going to end up reaching a lot of people. But if you start adding layering on certain demographic traits, you start now narrowing down that list of people that you end up reaching. And further on, you can also add other factors around geography to say I want to focus on a certain division that I know is in Ohio versus Seattle. And then you start adding further granularity that allows people to focus on a certain segment of people. But absolutely. I think in the long term, we also offer, we work with some of our partners, especially on the measurement side that allows frequency capping at the account level. So to make sure that you don't run into that same problem that you just referenced.

[A]

Right. And my second nerdy question is, you're reaching, let's say, an IT manager in Cleveland, Ohio, blah, blah, blah, do you still reach them when they're checking The Internet from a Starbucks or from their house?

[B]

Yes and no. So because, and this now I'm going to go into the nerdy identity piece of how we do things.

[A]

Yeah, I wanted to go to identity.

[B]

Yeah. So if you are in a Starbucks, so if you're an IT manager in a Starbucks, as long as you're using a laptop that has a certain identifier or you're interacting with a website that has identifiers that we have resolved back to being an IT manager in Callit, Akron, Ohio. And that's what the advertisers want. Absolutely. So you might be in a Starbucks, you might be in a Marriott, wherever you are. We will be able to do that. But if what we will do is if you're not using the IP address from a location like that, we would obviously then we screen those out to make sure that all these, these small businesses or kind of social gathering sites are not included in our targeting criteria.

[A]

Right. And how sophisticated are your customers in like these things? Do they say, well, I want to, you know, I want to pay more if they're in the office, I want to pay less if they're in a Starbucks. I want to like, you know, use data differently based on certainty. You know, what's going on in the marketplace for B2B.

[B]

I think it is an evolution. Right. And I think you will see a spectrum of folks, some of them starting to dip their feet in, in terms of making sure that they want to be able to see who they can reach. In some cases, they may come and set a certain frequency cap or set a certain targeting criteria, look at, see how it manifests on the dsp, run the campaign for some time and then optimize their targeting criteria based on what they see. And there will be others that are very prescriptive on exactly what they want. And they're going to come in and say, I want this and I want this. Maybe split up into five different audiences because I am going to have different ways in which I'm going to reach those with different messages, with different CPMs. All of that being implemented on the programmatic channel. So you'd see clients that you run into with that level of sophistication as well.

[A]

Yeah. And I'd imagine if I'm really fine tuning my model and I'm saying, like, I want this IT manager in Cleveland, CPM isn't that important. I'm willing to pay a pretty high CPM to reach that person.

[B]

Absolutely. And that is one of the things that differentiates B2B, because typically, I mean, I don't know if this happens, but likely nobody is choosing on their CRM software based on what they saw on TikTok.

[A]

You never know. I see a lot of B2B ads on TikTok because that's how I roll.

[B]

I'm hoping that's all brand related, not necessarily like conversion. They're not focusing on conversion there. But a lot of this B2B spend, the B2B conversions are, call it tens of thousands of dollars, maybe hundreds of thousands in remote cases, millions of thousands of dollars. So much more complex. You're trying to reach not a single person, but the decision is being made through some sort of a buying committee that might have 10, 15, 20 people, each of them wanting something slightly different. You might have the IT person who wants to focus on security, you might have marketing person focused on usability. You might have the finance person on cost. Each of them kind of getting their own vote on through the decision making process. All of this is going on for months, multiple, multiple quarters, maybe in some cases year and a half, two years. So when you think of all that and you're able to find that person that you want to target. Yeah. I mean typically you're not, you're not as budget conscious as you would be if you're, if you're trying to reach a person in a Starbucks.

[A]

Yeah, and I guess we sort of skipped over this. We're not just talking about targeting companies, but also titles or roles. How do you infer that stuff? How do you figure out what roles people have?

[B]

Yeah. So again, there are two different ways in which we are able to get to that answer. One is we work with a lot of data vendors in the industry to bring in data assets that give us that level of granularity that we are able to translate back into an identifier. The functional area that they're in, the, the seniority therein as well. And also we have a footprint of about 5,000 plus publishers that we work with on whom we have our first party tags. So we are able to interpret based on content consumption across all of these sites. We are able to model where we think they fall both in the functional spectrum as well as in the seniority spectrum.

[A]

Right, that makes sense. So I have to ask, because it's 2025, what's the role of AI in this, in this world? Imagine it seems like if customers know exactly the people they want, maybe they don't need AI, they're just working through a list of abm. What's your perspective? What's your customer's perspective on that?

[B]

Yeah. And from where I sit, probably every two days I get a link or something that somebody pings me saying about some platform launching something agentic within their platforms, about how they can optimize campaigns or whatever it might be. So for us, we have some hearty debates internally about what our role in that supply chain needs to be. And for us, if you think about a company of our size, it is important to retain some amount of focus. And as we think about our role in the industry, and if you think about where all these models are going, all of them are going to rely on some quality data. You're not going to be able to train these models on substandard data and expect superlative outcomes. So you need to have people. And I think that it's going to be value for people who have that maniacal focus on quality data. And that is something that we are going to continue to focus on in terms of making sure that for the models that are being run on these platforms where people, our clients are interacting with, that we offer our data, continuing to have the quality standards that they have come to be used to in the shape form that they are comfortable using, as well as on all the locations that they have that they are interacting with data. So with that focus, our role on AI and being able to maybe have agents of our own takes a little bit of a backseat, but not a huge one. So we want to make sure that where we have a right to play, that we do play and win. But we also think that we should make sure that we give sufficient lubrication using our data to all the other places where AI and AI agents are going to be used.

[A]

Right? Right. Yeah. I can certainly see uses for AI here in coming up with your ICP and identifying companies and things like that. But a lot of it's ultimately going to be deterministic.

[B]

100% right. I think there's going to be a. Because of the amount of spend that we are talking about, I think there is going to be a largely deterministic aspect. But I think even in our world, if you think about, let me take an example around frequency capping that you brought up. There is a world in which you say that. Yep. Hey, your new client came up with these 10,000 accounts that you wanted to reach. Based on the campaign performance that we are seeing, we are seeing that these 10 accounts have not been clicking on your ads, but these other 10 accounts have been actively clicking on your ads. You might be able to actively then Figure out how you qualify and disqualify accounts based on feedback that is coming and using AI models that is able to understand and then deliver towards outcome. So even in that world, I think there is a role that we get to play in terms of what we think is qualifying IDs against that audience. That's an area, for example, where you would see us leaning in, right?

[A]

Yeah. Okay, well, let's transition to our lightning round. Relatively quick questions, relatively quick answers. So what Bumbora's number one competitive advantage?

[B]

I think our core data asset, our publisher partnerships that we have across the ecosystem, to be able to get that source of truth on what people are researching, I think that's, that's definitely our strongest asset.

[A]

And what's your biggest challenge in the market?

[B]

I think for us, in a lot of cases it is about making people realize that P2P is different and what role that we have to play in the market and being able to bring them along that entire journey. I think that's a challenge.

[A]

So we ask this of everyone, so don't be insulted. So why won't the big guys like Google and Meta ultimately crush you?

[B]

I think the from aspect of specialization, I think the assets that we have and the comfort that we have around data is not something that we have necessarily seen. Especially on the third party side with these larger platforms. They seem to be more comfortable with first party data. They're kind of closing their walls, not opening their walls. So from that standpoint, I see them having the need to work with parties such as ourselves less so, kind of competing with one of us.

[A]

Right. All right, last question. If Bambora was an animal, what animal would it be?

[B]

I think maybe this is the nerd in me coming out saying an owl would be great. Owl.

[A]

Okay.

[B]

Yeah, it's smart. I think they can see in the night as well, so they can see through darkness. So hopefully, as we live in this, call it, uncertain times, having that particular skill will be helpful.

[A]

All right, great. You heard it here first. Ajit Tsupil, the CPO of Bambora. Thank you so much for joining us.

[B]

Thank you, Ari.