B2B Sales Strategy and AI in Sales: Deal Intelligence That Drives Revenue

Jeremy Donovan: It turned out that people who played individual sports, there was a correlation with success, but uh, people who played team sports, there was not. So if you did gymnastics or swimming or golf, individual sport, then there was a correlation. But if you played soccer or softball or lacrosse or basketball or football or whatever, then the correlation was not there.

Narrator: You're listening to the B2B revenue executive experience, a podcast dedicated, dedicated to, uh, helping executives train their sales and marketing teams to optimize growth. Whether you're looking for techniques and strategies or tools and resources, you've come to the right place. Let's accelerate your growth in three, two.

Jeremy Donovan: Uh, one.

Corey: Hello everyone and welcome to the B2B Revenue Executive Experience. I'm your host, Corey, and today we're talking to Jeremy Donovan. He's a well known B2B Sales and Revenue Strategy Leader, Authority TEDx organizer and speaker with more than 25 years experience across engineering, finance and marketing. Currently he serves as EVP of sales and customer success at Inside Partners, venture capital and private equity firm. He is the author of several books including the international bestseller how to Deliver a TED Talk and Predictable Prospecting, which lays out repeatable frameworks for outbound pipeline generation. Jeremy, welcome to the show.

Jeremy Donovan: Yeah, thanks so much. I'm excited to chat and geek out about sales and customer success. At least in B2B. I can't really talk about B2C, but at least in B2B.

Corey: Fair enough. No, me too. We're in the same boat. I'm excited to get into it. Uh, but first, uh, I'm kind of curious about your career journey into sales because on paper it kind of looks like you're an engineer and a data scientist. Right. Uh, and then made this leap into sales early and obviously found a lot of success there. So drew you to that path to begin with.

Jeremy Donovan: Yeah, I mean it uh, was definitely a circuitous path from semiconductor engineer to what I do today, which is an extension of revenue strategy and operations. Right. It's the venture side of I was doing on the operating side. I think the key to decoding that is. I absolutely do still consider myself an engineer and statistician and data scientists and computer scientists. Like I still definitely consider myself all of those things. And I was drawn throughout my career to a whole bunch of different areas where I could leverage that fact based analytical approach, uh, into an area that, you know, would benefit from that. And marketing and sales were natural places. The one I did, never did do, although I sort of dabble, I guess in it is hr because I think that's another Area where analytics is really useful. But the intersection for me on the HR and analytics side is I, uh. You often have, you hear a lot of opinions about should, should you hire reps who have, who, who were athletes, should you hire reps who worked in this area? And that area went to this school, went to that school. So I actually have run a lot of data on those types of things for hiring SDRs and AES and so forth. So to be able to make again a fact based decision. And uh, so it's, it's nowadays, I guess the expression is to apply those Moneyball techniques or AI techniques in order to make decisions, whether that's in HR or other areas. So that was the, that was the draw was, you know, um, it just felt intellectually very, very interesting from, from an engineering point of view. And there, there weren't, if you look back whatever, 10, 15 years when I started doing this, there really weren't a lot of engineers looking at uh, sales. Right. It was just people grew up through sales organizations and oftentimes, you know, 10, 15 years ago, they didn't necessarily come from engineering backgrounds. Right. They came from more liberal arts backgrounds and that sort of thing.

Corey: Yeah, no, I think it makes a lot of sense in a way. Right. Especially, I mean you're still dealing with systems in a way. Right? Building and analyzing constantly and tweaking and improving. So it's sort of a difference of, of degree. Not kind in a way, but uh, for sure.

Jeremy Donovan: It absolutely is. Yeah, it absolutely is.

Corey: I'm, I'm curious now. I feel like I have to ask though. What, what, what is the ideal SDR or salesperson based on?

Jeremy Donovan: Yeah, the ideal. Uh, I could answer both of them. The ideal. So we looked at something like 1200. It was somewhere between 1200 and 2000. It's been a minute SDR profiles to figure out what made them successful. And you have to define if you. We looked at LinkedIn profiles. So we don't know how many opportunities per month they were creating. Right. We don't have actual performance data, but what we do have is whether or not they were promoted into AE at the companies they worked for. So that was our success criteria. Were they promoted to AE or did they last, I think two years at the company? Because maybe they got promoted into a different role, which is not uncommon. So two plus years or promoted into ae and what we found there wasn't like a whole. Yes. If they actually had a stem degree like that actually was a signal. But it's, there's so few of Them with STEM degrees that you, you can't practically, you know, exploit that. But if you run across one, go hire them. The, the ideal profile was someone with two years in um, in professional services before they, they came on board. And very specifically because there's a lot of them people who worked for two years as executive or sorry, not executive, but as recruiters. So if they worked for an agency as a recruiter for two years, that to me was like the golden profile. There's a whole bunch of other things. Uh, a side note on, on that study because everyone always asks about this is like, does sports matter? And it was a very fascinating result that when we looked at whether sports mattered overall, whether they played sports in college, um, it did not matter overall, which was a little unexpected. So we double clicked on it and it turned out that people who played individual sports there was a correlation with success. But uh, people who played team sports there was not. So if you did gymnastics or swimming or golf, you know, or uh, whatever, you know, individual sport, then there was a correlation. But if you played, you know, soccer or softball or lacrosse or basketball or football or whatever, then I think the correlation was not there. But anyway, I mean uh, that was, that. That was kind of noisy. The big thing is two years in, in recruiting. So that was, that was the case for, for AES. Sorry, uh, for SDRs. For AES. There wasn't as strong of a signal. The biggest thing was really. And this is, I mean these things are going to be obvious in hindsight, but that they lasted for two years at their immediate prior employer and that they didn't have an employment gap. So like that you could snap them up out of their, not past. Everyone has employment gaps here and there. But that you were able to snap them out of the company they were at. And they were at that company for two plus years. I mean that's kind of obvious. But um, yeah, that was a big one for them. And then a lot of stuff that would have been in the LinkedIn profile about whatever academic profile and this and that once you're in the workforce for a few years and there's well established. I read a lot of academic research on this until I kind of geek out on it. Once you read the, you know, the academic research on this and once people are in the, in the role for a while or uh, out in the workforce for a while, stuff that they did when they were kids, you know, it just doesn't. Is not relevant anymore. It does. There's no correlation. The biggest correlation is going to the academic literature, the biggest correlation between like job performance and any other factors, there basically are three of them. Two of them easier to assess. One. Well, you can judge, you can be the judge of whether they're easy to assess. The number one is iq and humans actually do a pretty good job of assessing the IQ of other humans. So that's one. Although there are cognitive tests that you can do like the Wonderlic or the Criteria Corp test. So IQ is one, two, uh, is conscientiousness and there are, you know, written tests that you can do to assess conscientiousness. I, I, I, I don't know, people would argue with me, but I feel like they're less reliable than the IQ tests are. And the, but I, I mean they're, they're decent. I think you assess conscientiousness during the course of the process, right? Like did this person follow up with a thank you note in a timely fashion? Right. Whatever else, right. Whatever else it is that you just looking for signs of conscientiousness throughout the thing, uh, the interview process and also your back channel reference checks. Right. Like you can definitely assess conscientiousness and even intelligence right in the back channel reference checks. And then, um, the third thing is a test of job skill. And that's I think the harder one, right. To assess because you can't have them sell your product. Right. I guess you can role play with them, which is, which is probably the best that you can do. But otherwise you can't, you know, that one's a lot, a lot harder to test.

Corey: Wow, that's intriguing. There's so much in that. And uh, I don't want to spend too much time on it, but I want to zero in real quickly.

Jeremy Donovan: Why the recruiters?

Corey: What was the underlying skill set or mentality that really, that was a big marker of success for SDRs?

Jeremy Donovan: It's a hypoth, right? Like none of this is, we don't know why, but the major thing is that's a sales job. A B, it's a high call volume sales job, which is really, really, really important. Right. Because we know also more data, right. We know that something like 70% of all meetings scheduled from an outbound perspective are scheduled by a phone call. So that's kind of part two. And then, um, yeah, it's like a, it's a hustle, you know, it's a hustle job with. And I guess number three here is persuasion that you are, you are selling, right? I mean you're selling somebody on one of the biggest decisions that they could make in, you know, in, in a given couple of years of what job they're going to take. So yeah, there's a lot, a lot of elements of sales personship in, in that. So to me again I'm explaining it in hindsight now that I have the data but to me those things really make sense.

Corey: Yeah, no they really do. Once it's all kind of laid out for you, it's fascinating nonetheless. Okay, so let's get into your role a little bit, uh, leading sales and CS at Inside Partners. So it sounds like you're on the front lines kind of helping portfolio companies scale. Can you walk us through sort of the scope of that role and what the day to day looks like?

Jeremy Donovan: Yeah, I'll kind of zoom in on it. And I mean the good news is like we don't actually sell anything, right? We invest in companies but I'll still give you the sort of top down just to give folks an understanding.

Corey: So.

Jeremy Donovan: I'd never heard of Insight when I joined the prior startup that I was at but Insight is one of the bigger VCs on the planet. We're 90/billion of assets under management. We have over 550 um, portfolio companies. Nearly all of them are B2B SaaS. We're a software investor and most of them are minority investments as opposed to like the PE world where they're majority investments. So that scale gives us the ability to have um, ah, a significant number of operating advisors like myself. So I think we have something like 40 of us uh, across every business function, right. Sales and CS or marketing, product and engineering, finance, hr like you name it. And so our team of sales and CS advisors is about 10 or so of us. And so I'm narrowing, narrowing, narrowing down. Now you get to me right? And my team. So um, I focus personally on um, B2B SaaS, companies who are in the 10 to 100 million ARR. Because that's the scaling journey that I have had experience with. And then um, you know the day to day is a lot of advisory work. Just hey, they're trying to make a decision about all the classic things, right Is how should I structure the roles on my team? Should I have AES separate from AMS or should I have them combined? If I you know, hire for this new type of role, what's the compensation structure? You uh, know how do I go about territory design? On and on and on right. So it's just how do we advise them on these types of things? And then we're also involved in the diligence process. So if we're going to invest in a new company, we want to understand the strength of their go to market strategy, the strength of their team, the strength of their pipeline. So that's another big piece. There are other pieces too, but those are the big elements.

Corey: And since we were talking a little bit before the call, I mean you've worked with hundreds of companies and you're seeing a lot of use cases right now, um, when it comes to AI, AI in sales and cs, what's um, working and what's not.

Jeremy Donovan: Yeah, no surprise, right, that you have someone who works for a venture capital firm who's just talking and thinking about AI all day. I mean, I think that's probably true of everybody all the time. But um, I think this also requires some contextualization which is, right, there was a big correction, whatever that was a year or two ago or maybe a little bit more now, and I lose track of time. But um, and in that correction, right, we went from valuing companies heavily on growth to really being more mindful about the efficiency side. Right. Efficient growth. And the manifestation of efficient growth is rule of, uh, you often hear it as rule of 40, right? That you want your growth rate, your year over year, ARR. Growth rate plus your margin, your EBITDA margin to be 40% or higher. So if you're super high growth, then you know, you can be spending more money, you can be burning more cash and be over rule of 40. Uh, be, you know, rule of 40 plus. Um, but if you're growing slower, which is what happened to a lot of companies in the software business in the last couple years, then you got to tighten the purse strings in order to keep that up. And the reason that rule of measure matters so much is because that measure is the single highest correlation with market cap with enterprise value. Right? So value, if you, if you want to increase software company value, the lever to pull is, is rule of. And then the sub levers there are growth and uh, ARR. Growth and efficiency. I mean, I don't think that's rocket science, Right. I think it's pretty obvious if you think about it, right? Is grow and do it profitably or as profitably as uh, possible. So uh, transitioning this over to your question about AI, um, there are a lot of use cases that AI can use to accelerate both growth and efficiency. I do think the whole industry is a little bit at risk of not just venture. I'm just saying all the software industry and probably other industries is a Little bit at risk of having AI blinders on that they think AI is the solution to every single problem. If you were to. We had this conversation amongst my peers and I with some frequency about if you were to go in, back in on the operating side, either as a head of Rev Ops or as a CRO, like what's the one thing, what's the one play you would run that you've run in the past that you know is likely to work or that you've seen at portfolio companies that you know is likely to work? And uh, at least for me personally, the first play I would run is not an AI play. The first play I would run is one of two things. For the longest time, the play I would have run and let's just move this to number two position. It's just really, really intensive deal reviews because I see a lot of companies and I see a lot of very poor execution on deal reviews. So that was for the. I've been doing this for four and a half years, I would say for like two to three years. That was my answer. My answer shifted about a year and a half ago. Uh, and a year and a half ago I, I kind of observed that. I think that that's the obviously top of funnel matter matter so much. But I would give AES a weekly meetings target with an weekly external meeting target. And it sounds really in the weeds, but I just became a super duper um, fanboy of the ptc, the Parametric Technology Corporation like Mafia and Diaspora. And um, you know, they were really for folks who've heard of Medic or Medpic, right. They PTC invented that and, and they invented most of what we think about as modern enterprise selling. And you know, the, the folks like John McMahon and the, and his, his disciples, right. A lot of them adhere to this, this metric of eight meetings per AE per week, of which two need to be first demo disco meetings. And I've actually done them, uh, as you would suspect, I've done the math on that. And if you kind of do the math, you can do reverse math here and figure out how many meetings you need AES to do per week, given your win rate, given your ASP and so forth. So for like a typical enterprise sale, that is the right answer. It's eight meetings a week with. Of which two, two are uh, disco demos. I was just on the phone with one of our portfolio companies an hour ago and in their case, right. They're super duper transactional and it's all in, it's like inbound transactional one or two call closes those reps. If they only did eight a week, that would be a disaster. Right? Like they have to do scores and score. I mean they'd probably do eight a day. Right? And they're just closing, closing, closing, closing, closing. Um, but, but at least in enterprise selling, you know that metric matters. And it's for AES, it's that metric. For SDRs, it would be dials per day, not dials per week, but dials per day. Because we know that outbound, um, is all about the phone at this point. So anyway, that has become my uh, like what's the number one play that I would do? So that was a whole long digression from AI. Okay, So I would do those things. Yet I think if you're a CRO and you're not doing AI related optimization, you know, you may not be long for the job because you just can't credibly get up in front of a board meeting and say, oh, you know what? I think I'm not going to do AI. I mean there's not like super conclusive proof that it's actually good or bad. Right? Is, uh, we track a metric which is ARR for FTE right. I mean, uh, that's like a pretty golden metric. And ARR per FTE per full time equivalent per employee has been steadily increasing quarter over quarter after quarter after quarter for like years now. But there's no discontinuity at any given time that you might see, for example, when a new Foundation LLM model comes about. All right, so that was many digressions. And now I'm going to answer your question. So, uh, one of my colleagues and I, Aaliyah Kennedy and I have been speaking to our CROs over the course of the last two months. And in the last two months we've talked to uh, somewhere between 40 and 50 of the 550. And every week we're talking to more and more and we're trying to learn what AI use, cases they've adopted and which ones are working, which ones they're getting value out of. We've coalesced on four of them, but there's a fifth. And I'd also urge like, do. If someone's listening and wants to adopt something, just do one at a time and get that right. Like don't go after all four or five simultaneously. So the number one, the. And I'll talk to them, I'll talk to number one and then I'll pause. Um, number one, I think is best described as deal intelligence. And this is an extension of what I was talking about about deal reviews. So this is to under, you know, use AI to deeply understand what's going on with the deal. And that could be gaps in whatever methodology you're using. You know, maybe, or maybe gaps in multi threading. Uh, whatever it is, whatever the deal gaps happen to be, it can also be coaching the rep on the call that are associated with the deal. Right. So it's not like the, the general role play, it's the, the very, very specific call level feedback on the deal. So I'll pause there. So the first one's deal intelligence.

Corey: Yep. No, I think that makes a lot of sense because I was surprised and not surprised at the same time when you said deal reviews. Right. Was one of the first plays you'd run. Right. That resonates and I think that's still very crucial. But then my M. Immediate next question or the question that was floating around in the back of my head was okay, well what about AI for deal intelligence? So we've come full circle.

Jeremy Donovan: It seems kind of full circle. Uh, and there, uh, the other comment I'll make on each one of these is like buy versus build. If the companies already had some, I have this restriction that I can't talk about specific vendors, even whether we've invested in them or not. If they've already invested in one of the common deal intelligence platforms, you can figure out which ones are most common. Um, you can say, no, I can't. Uh, but if you've invested, you know, if you have one of those things, then they're leveraging what they have. Um, if they didn't, then they might be building their own. So one of the more interesting ones we ran across of a self build. And again there's no, I can't say that there's correlation yet with performance. But one that was fascinating uh, to me was one of our companies has a deal intelligence agent that they built. And so let's say I'm one of your reps and I'm working on Acme company. You can ask the agent, hey, go run a deal review, a preparatory deal review with Jeremy on Acme company and report back to me on what he says. And then uh, so, so now the agent goes off, it finds me, it runs me through a set of very intelligent medic style questions, it reports back to you on the key essence of that. And, and I think if it stopped there, that would be a problem. Like it's. But what you really want is then next time you Have a one on one with me. Right? You've, you've got that and you don't have to think on the fly so much. You can just double click into those things that the agent reported back. So I thought that was a super interesting use case and one, we're so used to AI like directing it to go do something in the digital world and then coming back and just um, giving us information. To me it was fascinating because I'd really never thought about asking an AI to go interact with another human, gather information from the human and then report back. I thought that was a super cool use case.

Corey: Yeah, no, and it's interesting. Something we're seeing as well. Right. We've thought a lot about like the hybrid approach to coaching and you know we, full transparency. I'm not going to turn this into an ad, but we have an AI coach and that's one of the use cases that we've seen to be the most effective. Right. It's like you run something like that and have the AI actually you know, we can talk like this to it, we can talk in Slack, do whatever you want but then, then that information is already there for you to have a coaching conversation later on.

Jeremy Donovan: Yeah. You guys have the QP formula, right? That's, that's uh, qualified prospect formula.

Corey: Yep, that's our, that's our bread and butter. That's our deal review formula.

Jeremy Donovan: Exactly, exactly. So you can have it go off and you know, and do that. Is there uh. Yeah, yeah. Differentiated uh, vision match. I'm trying to remember all the.

Corey: Hey, yeah, you're an expert. You got it all.

Jeremy Donovan: Uh, yeah, well it's been, I, I, it's Julie. Uh, I don't know if she's, I assume she's still associated with the company, but uh. Yeah, yeah, yeah. So um, years and years ago I was a Gartner and we brought the value selling methodology and across the entire company. So we were, we were really well, well steeped in it. And then I think she's got two books out. I think I've read both of her, both of her excellent books and then I, I do stare at the QP formula from time to time. Um, um, all right, use uh, case number two is uh, is lead enrichment or account enrichment and um, you know, ditto there. I'd say there is much more buy than build. Right. There's one or two, you know, major AI driven lead invest, lead enrichment vendors that are out there that you know, people are pretty, pretty commonly leveraging so that one's more of A it's lead enrichment but it's also contact identification, account identification and so on. What I don't see that, what I don't see as much of as I would have expected is um, it's like more sophisticated account scoring. And that was if I were to look back at my last gig and I thought about some of the things that our Rev Ops team had the biggest impact on the business on one of them was definitely account scoring.

Corey: Right.

Jeremy Donovan: Because we had a uh, something like 250,000 potential account universe and. Right. The AES were spending a lot of time trying to figure out which accounts in their patch to go after and we just did that. This is what you know, pre gen AI because this is really a predictive AI machine learning, you know technique that we, you know we put a ton of effort into machine learning in order to figure out and prioritize for the reps which accounts they should go after. And, and that was, that was a big growth unlock for us. And yet I don't like, I don't really see a ton of that. And it's part of this I think is the shiny object syndrome of Foundation LLMs and generative AI as opposed to the predicted AIML side. I think we're probably slightly under it. I mean we're under investing in a lot of things I like. We're slightly under investing in the predictive and a predictive side.

Corey: Yeah. It does feel like we've. You mentioned blinders earlier and that feels very apt. Um, and it feels like that's part of that. Right. We've sort of been swept up in the gen AI is like look what it can do doesn't necessarily mean that that's ideally what it should be doing.

Jeremy Donovan: Yes, yeah, yeah, exactly. There's a lot of, I mean as we're going through this like a lot of great stuff. The next one actually is um, probably it's a good segue because it's a combination of the two which is customer success augmentation and automation. Um, on the predictive side just to carry that through. Right. Is those same techniques like binomial logistic regression that you would use for account scoring. Um, you could use for customer health scoring. So um, the customer health score thing to figure out predictably which accounts are at risk. Um, and then the generative side. Right. Is to be able to actually interactively engage with those accounts and or help with workflow orchestration using AI combined with agents in order to go after those folks. Um, I'm particularly enamored with the CS Related ones that could also by the way, I would put account expansion in that category because a healthy account is one that's probably ripe for expansion. I think a lot of people focus on, I'm going to score my accounts to figure out which ones are at risk. But there's another side of this and I learned this from, I think it was the former either CRO of head of Rev ops at Looker, um, where what you really want to do is you want to never get red on the account and you want to figure out what things are correlated with health and then proactively go and run plays, a small set of very, very curated plays in order to drive health. Um, so for example, in the software business, right, a lot of people have integrations that maybe it turns out that if you have two or more integrations then you're much more likely to retain versus if you have 0 or 1. So you can go out and run a play where your CSMs, you know, uh, go out and help ensure that there's additional valuable integrations for your customer. So anyway, like that the CS1 is, the CF1 is number, is like a number three here.

Corey: I'm kind of, I'm fascinated. I kind of want to pause and dive into that a little bit more. I almost want to take back my last statement. You know, I still, I still think in a way like there's a little bit of shiny object syndrome around the gen AI side of things still. Um, but going back and looking at the more predictive analysis, machine learning side of things, do you think that data quality is still a problem?

Jeremy Donovan: Uh, I'm sure there are people, people would argue with me on this one. I don't think it's, I'm not seeing it as much of a problem as people paint it to be. It is 100% the case that you can't do any of this without good data. Right. But I think, I feel like when I uh, look at the, again, I have this particular universe of 10 to $100 million ARR companies, right? So they're relatively younger companies, these are software companies, so they're relatively tech forward. So in my tunnel vision world, these companies have data that's well enough structured, right. They're recording all their calls, so there's all of that. They're using a CRM. They are, they have really good telemetry on the post sales side on product usage and engagement. So uh, yeah, again in this little world I live in of 10 to $100 million software companies, I don't see Data as good, uh, data as a problem. I'm sure if you went to, you know, outside of my special little world, it's a problem. Right? A bit is probably a big, a big problem. I mean the biggest problem, the companies I talk to site with respect to. Let's go back to that first use case on deal intelligence for example, is that so many of the conversations are happening on mobile phones, um, on an ad hoc basis or text or in person. And like the texting especially or the whatsapping and an ad hoc mobile conversation and the face to face stuff. That stuff's not being recorded and a lot of stuff is there. Um, a lot of stuff is there. But that's their biggest complaint. It's not this other, it's not like this other stuff, if any, if anything they're drowning in data and underutilizing it.

Corey: Yeah, it definitely makes sense for that space. But that's a really good point about the, about the text and in person conversations because I don't, I don't hear a lot of people talking about that. But then I'm thinking and talking to our own sales team and thinking how much they do. Right. Just informally over text or WhatsApp. That's a significant part of the deal actually.

Jeremy Donovan: Yeah. I think there's at least there's like one vendor I think that you can wear like a pin or something on your shirt and it's maybe Bluetooth or NFC connected and there might be a few vendors of this. Right. And that records your conversation. I think there's a phone app that's really popular also. But it's probably very awkward to be sitting in a meeting with a client and either wearing that pin. I read some articles about um, you know, like the creepiness factor of M AI glasses also, you know, like recording everything and maybe we'll get to that point. But it's still, there's definitely an ick factor to it or an awkwardness factor to it. And regulated industries also can be really, really tricky. Really tricky because of like in healthcare, hipaa, HIPAA compliance in Europe. Tricky because of gdpr. Like there's still a lot of trickiness around this stuff.

Corey: Yeah, a lot of barriers to that and maybe for good reason. I don't know, the, the pen is a little, the pen of the glasses. Me personally, you know, I, I think I would behave differently, you know, even though I know that when I'm in front of this everything I say is virtually recorded.

Jeremy Donovan: Right.

Corey: Yeah. Um, I want to zero in on SCS and I promise I'LL let you get to your fourth lever. But I was intrigued when you mentioned the two or more integrations and how that was the factor of success. Are there other ones that you've seen in the cs?

Jeremy Donovan: That's just an example. Um, so this is also before the Gen AI days in my last gig we did do this thing I'm talking about there also and using predictive, uh, AIML techniques. So. So we had probably 40 different factors we were looking at not just like usage but also things like if they had filled out a CSAT score, nps, if they had the integrations, if they um.

Corey: Uh.

Jeremy Donovan: Support Tickets was another interesting one. And Support Tickets is, is, uh, is kind of a little. I mean it's not counterintuitive, but it's, it needs to be handled very specially that zero support tickets could be as bad as 100 support tickets. Um, for. Yeah. Because if, if they're not. They're just not communicating right then. Then that's a problem. Um, so yeah, they're. I, I maintain a. It's a completely open to the world. No, no, nothing. Right. It's just I basically put my. It's a playbook that I put my learnings there. So it's more of like my notebook, but you can actually access it, access it as revenue-playbook.com. so there, I believe there's a page there on customer health scoring that has like those 40 factors all written out. I don't really drive traffic. I mean it's. It's like I put my personal notebook online, but it's. I don't really drive traffic to it. Uh, but every time in the last four and a half years I've gotten a question and. Or just heard something on a podcast or read something in a book that I thought was interesting. I've. I've structured it in this playbook. I still do it. Although I do feel now like I could just ask the question of one of the LLMs and probably get just as good of an answer. But there are, there are some really nuanced things that it's hard to find answers for. I mean, I'll throw one of those. I always like to, um, not just say something without an example. An example would be like, how do you compensate reps in military and defense? That data doesn't really exist. And yet, you know, we have a good number of military and defense portfolio companies. So I, I've like seen it and have a better. I wouldn't say I have a super great understanding of it, but I probably Have a better understanding of it. So that's the kind of thing that I think is still valuable in the playbook.

Corey: Yeah, that's a great example. Thanks for sharing. I'll put it in the show notes. Be sure to get that out if people are interested. Um, yeah. Uh, okay. So what's number four?

Jeremy Donovan: The fourth one and I promised the bonus bit. So the fourth one, um, is RFP response. And I would put like filling out security questionnaires and proposal generation. Not just all in that category. Kind of an obvious, really good use of uh, of AI. If again, this is another one that was like deal intelligence. If they already had one of the uh, you know, two or three common RFP response tools. They're evolving and adding AI features and functionality pretty, pretty quickly. So they're, they're continuing to use, use those. We haven't really heard that one was better than any of the others. We heard about a third. It's like two big ones. We heard about a third one that people are using and also getting value out of recently. Um, if they didn't have that, there is a lot of build. So people are just you know, basically rolling their own on the rfp, on the RFP responder. Um, and that's a very. I mean that's like the perfect down the. I'm not a sports person, but down the center of the fairway is that it's a golf thing. I think.

Corey: I'm not a golfer either, but it sounds good.

Jeremy Donovan: I think that's where you want it sounds right. Yeah, it sounds right. Uh, that's, Yeah, I think that's the, that's the pretty classic use of AI. The bonus fifth one leads back, I mean full circle to I think maybe the deal intelligence piece, which is when we first started this. Well, actually it was even before that, like middle of last year to late last year. Role play, like AI based role play was a big one that a lot of our portfolio companies were adopting. And I. It's still important, but it's decelerated, um, in favor of the call by call stuff. Um, it's still happening, but it's. It does, it's. I put it number five now it's sort of knocked its way. So that's the bonus one there. Still interesting, still useful. Um, but not to the extent that some of these other things are.

Corey: That makes sense. I mean we saw that evolution in real time ourselves. Originally we built our tool to be a role play tool. Right. And then we saw like other people kind of taking over that space and then the interests and still valuable.

Narrator: Right.

Corey: But sort of weighing in favor of more like actual proactive coaching, deal by deal, that kind of thing.

Jeremy Donovan: So yeah, And I guess none of us should be surprised, right? Because yeah, the closer you can get to the actual live thing. Right. The better. And even at least people were role playing. Right. Like it does substitute in a very effective way for something people are already doing. We've seen a lot of pitches of AI related stuff that was solving problems that people didn't feel were that pressing, weren't already doing it. And it was another evolution I've seen in this job is there was a period of time where the investor side of VCs was very enraptured with category creation. Everything was all about category creation. Uh, it's not that you can't category create, but I think a lot of investors now and uh, entrepreneurs feel like that's very, very, very hard. So it's, it's the last couple years have been all about how do I apply technology to solve and address existing problems as opposed to creating something that is, is like allowing people to use to do something that they, you know, that's totally, totally different. Right. I mean, I guess AI, right. Is category creation the thing to be meta and a little ironic. But what they're using it for is not right. What they're using it for is this. Other things.

Corey: All right, I want to, I want to switch gears a little bit because you mentioned that intriguing example on compensation like how do you comp a rep in defense sectors and things where obviously there's not going to be a lot of public facing data. And then I saw on your LinkedIn the other day, uh, you did a poll I think which was, you know, how do you want to be compensated? Cash, um, you know, equity, A mix of the two. Just show me the answer.

Jeremy Donovan: Yeah, yeah, yeah.

Corey: I think we ended up somewhere like 47% cash only 33% of mix and about 20% were this, were just there, you know, to kind of see how things shook out.

Jeremy Donovan: Um, yeah, I strip out the just show answer. So yeah, it was like, I think it was, it was nearly 50, 50 of cash or a mix of cash and stock. And then 1 or 2% said I just, you know, this was very, this was variable comp. So I, I just want variable comp in stock. But it was, they, they definitely wanted him and I think yes, they definitely wanted a mix.

Corey: And, and so I'm fascinated because I was talking with a sales leader yesterday actually. Are you familiar with the platform?

Jeremy Donovan: Snappy I don't think so. No.

Corey: So they're really cool. They do, they do gifting and they've really tried to elevate the gifting space and they done it really successfully. I think they work with about half of the Fortune 1000 now. So actually a strategic business driver that's really aimed at employee retention. And they were sharing some fascinating stats and, and one point they made, um, was, you know, Jeremy, you get, um, let's say you get a raise this year. Right? Um, and let's say you get a very personalized, meaningful anniversary gift. Which one are you going to Talk about on LinkedIn?

Jeremy Donovan: On LinkedIn, probably the gift, not the raise. Like. Yes, because it's, it would be a little bit non, uh, classy to talk about the money.

Corey: Right. No one's going to go, you can't

Jeremy Donovan: take a picture of the money. Right. You could take a picture of the gift.

Corey: Yeah, exactly. Um, and what was fascinating about them, I'm thinking, okay, that's cool, that's great for your brand. Great little PR built in there. But they actually had hard data on how much more likely that was to make people stay and to make people connected to their coworkers in the company. And so I guess I'm trying to form a big question around trends you've seen in compensation. What the mix is, what the different, different levers are specifically to, you know, retain high performance and top talent.

Jeremy Donovan: Yeah. Um, side note, like, I think the real data would be interesting on that other one, which is if it was money only. Money and a gift. Or gift only. Right. Uh, I would think it would be similar to this poll that I did, which is, I think it's probably like they're equally happy with money and money and a gift, but if it was a gift only, they would not be happy. I think it's been less dynamic than um, than almost anything else. Like in the last whatever, even 10 years. The standard is a 5050 comp plan. Um, the biggest thing that's changed in over the years is, is quoted, ah, was like ote and quoted ote that uh, something really bad happened for a while which is reps were going and demanding higher and higher OTEs. And, and then companies were needed to retain people or hire people. So they're like, okay, sure, we'll, we'll raise you from 150,000 to 200 to 200,000. But you're, what we're going to do though is we're going to increase your quota to ote, right? We're going to increase Your quota. So from a CFO's perspective, the CFO is like uh, sure, they can have their 200k OTE because I'm still going to pay them the exact same amount anyway because there's no way they're going to hit this. And we saw the manifestation of that right is I think there was a lot of data that showed, I think typical attainment uh, was maybe 35ish percent were meeting or exceeding quota. And that's terrible but it, it's there the industry slash the reps, slash the companies like they all did it to themselves by allowing this OTE kind of escalation to occur. And I think we're getting a little bit back to, to normal. Like the escalation occurred, inflation occurred. Right. Uh, we experienced inflation. They want to use the passive voice there. So we, we experience inflation and but I think it's like stabilizing the OTE and the quota to ote thing that we see benchmark wise. So median is about a 4x quoted OTE. And then we recommend this best practice for efficiency purposes, 5x. But yeah, like uh, I don't think a major change there. And then ditto on stock because we're able to look at you know, what percentage stock allocation people give and you know that follows a really, you know when you coincidentally because I don't think the company's worth thinking about it but it follows a really clear uh, mathematical formula that's also by the way on the revenue playbook of what that formula looks like for um, for that I think uh, a closing comment on this topic is I think because I talked to a lot of reps and they asked me about stock. I mean the reps don't understand stock first of all because they get excited if they get 5,000 shares but they have no idea what the float is and, and the share is outstanding. So they don't really understand it. If I'm uh, an individual rep unless I'm an early, early rep in a hot company. I mean you're, you may, you're probably never going to, I don't know, I shouldn't say that like you to look at the statistics. I think the odds are low that you're going to see any money and if you do see money, m and like a car is a great thing, don't get me wrong. But like maybe it helps you to pay for some or some or all of a car which is pretty awesome. But um, and we're talking about high class things right? Because there are plenty of people who struggle to afford, you know, basic things. But. But for, you know, B2B SaaS, AES, like, that's what you could. That's what you could expect.

Corey: Yeah, no, I think that's. I think that's right on. I think one of the comments on your post was you're kind of buying a lottery ticket, and your lottery ticket's going to cap out at a certain amount. That seems to resonate. It feels right.

Jeremy Donovan: Exactly.

Corey: Okay, Jeremy, I want to be mindful of time. So as we get to the end of the podcast, there are two questions we ask every guest. Um, the first one is we're recording this, um, April 2026, looking out five years into the future to April 2031. Um, what's the one big shift coming for sales and cs, um, that a lot of people aren't talking about right now?

Jeremy Donovan: Yeah, I probably should have premeditated this. What I would like to see is, I would like. I want to pull it full circle. What I would like to see is just really, really strict operational discipline. So I, I guess maybe one that maybe not a lot of people are talking about is, is sales first line, sales manager, automation or augmentation. And so we get to the point where we have larger spans of control but better performance. So that's my answer.

Corey: No, no, I think it's an excellent answer. And not a lot of people are talking about that right now. Um, okay, final question for you. Looking, um, back on your own career. If you could go back five, 10, maybe even 15 years in the past, what is one piece of advice you would give your younger self, and why?

Jeremy Donovan: Oh, I know that one for sure, which is my younger self. I thought the pinnacle of success was that your manager didn't bother you. Like, your manager left you alone. And that was such a mistake. I lost 10 years at the beginning of my career, my entire 20s with that mentality, until I got an awesome manager who coached me. So I think just don't think you know it all and never, ever. Like, I'm 52, and sometimes my managers these days are younger than I am, and my current manager is younger than me. And, uh, never, never get so proud, uh, that you don't think you can learn something every day. So, uh, the willingness to be coached, I think is, is, is. And that feedback is a gift, right? Not. It's not. You shouldn't get nervous. You should be grateful.

Corey: Um, vital advice. Vital advice. All right, well, thank you so much for joining. Joining us, Jeremy. It's been a lot of fun. If people want to learn more about you, more about Inside Partners. Where should they go? Uh, just.

Jeremy Donovan: Yeah, uh, me is LinkedIn. Uh, and Insight Partners. Yeah, just insightpartners.com.

Corey: since we don't sell.

Jeremy Donovan: We don't sell anything. So, you know.

Corey: All right, thank you.

Narrator: You've been listening to the B2B revenue executive experience. To ensure that you never miss an episode, subscribe to the show in itunes or your favorite podcast player. Thank you so much for listening. Until next time.

Jeremy Donovan: Sam. Mhm.