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What Private Equity Portfolio Companies Miss In Their Own Billing · Private Equity Data Guy

• 0:00
  My former CEO used to say, automation allows you to do things faster, but if you're still doing the wrong thing, you're only doing the wrong things faster.
• 0:06
  And so let's figure out what the right thing is, which is harder.
• 0:09
  We created 104 million in pipeline in 60 days because we took what was a research tool and said, well, no, if you could just apply research in the right way at the right time and own the process of
• 0:18
  communicating it to an executive, getting their attention, hosting a meeting with them, figuring out what their priorities are and then mapping on a solution, you can run that and you can actually help win with them.
• 0:27
  When you're operating a billion dollar operation, the downside risk of making a bad choice is much higher than even the upside risk of making a good or even excellent choice.
• 0:37
  And so that's where you get this.
• 0:39
  Like, if you need a CRM, you probably just take Salesforce because how else.
• 0:43
  Are you going to go behind every value creation plan?
• 0:46
  There's a data problem nobody wants to talk about.
• 0:48
  Fragmented systems, metrics nobody trusts, and decisions made on gut feel dressed up as analysis.
• 0:54
  Welcome to the PE Data Guy.
• 0:56
  Each week, host Graham Crawford talks to the operating partners, advisors and practitioners who are doing the work inside portfolio companies.
• 1:03
  If you care about what actually drives returns in the market, then you're in the right place.
• 1:07
  The PE Data Guy starts now.
• 1:10
  Carlson Ma has spent his career on both sides of the enterprise deal table, advising executives at firms like Blackstone, Vista and Thoma Bravo on buying technology and the teams building and selling it.
• 1:23
  He got tired of watching deals that were built around access instead of outcomes.
• 1:26
  So Carson built Buyable, a marketplace that brokers guaranteed outcome backed offers into PE portfolios.
• 1:33
  His flagship find, $1.8 million of revenue sitting in the gap between one company's contracts and its billing system.
• 1:40
  Today we talked about where the lost money hides and what it takes to promise a result before you've even seen the data.
• 1:46
  So welcome to the show.
• 1:47
  Carson, thanks so much for coming on.
• 1:49
  Thanks Graham.
• 1:49
  Great to see you.
• 1:50
  And what an incredible background that is as well.
• 1:52
  And you know, I always like to ask people who've landed in the world of private equity, like, how did you, how did you end up here?
• 1:58
  Did you take the traditional route through banking?
• 2:00
  Like what, what, you know, there's, there's, every story is different, what was your path?
• 2:05
  So I came out of the University of Pennsylvania and everyone there talks about, oh, I'm going to go into IB or I'm going to go into management consulting.
• 2:11
  So I went the management consulting route and could work with almost any company on the planet and really just became infatuated with this concept of private equity.
• 2:19
  How do you, how do you create value over the long term?
• 2:22
  How do you run complex value creation playbooks and so forth and then got the opportunity to work with some really, really incredible firms over the years?
• 2:28
  Yeah, absolutely.
• 2:29
  Obviously Blackstone jumps out there from, from that intro.
• 2:32
  What was your interaction and experiences with Blackstone?
• 2:35
  Yeah, so, so RSM was.
• 2:37
  The company worked with 60,000 consultants globally, 10 billion in revenue.
• 2:40
  And Blackstone is a really key customer for, for rsm and so got the opportunity to go in and become one of their technology sort of preferred providers.
• 2:48
  And so it could be one of the first calls when they buy a portfolio company and they're getting their hands around what's a tech stack, what are they doing, what should they do next?
• 2:55
  We would get to come in, parachute in and help them think that through.
• 2:58
  That's interesting and I love that it's interesting that the model works that way, that they lean on consultants, you know, to some degree there to do it.
• 3:07
  All the talk these days is about how.
• 3:09
  All about value creation.
• 3:10
  Right.
• 3:10
  The financial leverage is less of a thing and it's all about value creation.
• 3:13
  Now do you see that dynamic shifting even for the big shops like Blackstone?
• 3:17
  And you know, I've heard about the enterprise partnerships with Anteropic and whatever, these big houses, but do you think more PE funds are going to start having Technology Resource as part of their team rather than using consultants?
• 3:29
  The big dogs have already gone there.
• 3:31
  Blackstone's a bit, a bit different in the way that they've staffed up.
• 3:34
  But Vista has, you know, their whole value creation team.
• 3:36
  They don't want to work from our experience, they don't want to work with very many consultants.
• 3:40
  They want to have it in house and then they want to basically deploy those teams and then get paid to use those teams.
• 3:44
  But Blackstone's a bit of a different beast and they've structured around, you know, preferred partnerships or people that they trust and who are kind of on the bleeding edge of what they're trying to do by industry niche.
• 3:53
  And so I think it's going to continue to expand for these really mega funds.
• 3:56
  But for all of the guys in the middle, having experienced experts on the outside is a really helpful play that I think will continue.
• 4:03
  Yeah, I think I saw, I can't remember the number, so I'm not going to quote the stat, but I saw the number.
• 4:07
  I think it was 50 plus percent of funds have taken on someone with AI in the job title in the last year.
• 4:13
  That sounds about right.
• 4:14
  Yeah.
• 4:16
  And that's not including the AI themselves, but it's a fascinating one to me.
• 4:20
  Like watching companies build knowledge graphs because they realize that they need to have good data to get the competitive advantages from AI.
• 4:26
  But it's certainly a really fascinating time to be in the, in the industry at a time where there's actually quite a funnel of pressure from the, you know, elongated hold periods that are going on and the, you know,
• 4:38
  that we can't just bring in some new deals because we've got cheap debt available to us because we don't.
• 4:42
  And it's getting more expensive every time that, every time those rates go up.
• 4:46
  So we'll.
• 4:47
  Which is all the more reason actual value creation needs to happen.
• 4:49
  You can't.
• 4:50
  Financial engineering is not, is not the primary playbook anymore.
• 4:53
  Absolutely sold on that one and you know, very excited that we both get to run around and be part of that vibe.
• 4:59
  So, yeah, welcome to the show.
• 5:02
  The other thing I mentioned in the intro is this great story that I heard the last time we talked about you finding, well, nearly $2 million in a $60 million business just sitting in a gap between systems.
• 5:12
  It doesn't surprise me actually from the work that I've done, but I'm sure it will surprise a lot of people.
• 5:17
  Can you talk about where that sort of money actually hides in companies?
• 5:21
  Yeah.
• 5:22
  So at the highest level at Bible we want to go, we want to basically bridge that gap from the concept of outcome or of access based deals where, hey, you're going to, you know, rent an employee or you're
• 5:33
  going to buy access to software.
• 5:35
  We run those deals and we've, we've sold them at these, at the highest levels and they can be really effective.
• 5:39
  Right.
• 5:39
  If you need to buy Salesforce, if you need a CRM, buy Salesforce, you need to do it.
• 5:43
  But we've also experienced what it's like to come in and say, this is the thing we're going after you hire the team, you run for a year and there's no outcome to show for it.
• 5:51
  There's no, there's no, there's not enough momentum.
• 5:53
  And so what if you began with the end in mind and said this is what we want and let's guarantee it.
• 5:56
  The case study you're talking about is actually a little bit bigger than you mentioned.
• 6:00
  That was just one, one series of deals that we found within the company.
• 6:03
  So the actual company is $1.2 billion in revenue, we reviewed all of their contracts to basically find, to make sure that they were billing the correct amount to their customers.
• 6:13
  So super simple.
• 6:13
  Before we get into more complicated value creation plays, we start and in 60 days we can basically review every piece of paper that's never been reviewed before to make sure that you're actually billing accurately and then collect on
• 6:23
  the pieces that are missed.
• 6:24
  And it's anywhere from 3 to 5% of revenue.
• 6:26
  Usually in the case of that business, it was 31 million of, of revenue that we.
• 6:32
  Yeah, that's, that, that's, that's a real outcome play and absolutely how I love to do work as well.
• 6:36
  You know, I spent time in, in big management consultancy as you did as well.
• 6:40
  And I often tell my clients up front, what I'm not going to propose is a multi year, generically titled data transformation to democratize your company's data.
• 6:48
  Because that will be good and not that democratizing data is bad, it's just, it's not an outcome.
• 6:53
  Right.
• 6:53
  And it's one of the reasons I love working in the private equity sphere.
• 6:58
  Two questions for you about setting up deals like that.
• 7:00
  Because I'll say I've found some resistance.
• 7:02
  Like I'd love to work on more outcome based deals but actually I've found it's almost like calling the bluff a little bit.
• 7:07
  I've found with clients where I try and move in that direction of like, oh hey, you don't want to pay the monthly for this fee.
• 7:12
  How about we like cut this and we go outcome based.
• 7:15
  And most of the time companies then actually once they see you've got the courage of your convictions then say wait a minute, yeah, actually no, we'll keep the outcome.
• 7:24
  You just come and do, you just come and do the monthly.
• 7:27
  So how do you, how do you find a way to negotiate with companies to.
• 7:30
  Yeah.
• 7:31
  To make that sort of deal possible.
• 7:33
  So there's two key pieces here that we try to focus on.
• 7:36
  Your buyer has to be hungry for this outcome and has to believe that they cannot do it without you.
• 7:40
  And so the reality, you have to recognize you're always as strong as your bot, know your best alternative to negotiated agreement.
• 7:46
  And if they think hey, I just need to go hire one person or I can go find 50 companies who could do this, there's, it's very, very difficult to create an outcome based deal.
• 7:53
  There, there has to be belief and conviction that you guys specialize in this niche.
• 7:56
  You've run this before, it's going to be faster, better and frankly even cheaper in the long term to run this with you and get it done.
• 8:02
  So that's on the hunger side.
• 8:03
  Has to be personally effective for this executive or for the operating partner, whoever it is, it has to actually help them hit their bonus.
• 8:10
  And so as you can imagine, you cut a whole world of like great projects down to a narrower set of outcomes that you can run for them.
• 8:16
  The second piece, once you have someone who's hungry and they're motivated, they say, I actually want this and I believe you guys might be uniquely capable, then you still get into the problem of causality and correlation.
• 8:25
  And so as soon as you become correlated with an outcome, the value gets downward compressed.
• 8:31
  And so that's where you end up being, okay, we're a consultant.
• 8:33
  Who knows, maybe we did contribute, maybe we didn't.
• 8:36
  And so we always focus on specific areas of the business, no matter how small they are.
• 8:40
  Where we can say that we are causal, we are causally creating this outcome in this space.
• 8:44
  Even if we start incredibly small and say, wasn't that amazing?
• 8:46
  Let's run it more broadly.
• 8:48
  We need to prove that there's spaces.
• 8:49
  And so it's, we basically have to agree ahead of time.
• 8:52
  This is a spot where nothing's going to happen, or better yet, something bad is going to happen if we don't act.
• 8:56
  And until we find that, as you can imagine, there's lots of deals we don't take because, because we don't agree on those businesses.
• 9:02
  But that's what it really requires to get into an outcome based deal.
• 9:05
  Yeah, and that was my question.
• 9:06
  I think you saw it in heading into the answer to my second question on that, which was around, how do you set yourself up with this, you know, might be a company you've not even seen in the data
• 9:14
  yet.
• 9:14
  How, how do you get committed to the belief that you can create that outcome without, you know, something up front?
• 9:21
  Yeah.
• 9:22
  So we, we run very specific niche case studies.
• 9:25
  So very specific by industry, by business size and by specific outcome.
• 9:30
  And so basically by style and shape of the business.
• 9:32
  And so we know that any business that hasn't looked into XYZ problem and we can walk through a few of them together will have some sort of gap that is worth finding.
• 9:40
  That gap may be smaller than we expect, but because of the commercials, we can still make it profitable for everyone to be involved.
• 9:46
  And so that's where it is.
• 9:47
  It's not, oh, hey, this sounds similar.
• 9:49
  Could we go into this industry?
• 9:50
  It's, we are very vigilant about not touching specific industries where we don't have a case study yet.
• 9:55
  And so that focus is what helps us also drive more value in that specific area.
• 9:59
  Because that playbook can get run faster and faster.
• 10:01
  Because the true value here is time is time to value.
• 10:04
  And when you have a specific niche, that's one of the ways that you can get faster and faster.
• 10:08
  Yeah, yeah.
• 10:09
  And I guess the other thing which is really nicely PE shaped about that is you achieve the outcome, you get the share in the outcome and then unlike a traditional consultancy deal, you don't hang around on the P
• 10:20
  and L, which of course the P fund doesn't want you to be doing either.
• 10:22
  Like the last thing they want is someone coming in and creating a dependency.
• 10:25
  They want you to come in, add the value, get off the P and L.
• 10:28
  Right.
• 10:30
  To see it.
• 10:31
  Back to the specific example of people undercharging the contract says you should have billed, you haven't billed in your experience.
• 10:37
  How do gaps like that open up in the first place?
• 10:39
  Like how do we get into that situation?
• 10:42
  So think about a business that's 50 million in revenue, they've got a controller, a few directors of finance, a deal gets signed and that the paper, 50 pages of contract land on their desk.
• 10:55
  Typically a real human reads that contract, tries to understand what the amendments are, what the entitlements are, what the process should be, and then punches it into a billing system.
• 11:05
  Then imagine that you do that over 100 contracts a week.
• 11:09
  Something may or may not get missed.
• 11:10
  The real multiplying effect is that $50 million business gets purchased in a roll up by a larger business and then gets merged together on a deadline across multiple billing systems where eventually something gets dropped.
• 11:22
  And like I said, it's usually 3 to 5% of revenue.
• 11:24
  You can just imagine something as simple as an escalator that's supposed to be 3% or CPI that's supposed to hit every January 1st, the first year it fires.
• 11:34
  The second year we add a meter to the system in the way that a complex company is billing.
• 11:38
  But the CPI never fires on the meter.
• 11:40
  So it hits the basically base rate, which is great.
• 11:43
  And so you see that step up monthly or annually.
• 11:45
  But the meter itself is constantly fluctuating and it's impossible to notice without doing the math and going back to the original paper to figure out that we never fired it there.
• 11:54
  So these are not massive sums of money on the individual case, but they become quite large across a portfolio and across a compound.
• 12:01
  They really compound, don't they?
• 12:03
  And it's not like executives don't ask the finance team for reconciliation.
• 12:09
  It's just tricky to find.
• 12:11
  You're talking hundreds of thousands of contracts, and then now imagine that controller who originally punched it in, leaves the business as part of the acquisition.
• 12:19
  And so now that tribal knowledge isn't there, there's nobody to go back and reread it.
• 12:22
  And those contractors sitting in a deal room that nobody's touched for three years, it's like that type of thing can just multiply the value and what we find there.
• 12:30
  We've spoken a couple of times about the old style, the old style of consultancy business, which I think, you know, we both, we both participated in ourselves.
• 12:37
  And you described it as, you pay us, we give you access, maybe you get results.
• 12:42
  Right.
• 12:42
  Why does that no longer survive a board review, do you think?
• 12:45
  Because everyone has been burned by that experience.
• 12:48
  All right, this is what we're here to do.
• 12:50
  We're going to set out.
• 12:50
  We've got the beautiful slide deck.
• 12:52
  I've been a part of that, where I personally contributed to that, where this is what we want to do this.
• 12:56
  We all had the best intentions, but as soon, as soon as that deal is signed and it's time to launch, now we all have to figure out, what do we do next and when.
• 13:03
  Your mindset is, I need to get to 2 million in billings.
• 13:06
  You don't always make the perfect choice.
• 13:08
  This is just human nature.
• 13:09
  It's not because you don't love and care, it's just that you don't always come back to, here's the outcome we're going after.
• 13:13
  And instead when you can start backwards and say, here's what we're going after, let's do the right things to protect the right data and pull it in and do it rapidly in a way that we've already done it
• 13:20
  before, you change the game.
• 13:22
  And so you're able to create more innovation and more simplicity in the approach.
• 13:25
  And that's just been our experience.
• 13:27
  I think consulting will stick around because you always create arbitrage on finding experts that you don't want to hire into your business.
• 13:32
  But in the worlds and in the spaces where outcomes are possible and are the primary objective, I think that we'll be able to create more.
• 13:39
  More value there.
• 13:40
  Yeah.
• 13:41
  And I still think the space of consultancy, especially in the, you know, the, the larger enterprise business where.
• 13:46
  And I know, because I've seen people use them this way, like I've been on both sides of this table, you know, especially IBM, was the original.
• 13:54
  You'll never get fired for hiring IBM.
• 13:56
  Yes.
• 13:57
  And it's it's the, you know, you're, you're a big, a big executive inside.
• 14:01
  You've got big initiatives.
• 14:02
  You hire IBM to do it, and if it happens to go wrong, then you can hold your hands up and say, what could I have done any better?
• 14:08
  I hired the best in the business at this.
• 14:11
  It's not my fault.
• 14:12
  And it's almost people running it like an insurance policy.
• 14:16
  And from the other side of the table, back to your original point.
• 14:19
  Humans always respond to the incentives that are put in front of them.
• 14:22
  And I agree.
• 14:24
  I've seen very, very few bad actors in any company, including big consultancy companies, including in private equity, all these, including investment banking, all these jobs that get a very bad name.
• 14:34
  Very, very few bad actors that I've ever come across in my career.
• 14:38
  But humans respond to the insult incentives in front of them.
• 14:40
  And if you're in a big team at Accenture or McKinsey or wherever else and your objective is billable hours, then that's what you're going to make.
• 14:49
  There it is.
• 14:50
  I want to dial into one of your points there around.
• 14:54
  We sort of call like risk reversal.
• 14:56
  That's basically what's happening when an executive says, I want to take on Salesforce because nobody gets fired for taking on Salesforce.
• 15:02
  I want to take on IBM back in the day.
• 15:04
  And I want to recognize that that's actually an important characteristic.
• 15:07
  We talk about it almost as if it's a bad thing.
• 15:09
  It's actually not.
• 15:10
  These guys have a lot to lose.
• 15:12
  And so the reality, we're excited.
• 15:14
  Oh, I know that, you know, so I just want to call it out for the audience that these guys have a lot to lose.
• 15:18
  When you're, when you're operating a billion dollar operation, the downside risk of making a bad choice is, is much higher than even the upside risk of making a good or even excellent choice.
• 15:29
  And so that's where you get this like you need a CRM.
• 15:32
  You probably just take Salesforce because how else are you going to go?
• 15:36
  But where we think that the opportunity is in the space of outcomes is we want to actually create that risk reversal at another level where we're actually guaranteeing, rather than saying, hey, buy the software and maybe you get
• 15:45
  this result, we're saying this is the result and we don't get paid unless you do it.
• 15:48
  And better yet, here's how we protect you along the way.
• 15:51
  Here's how we reverse risk, here's how we manage data, here's how we make you look good, and here's how we bring these emotional moments that can support you and your dialogue with the board to show why you made
• 15:59
  this decision.
• 16:00
  We're just trying to realize how important it is to have the human on the other end who makes these calls and want to support the way that they think and see the world.
• 16:07
  Yeah, or even, you know, you're an executive at a large company and I think to your point, there's a risk reversal point that applies to a large company as well.
• 16:17
  So say you've got a billion dollar company, it's clearly very successful.
• 16:20
  Why should we go out on a limb and try something?
• 16:22
  You know, maybe someone's got an AI generated CRM that is super cheap and will do the same thing for you.
• 16:28
  Why?
• 16:29
  Where's the incentive to take the risk personally?
• 16:32
  Like, you're an executive and, you know, you're not only bothered about how the board perceives you, but how the promotion panel perceives you and all of, all of that other thing.
• 16:40
  And it's hard to do innovation in big companies, and I know because I've worked in big companies, but there's a good reason for that, because if it ain't broke, don't fix it.
• 16:47
  And we've got a billion dollar company here, so why do we want to f around with it?
• 16:51
  Right.
• 16:52
  So, you know, it's, it, it is a totally reasonable, reasonable stance, but the, the psychology of it and the, and the way humans are driven is a, is a really interesting nuance of it to me.
• 17:03
  So.
• 17:03
  And that's one of the reasons.
• 17:04
  So, Graham, I just want to double down on that.
• 17:05
  That's one of the reasons we designed buyable in the way that we have, because executives are also graded.
• 17:10
  Right.
• 17:11
  I mean, one of the key reasons these folks are in these businesses, they want to continue to create value in the world.
• 17:15
  They want to continue to get promoted.
• 17:16
  They want to continue to get opportunities and attract resources to their families.
• 17:19
  And when they are a part of initiatives that are correlated with outcomes, it's harder for them to argue this was valuable.
• 17:27
  This is why we did this.
• 17:28
  This is what we're going after.
• 17:29
  So we put a CRM in place and we bought Zoom Info and our sales grew 3%.
• 17:33
  Did I contribute to that?
• 17:34
  Did I not?
• 17:35
  Whatnot is, it's a harder story at the board level.
• 17:39
  But hey, we decided to bring in a partner who looked exactly at this specific area and found these specific outcomes and brought in an extra 31 million for us.
• 17:46
  That's actually a story that they can stand on.
• 17:48
  That's an outcome that they can be causal with rather than just having to be correlated.
• 17:51
  And so that's part of why it's also should be exciting and is exciting to the executives that we work with.
• 17:56
  Yeah, I love it.
• 17:57
  It's the right way to do business.
• 18:00
  You don't work with a large AI company.
• 18:02
  Yeah, yeah.
• 18:04
  I mean that must be a very interesting and fast moving place to be.
• 18:08
  Is there anything you can share about that interaction around the outcomes?
• 18:13
  So with one of our original outcome partners with the company I was working with at the time, they go into market based on access and based on basically a usage based access to a platform that would help you
• 18:23
  DO Research and ChatGPT.
• 18:26
  Claude the large LLMs started to downward compress the value of a research based platform.
• 18:32
  And so we were attempting to sell into and create value for this massive AI data company.
• 18:39
  And we came to them with, with the standard pitch, hey, we, you know, you can research better, you'll prepare for your meetings better, this type of thing.
• 18:46
  And the response was, guys, this is, this is, it's not enough to get us going like thank you.
• 18:51
  And then we said, wait a minute, what if we could guarantee you pipeline?
• 18:55
  Like the actual reason that you want great research on your companies, on your customers, is so that you can bring better offers to them, so that you can create more deals for yourself to serve them better.
• 19:04
  What if we could do that?
• 19:05
  And the answer was, okay, all right, let's give it a go.
• 19:07
  And we created 104 million in pipeline in 60 days.
• 19:09
  Because we took what was a research tool and said, well, no, if you could just apply research in the right way at the right time and own the process of communicating it to an executive, getting their attention, hosting
• 19:19
  a meeting with them, figuring out what their priorities are and then mapping on a solution, you can run that and you can actually help win with them.
• 19:25
  And so that's one of the key case studies that got us going on.
• 19:27
  Wait a minute.
• 19:28
  We need to go deeper into the world of outcomes.
• 19:30
  We need to go deeper into the space of guaranteeing results for folks so that we make it that much easier to buy.
• 19:35
  I love that.
• 19:35
  And in doing so, you walk across the bridge and actually get more deeply embedded in the business as a result.
• 19:41
  Right.
• 19:41
  Because I've seen this with data projects throughout my whole career.
• 19:46
  It's one thing creating the right data product, whatever that data product might be, even if it's just something as simple as a dashboard or something as complex as a whole new, you know, environment, the value is in using
• 19:57
  it.
• 19:57
  The value is not in the data data product existing, the value is how you use it and how you then apply it to generate business value.
• 20:03
  And one of my other favorite phrases with my clients is that technology, including AI data, you switch any one of those and you want is not a business case in its own right.
• 20:13
  We're not doing AI to do AI.
• 20:15
  We're not transforming our data quality to transform our data quality.
• 20:18
  Like something has to lie behind it and there has to be an outcome.
• 20:21
  There has to be an outcome that we're chasing.
• 20:23
  I'm heartbroken by the number of dashboards that I helped create that are on a shelf somewhere.
• 20:31
  Because when you're, when you're the outsider and you want to create value, I mean, you want to get hired, you want to get promoted, like everyone is dreaming of the same stuff.
• 20:37
  Like you said, there's really no bad actors here.
• 20:39
  It's just then you follow the incentives of like, you've been hired to build a dashboard, you've been hired to build a data warehouse.
• 20:44
  What do you start doing?
• 20:45
  You build the world's greatest data warehouse.
• 20:47
  But if you forget and you don't hold on to like this is marching towards better understanding our customers, to better bring offers to them, to better structure our deals with them, to better close and create revenue, then you'll
• 20:57
  lose, you can lose that whole forest of the trees.
• 20:58
  And so that's part of why we think that the world of outcome based will continue to become more prevalent is because I do need a data warehouse, but I need it directed towards something and it will keep me
• 21:07
  accountable and it'll keep me moving in that direction.
• 21:10
  It's harder, but it also does bring you across the bridge to say, now we're in this together.
• 21:14
  What are we trying to do?
• 21:15
  How do we make it work versus we're going to build this beautiful thing and ship it over and good luck with it.
• 21:20
  Which may be sometimes how it's how it comes across.
• 21:23
  Yeah, yeah.
• 21:23
  If you use this well, I'm sure you'll do very well out of it.
• 21:26
  Exactly.
• 21:26
  And from someone who's never led a business like that or never been in that seat, it's very, very difficult to do that.
• 21:31
  Just it's a hard, hard place to win.
• 21:34
  I mean, even taking it back down to the basics of a simple dashboard and you know, those on shelves and those not on shelves, like I always ask people when they say, or this must be a title, this
• 21:42
  number, whatever that number might be, maybe new revenue, maybe new pipeline or whatever, to use your example.
• 21:48
  But you know, I always Ask, well, what's a good number?
• 21:51
  Like, what number do you want to see on here?
• 21:53
  What are your tolerance thresholds for this, being extra good or bad.
• 21:58
  And in either in each of those two edge cases?
• 22:00
  Because the answer is, oh, I'll be either a bit sad or a bit had, but there's nothing I can really do about it, then I'd question the value of even having it on there in the first place.
• 22:08
  Graham, that.
• 22:09
  That brings me to the.
• 22:09
  The real purpose of what we're trying to do here is everything comes back to what am I going to do next?
• 22:14
  Like, there's plenty of window dressing, there's plenty of this makes me feel good or feel bad.
• 22:18
  But the reality is, like, what are we going to do next?
• 22:21
  And when you get into the world of outcome based, thankfully you're both on the same page figuring out what is the most effective action to take next.
• 22:27
  It's not just dressing it up, building a dashboard, because that's frankly, why dashboards fail, is it's not orienting you towards, like, here's the emotional moment to understand all the contacts in history.
• 22:36
  And here's the three paths we considering.
• 22:38
  This is the path to run on.
• 22:39
  Let's action it and then better yet, action it consistently for 30 days, not just pivot tomorrow, pivot tomorrow, pivot tomorrow.
• 22:45
  And so I think that's what's interesting is more data doesn't get you to better actions, but more interpretation of data and a niche and specific area can get you to better actions.
• 22:52
  And that's the actual game we're trying to play.
• 22:54
  Yeah, exactly.
• 22:55
  Or what band aid can we apply to make sure that this red turns back to yellow on next month's dashboard?
• 23:01
  And then people don't pay attention to it anymore because usually that band aid is adding some sort of technical complexity or diluting a system somewhere in a way that's actually harmful in the long run.
• 23:11
  Yeah, it's.
• 23:13
  Yeah.
• 23:13
  Or just changing the metric.
• 23:14
  Or you just change the metric and say it's yellow, it's green, everything is green because your brain wants that.
• 23:20
  But what are we actually trying to do here?
• 23:22
  And how can we see the full system and figure out what the bottleneck and constraint is on it and then apply our resources there?
• 23:28
  Yeah.
• 23:29
  Especially in the corporate.
• 23:30
  I used to struggle with that with people.
• 23:31
  I'm like, I'd see them dashboards, they prepare or even just status reports they're preparing and trying to make people put anything other than yellow, other than green on there.
• 23:43
  So you end up with this.
• 23:44
  I call it the green dashboard paradox where you get this beautiful, a series of beautiful green dashboards from every department and yet the business is overall struggling and no one knows why until it.
• 23:52
  All, it all flips the rash or whatever it is.
• 23:55
  And that's not the point.
• 23:56
  And that's.
• 23:56
  But frankly, what you're observing there, Graham, is like those are also very, very, whatever those problems are, are very difficult to turn into outcome based problems.
• 24:04
  And so that, those are the spaces we just don't play.
• 24:06
  Like if you need to implement a CRM, that's not, I don't, I don't.
• 24:10
  Maybe it will become one.
• 24:11
  But that's not perhaps a great sphere for outcome based projects because there's so much complexity and regular, so much nuance to like, should we structure it this way, should we do it that way instead?
• 24:20
  Bringing in experts to help you get the thing stood up so you can get back to the actual business.
• 24:25
  And then we'll be on the other side of the actual business which is we want to create pipeline and revenue, we want to have better conversations with our customers, whatever that might be.
• 24:31
  And that's where we can play.
• 24:32
  And so I think there will continue to be a need for niche specialization that doesn't make sense to hire.
• 24:36
  And so consultants will be able to say, hey, we have that, we have someone who does that.
• 24:40
  Yeah, I agree that the CRM replacement is a really interesting one.
• 24:43
  I've heard various reports of how bad name any piece of CR software, CRM software, any brand name you like in the market.
• 24:50
  I've had a client tell me how it's terrible software actually to the point we were making previously, you're probably just not using it correctly.
• 24:57
  All the data you're putting in there is terrible and therefore the outcomes you're getting are terrible.
• 25:01
  And you know one something I always, that I've heard that I always bear in mind if people come with the, our objective is to change our CRM is I remind them that any, almost any technology change or even
• 25:12
  a process change actually in a business is going to for free actually give you a negative 20% and how effective it is before it gets better.
• 25:21
  So if you haven't got a kick, you haven't got a sweetener on the other side of making this change or you're actually going to make things worse because the human beings involved and the business that still has to
• 25:30
  operate around while you change the train tracks is going to suffer.
• 25:34
  So yeah, which means that the sweetener has to be even bigger if you have a guaranteed 20% hit your sweetener can't just be 20%.
• 25:41
  We've got to be talking about a 50% improvement in something, something that we care about.
• 25:45
  And so proves to me that's what's happening.
• 25:46
  If it's not, keep it going and find something that does.
• 25:49
  And that's what we hope we're doing here.
• 25:50
  Exactly.
• 25:51
  Although the best micro example I've got of that is, and this happens quite regularly when people, people get on board with like, oh, let's, let's get better quality data, let's automate things where we can, and whatever else.
• 26:02
  And I have, I've had this countless times in my career, both inside and outside of big corporations.
• 26:07
  People can bring this, I want your team to automate this.
• 26:09
  And I say, okay, so it's currently manual, like how long, how, how often is it running and how long does it take to run?
• 26:14
  And like once a month and it takes five minutes.
• 26:16
  I'm like, hmm, so let's look at the ROI on this.
• 26:21
  Even if it takes a day, even if it takes my team, one, one person, one day to automate this, then it's, I mean, what are we looking at?
• 26:30
  My former CEO used to say, automation allows you to do things faster, but if you're still doing the wrong thing, you're only doing the wrong things faster.
• 26:37
  And so let's figure out what the right thing is, which is harder.
• 26:40
  And so I totally feel that I've seen those exact scenarios.
• 26:43
  Graham, totally simplify then automate.
• 26:45
  There was a book, I recommended this book last week.
• 26:47
  It's called the algorithm by John McNeil, who was the president at Tesla during their big growth phase.
• 26:53
  And he's got a five step algorithm that he goes through to grow companies.
• 26:57
  And it always starts with simplifying.
• 27:00
  Before we go any, we start waving the technology around, let's see ruthlessly how we can simplify something before we start automating it.
• 27:07
  And I think that's some of the best advice going out there.
• 27:10
  Speaking of advice, if Those operating partners, CFOs, whoever it is listening in a portco and they think, okay, this sounds great, I want to start looking for places in my business that money might have gone missing or that
• 27:23
  I might be able to come up with something outcome based, like how should they start doing that before they, before they call you?
• 27:28
  So frankly, if a, if we found that treasure hunts are not, are not effective uses of time for large complicated businesses.
• 27:37
  And so if a cfo, an operating partner is listening and they have the opportunity to think, oh man, what should I Go and review my contract.
• 27:45
  Should I look over here on the sales place?
• 27:47
  What should I do?
• 27:48
  The reality is they should probably get back to whatever their core initiatives are and there's hopefully three, maybe five, hopefully not 12 or 15 initiatives and hammer on those.
• 27:57
  Where we would love to come in is help them.
• 27:59
  Basically sitting underneath those initiatives and not jeopardizing those initiatives.
• 28:04
  Let's go run these outcome based plays while you're on the way.
• 28:07
  And they're specific by niche.
• 28:08
  We focus on B2B tech companies and that, that's sort of our, our space, B2B recurring.
• 28:12
  So we also work with like managed service providers, folks who have recurring B2B relationships.
• 28:16
  If you're in that niche, then that's where lost money or free energy or better cloud or a revenue sprint can be really helpful.
• 28:24
  These are the types of outcome based plays that we run.
• 28:26
  But a treasure hunt we've yet to find where it's like that's going to be awesome.
• 28:30
  Unless it's truly worthy of being a top three or five initiative.
• 28:33
  It's probably better to just get back to those.
• 28:36
  Yeah, go back to work.
• 28:37
  Is the.
• 28:39
  As it were.
• 28:40
  Yeah, exactly the short answer.
• 28:41
  And that's sensible as well.
• 28:43
  I think the simple answer is often the best one.
• 28:46
  And I think you're right.
• 28:47
  Going in search of complicated use cases really can expend a lot of energy and you could find yourself working around the edges of the possible benefit or the most interesting priority.
• 28:56
  It's emotionally attractive though.
• 28:58
  And so it's one of the things that we constantly wrestle with, particularly as entrepreneurs, particularly in a rapidly growing business, is there's the emotionally attractive response, which may be a treasure hunt, which may be this niche thing, which
• 29:08
  may be implementing automation.
• 29:10
  And then there's the actual most important next thing that you should do to hammer on your bottleneck and the key constraint of the business.
• 29:17
  And it's nice when they emotionally align, but it's not always the case.
• 29:20
  And so that's part of the, part of the difficulty of growing a business is finding what that most important thing or things are and then actually effectively hitting them.
• 29:28
  Oh, I live on a daily basis, Carson.
• 29:31
  Believe me, believe me, it's never the thing that you most want to do.
• 29:35
  It's never the thing.
• 29:36
  But you know, I'm full of coke this morning.
• 29:38
  But the one that springs to mind with that is the progress you're looking for is in the work you're avoiding.
• 29:43
  And I've seen that to be true in a bunch of different places.
• 29:46
  That's part of where we think there's actually opportunity for outcome based deals is because every time that you work a deal, no matter who it is, you bring on a consultant, you bring on even an employee.
• 29:55
  Anytime you bring on a technology, it takes mental energy and effort to support, sustain, check in, have accountability, whatever it is.
• 30:04
  And that's part of what we talked about.
• 30:05
  You're always going to have a 20% reduction.
• 30:07
  And so you need to have a large enough outcome, a large enough sweetener to make it worth doing.
• 30:12
  Our vantage point on outcome based deals is if we can reverse risk enough that we actually don't need to take mental energy away from you.
• 30:19
  You can stay focused and hammering on your most important stuff and we can work on the periphery, pulling in these valuable outcomes for you that don't take you off of your constraint, that don't take you off, because if
• 30:29
  you run it multiple times and you've run this exact playbook at a very similar company, you don't need to take as many resources in order to get it done.
• 30:36
  And so that's part of the vision as we continue to get better and better at running these, is that we actually make it easier for you to get these wins while you do the real work.
• 30:44
  Rather than saying, no, I can't handle anything else, we can run so autonomously that you can still get your wins while you get outcomes.
• 30:52
  Awesome.
• 30:53
  And that's the best way to do it.
• 30:54
  Minimize the impact to the current run rate.
• 30:57
  It's always an admirable thing.
• 30:59
  So for all the things you've been involved in, I think you're a good person to ask this question too.
• 31:02
  As with anything in the media, what businesses or surprising businesses do you genuinely think are at risk of disruption over the next couple of years?
• 31:11
  Man, Graham, you're getting into some philosophical,.
• 31:14
  Deep and philosophical this week.
• 31:15
  Yeah.
• 31:17
  So probably at the highest level, artificial intelligence as a promise for human flourishing is unlikely in part because we can look backwards, because I don't know how to look forwards, but we can look backwards and see most technological
• 31:30
  improvements, like the dishwasher, whatever, humans, as soon as you get that time and energy back, you find other things to stress about.
• 31:36
  And so the fact that you and I, Graham, we started before this calling started, the fact that we're not talking about guns and war and bullets and food right now is remarkable in the course of human history.
• 31:45
  So we can even have this conversation about EBITDA and ARR and the whole thing.
• 31:48
  And so a simple vantage point on AI is that it's not necessarily going to change human flourishing.
• 31:54
  But will it change the type of things we can work on and give us more opportunity for those who are intentional to have more freedom and perhaps be on the beach?
• 32:01
  Yes.
• 32:02
  To that end, which industries will be most disrupted?
• 32:05
  It seems we hope to be on the bleeding edge of the ones that can be disrupted.
• 32:10
  The places where humans are involved and tech are involved without efficiency.
• 32:14
  And you can instead take artificial intelligence who can run a consistent playbook.
• 32:19
  That's where we think it'll hit first.
• 32:20
  And so we think we're actually creating more industries than we are just displacing in the immediate term in the form of outcome based deals that don't require a bunch of humans in the loop?
• 32:29
  Yeah, that's usually the way it goes.
• 32:31
  I agree.
• 32:33
  Even if they were goaded into it, it's perhaps unhelpful when all the leaders of these AI companies get rolled out on the White House lawn to talk about how the world's going to change in.
• 32:43
  It is changing.
• 32:44
  This is.
• 32:44
  I mean, it's incredibly powerful.
• 32:45
  You think about, we've let the cat out of the bag now, so you can't go back.
• 32:48
  But the downside risk of artificial intelligence is not zero.
• 32:52
  And the upside, as I mentioned, may be less than we would hope.
• 32:57
  And so it's something to be aware of.
• 32:59
  If the cat's out of the bag, you can't go back now.
• 33:01
  But will it make life better?
• 33:03
  Partially, remains to be seen.
• 33:04
  The question is, can we have the intentionality to live a better life along the way?
• 33:08
  And we shall see.
• 33:09
  Yeah.
• 33:10
  I'll give Gartner credit for the.
• 33:12
  What's it called, the hype cycle, where you have the peak of inflated expectations followed by the trough of disillusionment.
• 33:18
  Where do you think we are with AI?
• 33:20
  I think we're still in the peak of expectations.
• 33:22
  Maybe just flipping over the side of the hill towards the trough.
• 33:24
  I don't know what your thoughts on that are.
• 33:27
  Anyone that we've run an AI implementation with has been willing.
• 33:32
  This is sort of my former life prior to outcome based deals.
• 33:35
  The only reason you're willing to run an AI based thing is because you are in so much pain of your current situation that you thought this has got to be better.
• 33:42
  Like, I'm hiring these folks.
• 33:43
  They're not saying the right things on the phone.
• 33:45
  I would much rather have an agent that I can control and have playbooks to run for who can do this for me.
• 33:51
  And so my experience is that.
• 33:52
  But everyone who started in one of those projects got burned because it was like, oh, just push me.
• 33:58
  Just like let the agent respond.
• 33:59
  And then the agent went completely haywire and started to lose the thread.
• 34:04
  Which is why businesses like Sierra came along and said, wait a minute, wait a minute.
• 34:07
  We have to actually control these things and specialize by niche.
• 34:10
  Like customer service is a very niche use case, but we're going to crush it and run it through every Fortune 500 business and change the way that they do this.
• 34:18
  They were, they were one of the ones who got us incredibly inspired in the world of outcome based deals, as you know, that they, they structure all of their work based on the outcome that they deliver.
• 34:26
  And so I think that we're still plenty of folks in the hype cycle, but if you've tasted it and really tried to run it and change a piece of your business using artificial intelligence, you realize, oh, wow, we
• 34:34
  might not be as far as we thought, like it can give outputs, but are they actually better than a thoughtful human?
• 34:39
  That remains to be seen.
• 34:40
  And so humans using AI is going to continue to be really relevant.
• 34:43
  Yeah, yeah.
• 34:44
  It's an exciting time to be involved in building and growing companies for sure.
• 34:48
  So I'm excited for both of us.
• 34:51
  For folks who want to hear more from you, Carson, where should they go and find you on the Internet?
• 34:56
  I'll put some links in the show notes below, of course, but where should the good folks go find you?
• 35:01
  Let's start with LinkedIn so you can just check me out at Carson Mar there.
• 35:04
  And then over on Substack, we love to write about complex deal making, the world of B2B tech and how do you build better deals?
• 35:11
  How do you build outcome based deals?
• 35:12
  And then really, you know, lessons and field notes from, from being out in the world.
• 35:16
  Those are the two places to get started.
• 35:18
  Love it.
• 35:18
  Great.
• 35:19
  Substack's a real growing platform as well, I think.
• 35:21
  I really like the, the vibe they've got over there and a really nice, again, outcome based as well over there.
• 35:27
  It's like you bring a load of eyeballs, you get 90% of the cash that you take and it's very simple and straightforward.
• 35:33
  And you know, I think they saw an opportunity with Medium.
• 35:35
  You know, Medium's algorithm for how much people were getting paid was kind of all over the place and they changed it behind the scenes and so there was no clarity and, you know, folks didn't feel that the outcome
• 35:46
  based incentives were aligned.
• 35:47
  So they all walked across the substack.
• 35:50
  Yeah, it's a nice note to finish on, I think, Carson, thank you so much for coming on.
• 35:53
  It's been great chatting with you again.
• 35:56
  I hope it's our last conversation.
• 35:57
  I'm sure it won't be.
• 35:58
  Go.
• 35:58
  Go out there and keep creating those great outcomes.
• 36:00
  It's been great having you on the show, and thanks for sharing with everyone.
• 36:03
  Thanks for magic.
• 36:05
  All right, take care.
• 36:06
  Thanks for listening to the PE Data guy.
• 36:09
  The place where private equity meets data.
• 36:11
  Please forward this episode to your favorite private equity friend.
• 36:15
  Thanks for listening.
• 36:16
  See you next time.