AI and the Science of Sales

This transcript was created using AI tools and is not a verbatim, word-for-word transcript of the episode. Please forgive any errors or omissions from the finished product.

Courtney: [00:00:00] How is AI going to change the world of sales? And if sales reps today only spend 28% of their time selling, where do they spend the other 72%? Hi, I’m Courtney Baker, and this is AI Knowhow from Knownwell, helping you reimagine your business in the AI era. As always, I’m joined by Knownwel CEO, David DeWolf, Chief Product Officer Mohan Rao, and Chief Strategy Officer Pete Buer.

Friend of the pod and founder of TIGA Labs Andre Yee will also join us for this week’s panel discussion, and we’ll also have a discussion with Nick Toman of SBI to dig into his company’s research around B2B selling profiles and the impact AI will have on them. But first, let’s bust some myths around AI.

This week, Mohan Rao is with us for our first segment, one that we introduced a few weeks ago called AI MythBusters. Pete [00:01:00] did such a good job with our first one that we had to go another round. So Mohan, hey, welcome to the show. Early.

Mohan: Hey, Courtney. Such a pleasure.

Courtney: Yeah, so you don’t, for the audience, you actually don’t know what myth we’re gonna be covering, so here you go.

Uh, the myth that I would love for you to conquer once and for all is artificial intelligence is a brand new technology.

Mohan: Oh my God, no. It’s been around since the 1960s. Um, maybe, maybe even before, um, you know, in, when I was in college, um, I’d, uh, read this book, A book by Elaine Rich. I. I have it right here because I love that book so much. Uh, I think she was a professor at University of Texas at Austin, um, and that she had a definition back.

This is 1980 textbook about, um, you know, it’s a study of computers to do things at, [00:02:00] uh, at the moment, which people do better in. Right? So it’s about making computers do more human-like activity is how she defined it. Mm-Hmm. the definition just because it’s such an evergreen definition. So AI has been around for a very, very, very, very long time.

Courtney: And just for some. Clarity here. If you’re not watching us on YouTube, I know Mohan sounds like a sprite Gen Zer. Um, but you did go to college pre two thousands?

Mohan: Uh, let’s say that without dating myself. Yes, it was pre two thousands.

Courtney: It has been a hot second. Um, do you, can you help put in some reference why now, obviously with generative ai, obviously there’s been all this emphasis, and certainly it makes sense why this myth exists. What, why do you think that is that it has not been as prevalent or people haven’t [00:03:00] understood it up until more recently?

Mohan: Yeah, I guess AI is like Tony Bennett, right? It’s relevant for the new generation. So, so it’s been around for a long time, but what’s happened in the last few years, um, is that the, uh, you know, everything is generating data, right? So, so. We, um, you go into a store and your wifi signal gets captured, and that’s a data point that somebody with this MAC address came in.

So, uh, you know, the internet has been a, a vast repository of data, right? So it’s been doubling every three years. So that was the first thing. Followed by dramatically cheaper cloud computing costs, right? In Mm days, nobody could afford the processing power, but now we can. And because we buy with a drink and we can afford it, uh, and we can scale up as our businesses scale up.

And because of these two. Items around data increasing and dramatically, uh, lower, uh, computer and, [00:04:00] um, and storage costs. There have been some very important architectural unlocks in the last few years. Uh, and then what happened was in November of 2022, chat, GPT made it real for Mm-Hmm. Yeah. we all think that it’s, um, uh, new and uh, refreshed.

But it’s been around, we’ve been doing ai. With game playing and theater improving and those types of activities for a very, very long time.

Courtney: That is so helpful, and I love your insight on what had to happen to bring it to its current form. Existed for a really long time, but not in the usage that we have today. Mohan. Thank you for breaking that myth.

Mohan: Awesome.

Courtney: Good job. You are now a myth buster.

Mohan: Yay.

Courtney: [00:05:00] Sales has always been uniquely human, yet. B2B sales is often about everything but selling. I sat down with David DeWolf, Mohan Rao, and special guest, Andre Yee of ticket AI to talk about why B2B sales is ripe for disruption in the age of ai.

So, David Mohan, I have a guest for you today. Andre, welcome to the show.

David: Welcome to the

show, Andre. It’s awesome to have you.

Andre: thank you. It’s great to be here.

Courtney: So Andre, I’m gonna start just right outta the gate. And I also, David Mohan, don’t keep the gloves on for Andre here.

David: Okay. true friend of the firm. All right. She’s always trying to stir it up, Andre.

Courtney: yes. Yes. And listen, when you’ve been invited to come into this panel discussion, you are, you’re one of us at this point,

Andre: All right.

Courtney: uh, whether or not you want to be or not.

But what I wanted to start this [00:06:00] conversation off with is just to get your take on what you’re seeing happening with AI and sales and the changes that you’re starting to see. And I would love David for you and Mohan to get your takes on those as well.

Andre: so I, I think the way I think about that is, uh, I, I think that sales B2B sales in particular. Is in a, uh, period of transition. If you go out and you talk to sales leaders, what you find when you get them, you know, over drinks or whatever, and they’re having an honest moment, they’re going to tell you stuff.

Like they’re rethinking the way they go to market and, uh, and how to engage, uh, the buyer. And, um, you know, they’re rethinking how SDRs are used, whether that still works in the future. I think that’s the starting point. And then the question is does ai, uh, help solve that problem?

Right. But I think it starts with, I think, um, sales, uh, especially B2B sales is at a sort of point of [00:07:00] transition in, in, in my opinion,

David: Andre, when I think about that, I think about kind of a continuum that we’ve had, like the first wave in the digital era. A lot of times I call it the, the era of the CMO. Right. We saw marketing first and how we outreach and engage buyers really change fundamentally. And the challenge was always taking that further in the go-to market kind of commercial cycle was that sales, especially B2B sales, as you’re talking about, is so relational Mm-Hmm. there is not kind of black and white metrics that we could measure and, and do things in the exact same way.

That we do marketing. And so the, the paradigm didn’t really fit. the change that’s being forced is being forced, number one, by that change that is cascaded through. And there’s been almost this disconnect between marketing and sales and the approaches. And this. This relational aspect now being able to be grappled with, [00:08:00] because AI has an ability to do more than just compute and make calculations, is making inferences and judgements and starting to be able to approach upon that relational ability.

And so when I look at that meta picture, I kind of feel like what you’re talking about is. We are at a point in time in a lifecycle of, in terms of revamping the entire Go-to-market model. Um, maybe we’re on step three or four and we are just kinda working ourselves through the pipeline.

Andre: I let, let me just say, I think there’s some truth to that. What I guess I was saying earlier was that whether ai, um, actually, um, you know, generative AI has come along, uh, you know, let’s say in a last year and a half or whatever, or at least in public consciousness, Whether that happened or not. I think what I was really trying to say is that there have been issues in the the B2B go to market motion and, uh, people are rethinking that. It just happens. They’re rethinking that in an at a time, as you [00:09:00] said, where, um, there’s this really powerful technology and that technology in, in my opinion is, is a game changer.

Right? And, and so we can unpack that a little more, but.

Mohan: Yeah, the, you know, the way I think of it as, uh, sales traditionally back when was really. Uh, more about the heart than the head. And in the last 10 or 15 years, we’ve made it more and more and more about the head, uh, Hmm. analytics and uh, you know, we know exactly what they should buy and that sort of, uh, precision, which really does not exist.

And that’s where I think the mismatch is. And I’m kind of wondering if AI would be a force. To make it as much about the heart as it is about the head, because we know both are important in the sales, uh, Yeah, true. how do you think AI could help with that? Uh, resetting back to the heart and getting to a good tension between the heart and the head in terms of the attunement that you’re gonna have with the prospect.

I.

Andre: Yeah. I, I think, uh, I think that’s, that’s [00:10:00] true. That’s absolutely true. And it ties in with what David was saying, which is Jennifer AI has this ability to Be able to be leveraged to in, in language and in in relational connection in a way that, um, you know, prior technologies, you know, didn’t allow for that. thing that you might find interesting is last year as, as we started building, uh, tega, as the guys were, uh, coding away, one of the things I did was I just met with, uh, sales professionals. Uh, and, um, some of them were VP levels, some of them were account executives, some of them were even senior BDRs.

I was trying to explore this one question, which I, I think you might find interesting, which is, what do the best sellers do?

David: Hmm.

Andre: Like, uh, we know that this a transition, um, happening or at least one, you know, that’s one of the things I discovered in my conversations. Uh, some of the existing, uh, outbound and and selling methods are a wall. Um, what [00:11:00] did the best sellers do? And everyone gave a slightly different answer, but believe it or not, there was a pattern to this that I think you, uh, is, is really interesting. It’s, if I find, uh, in the answers, it, it appears all the best sellers do a, a, a version of this deep research, relational connection and then insight based, uh, outreach, right?

David: Hmm. engagement, insight based engagement. So. All the sellers do a version of this. And, um, and so I, when I think of, you know, bringing full circle, when I think of Jennifer ai, I do think that it is, uh, b uh, B2B Selling is human to human. it’ll Hmm for the foreseeable future. all the best sellers research do deep research.

Andre: All the best sellers relationally connect. All the best sellers, uh, have insight based en engagement. How does generative AI help the seller do that better? So to me, it’s not about replacing the [00:12:00] human, it’s about how do you

David: mm-Hmm.

Andre: augment the human seller so that they can do deep research better so that they can relationally connect better so that they can actually, intentionally, uh, engage the buyer with insight better.

Right? So that, that I think is, is the opportunity.

David: I love that frame, Andre, because I think it brings us back to one of the fundamental principles we’ve seen over and over again, which is you, you can use the word augment. Uh, I think it’s even greater of how do we leverage AI to, I. Really allow humans to do what only humans can do.

Andre: That’s correct.

David: And when you talk about the second phase doing research, you know what?

A computer can probably do it better than I can now. Now finding that needle in the haystack that maybe isn’t as computational. We can have arguments about creativity in those types of things. But research by and large. Let’s let the AI do building connection. I think there’s parts of that that we may be able to get help with, but to truly make a human connection, [00:13:00] to know and to be known as a human individual, only a human can do that.

We can’t replace that. And I think the more we leverage that frame of, hey, this is not just augmenting and somebody coming in to create a little bit of arbitrage, this is about really empowering people to be truly human, to live in their humanity. To exercise what only they can do more, I think helps us in all domains, right?

We focus on it in the leadership domain. You’re sitting here talking about cells. I couldn’t agree more. I love how Mohan framed it, that the heart over the head, that’s what we have as humans over these computers. Um, and I think it’s such a key insight for thinking about these different roles and how we can leverage ai.

Courtney: think right now everybody that’s in sales that’s listening to this conversation is just cheering because they’re like, yes, let us please give us the technology to just help us do what we really wanna do. And I’m, I think all three of you would agree with this.

Every salesperson I’ve ever worked with [00:14:00] just wants to be selling. They don’t wanna do those other pieces that Andre talked about, the research. Maybe that’s what the great ones do, but ultimately what they. You know, 98% of the time is they just wanna be out there selling. Um, and I think the stats bear this out, you know, the stats show that sales reps only spend about 28% of their time actually selling.

1. what is your path, I think for pe, people listening, executives that aren’t necessarily sales reps themselves, how do they help equip their teams with this new technology? How do they deploy it in their organizations?

David: You know there, there’s a bit of flipping the script that I think needs to be done. Courtney, when you say that, if you think about how we’ve talked on this podcast about where we’re seeing AI actually being rolled out and where we’re not, it’s in that execution layer of work. It’s actually doing the work itself, that AI is being used the most, right?

It is things like. [00:15:00] Writing copy or writing code, right. That we’ve seen most people grasp onto. But if you listen to what you said in sales, it’s actually not about doing the work. The work of sales itself, that’s what the sellers want to get back to. That’s what they’re best at. It is the exact opposite. It is the orchestration of the work.

It is the preliminary work. It’s that operational layer that we have talked about and how do we make sure as leaders that we are not just trying to replace somebody’s job, know, actually empower them to do it better, and helping to orchestrate the work, help helping to empower the work that is so important in my mind.

Andre: Yeah, that’s, that’s absolutely true. And then, um, when you look at the 72% um, non-selling activities, what you see is, a lot of stuff that is kind of important, but to the point that was made earlier. they’re not, they, they, [00:16:00] they are not necessarily, um, they, they don’t have to be done by a human right.

Or, you know, um, what, what a, what a what comprises the 20, uh, 72% stuff like prioritizing your leads. Um, stuff like, um, you know, preparing for meetings, creating proposals. Research actually was one. And then, and then, um, ironically, um, some of it is entering information into your CRM and other systems like that.

Well, I think, I think that’s the one thing that I find really interesting, which is, you know, um, in the past, um, we’ve come up with a lot of good software, you know, to help people become more process oriented, efficient. but that’s created its own body of work, you know, and, and then that picks a life of its

David: In work that every seller hates, by the way.

Courtney: Everyone hates.

Yeah.

Andre: Yeah. I think what’s exciting is that, um, and what we’re trying to do at Tega is, is actually flip that a little bit where, you know, hopefully, five years from now [00:17:00] pe you know, you’ll salespeople are going to spend 72% of their time actually selling and 28% of the time in other activities.

Right. So I think that would be. fantastic. But, that’s where generative AI can really help, I think, is, is all these, you know, sort of, I don’t wanna say mundane tasks because they’re actually kind of important, but they’re very repetitive tasks that, that AI can actually, um, you know, solve for.

And that leaves the seller the human with more time. And, and I think you said it well, David. To actually do the things that they do best, which is actually be in front of the client, think strategically about the account. talking to sales people, one of them said, you know, love being in front.

I, I, I love this job because I love being in front of customers. It’s all the other things that I don’t like

doing. Right. I love being in front of, of people, you know, and, and, them the product or, or whatever it is that they’re selling. So,

Courtney: okay. technology is, um, you know, fully there or [00:18:00] substantially there. in most sales organizations, um, you know, maybe 30% of the Salesforce produces 70% of the outcome, right? So that’s a natural thing mm-hmm. Do you see that percentage, uh, getting reset, um, as there is more of, um, this technology and maybe more of the intubation based thing that’s removed?

David: There’s two ramifications of that that I think of immediately. Um, there, there’s almost a leveling of the playing field and it’s because we’re able to give more of the work that is required, but may not be the secret sauce and the key thing, um, to. The computers to do, and when you do that, two things happen.

The first one is I think you have to compromise less in your actual hiring, right? As a leader, you can go out and say, what is that one thing? The person that excels in front of the clients, and I don’t have to worry about. Their ability to discipline themselves to do research or data entry because most of that’s gonna be [00:19:00] done for them.

Right. And served up on a platter. And so I can get much more specific, that’s infinitely more scalable. ’cause you’re looking for one skillset. You’re not looking for the intersection of 17 skillsets. Right. Um, I think the other part of that is. You also are able to attract more people to the profession that can do the job.

’cause there are some people that they don’t want to do the other aspects and they’re not energized by it. And so they go pursue other careers even though they could be a good fit for that ideal. And so in both of those ways, I think we make it. A better world, right? For these individuals doing this, doing this job, and it makes it easier for us to hire that.

That leads to scalability, not just the time we’re freeing up and not just how we’re empowering people.

Courtney: Last thing, I do think this is an interesting model. If we look at roles where the turnover is very

David: Hmm.

Courtney: You know, with SDR roles, if you get an SDR that can survive in that role two years, you know, you’re kinda lucky. You’re like, we [00:20:00] did it. That was amazing. It’s just such a high turnover role.

Andre: you know.

Courtney: I think that’s an interesting way to maybe look at other roles that we can say, Hey, what can AI do to actually make this role have more longevity, to be more fulfilling?

You know, all those things that we value in a long-term career.

Andre: that you’re bringing a really interesting point. If today, when, um, roles, important roles like an, you know, a salesperson, when they turn over, lot of the knowledge goes with them.

Courtney: Yeah.

Andre: Um, and one of the things, you know, as, as you have generative ai, and I, I, I do believe you’re going to have different sort of purpose-built ais for different roles.

But one of the, uh, net benefits, um, that people don’t talk about a lot is, is, um. Is just a preservation of institutional knowledge within the organization. So you know, if a particular salesperson leaves, you know, that person may have had four or [00:21:00] five years of deep experience about the product, about, you know, nuances about how to engage the buyer.

All that is, is captured in your ai. It doesn’t. Of course some of it will leave, like the relational connection with the, with the client. You know, that’s as, as, as David said, fundamentally a human thing. But a lot of the knowledge, institutional knowledge, uh, and experience about the client is, is retained.

Uh, and, and I think that’s, that’s a really powerful thing.

David: And retained without data entry, which we already talked about.

Andre: Yeah, exactly.

Courtney: Andre, thank you for joining us. It was a real treat to have you, David Mohan. Thank you. As always.

David: It was great to have you, Andre. Thanks,

Courtney. Thanks. It was great being here.

Courtney: you know, what’s closely related to the science of cells. The science of client intelligence. ’cause let’s face it, your best customers are often the ones you already have. Be one of the [00:22:00] first to get access to the client intelligence platform we’re building here at Knownwell. It’ll give you insights into how you can better serve your existing clients, resulting in reduced churn, increased loyalty, and more sales.

you can join the beta wait list right now at Knownwell dot com

​

Courtney: Nick Toman joined Pete Buer recently to talk about his company’s research into the world of B2B, selling the four distinct profiles they uncovered and how they relate to deal size.

Pete: Nick. Welcome. So great to have you on the podcast.

Nick: Pete, it is awesome to be here. Thanks for having me.

Pete: just to frame things up for our listeners, can you give us a little bit of background on SBI and your role?

Nick: Yeah. So, um, my role at SBI is, I, I had a product and strategy. Um, for us, what we do at SBI is, we’re a growth advisory firm, Pete, so we focus on all things value creation and growth for our clients. [00:23:00] Um, we’ve, we cover everything from consulting training. Uh, rev Tech Services, uh, we do things across talent, operations, execution, sort of all the commercial functions that we support our clients on.

And we also have, uh, advisory services too, Pete, so we cover quite a bit and help our client clients through quite a bit in terms of their, uh, their growth journey. I.

Pete: And you specifically.

Nick: Yeah, so I head a product and so, uh, my responsibilities include research for us, which I think we’re gonna talk a little bit about some of the research, uh, strategy, um, product Corp dev. So Pete, I get to do all the fun things to help our firm grow.

Pete: I was hoping to, probe you there because I want to get into the research. You guys have published some cool stuff recently, uh, around B2B sales and was hoping that you could tell us the story. I.

Nick: We surveyed over 2000 go to market professionals across a variety of roles, be it customer success, sales account management roles, sales engineers, uh, various managers of those functions [00:24:00] to understand what do they do, what are they doing differently, and ultimately what performance are they driving for their companies.

and you know, Pete, I’ll just give you the, the sort of punchline of what we found in the research is, uh, through the course of roughly 90 different questions, we did an analysis. We found that there were four kind of commercial approaches, I’ll call them. the first Pete was what we called narrowing.

Uh, and it was the most prevalent that we saw. And what we mean by narrowing is simply speaking. It’s all about sort of closing the aperture quickly for the customer as they’re engaging in a purchase process. So I really try to drive commercial closure. Um, you know, in some ways shapes and forms. It sort of, it sort of hearkens back to the old Glengarry Glen Ross approach.

Um, but really try to compel the customer quickly. We think of it as kind of the sales forward sort of approach. Um, second was what we called provoking. Uh, and I have to kind of give a nod back to my heritage in, in, in some of the challenger sale work. Um, but it was very similar to what we found in a challenger sale where you’re essentially trying to [00:25:00] reset the problem, reset the buying criteria, reset how the customer reframe, how the customer’s thinking about, um, their business and the actions they need to take.

Um, the third one we found was what we called translating. And translating was an interesting one, Pete, and, we’ll, we’ll probably dig in a little bit more into translating and then the fourth one in a minute here. But translating was all about essentially, I, I very good at denominating. The purchase in terms of business impact for the customer.

So very good about sort of translating the impact of my product or service into what it means for the customer. And I only talk about it really in terms of what it means for the customer. So very much able to put myself on their side of the buying equation when I’m talking about value. And lastly, anticipating piece.

Um, which was, uh, all about, uh, essentially being one step ahead of the customer, kind of bread crumbing them through their decision making, how to think about the purchase, how to think about the change management, what the implications for the business were. It wasn’t so much selling or sort of pushing a sales motion as it is kind of pulling the customer through a decision, a series of [00:26:00] decisions that they have to make.

Pete: , let’s get into the results. What,

Nick: Yeah, yeah, yeah. So, so without going deep, deep into the data, Pete, what we generally found, Pete, was that that narrowing profile, the most prevalent or most common profile or approach, has the lowest performance net net. and what we’re finding is that that kind of of approach where I’m really trying to push the customer through a decision making process.

Um, one when you’re getting pressure at the end of the quarter and we’re all sort of watching our own productivity numbers as suppliers or vendors, you know, this is the pressure we inadvertently begin to put on the teams. And what’s really interesting, Pete, is as you move up the food chain from frontline to management to senior management, we actually begin to see the prevalence of that narrowing profile.

Increase. So that one is the least effective, most common, sort of the lower left quadrant, if you will. The bad, bad quadrant. provoking is the next most powerful. Um, uh, doesn’t generate a huge amount of. Upside. Um, but it doesn’t hurt things, [00:27:00] Pete, and this is one where I’m very careful with how we talk about that provoking one because it has a time in place.

There is sometimes where the customers just got it wrong and we’ve gotta help them see the world a little differently or perhaps a little more clearly. Um, so it has a time in place and it’s one of those important sort of minors. Um, just as narrowing, Pete is a good minor. You gotta get commercial sometimes you gotta kind of nudge things through the funnel.

Translating and anticipating on the whole Pete, those dramatically outperform across all those different outcomes. Um, and the one that really does stand above the rest is the anticipating approach. Um, and what I’ll tell you is it’s both translating and anticipating they’re the least common, particularly anticipating.

Um, and I think in many cases, Pete, we sort of look at it and we say, wow. It’s least common because it’s hard. It’s hard to be one step ahead of the customer. But Pete, I think this is where, with some of the AI advances and some of the approaches that we see these individuals really embracing, where they’re very much oriented towards planning, they [00:28:00] major in planning, they’re thoughtful, going into the, the commercial interactions.

They’ve done their homework and they’re highly collaborative within their companies to get the information about the customer. And relay that back to the customer. It’s really not rocket science, Pete, what they’re doing. It’s just good hygiene around planning. And I think there’s some tools as we can talk more about, um, that really do help these individuals embrace that kind of an approach.

Pete: Are an AI focused podcast, uh, and you waved at this a moment ago. Let’s go there. Um, is there anything about the profiles, um, that links to use of AI tools or AI empowerment of them or.

Whatever.

Nick: Yeah, and we, we’ve started to include some, some questions and some examination of, you know, use of these kinds of technologies as part of, as part of the, the, the approach. You notice I keep using that word approach because I think it, it really does combine sort of what’s my behaviors, what’s my mindset?

Where do I focus my energy, um, as a, as a, you know, [00:29:00] frontline, employee. And what we’re finding, not surprisingly, Pete, is those with that anticipating as well as the translating, uh, approaches tend to be more inclined to use AI driven tools, particularly generative AI tools. Um, and I think what you’re seeing here, Pete, is they’ve really figured out how I can do quick diligence against a customer, not only understanding the business and economic drivers of that customer’s business, but more importantly.

Um, really thinking through how does that business work and what can I do from sort of a pain point amplification or attention getting amplification standpoint. Um, to really dig in and help senior stakeholders realize, there’s something more to this. We, we need to better understand what’s happening here.

And Pete, for years we, we’ve sort of scratched our head in the sales discipline around business acumen. It’s like this mythical thing that is so hard to teach to the front line and what we’re finding these individuals doing. You know, I, I wrote a piece in HBR on this, um, [00:30:00] a couple months ago, but it, it’s almost like the old five whys exercise that you do from kind of a critical thinking standpoint.

You can leverage, I. These tools to ask those five why’s and really go deep into, okay, well if, if working capital is an issue for this particular customer, why? And what does that mean? What would the implications be and what would the senior stakeholders be thinking about? As a result, suddenly I’m much better armed on a topic that maybe I’m not the most comfortable with personally, but I can begin to have a meaningful conversation and relate what my solutions do better to where that customer is in their own business functioning.

Pete: So the, the translator and anticipator approach, finds, commercial executives more likely to take advantage of AI tools to help with the approach. AI be, um, enabler of, uh, narrow and provokers? Like, could we, could it be the tool to help bring them up the productivity curve?

Nick: A hundred percent Pete. Okay. percent and I [00:31:00] think this is where we get into some of that major and minor. Um, I think there can be, you know, even if my DNA is, is sort of strongly and overtly narrowing and just who I am, uh, I think being able to really pause and ensure that I’ve got solid understanding of the situation I’m looking at, not just rush to closure.

Right. Um, I think this can be hugely helpful and this is where we get into forms of enablement, forms of playbook. Playbooks that just help remind our teams as they’re moving quickly in the field to take that minute and really do that proper diligence. Really understand what am I up against? What do I need to be smart on, and how can I better relate what we’re selling into that customer’s business?

Pete: let’s look to the horizon. what are you seeing out there? Uh, AI related, uh, AI powered, uh, tools and methodologies that companies are using that ha has you excited?

Nick: Yeah, there’s like, there’s dozens of, of really interesting use cases. [00:32:00] Um, I’ll talk about two Pete that have got me particularly excited. Um, given what we do, uh, here at SBI, we, we do help our clients, um, in many cases better understand sort of their market and the market potential and aligning their commercial resources against that market potential.

Um, one of the most powerful use cases, and this is something we, we’ve, um, we’ve recently, uh, been, been, uh, leveraging this kind of capability, um, when we’re working with our clients is, is doing market segmentation through an AI driven cluster analysis piece. So traditionally, when you look at sort of segmenting a market and creating prioritized accounts that you then move into, you know, a production environment for your, for your sales teams or your, or your CS teams, um, many cases we sort of apply what I call a top down model, which is you take the accounts.

You take traditional firmographics, right, from an a third party enrichment provider, and you sort of look at, okay, what are the common patterns that those firmographics [00:33:00] tell us perform better? Um, you’re kind of taking what, you know, a vendor like a ZoomInfo for instance, um, might be providing as sort of fixed gospel around how that market tends to present.

And you find patterns in that data. And it’s not a bad approach. I mean, we’ve, we’ve, we’ve driven a lot of value for our clients using this approach, but what I’ll tell you is it’s only as good as, as that enrichment data. And, um, what we’re using now is, is sort of a bottoms up method where we take the customer’s accounts, we can begin to pattern out companies that look exceptionally close to and behave like the customers that are best performing, do a cluster analysis where we’re scraping a tremendous number of websites to pattern it out, create these really tight clusters in the market.

What it allows us to do, Pete, is create these sort of niche micro markets. Where if you’ve got a handful of customers performing well there, you can tell that success story many times over to create opportunities, and it just doesn’t behave or [00:34:00] present the way a normal, um, a normal market vertical would.

Pete: naturally defined cluster of companies that hang together for how they show up or certain characteristics or of needs, which doesn’t necessarily line up to the top down

Nick: No.

Pete: segment business model, Um, that you normally start with.

Nick: Right, right. For, for instance, with one of our clients, Pete, we, we found a, it’s a really interesting sub-vertical within. Um, within sort of broader logistics, which, you know, logistics was kind of the, the vertical that they were using, uh, one called freight forwarding. Uh, and it was really more about technology orientation within that logistics market.

And it happened to be very specific, sort of highly data oriented types of providers. Within that market where it was just a real hotbed of activity. They weren’t focused on it. Uh, in fact all the accounts, almost all the accounts they had listed there were in the marketing territory. They weren’t actively covered, um, yet it was remark, a remarkably right market for them.

So able to quickly put that into a more active [00:35:00] territories and just helped them, you know, immediately create opportunity.

Pete: Super cool. Okay, you said you had two examples. Let’s hear another one.

Nick: Another one. So, um, Pete, as you know, I love talent. Uh, so another one and, and we’re doing some really fun experiments with this right now, working with, um, a professor over at, at, at Harvard. With that tool we mentioned earlier, what we’ve been able to do is marry that data, which captures kind of behaviors and sort of my beliefs as an individual contributor. Also go in and, and apply, uh, traditional demographic data. So how long have I been in a role? How much tenure do I have? So a series of different sort of demographic criteria around these individuals.

Pull in performance data of all kinds, Pete, um, sales velocity, uh, a SP, all kinds of different, um, performance criteria mash that into a pretty significant, uh, large language model and understand truly what drives performance.

Now what’s [00:36:00] really interesting, Pete, when you start to do that, you get this composite picture of okay skills, attributes, behaviors, competencies, that you can turn into, say, hiring criteria or screening criteria. And we’ve been pretty successful in helping our clients create these hiring phenotypes that they can go in and really, um, get targeted with a high degree of confidence.

Hey, this individual’s gonna excel in our roles. Um. Pete, we can begin to do things like predicting, um, the next great manager candidates for instance, and who should be up for progression

So there’s been a series of really exciting and fun use cases when you just let the data sort of step back, load the data in and say, Hey, tell us what, what we can glean, what’s really driving performance in our environment for our customers.

Pete: Nick, I could talk to you forever. Um, but we’re up against it on, on time. Of course. you’re, you’re so, you’re so deep on this stuff and you’ve got such great instincts on where to go for the insight. Um, tha thanks for sharing what, what you could in the time that we had together today. I have a feeling [00:37:00] we’ll be talking to you again, but, uh, uh, so appreciative of, of your time here.

Nick: Of course, Pete and happy, happy to be on. Happy to be back whenever, whenever you want to go deeper on this. But um, thanks for the opportunity.

Pete: Awesome. Take care, pal. All the best.

Nick: Thank you.

Courtney: Thanks as always for listening and watching. Don’t forget to give us a five star review on your podcast, player of Choice or over on YouTube. We’d also love it if you would leave a review or share this episode on social media. At the end of every episode, we’d like to ask one of our AI friends to weigh in on the topic at hand

Hey Claude, what’s happening? This episode we’re talking about AI and the science of cells. How are you going to change selling?

Courtney: now you are in the know. Thanks as always for listening and watching. We’ll see you [00:38:00] next week with more headlines, round table discussions and interviews with AI experts.