Dan Martell - From Ballet Dancer to Lead Investor with Emily Walsh @ Georgian Partners - Escape Velocity Show #46

00:00:00.000 Saying no is the worst part of the job.

00:00:01.540 Best part of the job is just meeting an incredible array of incredibly talented people

00:00:06.080 who have vision about what the future could look like

00:00:08.500 and getting the sneak peek of what that could be.

00:00:11.440 It's very exciting.

00:00:12.900 Ignition sequence start.

00:00:14.880 Three, two, one.

00:00:25.920 Emily Briggs, thanks for coming on the show.

00:00:28.260 Thank you for having me.

00:00:29.320 I appreciate you being here. So you are a principal at Georgian Partners, raised $1.2 billion across four funds. Can't say if there's more to come, but that's what people do is they raise money. What has been your journey to get to the point where you're now writing checks and investing in technology companies?

00:00:51.000 Sure. So I think across the investing landscape, people have very different paths. You often talk

00:00:59.240 to investors and you'll find they come from really different, bizarre sort of backgrounds.

00:01:04.620 Mine, I think, falls in that line. So I actually did my undergrad in dance. I trained my whole

00:01:10.080 life to be a professional ballet dancer.

00:01:12.820 Juilliard?

00:01:13.080 Yeah, I went to Juilliard.

00:01:14.520 That's crazy.

00:01:14.740 Um, spend four years there, uh, it was an incredible experience, um, made lifelong

00:01:20.740 friends, uh, graduated, was freelancing in New York, um, realized that, you know, my

00:01:28.120 career, uh, sort of the pinnacle you can get to in that career is, is some of my

00:01:33.100 teachers at Juilliard who I look to and, and just see that how hard their lives

00:01:38.160 were, um, you know, in their forties and fifties, um, you know, struggling to, to

00:01:42.440 make ends meet in some cases and realized that I wanted sort of a broader career and I had the

00:01:48.780 opportunity to join a nonprofit that had been started by two colleagues from Juilliard as the

00:01:53.960 first full-time employee. I ended up running that for about four years in New York. We did arts

00:01:59.460 accessibility work so we worked in schools, hospitals, and communities bringing professional

00:02:04.680 artists to work with folks in need and learned a lot of like hard business lessons during that

00:02:10.920 time, it was, you know, had very little kind of formal education.

00:02:14.280 Non-profits is probably like the hardest, I mean.

00:02:17.140 It's super hard.

00:02:18.060 Yeah.

00:02:18.800 So raising money, finding people that

00:02:21.360 want to work for significantly less than they

00:02:23.440 could get paid elsewhere, you need a really strong vision

00:02:26.820 and a really strong kind of driving motivation, exactly.

00:02:31.420 And, you know, the education at Juilliard was amazing,

00:02:34.780 but I literally danced for 12 hours a day, six days a week.

00:02:37.200 And then you're running this part time?

00:02:39.320 So once I transitioned to this kind of full time,

00:02:44.080 it was maybe three, four years after undergrad.

00:02:47.040 So yeah, I learned a lot of really good lessons

00:02:49.640 during that time, but realized I just

00:02:51.920 had no formal education on the subject of business at all.

00:02:55.280 All of my lessons were learned the hard way.

00:02:58.040 And so I ended up going back to my MBA at Cornell,

00:03:02.240 went to McKinsey afterwards for a couple of years.

00:03:04.880 What was that like?

00:03:05.760 Because I've seen that.

00:03:06.460 I forget the name of the show about like these,

00:03:10.300 I mean, they live on a plane, you know what I mean?

00:03:13.300 I think it's like Sunday night, Thursday,

00:03:16.700 and a lot of entertaining, a lot of modeling.

00:03:19.960 Is it that?

00:03:20.600 No.

00:03:21.020 No?

00:03:21.520 OK.

00:03:22.020 It's sitting in a dark conference room

00:03:25.500 in some forgotten windowless part of a client's building.

00:03:28.360 Corporate America somewheres.

00:03:29.760 Exactly.

00:03:31.300 I think we got to work on some really interesting problems,

00:03:34.300 And actually, some things that ended up being super relevant

00:03:36.640 to the work I do at Georgian, working with a lot of banks

00:03:39.640 and insurance companies going through digital transformation,

00:03:42.400 figuring out how do I adopt new technology,

00:03:45.460 but also how do I change the way my team works?

00:03:47.960 How do I put new KPIs in place?

00:03:49.800 How do I hire entirely new types of roles

00:03:52.000 that I've never had before?

00:03:53.160 I guess as an entrepreneur, I've always,

00:03:55.060 when I think of management consulting, or the McKenzie,

00:03:57.940 or whatever it is, I just can't see,

00:04:01.140 would these companies want you there?

00:04:03.060 You know what I mean?

00:04:03.840 Like, I just, I'm like, why would I pay so much money

00:04:06.080 for somebody telling me something that I probably

00:04:07.620 know I need to do?

00:04:08.280 But is it the data set they're looking for?

00:04:09.800 Or did they really not know what direction they should go?

00:04:13.380 Yeah, so sometimes, you know, I would say more often than not,

00:04:17.040 the solution is known.

00:04:18.840 The direction they want to go is known.

00:04:21.680 They're looking for external validation

00:04:23.720 and also the experience of a group that's maybe

00:04:25.960 done it before and can help with some best practices.

00:04:28.680 So they're looking at the fact that you come with expertise

00:04:30.540 in finance or banking.

00:04:31.880 Exactly.

00:04:32.600 And they know you probably worked with their competitor,

00:04:34.840 so maybe you guys got some stuff.

00:04:36.660 But it was a Chinese wall.

00:04:38.280 Right, exactly.

00:04:39.260 I mean, I think banks are adopting new tools,

00:04:42.020 new ways of working.

00:04:43.280 And that's uncharted territory for many of them.

00:04:46.420 And I think that bringing in a McKinsey or a BCG or a Bain

00:04:49.520 is one way to kind of guard against the potential pitfalls

00:04:53.420 of going down a very new path.

00:04:56.020 And I think, again, relevant to the work I do now at Georgian,

00:05:00.420 You asked, do they want you there?

00:05:02.840 I think we often worked in embedded teams.

00:05:05.400 So you'd be going into a team from a bank, a team from McKinsey

00:05:09.140 working side by side.

00:05:10.540 And I think there is that inherent tension of,

00:05:13.020 do they want us there?

00:05:14.460 Maybe not everybody.

00:05:16.140 And you really have to work together

00:05:19.700 and lead through influence.

00:05:20.840 And I think that's a very similar thing to working

00:05:22.960 with portfolio companies post-investment.

00:05:25.840 We're not majority investors.

00:05:27.180 We don't come in and bring in, this is the way 0.99

00:05:30.400 things are going to be.

00:05:31.880 We're minority investors.

00:05:32.800 And so everything we do has to be through influence.

00:05:34.640 And it has to be through kind of establishing a shared vision

00:05:37.000 and getting people on board.

00:05:38.020 And we're all working towards the same goals.

00:05:39.700 And that was a really good lesson, actually learned.

00:05:44.140 That's interesting.

00:05:44.800 Yeah.

00:05:45.300 Instead of just the influence side of how do we present

00:05:49.920 something where they kind of come to that conclusion

00:05:52.480 without feeling like we told them to do it.

00:05:55.900 What did you, what were some of the lessons learned in, you know, learning to dance that you actually brought to the business world?

00:06:04.640 Yeah, it's a, that's a good question.

00:06:07.820 It's actually something I've thought more about as I've gotten later in my career.

00:06:11.960 Anybody that's like great musicians or any artists, a lot of programmers I know are really great.

00:06:17.180 You know, they have other areas they've mastered and I'm just curious what translated for you.

00:06:21.500 Yeah, I think some of the things that I've appreciated more as I've gotten older,

00:06:26.380 I think when I started out in my career, I was like, oh, I'm missing all my business career.

00:06:30.220 I'm missing all these things. I don't know all these hard skills. Now, I've really started

00:06:34.620 reflecting on what do I know through all of those years of a very high discipline environment.

00:06:42.460 And I think grit is one of the big things. And I think just the ability to go in day after day

00:06:51.180 and and really push towards something i think with dance it's you know you go to these auditions

00:06:55.580 with 800 people in the room and your number like 757 and you might get told no you know 30 times

00:07:02.540 in a row before you get told yes and i think that that level of kind of just drive and determination

00:07:08.140 and and doing it just because you want to and with very little hope for other rewards is i think a

00:07:14.460 really good lesson that I learned.

00:07:17.100 And I think it's a very kind of long-term view

00:07:20.580 that you have to have about your career

00:07:22.200 and why you're doing what you're doing.

00:07:23.760 And that really translates over into the investment world.

00:07:27.480 I mean, we're in a very long-term business.

00:07:29.460 It's a tough business.

00:07:30.720 It's a competitive business.

00:07:32.640 And I think those lessons have kind of illuminated

00:07:35.760 themselves to me over time.

00:07:37.140 And I've appreciated them a lot more.

00:07:38.760 That you went through that experience gives you context.

00:07:43.080 Because it's fascinating.

00:07:44.340 I remember a long time ago, it occurred to me

00:07:47.440 that investors, they're entrepreneurs.

00:07:49.360 They got to go raise money.

00:07:50.320 They have LPs.

00:07:51.880 It actually made it, because there's always

00:07:53.700 this, if you don't know any better,

00:07:55.240 you're just like, oh, there's these people

00:07:56.600 that want to take a piece of my company for money

00:07:58.360 and not do any work, and I'm doing all the work.

00:08:01.780 But once you realize that they have

00:08:03.980 to do a lot of the hard stuff that founders do,

00:08:08.760 it creates more of a shared journey.

00:08:11.560 Your experience so far, I know you've led a few deals.

00:08:15.160 You help with board governance.

00:08:17.980 What have you learned about the influence side, what you just

00:08:20.980 mentioned, in regards to relationships with CEOs?

00:08:26.560 And are most of the companies you guys are involved in,

00:08:29.020 is it still the founder running these, or is it?

00:08:31.480 It's a mix.

00:08:32.560 It often is.

00:08:34.380 Sometimes we're coming into a CEO

00:08:36.700 has taken over from an original founding team.

00:08:38.540 But often, we're working with a founding team.

00:08:40.240 You don't do majority in regards to control,

00:08:42.460 but do you lead rounds?

00:08:43.700 We do, yeah.

00:08:44.380 We're usually coming in as the lead.

00:08:46.220 And more growth stage.

00:08:47.700 Growth stage, yeah.

00:08:48.600 So we're coming in kind of $10 to $20 million of revenue,

00:08:51.620 north of $6 million of ARR is our threshold.

00:08:54.520 That's the lower end, $6 million plus.

00:08:56.240 Exactly.

00:08:57.000 And what's the size of the investments, typically?

00:08:59.400 So generally, $20 to $40 million.

00:09:03.180 Sometimes we'll go a little bit higher,

00:09:05.020 but $20 is sort of the low end.

00:09:06.520 And then we're leading the rounds.

00:09:09.120 And I think to answer your question on how do you think about influence, I think founders

00:09:17.420 have the toughest job in the world.

00:09:19.840 It's incredibly hard running a company.

00:09:21.560 And I think bringing something authentic and real as an investor is really important and

00:09:27.520 always doing what you say you will do.

00:09:30.320 So Georgian has set ourselves up very much like a software company.

00:09:34.920 We have an in-house R&D team.

00:09:36.760 We have an in-house customer success team

00:09:38.440 and operations team.

00:09:39.440 Wow, you call it that.

00:09:40.540 Yeah, we do, actually.

00:09:41.500 Nice job.

00:09:42.580 So we've very much taken the mindset

00:09:45.080 of we're going to model ourselves

00:09:46.520 after one of our companies.

00:09:47.860 And actually, we've been on our own growth trajectory.

00:09:50.620 When I joined four years ago or so, we were 17 people.

00:09:53.500 We're almost 60 now.

00:09:54.460 60, yeah.

00:09:55.080 So we've really thought carefully

00:09:56.780 about how do we build this company to resonate

00:09:59.280 with the companies that we invest in.

00:10:02.140 And from a value add perspective, when we go in

00:10:06.400 and we say, we think we can help you in these areas.

00:10:10.120 I think living up to that is something

00:10:12.040 we think about all the time, is how do you live up

00:10:13.960 to those early discussions and make sure

00:10:16.180 that you're, that trust, that ability to influence.

00:10:18.700 What you said you would do.

00:10:20.320 Exactly.

00:10:20.980 Yeah.

00:10:21.640 Exactly.

00:10:23.020 The ability to influence is predicated on trust.

00:10:25.820 And I think trust comes with being authentic

00:10:28.840 and living up to your word.

00:10:30.460 And you said that that was two things, authentic and say,

00:10:34.720 do what you're going to say.

00:10:36.280 How does the authentic part, because that's

00:10:38.100 what I find really fascinating, Emily,

00:10:39.540 when I've seen other interviews of you online,

00:10:41.460 is, you know, and I think your creative background,

00:10:45.800 you're very honest about who you are,

00:10:47.840 and this is, you know, my journey.

00:10:49.900 And I think that's what, and obviously,

00:10:52.060 the Mackenzie experience, I'm assuming, you know,

00:10:53.800 trying to go to, like, middle America

00:10:56.620 and try to connect with these people that may, you know,

00:10:58.900 like, is it that you're more transparent and honest?

00:11:03.220 How do you build that trust quickly?

00:11:04.920 If a founder wants to do this, what are some of the approaches that they could do that you've found work really well?

00:11:12.460 Yeah, I mean, I think that is hard.

00:11:14.160 How do you establish that relationship in an efficient way, in a quick way, right?

00:11:18.400 Sometimes you're building a relationship in, let's say, a month, a six-week diligence process.

00:11:23.380 How do you do that?

00:11:24.100 I mean, I think that, you know, one of the things we always encourage people that we're talking to to do is talk to other CEOs that we've worked with to hear their story.

00:11:33.060 you know, call anyone in the portfolio, get their story about Georgia and about the people

00:11:36.320 you've worked with there. You know, that's important. I do. And I do think, you know,

00:11:40.500 opening up, certainly there's things you can share. There's things you can't share in any

00:11:43.960 context, but opening up and sharing what you can in a very kind of transparent and forthright way.

00:11:49.360 And sometimes that's, this isn't a great investment for us. And here, let me explain to you exactly

00:11:53.860 where we invest and why and how I think you can, you know, further your business.

00:11:59.400 Let me provide you with some meaningful feedback and some help to get where you want to go, even if it's not a fit.

00:12:05.180 Can you do that, Emery? Because that's tough, right?

00:12:06.280 It's very tough.

00:12:07.620 I've invested in 40 companies, and I've always wanted to be not just a no, but a no, and here's why.

00:12:14.920 Some people don't want to hear, but I just felt like, you know, how do you do that for founders?

00:12:21.280 Yeah, and I think because we're at a later stage, we tend to have a fair bit of data to work with.

00:12:26.360 So we've usually done a lot of meaningful work.

00:12:29.040 By the time we get to a no, if we've

00:12:30.800 gotten into a process with someone,

00:12:32.320 we often offer to share back that work,

00:12:34.320 whether it's a financial model we've built.

00:12:36.740 You know, we won't share, let's say, a return case.

00:12:38.980 But we'll share, you know, here's a model we built.

00:12:41.520 So that's our internal case that says.

00:12:43.360 Let us know the inside.

00:12:44.460 This is it.

00:12:45.260 We're getting the good stuff.

00:12:46.460 Yeah, so we build our own model internally

00:12:49.640 to validate whether or not a potential investment

00:12:52.980 will meet our return criteria.

00:12:54.600 So we take a company, a company gives us a management plan.

00:12:58.260 We build a bottoms up view of that plan.

00:13:02.420 It's, I think, almost always lower than what

00:13:05.280 the management thinks if we project out three or four years.

00:13:08.040 That becomes our worst case or like?

00:13:10.120 That's our kind of mid case.

00:13:11.420 This is our practical reality, Georgian views.

00:13:14.440 We're going to get here in four years.

00:13:16.040 And that case needs to meet kind of our return threshold.

00:13:19.260 And then our low case would be, this thing really

00:13:23.100 just doesn't take off.

00:13:24.280 We end up in a bad place, really much lower growth.

00:13:27.160 Acceptable risk, like you're evaluating the risk levels.

00:13:29.880 And so we look across all these three cases,

00:13:31.860 and we kind of get to a view.

00:13:33.600 Return case.

00:13:34.960 Is there a standard norm for investments?

00:13:38.900 Is this an investing kind of thing that people know?

00:13:42.580 Like McKinsey, do they do this for companies?

00:13:46.120 I don't know if they do it in the same way.

00:13:48.280 Does Georgian have an IP around that?

00:13:50.500 No.

00:13:51.500 Our peers were all doing very similar things.

00:13:54.200 Got it.

00:13:54.700 Like in the growth stage, I think it's a pretty common model.

00:13:56.880 And the inputs are traditional inputs,

00:13:58.660 or do you guys look at geopolitical stuff?

00:14:01.980 Like what are you looking at as inputs on?

00:14:03.860 Yeah.

00:14:05.160 So we do look at market risk really carefully.

00:14:08.300 So we have kind of a separate market process

00:14:10.640 that we go through.

00:14:11.900 On the financial case, we do a very kind of in-depth granular

00:14:15.720 buildup on every lever of the business.

00:14:18.020 So that looks at reps that you have,

00:14:21.560 how fast are you going to hire people,

00:14:22.920 how fast are they going to churn out of the business,

00:14:25.760 what are their quotas, what's attainment been previously.

00:14:28.500 I can go on and on.

00:14:29.780 But they're very, very detailed builds.

00:14:33.080 And then if we're looking at, let's say,

00:14:35.480 a model that's more in the SMB space,

00:14:37.160 we might look at a macro view of products,

00:14:40.460 how those sales cycles have worked over time,

00:14:43.540 what is the impact of churn been over time,

00:14:45.680 how do we project that forward?

00:14:46.820 How much data are you buying, like, to create these models?

00:14:51.580 Like, I'm assuming you guys have, like, other companies that provide data feeds for this?

00:14:55.740 Or how to?

00:14:56.620 This is actually, we do, every company we get kind of deep into a cycle with, we do it bottoms up.

00:15:02.540 Oh, you're learning, too.

00:15:03.760 Exactly.

00:15:04.420 Yeah, so we do it bottoms up for every company.

00:15:07.400 And sometimes we will look at benchmarks.

00:15:09.680 So we'll benchmark a potential investment to the top quartile of our portfolio, for example.

00:15:14.120 But is there companies that sell this kind of industry data?

00:15:19.120 Like are there brokers, data brokers for,

00:15:23.700 I feel like in the market investing,

00:15:26.120 like high frequency trading, there's

00:15:28.080 all these different data sources.

00:15:29.240 But is there like for VCs or private equity

00:15:31.480 or growth stage equity, is there companies?

00:15:34.880 Because as a founder, it sounds fascinating.

00:15:36.720 Maybe I should do this for my own planning.

00:15:38.960 I think it is a good exercise for companies to go through.

00:15:41.720 So we'll often, we won't share kind of the ultimate output.

00:15:44.760 But what we will share is the bottom-up build that we do.

00:15:47.400 Yeah, this is the thinking process.

00:15:48.740 Exactly.

00:15:49.400 And we'll share that back to people.

00:15:50.620 And hopefully, maybe they find value, maybe they don't.

00:15:52.940 But at least it shows the kind of depth of thought

00:15:55.380 we put into it.

00:15:56.080 Here's, especially if you say, no,

00:15:58.160 these are the reasons we don't think it's

00:16:00.840 going to fit for our return.

00:16:02.060 And it's not bad, because there's also the fund age.

00:16:05.380 So I know as founders, if you raise from a later stage fund,

00:16:08.940 then they've got to do a return.

00:16:12.700 So there's sometimes pressure at the board level.

00:16:15.120 These are things that first-time founders probably

00:16:16.860 don't know that well.

00:16:18.660 I find that really fascinating.

00:16:20.320 Yeah, we put a lot of thought into it.

00:16:22.320 And we do try and share back as much as we can

00:16:24.460 to the companies we talk to.

00:16:27.260 To go back to your question, we really

00:16:29.940 do try and show value, even if the answer is no.

00:16:34.020 We try and be a process that a founder looks back and says,

00:16:37.020 like they said no but I learned something and it wasn't a no and I don't

00:16:41.460 know why it was and when's the last time you don't have to give me the the case

00:16:46.800 that somebody passed on you guys you wanted to do the deal and they didn't do

00:16:52.520 it how'd that feel like that you were like meaningfully involved maybe you

00:16:56.400 created this whole like whatever plan back thing or how does that feel how do

00:17:00.480 you deal with it it's really tough when when you you know you you don't get a

00:17:04.200 a deal that you really want.

00:17:05.960 We spend a lot of time thinking about those deals.

00:17:09.980 Where did we miss?

00:17:10.800 How long does that process last?

00:17:12.700 The retrospective?

00:17:13.820 No, it's the diligence process.

00:17:15.820 Yeah.

00:17:16.320 Usually, so honestly.

00:17:17.680 Do you have a full process after if you don't get a deal

00:17:20.420 to the retrospective?

00:17:21.940 We haven't formalized it, but we do think a lot about it.

00:17:23.540 OK, it sounded like very formal real quick.

00:17:25.440 We do think a lot about it.

00:17:26.400 We talk a lot about it.

00:17:27.380 Yeah.

00:17:28.040 There's a feedback loop into what

00:17:29.780 can we do next time to make sure this doesn't happen.

00:17:31.420 Exactly.

00:17:31.960 And we've, over time, made a lot of changes to our process

00:17:36.580 when we have reflected on things that we really wanted

00:17:40.020 and didn't end up getting.

00:17:42.420 And usually, it goes back to not understanding

00:17:45.860 the motivations.

00:17:47.540 So we spend a lot of time trying to understand right up front,

00:17:50.380 when we start talking to founders,

00:17:52.420 what does an ideal partner look like for you in this round?

00:17:56.060 Who's going to make the most impact on your business?

00:17:59.140 Who do you want to bring in? 0.97

00:18:00.340 And you're asking the CEO that?

00:18:01.960 Yeah.

00:18:02.620 Yeah.

00:18:02.860 So usually now, every call, first call we do with a company,

00:18:06.040 we'll ask, who's your ideal partner?

00:18:08.480 What do you want to get out of a partner in this round?

00:18:11.340 Because if you know that's not the characteristic

00:18:13.580 of what you guys can do, you will just bow out?

00:18:16.860 Or you want to make sure you present it?

00:18:18.820 I think sometimes the answer is,

00:18:21.960 it may just not be the right fit.

00:18:23.700 I think more often than not, whether or not

00:18:25.720 it's exactly what we're thinking we bring to the table,

00:18:29.200 There's enough information there that we

00:18:31.200 can adjust how we communicate in our process

00:18:35.900 and present those aspects of what we do,

00:18:39.400 because we have a pretty broad offering across Georgia

00:18:41.740 at this point, in the best light to answer that kind of need

00:18:46.080 or concern of the founder.

00:18:47.500 So it's really just about if you go to a proposal,

00:18:50.740 you need to make sure that you address the things

00:18:52.700 that they're asking.

00:18:53.300 I mean, it's pretty straightforward.

00:18:54.700 Right.

00:18:55.000 Sales.

00:18:55.600 It's sales, right?

00:18:56.600 It's 100% sales.

00:18:57.400 It's 100% sales.

00:18:58.860 And it's a competitive industry, and so I think thinking along those lines has become something that's very much ingrained in our team.

00:19:06.100 How do you guys look at differentiation? Like, what's the unique things that you guys have decided to tell the market about Georgian?

00:19:13.980 Yeah, so our kind of core differentiator and how we were always set up right from the beginning, 11 years ago when Georgian got started,

00:19:21.960 is that we would have what we call our impact team, and this is basically an applied research lab in-house at Georgian.

00:19:28.280 So we have a group of machine learning, researchers, engineers, data scientists, NLP folks, cybersecurity folks on staff at Georgian.

00:19:36.560 And they work with our portfolio.

00:19:38.680 So that's really how we lead.

00:19:42.380 That's some value adder.

00:19:43.400 And our differentiation.

00:19:45.060 So going back to the CEO con, we really look of where can we add value in product?

00:19:49.840 Where can we add incremental knowledge to your organization to help your R&D team move faster?

00:19:56.480 So they may not have the internal resources to do the analysis.

00:19:59.820 Now I see where your background from McKenzie really can be like a differentiator because you already know how to crunch the data or what to look for, how to bring that up.

00:20:08.480 Because most founders are too busy thinking about the future to actually even optimize the stuff they're doing.

00:20:13.060 And there could be some like crazy low hanging fruit.

00:20:15.520 Yeah.

00:20:15.820 And we'll often see that with companies that have amassed a really valuable data asset.

00:20:20.020 Yeah.

00:20:20.480 And maybe haven't monetized it yet.

00:20:22.700 Yeah.

00:20:22.900 And there's incredible insight to be kind of unlocked from that.

00:20:26.860 And so in those cases, when we invest in that type of company, we'll often help them identify what those insights would look like, build up a data science team, and then maybe even do a dedicated research project where we'll come in for three to six months and help them build that first product module.

00:20:41.080 Really? Help them like hire, build out the product?

00:20:44.180 Exactly.

00:20:45.340 And that's really cool.

00:20:46.660 Yeah.

00:20:46.920 So that's like really where we lean in with differentiation when we're talking to potential investments.

00:20:52.900 interesting and are all fun all four funds that you've raised so far had the

00:20:59.020 same like investment characteristic or thesis yeah so actually our first two

00:21:05.260 funds were all around the thesis area of applied analytics so we got started in

00:21:09.580 2008 big data was very much a new term in 2008 and and the idea that you could

00:21:16.640 draw these insights from a business process feed those insights back to

00:21:20.500 to consumers, they were going to find incremental value

00:21:22.340 that they would pay for.

00:21:23.900 From those new insights, that was all very much emerging.

00:21:26.360 And we really saw from 2008 to about 2014, 2015,

00:21:31.000 that maturation happened, where 2015, 2016,

00:21:34.060 you're starting a business, you're not

00:21:35.400 thinking about those things, you're probably behind.

00:21:37.260 At a disadvantage.

00:21:38.280 And so that was the maturation of applied analytics

00:21:41.560 as our thesis area.

00:21:42.460 We evolved applied analytics to what we now invest in,

00:21:46.020 which is applied AI.

00:21:47.880 So increasingly using machine learning technologies

00:21:50.460 to unlock new insights, automate workflows.

00:21:54.120 Second one is conversational AI.

00:21:55.980 So you look at messaging and voice-based interfaces,

00:21:58.140 how you can personalize the customer experience

00:22:00.840 through new communication channels.

00:22:02.940 Are you guys an investor in Chorus?

00:22:04.360 Yes.

00:22:04.860 Yeah.

00:22:05.360 Yeah.

00:22:05.560 Such great.

00:22:06.220 I mean, it's next level.

00:22:08.740 Like this is where people, founders that

00:22:11.780 are not using tools like this, they're

00:22:13.620 100% at a disadvantage.

00:22:15.180 Because the ability to work with your team

00:22:17.820 to identify the bright spots amongst your sales reps,

00:22:23.040 clip, add to a training database, onboard,

00:22:26.280 train, all through automation around the voice level.

00:22:30.000 I mean, it's like when, so if people,

00:22:33.000 you guys can Google all this stuff,

00:22:34.220 but when, do you have companies that you've

00:22:38.160 seen that are starting to do the conversation?

00:22:41.020 Like at some point, because there's enough data,

00:22:43.780 the conversation might not be with a human.

00:22:46.900 Right.

00:22:47.740 I could see that because I mean, I train salespeople

00:22:50.140 and like if they just do what I tell them to do,

00:22:52.260 they usually get a fairly decent result.

00:22:54.060 And it's a lot of it's question,

00:22:55.340 do you think there'll be a point where for maybe

00:22:57.220 on the SMB or the support side,

00:22:59.280 the conversation is being done with non-human?

00:23:03.480 Definitely, I think we're seeing more and more

00:23:05.160 of those kind of front end conversations,

00:23:07.040 early conversations being automated by-

00:23:08.960 Through the bot stuff for sure right now, yeah.

00:23:10.800 Exactly.

00:23:11.780 And they're getting smarter and smarter over time.

00:23:13.660 And it's an interesting space

00:23:14.740 because you're seeing this three kind of pillars.

00:23:18.180 You're seeing the big companies like Microsoft

00:23:21.140 getting into this, Salesforce, Einstein getting into this.

00:23:23.920 You're seeing a really interesting cohort

00:23:25.780 of kind of up-and-comer companies.

00:23:27.360 Ada support in Toronto is a really good example

00:23:29.880 of a company in that space.

00:23:31.360 And then you're seeing the in-house, the do-it-yourself.

00:23:33.960 So there's frameworks like Rasa,

00:23:35.480 which is a conversational AI or an NLP framework

00:23:39.580 that companies can use to build their own.

00:23:41.440 So it's interesting kind of seeing the three different kind of competing angles of how do we get this market where you can really automate a lot of that customer interaction.

00:23:51.940 You can save a ton of money for companies running contact centers by deflecting a lot of these calls or answering a lot of these concerns through an automated approach.

00:24:01.000 And it's also much better for consumers.

00:24:02.960 It's faster.

00:24:03.600 It's way faster.

00:24:04.640 And you're seeing interesting opportunities like, you know, generating revenue through those conversations.

00:24:09.640 Like, how do you hook into the back-end systems in order to access the customer account so you can offer them the right products as you're, you know, working on changing their mailing address or whatever?

00:24:20.100 It's a very interesting space.

00:24:21.800 So I think we're seeing more and more companies either going to market with a product around, you know, 100% kind of solving a problem in that space.

00:24:33.160 We're also seeing companies that have a broader solution and are adding those kind of conversational capabilities up front.

00:24:38.580 Do you think like in the future, maybe three or four years out, like AI is not like something unique to your business, but it's just like every business should have a team that specializes in insights from data or.

00:24:51.980 A hundred percent.

00:24:52.820 Yeah.

00:24:53.000 Like it's not, it's like saying I've got a database.

00:24:55.460 Yeah, exactly.

00:24:56.320 It's, it's, it's going to be table stakes.

00:24:58.340 Yeah.

00:24:58.580 We think.

00:24:59.680 And where do all these trained AI people come from?

00:25:02.980 Like where are your portfolio?

00:25:04.080 Like the fact that you have them on staff, I'm like, okay, that's not fair.

00:25:07.360 Yeah.

00:25:07.820 Yeah, there's definitely a deficit of talents in that area.

00:25:11.460 If somebody's trying to lean into that, what would your suggestion be if they wanted to

00:25:15.560 have somebody at least give them some counsel, some thoughts around auditing their data sets

00:25:23.000 and say, here's some opportunities for founders that are at $3 to $6 million in AR, have a

00:25:30.220 clear value problem.

00:25:31.340 Exactly.

00:25:31.820 And that's the right approach, by the way, starting with get some advice, understand

00:25:36.720 the data set because bringing on those resources is incredibly expensive and if you don't have a

00:25:43.040 really well scoped thing for them to work on and data that's in the right place often we'll say

00:25:48.640 start with a data engineer first and make sure you have a really strong what would that person do

00:25:54.480 so they would look across the data set that you have they would organize it in the right way they

00:25:58.240 would think about what could be done with with this data they would make sure that it's in a

00:26:03.760 in a kind of format and universities teach this data

00:26:07.600 engineering that's like a thing that you can get that it is it is

00:26:11.680 um so we'll often say you know start with just understanding your data first

00:26:15.360 before you bring in a data scientist before you bring in you know heavy duty

00:26:18.480 ml talent yeah um really have a firm handle on the

00:26:21.920 data the structure of the data um and the ability of it to be used

00:26:26.080 uh and an understanding put it in a format that could be digested or useful

00:26:29.200 exactly and and you know go and find maybe it's at a local university

00:26:33.120 You know, a computer science team there can help you get an understanding of the art of the possible.

00:26:38.360 You know, do some consultative work with folks before you start hiring up a team.

00:26:42.380 Because if you don't know exactly how they're going to be deployed, it's a ton of cash and frustration on the part of the people that you hire.

00:26:50.240 I just have a lot of people message me and they're like, I need an AI expert.

00:26:53.480 I'm like, that's like such a broad question.

00:26:56.540 It's like you don't even know what you need to do, but you just think it's AI.

00:27:00.040 Right.

00:27:00.480 Um, and so like, do you, when you look at the landscape of opportunities where you guys

00:27:05.220 are investing in, um, is, is it, is it just around like voice or applied AI?

00:27:12.160 Is it, um, what's the categories that AI seems to be, um, in a B2B context, not consumer,

00:27:20.340 right?

00:27:20.460 Cause we have self-driving cars and all that stuff coming online.

00:27:22.480 But like, what are the use cases from a business point of view that you feel are, um, low hanging

00:27:28.280 fruit you know the next year to three years out. Right, so we're seeing a lot

00:27:32.780 of interesting applications in companies that are selling into banks, insurance

00:27:37.040 companies, they have very high value data. They have a ton of it too. A ton of data,

00:27:41.840 very high value data, so we're seeing a lot of interesting companies there. One

00:27:47.180 of our newest investments is in real estate space. PropTech. PropTech. New word, I just

00:27:51.960 learned it Emily, thank you for teaching it to me. A company called Reonomy which is based in New York,

00:27:56.660 And they're in the commercial real estate field, which is an industry that historically

00:28:01.860 has had very analog, very messy data.

00:28:04.780 And we're kind of coming into a world with the introduction of all of these different

00:28:08.980 ways to manipulate and read data and ingest data, where a lot of that offline data is

00:28:14.620 now being able to be digitized and you can do really interesting things with it.

00:28:18.540 So that's a space we're excited about as well.

00:28:20.120 Have you seen, what's the company SoftBank invested in, in the real estate that essentially

00:28:25.100 buys houses?

00:28:26.100 Did you see this?

00:28:26.800 Is it XO?

00:28:28.000 What's it called?

00:28:28.640 Yes, I know what you're talking about.

00:28:30.340 Keith Reboi was an early co-founder.

00:28:32.620 I mean, here's a company.

00:28:34.040 I mean, billions of dollars now deployed as a debt fund, essentially, to buy homes.

00:28:40.080 And they have a whole infrastructure to renovate, to then resell, guarantee the sale prices, guarantee the buying, all powered through machine learning and data sets.

00:28:52.120 I mean, this is crazy.

00:28:54.360 Yeah.

00:28:54.800 Yeah, it's a really interesting time.

00:28:57.700 I think there's a lot of opportunity in PropTech.

00:29:01.520 Historically, we're all SaaS investments.

00:29:04.940 We've broadened that scope a little bit.

00:29:06.740 We have some businesses that are more transactional in nature

00:29:09.080 in the portfolio now.

00:29:09.820 OK, success fees versus reoccurring.

00:29:11.680 Exactly, yeah.

00:29:12.980 Which is a great, great pricing model.

00:29:15.220 I mean, pure alignment, value metric, the more success,

00:29:18.640 the more revenue.

00:29:20.240 Right, exactly.

00:29:20.960 And we see some good examples of marketplaces where

00:29:24.080 investors in a company called Ritual,

00:29:26.240 which is based out of Toronto, to more of a marketplace

00:29:28.700 model, transactional model.

00:29:30.740 But I will say, we look for predictability.

00:29:34.240 And I think there is a certain amount of data points

00:29:37.840 that we would need to see around a model like we're

00:29:39.940 talking about, where we're raising a huge debt

00:29:42.600 fund to buy out these properties.

00:29:44.460 And how does that model mature in age and play out?

00:29:48.360 I think we'd need to see more examples of that being

00:29:50.780 successful to feel comfortable just

00:29:52.800 from kind of our mandate of working for a property.

00:29:54.860 But I guess the way I'm like, if I just

00:29:56.760 look at like where the big opportunities are,

00:29:58.540 it would be in industries that have been around long enough

00:30:02.040 that have collected enough data to be

00:30:03.600 able to do these modeling activities, right?

00:30:06.780 So financials, real estate.

00:30:10.560 I've got to assume food, right?

00:30:12.300 Like agriculture, logistics.

00:30:14.680 Yeah, health.

00:30:17.000 There's some really interesting stuff happening

00:30:18.760 in the health care industry.

00:30:20.280 How neat is it in your role, because I don't think

00:30:22.740 people realize this.

00:30:24.060 As an investor, you're essentially

00:30:25.580 seeing into the future on a daily basis, probably, right?

00:30:29.560 As you meet with CEOs, they're showing you their roadmaps

00:30:33.100 that aren't public.

00:30:34.840 It's the best part of it.

00:30:36.820 Saying no is the worst part of the job.

00:30:38.300 Best part of the job is just meeting

00:30:39.840 an incredible array of incredibly talented people

00:30:43.020 who have vision about what the future could look like

00:30:45.320 and getting the sneak peek of what that could be.

00:30:48.260 It's very exciting.

00:30:49.200 Wow.

00:30:49.700 And then how do you, from a personal development point

00:30:54.520 of view, Emily, I'm just curious, what books do you read?

00:30:59.240 What do you do to better your skill set to be a better

00:31:03.720 investor, board member, partner?

00:31:06.600 Yeah, so read a fair bit.

00:31:08.500 I'm actually reading a book called Multipliers right now,

00:31:10.840 which is more on the team side.

00:31:13.120 Yeah, but read a fair bit.

00:31:15.940 read the blogs and the news posts and all of that.

00:31:19.980 And I think just talking to people

00:31:22.080 and kind of when I find something I'm really

00:31:24.520 excited about going deep and really understanding

00:31:27.040 what's happening in that industry.

00:31:28.320 And so that tends to be just driven by companies

00:31:30.960 that I'm talking to at that particular time.

00:31:34.120 But we do have the fortune of just

00:31:36.960 being in the cross-section of a really incredibly

00:31:39.760 talented group of people.

00:31:40.680 So I spend a lot of time just talking to people as well.

00:31:43.000 I mean, if you need to go deep down,

00:31:45.320 That's really cool.

00:31:46.220 Yeah.

00:31:48.140 When you look back at, like, your journey and kind of, like, who you are today, what, like, who did you need to become to be successful?

00:31:59.000 You know, mother, investor.

00:32:02.080 Yeah.

00:32:03.480 Like, kind of, when you look back at the person dancing at Juilliard to today, what were some of those life lessons and how did that impact your character?

00:32:12.420 Yeah, that's a really good question.

00:32:15.780 I know you weren't expecting that one, so that's the kind of question I like to ask.

00:32:20.140 Yeah, I think that, you know, dance is an interesting thing because it takes so much

00:32:24.800 kind of drive and dedication and precision and all of those things, and you don't talk.

00:32:30.920 You know, I basically spent the first 20 years of my life communicating physically and not

00:32:36.520 through speech.

00:32:38.360 So there was this whole education process for me of how do I learn to communicate with

00:32:42.460 people speaking and the confidence to do that, right?

00:32:46.760 And I think the confidence to, you know, a lot of our work, no matter how much you educate

00:32:52.340 yourself, you're always learning something new by the people you talk to.

00:32:55.780 And I think just the confidence that you can, you know, plug in and add value, even when

00:33:02.040 you may not, when something may be new, you can still bring a valuable perspective.

00:33:06.360 I think that confidence took time to develop.

00:33:09.660 I think the very first time I went into my very first finance

00:33:12.760 class at the age of 28 at Cornell,

00:33:15.700 I was crying afterwards.

00:33:17.660 I was so scared, and I was so just overwhelmed

00:33:22.240 by what I didn't know.

00:33:23.660 And I think it took time to just kind of realize

00:33:27.160 that that's OK, and that's a part of learning,

00:33:29.160 and that's a part of being valuable,

00:33:31.920 is having that mindset that you're always open to learn,

00:33:34.000 but you can still kind of step up and communicate

00:33:36.340 and bring value even when you may not

00:33:37.840 know all the answers.

00:33:39.460 So the building that confidence over the years

00:33:41.760 of being OK with not knowing.

00:33:44.980 That's a better way to put it.

00:33:46.060 No, it's interesting.

00:33:47.280 I think a lot of women are going to watch this. 1.00

00:33:48.820 I know my wife is as successful as she is. 0.99

00:33:52.480 She still has these moments where she doesn't feel like worthy

00:33:57.160 or confident or, you know, and it's just fascinating,

00:34:00.840 especially from my end, I'm sure.

00:34:02.400 You know, it's like, um, they're like, really, you're just, you should like, it's, it's interesting.

00:34:08.020 Um, and I know you do work with, I think it's girls in code or Canada learning code.

00:34:12.640 Yeah.

00:34:13.100 So how does that, like, where'd that passion come from?

00:34:16.940 How does that impact your life today?

00:34:18.380 And yeah.

00:34:19.440 Um, so this was, uh, an organization used to be called ladies learning code. 1.00

00:34:22.580 Um, they've, they've changed to Canada learning code and, um, have a mission of, of bringing

00:34:27.380 technology experiences to 10 million Canadians over the next 10 years.

00:34:30.960 That's a lot of Canadians and we're not a lot of Canadians yeah so they have

00:34:35.280 programs now that are focused on women, focused on kids, and then focused on

00:34:40.620 kind of you know broader community and you know we've been involved with them

00:34:45.660 for the last five years or so as donors and volunteers so all the Georgian team

00:34:49.720 volunteer one day a year for Canada Learning Code programming and you know

00:34:54.800 we feel very passionately about technology education I think to our

00:34:58.020 earlier conversation around how do you develop the skill sets well it can't all

00:35:02.760 come from one part of the population because this is going to be almost every

00:35:05.500 job is going to have that skill in technology and so we need to reach

00:35:10.680 broad and we need to touch a lot of people and and get you know these kinds

00:35:15.520 of educational opportunities into the hands of everyone no matter age or

00:35:19.980 background or sex or you know any of those things so that's you know the

00:35:24.600 reason that the Georgian got behind can learning code I think me coming from a 0.99

00:35:27.720 nonprofit background.

00:35:29.040 I was really excited when I joined the team to get involved.

00:35:31.560 So I've been working with them for the past four years or so.

00:35:34.920 And it's incredibly gratifying to see that.

00:35:37.860 I mean, it's a long gap for the nine-year-olds

00:35:41.760 we volunteer with to founder of a series C company.

00:35:45.780 But I think it's exciting to think

00:35:47.640 that there's a much broader exposure.

00:35:49.800 Exactly.

00:35:50.400 It's really cool.

00:35:51.280 And it's going to those classes and seeing every race,

00:35:54.900 Every gender, this whole cross-section of the population,

00:35:59.960 learning how to interact with technology

00:36:02.180 is so exciting for what this industry looks like in 20 years.

00:36:06.600 And if somebody's interested in reaching out

00:36:08.880 to chat about investments, career paths, that kind of stuff,

00:36:13.320 where do the people find you online?

00:36:15.540 So I have a Twitter account.

00:36:17.040 I wish I was better at it.

00:36:18.940 My email is always good.

00:36:21.320 You all should, georgianpartners.com and LinkedIn.

00:36:25.100 All right, LinkedIn, email.

00:36:26.800 Emily, I appreciate you coming on the show.

00:36:28.100 All right, thanks so much.

00:36:29.280 Cheers.

00:36:29.620 Thanks for watching this episode of Escape Velocity.

00:36:32.820 Be sure to like and subscribe and leave a comment with your biggest insight from our conversation.

00:36:38.300 Be sure to check out the next episode.

Dan Martell - January 28, 2021

From Ballet Dancer to Lead Investor with Emily Walsh @ Georgian Partners - Escape Velocity Show #46

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