A Radically New Approach to Segmentation - John Buten - Revenue Today - Episode # 055

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Sales, marketing, and RevOps. It's sink or swim out there, and yesterday's strategies and tactics won't help you today. This is Revenue Today, and I'm your host, Jared Robin. Join me as we interview revenue leaders in our community to learn what steps we could take right now to help you scale yourself and your company. Revenue Today is sponsored by Rev Genius, and we're on a mission to bring inspiration and creativity to all revenue professionals in the world. Want to shout out our sponsor Demandbase. Demandbase is smarter GTM for B2B brands. They help marketing and sales teams spot the juiciest opportunities earlier and progress them faster by injecting account intelligence into every step of the buyer journey and orchestrating every action. For more information about Demandbase, visit demandbase.com.

Another problem that I mentioned before is around operationalizing that data. And what we see with many of our customers is that they have so many different tools and so many different data points, and they're really spread around the organizations. Some of them sit in your Salesforce, some others in HubSpot, some information you take from LinkedIn, from this vendor and the other vendor. Can you really take all that data, put it together, see a coherent picture, and make some decisions based on that coherent picture? This is maybe the challenge. So even if you have access to all the data in the world, operationalizing it is a big problem. So at Explorium, we suggest a new approach, a different approach to segmentation and to the data that you need to use for segmentation. And we provide a very large catalog of data that you can see here. And it's focused around business data. So everything is organized around the businesses because this is the accounts. This is where we all want to start. But there's a lot of additional information here about individuals, about personas, about geos, locations, demographic information, stuff like that. And it's all available to you to look at and enrich your data with, but it's already organized, harmonized, and normalized. So we take that part out of the equation. We've— we're already doing the hard lifting for you. We're taking the data from some of the best sources out there, premium data providers that you're familiar with, open sources, and proprietary signals that we create on our own. All of that data is available to you, and it's already normalized and harmonized. to work with your internal data and allow you to do some predictions, segmentations, look at your ICP, etc., etc. That's the data part. And you have here on the next slide some of the numbers. You know, we have over 190 million company records, including a lot of small businesses, including a lot of professional emails, and 4,000— more than 4,000 data attributes that you can now— they're all available for you to use now for segmentation. So you don't need to settle for 2-dimensional segmentation anymore. And maybe the other part of that is the ability or the platform capabilities that allow you to take that data, manipulate it, shape it, and make it work for your specific business needs. So our platform helps you, number one, automatically discover some relevant attributes Because like I said, the problem today is that you almost have too much data. So how can you tell which attribute is really the one that is critical for your segmentation? Is it about the maturity of the company or is it about the technology that they're using today? How can you tell? Our platform looks at your internal data. It can look at stuff like win-loss analysis or other internal data that you have in-house, match it with our data, and then tell you, vast catalog of over 4,000 attributes and tell you, pinpoint which attributes are the impactful one that you should be looking at first. We can also help you, of course, enrich your data with those impactful attributes and of course many others that make sense to your business case. We can also transform the data in the way that it works for you according to your business logic, according to your business needs. For example, if you need to combine some data points from different data vendors, create a score based on them. If you want to build a predictive model, a scoring model based on machine learning, you can do all of that based on the data that we have in the platform and the platform capabilities, create that score and move it on to your system of record, whether it's Salesforce or other CRM, et cetera. That's our different approach. I hope it makes sense with data and capabilities on top of it, machine learning capabilities on top of it, which make it very unique. I want to share a customer story. One of our customers, I think it's a very interesting story because it shows how they used the vast data and the platform capabilities to make a big difference and support their growth. with segmentation. So the customer is a B2B, B2SMB solution provider in the United States, and they have tens of thousands of local businesses across the United States. And their challenge was growth. Isn't that the challenge for everybody these days? But what they needed to do was generate 50 quality leads every week for each sales rep. That's a tough one. Fresh new leads, 50 of them every week for every salesman. That was difficult. And they were using, again, the basic, I call it the 2-dimensional segmentation. So they were looking at company revenue, number of employees, location, and industry. That's it. Gave that list of 50 leads to the salespeople and got a reject, a big reject back. Some of the leads were not correct. The information was, wrong. The address was wrong, the telephone number was wrong, the email was wrong, the company shut down and it wasn't reflected. So it wasn't quality leads. And some, and some of the leads were not in the right segment. We thought they were in this geographical location, but in reality they moved to another city. We thought they were in this industry, but in reality they weren't really. The industry classification is a problematic one. We all use NAICS codes, But they're not entirely correct for every type of business. So the challenge was how to support this amazing growth with quality leads for the salespeople. So, um, what they did was of course use Explorium, and with Explorium they were able to get a lot more data available for them for the segmentation. And you can see some examples here on the right-hand side. So not only the Basic firmographic signals around revenue and employees, but also things like credit score that can be a good implication, or parent company, or a correct location, or maybe the technologies that we mentioned before. Is it Shopify that you're looking for is a good indication that this is a quality lead, or maybe it's other payment technologies that are in use, or maybe it's the number of social profiles is a good or bad indication of if this customer is a good lead for me. Of course, in addition to that, they could get very accurate contact information for every level that they needed to approach, email, phone numbers, and the industry classification was vetted and verified and validated and cross-checked across a number of data providers. Again, since we have all of those data providers, we can compare what industry they think this account is, and give you the best approach there. So with Explorium, they got more data, more data points and attributes, and they could automatically validate and check and provide the salesperson with a more accurate and quality list of leads. The initial results when we did a proof of concept with them were amazing. We were able to give them 600 50,000 new leads in the proof of concept, and they were all— they got very good reviews from their salespeople, very good leads, and also a lot of contact information, 500,000 contacts for those leads, for those accounts, which again were very accurate and very, very much liked by the salespeople. And the results of the whole project— why is it It's not moving to the next slide. Hang on. Here it is. It was an amazing uplift in conversion rate because again, there was greater precision. The targeting was better, the segmentation was better, and obviously those customers made more sense, were willing to look at or had a pain that indicated they need a solution that my customer was selling. And so the conversion was much, much better. But maybe more important is that salespeople could actually focus on sales instead of wasting a lot of time doing research around that customer. All the data available from Explorium that was relevant was also available through their Salesforce, and they could find it and know everything they needed to know about the customer before they call him, before they approach him. And also streamlined marketing and sales operations. And we talk a lot about RevOps. And it's very trendy right now, and the process and streamlining the way everybody sees the same picture is also achieved here. Because when everybody has the same data about an account, when everybody understands why this account is a good target and in our segment, or maybe why the quality score for that account is high or low, then there's no arguments. Everybody sees the same picture and can behave accordingly. So that's a great success story with one of our customers. Maybe they'll join us for the next webinar. And then just one last thing that I wanted to share with you is maybe taking it one step down, one level down, and making it more, you know, how to get started. is really the question. Okay, I get segmentation, I understand it's very important, I know there's so much data that I can use, but still, how do I get started? And I think the key here, and again, we learned that from our customers, is to implement a repeatable and scalable methodology. So even if you start small, the process needs to be defined in a way that can scale and can work on and on as you grow and as you become more successful. And I think it has a, maybe 4 steps here. The first one is to do some preprocessing, to look at the data you have today. Typically, that would be, again, your win-loss analysis, to analyze your customer and prospect base, to see where you win and where you lose. Our platform can then create some lookalike algorithms and tell you which— where are those specific attributes that make a difference. and also create some additional top-of-funnel leads based on those attributes. So for example, if you are successful with doctor clinics that are, what did you say, orthopedics that do knee replacement, then let me— Yeah. Now I can find for you more of those doctor clinics, very, very specific. And then we can, of course, enrich those leads with so much more information about them so that you can approach them, you can know more about the account, more about the people, and approach them correctly. And then you can also filter out some noise in the system, right? Like I gave you that example of the NAICS code, which all of us are using for segmentation. But in some cases, for some industries, for some local businesses, it's not always correct. So you need to figure out what is the right industry that you're really looking for. Maybe it's not the NAICS code, maybe it's the LinkedIn industry, maybe it's how the customer is defining himself on his website. So what's the right way to pinpoint those customers? And maybe last but not least is to give them a score so that you can learn from that and you can learn moving forward and also take that process all the way to beginning and learn again from the data as you gain more customers or lose more customers. You— the, the model can change and the system can learn and adapt itself to better segmentation next time.

Whoa, another great episode of Revenue Today. For show notes, links, and mentions, visit revenue today dot live. For all my friends in the Rev Genius community, thanks Thank you. It's been awesome to spend this time with you. Please DM me any feedback and ideas in our Slack channel or on LinkedIn. If you're not in Rev Genius, join us at revgenius.com. It's free and it only takes like 2 seconds and you'll be joining a group of 27,000 revenue professionals strong. We've got it all. Looking forward to seeing you there. Catch you on the flip side. Bye.