In this episode of the GTM Innovators Podcast, host Kyle James sits down with John Marcus III, founder of Fractional Ventures, to explore the cutting-edge intersection of AI and sales. John introduces the transformative concept of AI Qualified Leads (AIQL), dives into the power of bespoke data for uncovering non-obvious customer insights, and shares why context is the cornerstone of successful AI implementations. Packed with actionable advice and forward-thinking perspectives, this conversation is a must-listen for anyone looking to elevate their sales strategy and embrace the future of go-to-market innovation.
Subscript to GMT Innovators Series on the following platforms:
- SoundCloud: https://soundcloud.com/research-859405782
- YouTube: https://www.youtube.com/playlist?list=PLsoV6fwX4cpGR2Hg98rc1e1k2-cOqCbhb
- Spotify: https://open.spotify.com/show/1gvDzcl0jxpPIfu9WYu6U4
- iHeartReadio: https://iheart.com/podcast/258127960/
- Pandora: https://www.pandora.com/podcast/gtm-innovators/PC:1001097038
- iTunes: https://podcasts.apple.com/us/podcast/gtm-innovators/id1790738579
- Amazon: https://music.amazon.com/podcasts/1b000615-31cc-49dd-a5d8-f80d5098bf2d/gtm-innovators
Transcript:
Kyle James 00:00
Kyle, welcome to another episode of the GTM innovators by 3Sixty Insights I’m your host, Kyle James, and today we are diving into really all things bespoke data. Let’s just call it that, as John Marcus kind of told me. So I’m here with John Marcus. John Marcus is the founder, former founder of Bedrock Data, which he sold to form stack, and has done since then a range of VP and leadership roles in DevOps and sales. And today is kind of the founder of fractional ventures. John. Welcome to the show, and feel free to, like, tell us anything else about yourself that that I might have missed or that you want to kind of expand upon. We’ve known each other forever, but
John Marcus III 00:48
yeah, like, that’s, that’s, that’s, that’s the first thing. That’s the first mark against me is, I’ve known Cal for quite a while. We got to cut our teeth back in the trenches early on at HubSpot, where I was on the sales side of things. My background actually is in engineering, electrical engineering of all things. And I got into sales very early on post education, and was able to learn from just some of the best, like Marco Berge over at HubSpot. Later on, I had the fortune of being able to create a couple of my own companies and find those homes. Not all of them successful. Some startups don’t make it, which is okay. But as a serial entrepreneur, what I’m doing today is I’m helping businesses through that, that gap, that challenge of growth. And not everybody’s got the opportunity to go and hire or find someone full time to go do that. So that’s where myself and the partnership here at fractional ventures. We we help businesses out with that. You know, we provide fractional rev, op, sales, marketing, CS in particular. So really excited to be working on that for the past year and change here. Nice,
Kyle James 01:49
nice, awesome. And I know you and I have talked a lot about, like, you’re deeper in the weeds and all this AI stuff than pretty much anybody I know. But I think one something that you said that really resonated me, and I think everybody needs to hear, is, like, garbage in, garbage out, right? Like, everything starts with the data and the data analytics that you’re putting together, and you’re doing some really interesting, innovative ways to do that. But like, like, talk to us, tell us why it’s so important to, like, get that good data before you even do anything. Yeah,
John Marcus III 02:18
what’s really interesting, least at the time that we’re recording this, is you’re starting to see people understand how to use some of these frontier models, these agentic models, like GPT oh one, for example, and the kind of rumored oh three. And it’s a very different way that you have to use these llms. And everyone, kind of, myself included, tried it out and said, Hey, this is garbage, like, I’m going back to four. Oh, like, that thing works. I know how to use it. But the kind of next step in evolution of any kind of AI applications in business all comes down to context. So the more context that you provide these agentic models, the further they can run with it. And one of the very early problems, back before everybody started calling an AI, when we were doing machine learning, before transformers and all this LLM stuff, is that you needed a lot of data to be able to go and train the models in a way that they’d be able to make good predictions. Now what we’re able to do is we’re able to make these kinds of leaps in inference, because these are inference models. That’s what the llms are, and predictions as to, like, what the answers to these questions are. And it all has to do with kind of what it’s trained on. Now, the problem here is that sometimes there’s too big of a jump. You can say, like, hey, plan out an entire, you know, business for me and do all the marketing and all the sales and all that stuff. It’s not gonna be able to do it, at least you haven’t told it what
Kyle James 03:43
kind of business you want. You haven’t told it what are you selling? Yeah, totally, exactly.
John Marcus III 03:47
So the other side of it is to say, Well, I’m gonna go give it a ton of context, and that’s where the latest models are coming to play. And these are the ones when it comes to go to market function that are the most comparable to what some entry level roles are when we talk about the end of the SDR, or, you know, the end of the analyst, like, that’s where it is now you can take some of these frontier models and use agentic methods to produce what, you know, first year out of college analyst might be doing at a, you know, a bigger firm. So I think the challenge here is getting that context right. And to get it right, you need to have good data. And I think the big challenge here, when it comes to providing data for these models, to get this context, is up to this point, everyone’s just kind of thrown everything at it and saw what stuck. We’ve got to be a little more constructive and a little more constrained, because you can’t throw an entire CRMs worth of data and say, like, Hey, where are my best customers? Like, there’s we’re still not there
Kyle James 04:47
yet, right? So where do people get the data right? Like, how are you generating or are you working with them to generate the data for them? Like, generate data is being generated all the time. But I mean, is it in the right format? How do you standardize it? You know. What do you do
John Marcus III 05:00
there? Yeah, and this is the problem, because up into this point, data has been any data, right? Just a point of information. Let’s grab an example of a customer. I’ve got a business name, I’ve got a state they’re in. I’ve got like, you know, the CEO’s name, something like that. But that’s all very specific structured data. And we can only go so far with structured data, even with machine learning techniques, if we want to start to get beyond that, to make these non obvious correlations or non obvious inferences that llms can give us the ability to, do you have to go outside of the CRM, right. So the stuff that you would get out of zoom info, or out of Apollo, or out of, you know, whatever your favorite tool that has, you know, your list of information, they’re going to have stuff like industry. They’re going to have stuff like, you know, annual revenue. But what I’m finding when businesses want to go and target their best customers or best prospects. It’s not something that’s in that database. It’s not in any database. And I got a couple examples of that, but you tell me kind of how you want to think about that? No,
Kyle James 06:10
no, I think this is great. Like, I think I’m picking up what you’re putting down, but like, we’re talking very philosophical, right? Like, give some concrete examples that I think really help connect stuff. Like, we’re using kind of like CRM data, right? So, like, how are you identifying the person on the call and their background, and are they right for contact? And how do you thread all that together and pull out actual insights for let’s just stick with sales reps, right?
John Marcus III 06:34
Yeah. So today, there’s some good work going on, and I think Gong is maybe one of the companies that’s most at the helm today, and thinking about how to leverage that the others in the space are a little bit behind. But as opposed to trying to think about rep efficiency, let’s talk about prospect identification. Okay, one of the biggest challenges is not necessarily your team being consistent and like doing a demo and qualifying correctly, like you can do that right, you do sales training for that. But who they talk to and who they go after is actually the biggest challenge here. If you’re going to be generating your own pipeline, whether it’s ABM, whether you’re going to be doing outbound yourself and trying to pull you know, prospects out of a hat, you have to kind of figure out what makes your best customers work for your business. So I’ll give an example of one exercise that I did. I have a client that is really good at selling into the higher ed space. They crush it, except when they don’t, and they don’t know why. So we absolutely dominate in this particular vertical here. And sometimes we win and sometimes we don’t, but man, when we win we win, and when we lose we lose, we lose. So I took all of their existing customers in higher ed, and I said, you know, what do we know about these institutions? We’ve got the the student body size that all over the place. No patterns. There. Are they in a major metropolitan area? Are they geographically dispersed? No patterns. Does it have to do with what letter the alphabet like, what’s their mascot? Nothing. It wasn’t until I started feeding these groups into llms and letting the LLM leverage its existing knowledge inside of its training to surface out some of these details help me understand these non obvious links and some of the stuff that started coming up, vocational programs, trades, non traditional learners, two year programs, commuter colleges. And went back, and I looked at all of the wins and losses that they had, sure enough, universities and institutions that have that demographic needed their particular product, a particular product just absolutely crushes it in that space. So that’s not in a database. Can’t go in the database and be like, is this, give me all of the schools that have a, you know, 30% or higher commuter population, or exclude universities that have, you know, four traditional four year programs that focus on liberal arts or something like, you can’t get that. Just
Kyle James 09:01
the thought of trying to gather, to compare those kind of things, is overwhelming to people, right? Like, it is large companies that’s, that’s weeks on end, just to, like, try to compile that stuff, right?
John Marcus III 09:12
And not only that, you can’t even know, if you go into a database and say, give me everybody, all the higher ed institutions that you know of does zoom info have all of that you don’t know? Instead, go grab like an actual list, like to me, every university, there’s got to be something out there, and that’s going to be much, much bigger. And now I’m not relying on any of these data brokerages to decide for me who I should be reaching out to. Instead, I’m using my technology, and I’m using what I know about my own customers to tell this data what how it should arrange itself. Like this is the way that I think about it. Like back at Bedrock Data, we had this idea of, you know, data topology, and I’m seeing that again here. Now I’m seeing that how you arrange the information actually changes the kinds of. And the inferences that you get when you use llms to start to discover this stuff, and what you get, yeah, and what you get now is really interesting, because now I can say I have a bespoke data set that tells me if a university is or is not in these seven dimensions that don’t exist in the database anywhere. And I have that. And guess what, nobody else does. That’s your bespoke data.
Kyle James 10:23
Yeah, that’s like, traditionally. And you know my background, I used to sell high tech, is you might talk to 100 you would hope a rep would talk to 100 plus people, and you’re hoping they would connect some neurons here to, like, see like, Oh, I’m seeing this over and over again, but sounds like what you’re saying is, I’m just trying to repeat it back to like it helps me understand it and the audience too. Like you could go across reps into hundreds 1000s of deals opportunities, and say, Hey, what are the common themes that no one has even thought about looking at and just poke holes at a whole bunch of things and find connections that have never been made or never even thought before. Like, yeah. And I imagine you can do that in hours, not years, right? Like that. That’s, that’s like, you know, forget 10x that’s 100x
John Marcus III 11:19
Yeah. And, and this is where the new agentic stuff comes in, because in in a normal how would you do this if you just had a bunch of smart people in a room? Well, you would essentially do exactly that. You’d ask, like, Hey, we’re going to whiteboard and we’re going to come up with, like, all the non obvious connections. Or, like, wouldn’t
Kyle James 11:37
that be hypothesis, and then spend a week researching every single one of them. Yeah,
John Marcus III 11:41
right. And then you go and do that research separately, and then you have somebody kind of challenge it and have an adversarial view on that. Well, with agentic stuff, you don’t need to, because the agents go and do that, you have an adversarial agent that just goes and challenges and pokes holes and stuff, and another one that says, hey, this relationship that you’ve inferred is a little too vanilla, like, if I can find this in a database, it’s not a good idea. Yeah, right. And now we’re doing is we’re starting to look at things that are much, much outside of the normal realm of these relationships. The reason that, and you could say, Hey, I’ve got an infinite amount of sales reps. I got infinite time. I’m just going to work everybody. Why? And I see businesses get really scared of ramping down the volume because they think, Oh, well, you know, more is always better. It’s not because you’re reaching into people who the timing’s wrong. You’re burning your one shot that you’ve got. And it requires so much more effort to get onto somebody’s calendar, to get into their consideration set depending on where they’re at, and they’re buying life cycle. By the time that information hits six cents, or, you know, hits, uh, Apollo’s and 10 signals it’s over, right? They’ve already gone down that path. They’re already researching this stuff by the time it hits the big stream, the the ships already sailed, unless you’re in kind of the longest term of the longest term, types of sales that. So
Kyle James 13:01
that’s interesting, right? Because you hear a lot of entrepreneur, startup people, like, try a lot of different things and find out what succeeds and what failed, and kind of what you’re saying, and I can, I mean, you’ve done much more sales than I have, right? But, like, it was always a blessing when you get on a call with someone and you can instantly disqualify in five minutes, right? Because you know you’re not wasting any more time with them, yep. And you’re like, like, Alright, how do I take that approach and put it on steroids? Like, that’s what you’re saying. Yeah.
John Marcus III 13:26
Before you even get them on the phone, though, this is the thing, like, look at so here are the things that I’ll feed into some of these inference models. I’ll feed their homepage, the copy on their homepage. I’m looking at recent social posts. I’m looking at recent social posts from leadership. I’m going and grabbing all of this other information here to pull in and to essentially support or refute that that business is in that characteristic dynamic that we’ve, you know, built in our bespoke data. One of the things they always saying, you know, in the movies like, run the simulations. What are the simulations say? This is exactly what we’re doing. We’re running simulations on, like a sales cycle here, and you can go through and step by step these other agents, again, they’re trained on a very specific set of tasks, right? They’re, they’re meant to do a little bit of thing, right? Again, the jump. They can’t create the entire business, but I can tell you how to pull together, you know, maybe an AdWords campaign, right? And then there’s going to be another one that has access to tools that can actually fire up and create the campaign in AdWords, right? Yeah, you have enough of these little agents, and you combine them together, and there’s a whole bunch of different topologies for that, what you end up getting is a really interesting and powerful engine to run these simulations before you even go out to the market. And now, instead of going and saying, Well, we have to focus in on the 3000 companies that we think are going to be great fits, because zoom info, tag them that way. No, give me 30,000 give me everybody that’s in this space, and just throw the machinery at it. It’s getting cheaper and cheaper every day. Now, some of the really, I. Um, deep agentic stuff that goes, you know, very much, down a path that that’s going to get pricey, right? You’re going to spend a couple $1,000 on that, but for a couple 100 bucks, you can go and run a list of 100,000 companies through it, categorize them and see if they meet your bespoke data qualification. They literally have a prompt for fractional ventures, for specifically the revenue operations, which I focus on in the partnership, that will take the company, extract all the information that it can find from it, and then pull that out into a way that essentially services what that business is. Potential challenges are. It’ll go and say, Hey, their website’s pulled together, and they have these particular tech stacks, and it’s working well, and like all this stuff is in line, great. I don’t really need to discuss anything related to kind of the fundamentals of, you know, marketing, for example, but they’ve got a ton of people that they’re currently hiring, and they’re all AES and SDRs. So let’s go and explore what their plans are for growth, because they’re obviously in growth phase, and that’s potentially a good target for us. So it boils down to just a prompt or two that encapsulate all of these nuances that would take a rep a year or two years to learn. They’d have to look through hundreds and hundreds of websites and make all of these mistakes over and over and over. And you can run that even before you decide to market against them. And that’s where stuff like ABM is coming back pretty strong companies marketing now you can target 1000 businesses. You don’t have to target the entire industry and say, here are the 1000 that like meet this profile. It’s that university with the high commuter population, the non traditional learners. Go grab 1000 of them. I can run a reasonable campaign without infinite dollars on that.
Kyle James 16:35
So let’s take a step back like, because everything we’re talking about is like, this is all the work that you can do before you even have like, this is reinventing the sales qualified lead, you know, term, yeah, but what sort of data, or what sort of reps, or, you know, how much work to direct do you need from, you know, opportunities to like, build this like, or Does this even happen in the early stage startup? Before you know, reps have gone and started talking to prospects and, like, how much you need hundreds of opportunities to kind of analyze and in sales one or even thinking about doing this before you talk to anybody, because that seems like that’s a whole other, like, mind blowing thing, right?
John Marcus III 17:18
Yeah, yeah. Let’s go and let’s put a stamp on it, AI QL, right. Boom. We tagged it. Ai QL, if nobody said it before, we just said it. Now. So what’s an AI qualified lead? And how soon can you get to that? So what I’m finding in my research right now is that the generation of these prompts to discover those non obvious links. That’s hard, but I’m currently struggling with that, and I have to put a lot of my brain power and partnerships brain power, and then solicit that around to get that shored up. Once it’s good, though, it tends to hold pretty well, and you can use feedback from the sales team to essentially go and tune and adjust it so that’s always that’s human in the loop
Kyle James 18:04
you get the more it refines it. It might change over time as you the prompts
John Marcus III 18:08
going to change targets or whatever. Yeah, exactly. Because then I can come up with a list of 100 a sample, 100 businesses. And if an SDR, like a senior SDR, is like, really good at focusing on a particular type of business. They just got an eye for a good fit, great. I want them to score it great, marginal or poor, right? And then if most of that’s coming back great, some of that’s coming back poor, we’re going to go and adjust the problem to control for that. Now how much do you need? I’ve done this with as little as 10 data points like 10 companies, to be able to generate pretty good prompts that will get you through your first, say, 1000. So you can get about two orders of magnitude, again, real rough numbers here, about two orders of magnitude of scope, right and inference for a given amount of data. So if you and so after that 1000, for example, you’re going to need to go back and refine it. And you’re going to, you
Kyle James 19:03
know, a lot of industries, and B to B space, like, 1000 probably is most of your target audience. Some are much tangent, much longer, larger than that. But a lot are, you know, yeah. And
John Marcus III 19:14
you could, you know, run it over a big data set and then come back and say, like, Okay, we’re going to go try for the next six months. We’re going to work these X amount of 1000s that came back as great fits from this classifier. But I think you’d be missing something, because you don’t need that many conversations to go back feed this. Because, again, you got the call transcript right. You have all of the notes in the company. You’ve got their home page now you’ve got, you know, whatever your qualification framework, you filled that out, and that context right to the beginning of the call. That’s the important thing. So we take that context, we feed it back up to the top. I got this written down on a piece of paper. If I, if I made it bigger, I just put it right on the screen. Maybe share it afterwards. Yeah. Yeah, but the idea is to take that final prompt, see what’s happening, and then evolve it over time. And as that evolves, you can stamp that right, and we can say, okay, you know, v1 here did a really good job. It lets some tire kickers through, but it doesn’t exclude this very, you know, unique but important buyer. On the other side of things, you come into like v3 v4 of your prompting, and now it’s actually really refined, and it may have attributes that you you desire or you don’t desire, but as you get more of that feedback from the field, from their calls, that’s when the stuff really starts to dial in well, and you can get some of these prompts that are pages and pages long. I’ve got one that’s almost 10 pages long right now, and that’s all derived from follow on conversations to add nuance and context so that that whole prompt right the whole prompt space is loaded up with all of the tokens that you need for that particular model To make an inference about a new piece of information, which is the whole idea of AI and machine learning anyways.
Kyle James 21:07
Wow. So it seems like there’s no reason all of this would not exactly apply to churn analysis would not apply to account management upsell, like, instead of just the sales front end, like, how are you using it to, man, you know, account managers to manage it. You know, people that are trying to save, like, trying to find new opportunities up, like, right? Like, it just seems like you just reapply the same stuff.
John Marcus III 21:35
Yeah, there’s, there’s a bit of a tweak. There, 10 customers, right, right? Yeah, you need a bit of data first, and you need the event that you’re testing for. So you need some turns. The thing that you can start to do now, though, is you can look at the entire customer base, and instead of trying to identify the holistics of your potential market, instead, what we’re trying to do is we’re trying to identify the aberrations of the sick customers, right? We’re looking for the people who are sneezing and coughing on the train, yeah, slightly different technique to get there, but can still be done. And it’s actually similar in that you’re looking at the characteristics of the fit, because a great fit business, aside from, like, going out of business, they’re not going to churn. Like, if they’re getting value from your product that, by definition, the fit, and they’re paying money and they’re seeing the value from your solution, then great, unless there’s something tragic that happens on their side, like, they should continue to be a customer. That’s the whole idea of software as a service, yeah. Now the fit that is going to be the best customer every single time isn’t always the one that sales goes and sells. And as you were on the customer side of the line, and I was on the sales side of the line back if I was five, tell you not all mine were great, right? They all had potential, but sometimes you had to work toward them. And this is where I think it gets interesting, trying to identify which of your customers and your existing customer base are at risk. In a very different way. This isn’t about product usage. This isn’t about like, how many people are on the system, or even about how much they’re spending. Because everybody goes and looks like, oh, we have to look at the most MRR, and that’s how we’re going to sort everything. Like, yeah, that’s a good first order, like, don’t lose your biggest customers. But after that, how do you know who are the most at risk? Well, that risk is happening when the business and that client are separate, like they’re going different ways. And that’s just the same as going looking at somebody’s website, following the social feeds, like, going and pull that information together and then go and say, Hey, these were the four that churned out of the 10 or the 20, or whatever your your total population is. And then then you start to use the prompts to go and start to pull out the non obvious stuff. Because if it was just like, Oh, your customers that use your product the least are the ones that are going to churn Well, there wouldn’t be any business in CS. You just go, look at those people, fire those people, and then upgrade the others. This is why there’s still an art and a science to account management, because we don’t know, and the more information that we can provide the AMS and the CSMs, the better they’re going to be able to inject value and to realign, like the kind of chiropractic adjustment between the business and the client. And I think that getting to that absolutely, you can use a lot of the same, overall, tools, techniques, a little different, but you’re trying to find out, you know, who, who’s coughing on the train,
Kyle James 24:32
yeah, so, so let’s, let’s try to, I’m curious, like, to kind of wrap this back. Like, how to end users to like, do you integrate and send this? Do you set up fields and data properties inside of CRM, like Salesforce or HubSpot, or are you, you know, build, kind of your own little UI for this, for for teams, like, how is that stuff leveraged and, and, how does, how does you know, sales, sales, Sammy, just come up with a persona, use it and not feel over. Well, or do you do you present it to them in kind of, like a report? Or what does that come back? Like? Yeah,
John Marcus III 25:06
everything’s got to get back in context with the reps. If the reps are going to use it, it’s got to be in their context, and usually in their CRM like, that’s where they’re living. If, wherever you’re managing opportunities, or you’re managing your leads, if you’re thinking about account based outreach strategies. That’s the system where it’s got to go. Now, tools that I use, whole bunch of llms, bunch of custom built stuff, big stack, Python and data science, right? But what that generates are insights. And those insights go into the CRM and it generates some kind of classification, and that could be like, you know, at risk or doomed, or, you know, great. It could be great fit, marginal fit, not a fit. And that’s those fields set up in their system exactly, and that’s acting as guidance. So when you pull all of these non, non standard insights as bespoke data in, I like to think of any CRM, you know, all the accounts that I’ve got in there, all the leads that I’ve got in there as a deck of cards, and as a wrap, I want to be able to grab the card off the top, and I want that to be the best card that I could play, right? Give me the best prospect. And then I’ll keep going and going and going, and I’ll keep working through there. And if I run out of cards in the inbound deck, I’m going to go grab the outbound deck. And who do I want to make the first call to the most qualified potential outbound prospect I can find? So you, how do I know sort of
Kyle James 26:29
AI, QL scoring happening too then, yeah,
John Marcus III 26:33
and the scoring, a lot of times, doesn’t need to get much more advanced than great fit, and they’ve done something that our customers do right before they go and buy. So it doesn’t need to be super complex. The scoring mechanism doesn’t need to be super crazy. And in the beginning, the most important part is you pick up the phone and you call them, yeah, especially when it comes to the outbound stuff, because we’re trying to make sure that we’re identifying the non obvious things that lead us to believe that this business is about to have that moment, they’re about to go and make some decisions on whatever it is that we sell, back it up. So we talk about you’re either redesigning your website. You just redesign your website, or you’re about to redesign your website. Those are the three phases of marketing, right? And it was just one big loop, and that’s the case too, right? The loop of consideration, and I’ll have to send over some, you know, Ms pink graphics so we can share with the viewers as well. But there’s this loop. And, you know, by the time it hits six cents, by the time it hits Apollo or whatever, it’s too late, right? They’ve already started down that path. They’ve already had internal conversations. If you can find the businesses that exhibit the symptoms of wanting or needing your service, you can make that outreach, and your outreach is much less desperate and it’s much more educational, and you become the trusted advisor. You’re setting the framework for how they evaluate your software. It’s like whoever wrote, If you ever get an RFP and you didn’t write it, you know, you’re being shopped, right? Is one of the sayings that we ask in sales now we can go and start to start the conversation at a company and say, Hey, you’re looking to generate more leads. Hey, you’re looking to, you know, reduce costs. I see all these things on your website. See what you guys are talking about. Businesses that do this, a lot of times, are trying to solve this problem. Are you trying to solve that problem today? And they’re like, Yeah, we actually having some conversations around the water cooler about it. Great. Let me come in and help you with an assessment. Yeah, we’ll take 30 minutes, we’ll go through your business, we’ll plug in some stuff, we’ll give you some benchmark data. And if that all lines up, then maybe you’re fit for what we sell. If not, that’s fine too. You’ll come out smarter. And that is a fundamentally different conversation than 15 emails that I got off of some email sequence and like, 20 cold calls that I never picked up. It’s one shot, one connection, one sale. That’s uh and that’s going to require that we slow down and focus on the best prospects that are at that point in time, before they’ve ever shown a signal externally, we have people to determine that via that non obvious data, that bespoke data that we’ve got, and that’s going to give us the advantage. I think that’s the future of sales. That’s tech sales for the next five to 10 years. So,
Kyle James 29:13
so let’s go there. Like, right? Like, if I’m a rep, this is fundamentally changing the way I approach everything. I would imagine spending some time in sales. Like, this sounds too good to be true. Like, great. You’re telling me I’m going to have exactly what I need to close. But like, your work, what is their reaction to this? And you providing all this and, like, what are you seeing the results, you know, in hand to hand combat. How’s that? Yeah, yeah.
John Marcus III 29:37
So first there’s going to be, there’s always two chapters in anything that’s new, but the ones that are
Kyle James 29:43
using CRMs input data, right? That’s exactly. It starts with the systems
John Marcus III 29:47
themselves. They’re like, Oh, I have to use this thing. Can I just, can you just, like, set up calls for me? And I kind of changed the script a little bit. It’s like, you’re more than welcome to, you know, have a BDR do $100 for you every single day, and you can do. All those basic sequences. But if you choose what you want to leverage this information, we’re going to change the relationship of, you know, the AE and in prospecting, this is going to require that more AES go back to being full stack reps, right? Unless you’re on a plane flying to a site to do a demo with your sales engineer and your VP in tow and all that stuff. If you’re in any kind of velocity sale, or you’re selling SaaS, for example, you’re gonna need to start doing some more prospecting. And the reason is, and this is, you know, back in my day, we had to do all of our own prospecting. We did because we didn’t have meeting our expected spot. But the reason that that was so important is I knew the business cold, like that prospect. I knew everything about them. I knew everything about our product and why those two things go together. And we really lost our way when we saw the kind of, you know, delusion of efficiency by adding in BDRs and sequences. And I am, I’m just as much of the problem because I’ve built tools to do that. I ran the box even up, but I was the outbound guy, the inbound company, who has right but the thing that we did so different is we really, really knew our prospects. And when I say no, the prospect we knew about their business, we understand the space like we would subscribe to newsletters, and we would become experts in that, so that it didn’t matter how we got them on the phone. We had something intelligent to say that was going to provide value for them, and that starts this whole reciprocity loop where I’m going to help you out with some information. You’re thinking about generating leads online. I can help you take better act data. For example, Hey, you have these systems. I see that you’re having some struggles. You’re growing your sales team. You just raised some money. How are you going to keep all of this stuff in sync? Hey, I’ve got some tips for you. I got some strategies around that, best practices. I’ve got guides. And now we’re moving away from, I’m trying to push a piece of software on you, is I’m trying to ask you questions, get information out so that I can tell you if I can even help you, yeah. And along the way, I’m going to give you the value that you need, so that whether or not you go with us, you’re going to find something of value on this conversation. And that’s why you’re going to take the call. There’s a reason to have that qualification conversation for both sides well,
Kyle James 32:07
and I’d imagine too, right? If this is, this is how it’s playing out, I don’t see any reason that your hypothesis theory theories are wrong. Is it’s fewer contacts for the customer, right? It’s, it’s they don’t have to worry about dealing with as many people, like, maybe it does get down to a point where you don’t deal with the BDR, you don’t have to deal with account manager, you don’t have to deal with somebody onboarding. It’s like that one sales rep who is your point person throughout, because they’ve got all the resources kind of supporting them through different elements. Yeah. And that seems like a good experience for everybody. It is. You’re getting back to, like, relationship building for the long term. Yeah, and
John Marcus III 32:44
I think that there’s a challenge, because there’s, you know, 15 years out from, you know, my first, first inside sales role. And, you know, back then, that was the reality you had to because we didn’t have all the specialization. There’s going to be a real, big reality shift for people who, you know, maybe have been in it for five years, who are used to, you know, having BDR set up appointments for them. There’s gonna be a shift there. And I think that there’s enough slack in the system that it’s gonna take five to 10 years for this to really shake out. But you see how many people are just absolutely annoyed online. You look on LinkedIn, and everybody’s like, Oh, I had to talk to a BDR, I just want to see the software. And there’s, I think it’s like, marketing ops.com, they have a whole library of just no BS demos. It’s like, literally, this is the demo. You don’t have to reach out, you know, you don’t have to fill out a form. You don’t have to talk to BDR, like, you know what you need. Like, here’s the demo. Now in sales, we get scared of that, because now we have people going and trying to make inferences about their you know, need for a piece of software that they don’t they don’t know, and we want to talk to them. So I think what’s going to have to change is we’re going to have to cut out a bunch of the crap, bunch of the steps before having an honest conversation about the business. And I don’t know from a skill perspective, what percentage of the current inside sales practitioners are out there that understand that value driven selling,
Kyle James 34:10
let me, let me ask you this, because where you’ve got my head spinning right now, right? Is what this plays out, is people become where we’re I call it mercenaries, right? Like to the specialization role. You’re a specialized mercenary at BDR, you can go plug that in any company or account manager, whatever. But it sounds like, if this is correct, the way it’s playing out in my head is like people start specializing in businesses, relationships and products again, and that, and you, and because of that, you probably stay at companies longer, right? Like, would you say that’s not correct? Which, I think people are kind of tired of jumping around all the time anyway, right? Yeah,
John Marcus III 34:55
yeah. So, so here’s a follow on to that. Here’s, here’s essentially the, you know, not the lot. Conclusion, but the next step is you hit your number more often, and you make more money. And people who have ownership over their work and have the ability to kind of set their own destiny and to achieve that, to see how it’s going to happen are much happier if you look at kind of modern, modern knowledge workers, people are jumping around, not because they’re like, oh, I can make another $5 over there. No, it’s like, This place sucks. I don’t like the people I work with, or I’m not in a field that I enjoy, or I don’t like the product, or it’s just like there’s something bad there. And as humans, there’s only so much tolerance we have. I think in kind of current generation of the workforce, there’s only so much tolerance for for kind of bad working conditions. And it doesn’t mean like, Oh, I get to work from home and like, I get to expense my lunch every day. It’s like, I just don’t like the people. I don’t like talking to these prospects, like, I’m not excited about this business. Yeah, and those that do get excited, and we saw a lot of this at UPS, but and anybody that I hire, I really want to know, like, why are you excited about this industry, this particular company, is because they have some reason to be excited. Like, I love talking to early stage businesses. I love working with early stage businesses. I want to be hands on. Want to be hands on keyboard. That’s, you know what I do. I make sure that any of engagements and I’m getting my hands on keyboard. On keyboard and building something while we’re at it, yeah, and that creates meaning and purpose and drive. And there’s a ton of stuff written about this and how that’s meaningful for people to stay in roles, so I can see people doing better, making more income for that particular role, needing fewer people. Yeah, right, which is the big elephant in the room, yeah, yeah, people to get it done.
Kyle James 36:48
Because software is, you know, you level out the playing field when AI can handle more and more of it. So it is really the relationships, and it’s the industry specialization. Yeah, I see exactly what you’re talking about. Yeah, that’s, that’s really hopeful, honestly, compared to a lot of stuff you hear about AI in the future.
John Marcus III 37:04
Well, we just shove so many people and so much people process and technology. The stuff that I, you know, my personal website, is people process technology, but you shoved it all in there, right? And it’s like, oh, we need, you know, additional nurturing sequences to squeeze like that 1% more. There’s a whole shift in how people want to buy, and we’re going back to what it used to be, which is, again, relationships, expertise in the product, focusing on the qualification, focusing on the value that you can drive for a business. And nobody wants to go back and like, look at how many dollars they did that day and how many connects they did that day. So but when I say that to VPs of sales, man, they clamp up. Really, what do you mean? We’re going to stop doing all this outbound? I was like, Fine, give me five reps, right? And they’re going to go and work these specialized things here. They’re going to understand these businesses. They’re going to do the outreach. And if they don’t, we’ll go find five other reps that will, I think. What will happen in terms of labor force, though, this is wild speculation. Let’s go those that are above wild speculation. Yeah, those that yeah, reckless speculation, those that have essentially gone down and thought that they have specialized, like, their specialized BDR, like, you know, they have this very specific part in the process that they do that’s going to get thrown up on end, and it’s going to cause a lot of people to have an honest conversation with their relationship with tech sales, and they’re going to need to say, Okay, well, hold on, I said I was going to do dials, and I did the thing that I said that they said I had to do, and then I did it. And like, Okay, well, you know, Where’s where’s my success, where’s my instant promotion? Well, that’s gone now because it’s much more, you know, autodidact like self learning is going to be the key here to being better than the next rep or the competitor, because he did the research. Those that don’t get on that bandwagon and don’t start to obsess about their prospects and the things that make them different or focus on an industry or a product, they’re going to get cast off. And then I think what’s going to happen is you’re going to have a lot of very skilled people who understand these processes that hopefully get the joke at the end of the day, it’s like, oh, wait, it’s actually about, you know, understanding the market and the customer, the people stupid. Yeah, it’s the people stupid. They’re going to land at companies that wouldn’t have had access to, say, you know, high skilled athlete, AE tech, you know, came through an awesome BDR program. They’re going to get access to those kinds of people. You’re going to find, you know, manufacturing businesses where you’re going to get somebody who is at, you know, a HubSpot or an IBM, or came through the PTC Training Program, all of a sudden be able to focus in on something that they care about. Hey, maybe they’re really into, you know, like animal husbandry. What kind of skills could you bring about in, like, negotiating, buying and selling cows, right? So I think we’re going to see a lot of the condensed, concentrated skills for. That are trained in these, you know, tech institutions shoot down into a lot of other industries. They’re going to see a huge dispersion of what are traditionally tech sales tactics in other fields. So you’re going to start seeing more marketing automation in things that are, you know, kind of, you know, paper and pen, kinds of businesses, and I think that that spread is going to be a good thing, because that’s going to raise the floor on how business gets done. It’s
Kyle James 40:27
fascinating. Let’s time stamp that here. Like, let’s, let’s come back and look at it in two to three to four to five years. And like, were we crazy, or were we absolutely spot
John Marcus III 40:37
on the AI qualified lead? Let me see if I can see I got a website. Goodbye. Yeah, thanks.
Kyle James 40:42
Thanks, John. That was absolutely insightful. And I’m sure, like, if the audience is like me, we all need a chance to go back and, like, probably listen to it again, right? Just to, like, think about, wow. Like, there’s a lot there, but, but tell, tell everybody. Like, how could they contact you? You know, what are, what are your lat long on online and anything upcoming that you want to share or promote,
John Marcus III 41:04
yep, really easy to get a hold of me. Just John at fractional dot ventures that’s with an H. Go check out fractional dot ventures that’s a website, and pick ax. Dot technology is the other tool that we’re building up. If you’ve got challenges in your business here in that growth phase, we’re happy to have conversation, and even if it’s not something that’s immediate right now, there’s probably an assessment in your future, and we’re happy to kind of get you pointed in the right direction. Aside from that, for me, this winter is going to be a lot of AI research. Going to be publishing some stuff, not into journals, but some longer form content. So I’ll make sure that everybody gets notified on that. But if you go to fraction go to fractional dot ventures, you’ll you’ll get all the information you
Kyle James 41:45
need. Awesome. Love it. So hopefully everybody enjoyed this episode. If you did, please subscribe, like, share, give it a five star review. If you didn’t like it, then don’t give it a five star review and just reach out to me. You just hit me up. Research at 360 insights, open to suggestions, topics, whatever, and thanks everybody for tuning in. We’ll be back next week with another go to market innovator. And until then, keep growing. Everybody. Have a good one. You.