The next CMO podcast explores topics that are on the minds of forward-thinking marketing executives from leadership and strategy to emerging technologies. And we bring these topics to life by interviewing leading experts in their fields. The next CMO is sponsored by Plannuh makers of the world’s first AI based marketing leadership platform.And hosted by me, Peter Mahoney, the former CEO of Plannuh along with my cohost Kelsey KrapfIn this episode of the next CMO podcast, Kelsey and I speak to Mark Johnson, the CMO in general manager of Bombora. The leading provider of B2B intent data. Mark has an amazing career with over 20 years of experience, helping build data businesses in the marketing and media technology industry. Before joining Bombora, mark was the CMO of resonate.
A venture back analytics company. And prior to that had senior executive roles at companies like Experian, buzz metrics, NPD Jupiter research in Ziff Davis. We have an incredibly expansive conversation about third-party intent data about key applications of this data how to use intent data to understand the buyer’s journey lots of different use cases and where the future of intent data and data in general in marketing is going i hope you enjoy the conversation i sure did
Kelsey Krapf: [00:01:36] Mark. I am super thrilled to have you on the next CMO podcast today. I know intent data is one of the hottest topics in marketing, we’d love to hear about you and about Bombora.
Marc Johnson: [00:01:48] Yeah, absolutely. Thank you, Kelsey. And thanks Peter for having me. My background is not the typical path to a CMO. I started actually in advertising sales in the nineties in New York city. After I left Boston for the big apple here, much to the chagrin of my fellow.
I guess the term is mass holes, but we won’t miss that. And from ad sales moved into research actually covering the early online advertising space for a research company and that then set me on a path to help create businesses that marketed and sold data. And that. Spanned some of the early or the first social media monitoring data to point of sale and consumer survey data to video engagement data.
To human values and political sentiments when it was okay to do that to mobile data to search term data and now to Bombora, which is intent data. And that is pretty simple in the sense that it is data that helps businesses understand what other businesses are intending to buy.
Peter Mahoney: [00:02:59] That’s fantastic market. What a background. Yeah. And coming from the the ad sales space, which is which I’m sure was interesting during the day. So you have probably a healthy liver based on that. And and then I obviously spent a lot of time in data and data is becoming more, more important for us all as marketers to figure out how to do a better job and more effective job reaching our customers.
So maybe dig down a little bit deeper there and help us understand what intent data really is. So what are the kinds of things that you would consider intent data and, what are the signals you look for that would be found in a platform like Bombora?
Marc Johnson: [00:03:44] Sure. Intent data and is different in a significant way from some of those other types of data that I’ve mentioned, I’ve had experience.
Around. There’s a lot of data, whether it’s firmographic data or demographic data or contact data that B2B marketers use and intent data is more often than not a compliment to those datasets. It’s fairly a bit more dynamic. In that it’s changing day to day, week to week because intent data is if it’s effective is reflecting the buying process.
And in Bombora’s case, reflecting the research that takes place from within a company as that company or that business is evaluating a product or a solution in leading up to making a purchase decision. So it’s interesting. It’s data that is often used more to prioritize activities to allocate resources then to describe the why behind particular things, which is often what other types of data is really focused on.
Peter Mahoney: [00:04:48] If you think about it, there are lots of different things that might be considered intent data. And I like your description that they’re really a lot of the data that we get is about who you are or who your company is versus what you’re planning to do. And by definition, that’s going to change from minute to minute, month to month.
So I assume there’s an interesting freshness challenge that you have with this kind of data. And how do you think about that is what’s the shelf life. Of an intent. I suspect it’s variable based on the kind of intent, but tell me how you think about that.
Marc Johnson: [00:05:27] Sure. And just a quick backup, cause you did go down an interesting path.
There are a lot of different types or ways that people describe intent data and of course your own first party data visitors to your site engagement can be categorized as intent because they’re exhibiting interest in your product and what you’re offering, but that’s a very small.
Data piece. Then there’s third-party intent, which is where Bombora lives and the Gartner stat is that by 2022, I think 70% of B2B marketers are gonna be using third party intent data. And that’s intent data collected. In our case from content consumption can also be things like G2 crowd, software reviews or even in some instances, data that’s pulled off the programmatic bid stream, which is another conversation.
But to your question, Peter, yet that the shelf life of the data. Is often dependent on the sales cycle and the research process. Big ticket items that can last months and months in terms of the evaluation and the sales cycle. Then that data is that’s the time for that. We’ve seen in our own data is that we can look at the buyer’s journey, if you will.
And see that the terms that a company is consuming and are more broad in the early part of their evaluation for a product or a service. And then as they get closer to making that decision, It becomes more specific about the brands, this specific name company versus another named company. And that would make sense and trying to understand the competitive differences between two products before actually making that buy versus the early phases of researching the space more broadly.
Peter Mahoney: [00:07:12] That’s really interesting, Mark. And I hadn’t thought about it and I shouldn’t be surprised that I haven’t thought about this as much as you have since this is what you do for your whole career. Pretty much. But that implies that as a marketer, you need to have a pretty good understanding of the buyer’s journey, before you start to think about intent as an example what are the signals? What are the what are the th the early indicators of intent? Before you get to, before you, they’re probably problem related before you get to solution related, or certainly before you get to specific vendor related stuff, obviously that stuff is gold, right?
If, Hey, you’re looking for Bombora, that would be fantastic. You obviously want to capture a lot of that, but you keep going clicks back. So it means that you as a marketer probably need to have a pretty good understanding of the buyer’s journey. And is that a first step as you put together, a data targeting strategy is really understanding the customer and the customer buying journey.
Marc Johnson: [00:08:20] Actually you can come at it both ways. And so if you understand your buyer’s journey, then it’s arguably that much easier to understand what topics Are relevant to buyers, your buyers, as you proceed through that process, the other side of the equation is using the data itself to understand the buyer’s journey, if you don’t, if you don’t have it.
And so for an example, what we do oftentimes with customers, Is we’ll take, we call it a historical buyer’s journey analysis. And so we will take a list of their closed one and, or closed loss accounts. And we will look at the historical content consumption by topics by those accounts. And what we’ll see is what I was just talking about.
Like here are the topics that when you started to engage with them in the sales process, and here are the topics the mix That they were heavily engaged with and consuming at the end. And oftentimes those topics are some part as you’d expect those that the brand knows about it their search terms their competitors set their how they’re positioning the solution the same time.
There are some other topics that are the way that the buyer is thinking about it. And, you can always the way that. CMOs has used this language, but the buyers might not always use that language or the CEO and the product people have this way of talking about it. But if we look at it from the buyer’s side it’s slightly different.
So if you can go at it both ways and actually arguably using intent to understand the buyer’s journey is even maybe a better place to start.
Peter Mahoney: [00:09:52] That’s fascinating. I hadn’t thought of that. I think we’re going to find there’s a lot, I haven’t thought about and the idea that you can. You can analyze content consumption and activity and infer what that buyer’s journey is in some aggregate form is super powerful because that’s one of those things that’s, it’s hard to get at.
It’s hard to really put yourself in that in that space. And I’d imagine that you could also look at. If you have the data to do it, you could look at buyers versus non-buyers, which might be interesting too. So there’s some really interesting segmentation of the data that, that you can do.
So do you have to be like a crazy data scientist to do this stuff, or even just the sane data scientists or. Are the tools of the trade. These days more accessible to the point where you don’t need a dedicated data science team to make this stuff work.
Marc Johnson: [00:10:48] You don’t anymore. You used to and it’s a rapidly evolving space where the data is becoming far more democratized.
So when. We started the business, our typical customer, which is quite different from the way a lot of companies start was Salesforce, Oracle who had big data science teams were using data lakes. And they understood if they could get a few percent lift on sales efforts across thousands of reps.
That’s a huge amount of money over, they’ve paved the way now for The data to be more accessible at a lower price point. Integrated into systems like HubSpot, that the example that I gave them. And Peter, you mentioned looking at non-buyers. So if you know the topics that are being consumed by your customers, you can go find other prospects that are consuming that same content those same topics.
And we have an integration in HubSpot. I know you guys are in Boston, they’re in HubSpot fans, where you can pull in net new accounts that look like your existing accounts based on content consumption. It’s not a, you don’t need a data science team anymore. And it can be as simple as.
Handing a spreadsheet and a list to a couple of SDRs to try it versus having to put the data into other systems. So that’s good news for everybody.
Peter Mahoney: [00:12:02] So we went by that pretty quickly. And I, that I learned another thing. This is great. I’m learning all day here. So one is, I always think of lookalike data as is more demographic look alike, but there is also an intent look alike.
So you can look at the behaviors. and activities, which makes sense. And in find a lookalike audience, just like you might do Facebook targeting and lookalikes and things like that. So that seems like it’s probably a really compelling approach. And I was going to ask. Mark about the next level of detail beyond.
Do you need a data science team is who’s really using this kind of data targeting approach today. And it sounds like kind of anyone but help us understand a little more about the kind of use cases you talked about. Hey, we can make a, you can make a list for an SDR, but talk about the kind of use cases as our audience can understand how they might.
Think about applying intent data for a sort of a first experiment within their company?
Marc Johnson: [00:13:05] Yeah, absolutely. The low hanging fruit is often sales prioritization. And if you think about it, sales people come in or they log into zoom every day. And look at their prospect list and look at their territory.
And are a challenge to figure out who to call first who’s at the top of the list and what to say. And so the first and most immediate path to value is taking that account list and scoring it and reprioritizing it based on intent topics and letting SDRs and AEs prioritize their outreach as well as the conversation based on those topics and that typically yields We have all sorts of stats, but pretty good, between a hundred to 300% increase.
Actually there’s a Gartner stat that I’m going to try to remember. It is a 13% average pipeline expansion. This is from Gartner’s survey of about 300 marketers with 50 million in revenue. And 35% of those surveyed got 15% pipeline expansion. When they use third party intent data that wasn’t unique to Bombora, that was that was a Gartner survey.
And so to your question, getting in the hands of sales to act on has immediate impact. The way that often happens now is that the marketing team or marketing ops or rev ops will buy it and work with sales to get that set up. And from there’s Sort of a whole continuum, if you will, of different use cases that will surround the prospect with relevant content at ABM ad targeting is a prime example of that.
And so delivering ads with the topics that speak to those topics, to those companies that have high intent at the same time that you’re calling them is it’s the sort of classic use case. And. The other one that’s emerging now is around churn. Expansion upsell and cross sell.
And so really, if you can understand what’s of interest to a prospect, once they become a customer, you can also look at that to identify potential risks that they start to consume content and have high intent on a competitor competitive solution. It’s a good opportunity to get in there and understand what’s going on.
As well as if you have a really big account, look for opportunities To sell a new products based on that. So again, I starting from sales and then expanding from sales, with sales and expanding to other supporting dimensions.
Peter Mahoney: [00:15:32] So some of the examples you mentioned there, mark were really about optimizing a flow of data that you may already have.
So that may be, as you said, you’ve got a. An SDR or BDR or a rep, who’s working with a a flow of prospects that they want to prioritize. If you look at what most marketers want to do is they want to add more to that flow. So there’s the, so what’s the application? What’s the approach you should use with data like this, to attract net new prospects?
That happened to have some level of attent intent, which is a different phase of versus the one that you mentioned, which is optimized once they’re in the flow. And then even later stage, you can see value across all points in the customer life cycle from a glimmer in your eye. They’re not even a prospect yet to being a prospect and figuring out which one you focused your time on to customers who have the potential for best upsell or the risk for churn.
All that lifecycle, but go all the way to the back to the beginning, because that’s where I think marketers struggle the most is identifying who can I bring in show my thing to who might have a need or an intent to solve a problem? How do, how should we think about that side of the equation?
Marc Johnson: [00:16:53] Sure. And to the point it is not just about optimizing your existing data sets, it’s more your existing motion.
And your existing processes, so you don’t need to change anything. You’re really just accelerating things in the first instance, but trying to find net new accounts. And this is we’re working on a piece with Gartner around ICP versus intent. And you think about the process of building an ideal customer profile?
I would do a customer list. So you can run ABM programs. And the ideal way is to do that and then score that with intent. And the prioritization we just talked about the other way is just to do what we talked about earlier and look for other companies that have the same behaviors that they may not be right in your ICP.
Or you may have not as again, I’m like on a Gartner tip today. 50% of companies are redoing their ICP. So at any point of time, that’s a bit of a a hot mess. So you might as well just get to finding companies that look like your existing prospects and going after those with demand gen if they’re already in your CRM with.
Custom nurture programs and, always SDR outreach. So that’s the way that you can add that in, by looking as we talked about behaviorally at companies.
Kelsey Krapf: [00:18:10] I think it’s, there’s a lot to unpack here, especially for me as a marketer, but it’s very clear that intent data is important to both sales and marketing.
And I think there are multiple types of intent data. There’s the demographic there’s, similar to your ideal customer profile. But I’m going to play a little bit of devil’s advocate here. When is intent data not good enough? Because as a marketer, I have come across intent data. And, we got zero results from it.
So how can we qualify that intent data and how can we know that it’s going to be beneficial to move pipeline around?
Marc Johnson: [00:18:45] Sure. Oftentimes if it’s a very small company depending on the provider, might there might not be enough of a signal there to show up. And oftentimes if you’re dealing with the.
Really small purse like click-throughs, but if you’re measuring and click-throughs on ad campaigns that’s a bad way to measure anyways, but we find a lot of companies will say, okay, yeah, we’re going to do intent data. We’re going to do ABM ad targeting. And we didn’t 10 X, the click-throughs, nobody clicks on that anyway.
And so if you’re a sort of metrics or. Questionable. We’re not gonna, it’s not going to help that much anyways. And so it, it is also this is. There’s a fallacy, that if you get it or you get this data and magically, everything is all your results are going to go up. And it really all data has to be tested and iterated on.
And whether that’s in what motion with what campaigns and what mix of topics and getting the topics right. Is a process. And so you don’t have to change what you’re doing. You just have to keep iterating on and testing and seeing the results. Yeah, that’s it mostly, and it segues into I don’t know what data we’re using, but where did that data come from?
What’s the provenance of it, et cetera. We do. Often say we try to get customers to test the data first, before they buy it from us and to say what specific instance or activity are you going to use it in? And frankly, where’s your level of sophistication? Like we talked about earlier. It is now available for growth oriented companies, but if you don’t have maybe mark, if you don’t have marketing automation, if you really don’t have any SDR sort of process.
You don’t like, if you’re just getting going, it might, we might say, Hey, get a few more things in place. Before we start doing, it’s like putting rocket fuel into you’re in 1978 Volvo. Maybe you need to, put some new tires on first and then do it.
Peter Mahoney: [00:20:39] So let’s talk about that.
Mark. What’s the starter kit for embarking on an intent-based data strategy. So it sounds like you need a CRM, you should have an SDR. What are the few things that you really need to have in place? And then take that and say, how do we apply that starter kit? What’s the first step. That I might take to a test, as you said to see if this approach can be valuable for me as a marketer.
Marc Johnson: [00:21:06] Yeah. The first step and then some other, a cop out answer to the first step is really looking at what you’re doing already. And for instance, you can start with LinkedIn advertising, you can use intent data to do LinkedIn. You can use companies like Terminus or roadworks and where we have integrations.
To do that. And I think the challenge is trying to start two things at once. So if you’re already doing some advertising or you already have an SDR program and they’re doing outreach there, maybe they’re using outreach. Or if you have some emotion that’s already happening, then that’s easier because you already have results.
You already have a benchmark, and you can add the data in very easily versus. For instance, a company has not doing ABM advertising, and we’re going to try to help them, figure out ABM and intent at the same time. And it just becomes that much harder. So it really, it is what are you doing already that you want to improve as opposed to starting fresh and putting a new process, new software, new stack solution, whatever, and data at the same time.
So it’s a lot of moving parts.
Peter Mahoney: [00:22:14] That, makes a ton of sense. If you have a benchmark, you can start from and say, I’ve got this baseline of performance across my marketing campaign strategy that I’m executing. And then pick one or two of those things and say, where can I apply data to try to create an a T try to create an uplift?
So that, that makes a lot of sense. And like you said, most people have a digital strategy that they’re running. Maybe they’ve got a landing page or web page conversion strategy. That seems like it’s probably ripe for adding data to the process. And and then even. Some of the things though that I find interesting is your approach talking about the the ICP, because I feel like an ICP is a blunt instrument for a lot of people, right?
They say it feels like it’s one of those. And I know w we have an ICP that’s actually too vague to tell you the truth. We have a pretty broad market people who are marketers and and we try to narrow it a little bit more than that, but it’s a little bit vague. And what’s more interesting is is to actually look in and explore.
The behavior to try to identify maybe areas of customer profile that you had no idea have a need for your product and services. So I, I assume that might be as a separate stream, right? One is the inbound, how to improve the performance, but then there’s the, how do I apply this data to, to really analyze my customer profile and really find out who else is out there.
So would that be a separate initiative? To do that kind of research mark,
Marc Johnson: [00:23:53] Yeah, it would be parallel. I think that’s the you hit the nail on the head with ICP and getting good data for that. You can come at it. It’s like a converging Venn diagram. Look at companies with high intent.
And we find, in any large. List of companies about 15%, this is very consistent, are active and in market, which makes sense because companies aren’t always buying expense software or security. And you can always find companies that are interested in a buying or evaluating what you sell.
And then in parallel you can Overlay that on your ICP or almost supplant it and just vet those companies. Are they where are they geographically? Are they the right size? Some of the other dimensions that might use from qualification against that, in that list of companies with high intent.
So yeah, that there, I guess parallel processes That should converge ultimately, but whichever one is going to get you quicker to value, oftentimes you might just shortcut your way and use the intent data while you’re building out that ICP over time.
Kelsey Krapf: [00:24:56] So I’d love to learn about how Bombora does data collection.
What is your methodology behind it? If you could walk that through our, for our listeners.
Marc Johnson: [00:25:06] Absolutely. So Bombora is data comes from a cooperative and it is a collection of publishing companies. And I guess about 4,000 B2B websites. And those are Large sites like business insider Forbes, the wall street journal as well as a very niche sites they’re focused on like source media or SourceForge that are focused more specifically on speeds and feeds or particular technologies.
And so we have Direct relationships with all of those websites and all of those publishers to collect the anonymous content consumption of their visitors of their audiences. And so it’s consent based we’re in the consent string and that what we’re doing with our NLP. Is looking at what is on the page of each of those publishers, every page of every site and where it’s, I guess the 20 billion interactions or events a month.
So it has massive amount of volume. And we’re seeing what is this piece of content on this page about? And those are the topics which are not keywords there. What is it, the meaning of this? And then we are associating. That content consumption with a company is, did GE did Boeing did Citibank consume more content against what they historically would have because we’re keeping a sort of historical baseline, so are more people at that company consuming more content on these topics.
And that is it’s a product called company surge. And when we see the increased content consumption, That is this indicator, that research activity is growing and it maps to the research process. And so really what we’re we’ve we’re doing with that is seeing. The this whole, the stats about 60% of the buying processes already decision is made before they show up to sales.
So we’re seeing that research happening across basically the B2B web and then associating those topics to specific companies and they get scores from there.
Peter Mahoney: [00:27:06] So is it harder to gather that data mark, when people are all working from home? Because,
Marc Johnson: [00:27:14] yeah. Yeah. Slightly when everyone went, worked from work from home We, we didn’t see so much of the drop-off in content consumption because a variety of methods, we have some logged in IDs that are anonymous to individuals, but they tied the domain.
So that helps us resolve the identity IP addresses to accompany domain as well as Cookies help provide some continuity over time. But what we saw is more of a, we didn’t see a drop-off in contact consumption. We saw a change in what people were reading about. When the pandemic hit, everybody was more about healthcare and the financial markets, et cetera.
And we were doing a weekly dashboards around what topics were interesting. And so we’ve seen it. Come back to the standard amount of the standard level of buying. So it’s it behavior change for a little bit. We’re still able to see it. And now it’s back to Back to normal as it were in terms of the buying process.
Peter Mahoney: [00:28:09] So you, you
answered one question. I asked in one bonus one I should have asked. And the one that I directly in probably inelegantly asked was about, Hey, these people are working for home. How do you get their company data in you? You answered that quite well. Is that because of these partnerships you have, and you have permission-based data to go after an associate things anonymously.
Privacy safe ways so that you can figure out close enough whether these people fit with those companies. But the fascinating thing is that you probably had this amazing insight into the change in content consumption over time, through the major disruption of a pandemic and You, you highlighted things like, obviously information about health care and things like that, but what weather insights came out of that?
What did you learn about how people changed and do you see it now changing back their behavior since the world is hopefully getting more normal again?
Marc Johnson: [00:29:12] Yeah. Th the one thing that we saw. Was, and it’s important because we’re able to look historically at past years as well. And so we saw a as the pandemic was hitting at the beginning of last year B normally we would’ve seen a spike in as the second quarter.
Came in. Cause although like a first quarter research activities dropped off because of the holiday and then budgets are released and Q2 happens and there’s a huge increase in all topics. Because the buying processes are kicking in for second quarter and beyond. So we saw a slowed recovery if you will.
And less of that activity as it relates to Sort of changes of behavior. I’ll do a quick plug. We have a partnership with media post and every week or so Mike McLaren. Who’s the CEO of Merkel’s B2B division. His team, they pull out insights from the data based on what based on the topics that we created an index, the 500, top 500 brands are consuming.
And so let’s see last on April, last week on there’s a huge increase in the topic, customer delight. And so you’ll see things like that and we’ll see. Increases in GDPR and CCPA, that would make sense because there are what’s going on in the marketplace, obviously, a lot of interested in cookie list.
And so for us, that’s not really it doesn’t help our customers sell any more or less. It’s more of a validation of what’s going on. That the data is reflective of of content consumption that is relevant to, to external topics, which sort of makes sense.
Peter Mahoney: [00:30:52] That’s great, mark. And is that data available publicly?
Marc Johnson: [00:30:54] You were saying. Yeah, it’s a pub. It’s a, it is on media post. Yeah, we’ll put a
Peter Mahoney: [00:30:59] link, put a link in there in the show notes. That’s fascinating. I’d love to check that out. And and so we’ll put that in the show notes and people can check it out when they when they check out our show notes. Cause I’m sure they’re scouring every detail of our show notes.
It’s probably the most
Marc Johnson: [00:31:14] important that they’re reading.
Kelsey Krapf: [00:31:16] So mark, how does the Bora use intent data? As part of their marketing strategy, because I’m sure you’re you create specific campaigns around the intent data you guys collect.
Marc Johnson: [00:31:27] Yeah. It’s weird to be an attempt data company using intent data.
It’s like an echo chamber. It’s very meta. And arguably we’re not, we are we’re not very accomplished children, fairly, we’re a decent user of intent data. There are customers who we learned from or more sophisticated. But we use it in a couple of ways. We use it for lead scoring for sure.
And as Inbound leads come in. They’re they get they get a bunch of different things to raise their score first party engagement, as well as firmographic, but also are they, do they have high intent on topics that are relevant to us? And so that’s one piece the our customer success team is always looking at and we have this integrated into Salesforce.
You can put it in the data’s quite portable. Salesforce is a good place for it. So they’re always keeping an eye on their accounts to see as I mentioned earlier, what are topics that are very interest and being consumed at that company. And we just had this just came up the other day where.
One of the CSMs customer success managers noticed one of our customers was researching one of our partners, our platform partners, and reached out and said, Hey, I see that you’re researching. And they were. Not only they’re taking, taken aback a little bit. It was good. Like you’re a customer cause the data works.
But it was also good because we could a conversation of how to use the data in that platform. As opposed to being taken by surprise and all of a sudden they’re doing this other deal and w we’d be back on our heels. So that’s the, on the retention side for sure. And then we do use it for for our ABM campaigns, probably a little bit less.
Because we probably want everybody just to buy intent. But it can help it prioritize the R I targeted,
Peter Mahoney: [00:33:05] Look into your crystal ball a little bit for us mark and help us understand there’s a lot going on in the data world right now. There’s obviously a lot of talk about the changing role of the cookie of in the app world around data privacy.
So there it’s getting more complicated, but using data responsibly is an incredibly important part of every marketer’s job. So what are the kinds of things that you think we’re going to see? What are the major things and changes we’re going to see in the future that we should all be thinking about as marketers when it comes to the world of data?
Marc Johnson: [00:33:46] Yeah. Specific to the world of data. Ethically sourced and data ethics and consent are now here to stay in a real way. And so that for data that we had the wild west for a long time around the Providence of data and buying and selling it on, that era is coming to an end or is at an end now.
And really being Conversant in and understanding the value exchange both with customers where this data is coming from what they’re getting back as well as the ecosystem and both the commercial terms and the regulatory issues associated with data is now Exponentially more important.
And in a way it’s good because they’ll just be less bad data out there. And so that’s a good thing, I think for everyone customers, marketers, and as well separate from that, I’m thinking about deep fakes and VR and how to use those in marketing because Those are coming on the horizon in a really big way.
Not necessarily related only to data, but.
Peter Mahoney: [00:34:50] Yeah, it’s interesting. I imagine that we’re all going to have our own NFT associated with our identity so that you can avoid a deep fakes or hopefully diminish the impact of them. It’s really fascinating to see how quickly that part of the world is changing.
But I have to believe on your earlier point, mark that with the increasing responsibility accountability related to being a good steward of data and being a responsible adherent to data privacy regulations. That’s gotta be really good for companies like Bombora, who obviously are doing this the right way, have the right kind of partnerships have a reputation.
And so I assume that’s gotta be good for your business. Isn’t it?
Marc Johnson: [00:35:39] It is. Mission and vision is to help make. Sales and marketing activity that people value. And ultimately, all of what we just talked about around data is going to be less marketing and less selling, but better and more effective because it’s relevant.
And none of us got into this to get 1% click-throughs and to spam people with email or ads that aren’t relevant or. Have SDR is just sending weaponizing marketing automation. And it feels outreach to send hundreds of thousands of emails in this sort of pray and spray way. So the it’s good for companies that say let’s do things more smartly, use data to get better results.
And that all is an outgrowth of being more relevant to, to what customers need.
Peter Mahoney: [00:36:29] That’s fantastic. And I’m a big believer in that. In fact, I can’t tell you how often we hear people say to us when they learn about Plannuh, they said, oh my God, where have you been my entire career? You mean, you can just fully automate and make that whole, budget and financial management thing go away and then deliver this great visibility.
I didn’t think that was possible. The only way you can communicate to customers that something is available is either just blast to the entire planet, which. No, we try to do a little bit of but really trying to understand the underlying intent and problems that people have. And if we can do that more effectively, and I’m sure there are lots of people in our situation where you can really serve a need that’s out there.
If you can make that sort of broader leap between what that early in the buyer’s journey kind of signal is and putting the right solution in front of them seems like it, it can be compelling for everybody.
Marc Johnson: [00:37:27] Yeah, and you don’t have to boil the ocean. You don’t have to solve all these problems, just being a bit more relevant and a bit more restraint, a bit more focused goes a long way.
Peter Mahoney: [00:37:39] That’s great. This has been a fantastic conversation. Unfortunately, I think we’re getting to the end of our time. I could talk about this stuff all day being a data nerd and a nerd in general. And but I think we probably have one more quick question from Kelsey before we wrap up.
And I guess before that if you could help our listeners understand how they can learn more about Bombora.
Marc Johnson: [00:38:01] Sure. Bombora.com is probably the best place to start. Then we’ve got a whole resource library there. Some amazing case studies that are not deep fakes customers that really have helped not just us, but push the whole intent data industry forward.
So that’s the best bet. And from there, you can also, there’ll be ways to sign up for sample data sets and things like that.
Kelsey Krapf: [00:38:22] Awesome. I’m going to echo exactly what Peter said. Absolutely have loved this podcast, learned a ton. So just to wrap it up, what advice would you give to those that are CMOs or aspiring to be one someday as a CMO yourself?
Marc Johnson: [00:38:37] Sure. I, the advice I would give aspiring CMOs is probably know why you want to be a CMO and which type. I have. In my career folks that have worked on my team’s very, a hard line where a whole. Bunch of them have become CMO. Like I want to do exactly that. Because they either more super had a strategic view.
They were very operational and it was different flavors of CMOs, a whole other set that, that is the worst thing I do not want your job. That sounds like a nightmare. And so I think Oftentimes, it just becomes, oh, CMO is the path to move to the top of marketing. But that’s not necessarily what you have to do.
You can either be more focused or not. And probably to think more about growth and revenue and customer value than marketing as we maybe traditionally defined it in the business school that I did not attend.
Great. Great. Kelsey Krapf: [00:39:29] Thank you so much for your time today, mark. This has been a phenomenal conversation and make sure to follow the next CMO and Plannuh on Twitter and LinkedIn.
And if you have any ideas for topics or guests, you can email us at the next CMO at Plannuh dot com. Have a great day, everyone.
Marc Johnson: [00:39:47] Thank you heart. Thank you, Kelsey. Thank you, Peter. Thank you. Plannuh.
nextcmo
08 Jun 2021
