Matt Heine: Hi and welcome to this episode of Between Meetings. Today I'm delighted to have my first, I think, repeat guest on the podcast series. My guest today you would have heard back in episode nine, I believe, when he spoke to us about AI and some of the work that he was doing. Joel Robbie, the founder of Nod. Welcome.
Joel Robbie: Thanks Matt. Good to be here.
MH: Now, we were just talking about some of your background and I think before we jump into the big topic of AI and what you're doing with AI, I'd love to really dig into the startup world. You've been now a successful company for two years.
JR: Yep, that's right.
MH: Having left a large Australian telecommunication company to build your company, do you want to just talk to us about that transition? What actually made you make the leap and some of the lessons you've learnt?
JR: Yeah, for sure. So Nod, been a bit of a journey with Nod, to be honest. So like a lot of people who start companies, was working, kind of burning the midnight oil alongside the day job for about 12 months there. Nod actually started as a marketplace business. So our initial idea was to connect consumers who had a question about money with a financial planner who could answer them, and build all the enabling technology for that interaction to happen online. Uber and Airbnb were really kind of hot and new at the time. So the whole marketplace concept seemed like a great idea.
And what I was really trying to do was solve a problem for family. So my family got into a really sticky financial situation. My dad got terminal cancer. Mom and dad decided to invest in a restaurant after that diagnosis. And essentially, I was looking for a way to build a platform that would make sure that people like mom and dad could get the advice they needed when they needed it and could extricate themselves from those situations when they could.
So yeah, it was kind of spurred on by a family situation. We were lucky enough to get some initial investment from H2 Ventures, which was, I had a mortgage at the time, still do. And that was the kind of bit of a catalyst for being able to leave the safety of the corporate salary and jump out and start to build a company.
And yeah, the business has been, as every startup is, it's a massive roller coaster. You learn a whole bunch of what to do and what not to do along the way. But I think I'd find it very, very hard to go back. Yeah.
MH: Now you've got a new addition to the family, a three-year-old daughter, Frankie.
JR: Three month.
MH: Three month old, sorry. Were you married at the time that you decided to go into the startup world or is that something that happened through the process as well?
JR: Yeah. That's a good question. So I got married very young, so I got married at 24 and yeah, we were already married. I've always had a side hustle. So Steph, my long-suffering wife, has always kind of known that I was a chance to go out and do something, knowing that it was always something I wanted to do. The deal, if I put that in inverted commas, was that I would do that in a way that didn't kind of sacrifice just that kind of baseline security of our home that we owned. That was kind of the red line, if you like. And so once we had that initial investment, venture investment, allowed us to kind of do that in a semi-controlled way and kind of start the flywheel from there. So yeah.
JR: So she's always been massively supportive. She's worked in the business all of last year as well. So we're kind of a family that's all in. But yeah, we've always been somewhat planned about how we go about that as well. Yeah.
MH: So your previous job was in a large corporate. What were you doing then?
JR: So I was, I'll say the word, I was at Telstra. We can name the company. It's okay. Please, please don't write angry letters to me. I don't run it anymore. But I was in business development and product development at Telstra for most of the time there. So I was working a large enterprise sales portfolio for a long time.
And then my last job was actually in big data, helping to spin up the big data business there at Telstra. So I was working kind of a hybrid product development and business development role there. Creating a product that took data from the mobile towers and allowed us to kind of map population movements over time in a kind of anonymized, aggregated way. Really good for transport planning and that sort of stuff.
MH: I was going to say. How is that different really to what Google Maps or Apple Maps or Waze is doing?
JR: Yeah, so the difference was around two things. So Telstra has 60% of the mobile customers in Australia, so it's a really large data set, larger than any other entity in Australia. So that ability to triangulate that and do stuff with that data was the best sample size in Australia for transport planning.
Also, we didn't quite get to this, but we obviously, you have a lot of other information about the way people use products and services and the idea to add kind of marketing, kind of consumer profiles to that data in a really smart way. So watching a consumer profile move around the map but do that in kind of anonymized groups of people was sort of interesting as well. So we never quite got to that. But that was sort of a long-term vision, I think.
MH: Was that data that they were collecting, was that actually being used by Google and the other mapping companies?
JR: Less so. So they tended to, so other mapping companies certainly use the Google data, I think. So if you think about like Nearmap and some of these other kind of mapping data providers, they were certainly using other cohort data, from Google and the like, to do that work.
MH: Might actually just take a step back. It's something that maybe we take for granted, but the way that Google Maps and number of these mapping apps actually work, is fascinating because-
MH: ... in its essence, it's so simple. But to actually arrive at where they did was, is so clever in that they actually use your mobile phone to look at where congestion is on the roads and see the population movement to your point earlier.
JR: For sure. Yeah. And it's a really hard task to, from a data, if we're talking data and data analytics, it's a really, really tough task to map data to a particular geography, which is only within, on a good GPS is only within maybe 50 metres squared and then actually snap that data point to a very specific road on a very specific point. It actually requires a whole lot of technical smarts that yeah, they do a really good job at.
MH: So given that you're in business development and sales effectively, did you have a technical bent or was that sort of part of the job you're expected to, because to go from sales to setting up a AI-driven startup is a big stretch.
JR: Totally. So I've always been really comfortable with data. So actually my early career, my early, early career was I was going to be a clinical psychologist and then I was going to be a research psychologist. So I got really comfortable working with statistics and data in that research psychology part of my early career. So I was working at a university, in a cold, dark lab crunching large amounts of data and getting really comfortable with that.
MH: Which is not how you picture a traditional psychologist.
JR: No, no, and certainly not a clinical... So clinical psychologist, they're just sitting there talking to you, kind of like we are now.
MH: I'm getting nervous [inaudible 00:07:04].
JR: How do you feel about that? No, but a research psychologist actually spends a lot of time working with numbers, working with data, finding correlation, finding causation. That always fascinated me. And so, I kind of spent a lot of time in my side hustles trying to use data to do things.
And even in my Telstra life, particularly at that last role, trying to use data to do things, that's always fascinated me.
MH: What were some of the other side hustles that you had along the way?
JR: So I was joking about this with the team actually. Because we've recently launched another iteration of our product that's got a different architecture to the original and they kept on saying, "Oh, it's version two." I said, "No, no, no. I've been iterating on this idea since 2012 and for me, it feels like iteration eight."
So side hustle one was a cashflow management tool for small businesses. So again, still trying to solve that problem of how do you stop my mom and dad from doing crazy stuff with money but in a really different way. So that was an initial one.
And then Nod marketplace came along shortly after that. We then obviously pivoted into document creation because that was the biggest barrier for advisors to use the marketplace platform. And we've been following that rabbit hole ever since.
MH: I think full disclosure, I'm a shareholder of Nod, as you obviously know, but when you first came to me, I think the first time I met you, it was very much a marketplace idea, which I thought was interesting, but wasn't interesting to me as an investor. And yet, when you came back with version or iteration two-
JR: Was iteration three, really. For me, it was iteration three, it probably felt like iteration two to everyone else, but yeah.
MH: Yeah. Suddenly I became very interested in, and again, we'll talk a bit about that at the end.
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In this podcast series Matt Heine, Joint Managing Director of Netwealth, chats to industry professionals and thought leaders on what opportunities and challenges they see for financial advisers and the wealth industry as a whole.
MH: Now, many of our listeners are probably wondering what is it that you actually do?
JR: Yeah. Who is this guy? Yeah. So Nod is a document automation platform. So we've been working in financial planning industry in Australia now for two years, automating statements of advice. We've just released a, calling our September release.
I think Celeste, our PM, is calling it Bloom. For whatever reason, she's got a flower theme going on, but that product basically allows us to be document agnostic, which we're kind of excited about.
So historically we've been limited to statements of advice and probably a limited set of statements of advice. We can now address any statement of advice, any record of advice, any fee disclosure statement, any client engagement letter. If you wanted to give advice about Lego on our platform, you could, which is exciting. So yeah, we're becoming a very much more scalable business this month, which I'm personally very excited about.
MH: So one of the things that got my interest initially when I made my first investment was not the fact that you were creating statements of advice. There's no shortage of companies out there or software providers doing that. What I was really interested in was the way in which you were actually approaching the problem, which was totally different to anything I'd seen before. Do you want to just talk through what that approach was and more importantly, why you think that's a better way than the traditional template?
JR: For sure. So we've always been a very data-driven business, so team full of data scientists and software engineers trying to solve this problem. Why have we solved it that way? We're of the fundamental view that the more data points you can associate with a piece of content, the more likely you are to be able to produce that content in the right document at the right time for the right client scenario.
One of the most annoying parts that customers tell us about the advice process is the fact that they spend inordinate amounts of time writing documents to provide that final artefact to the end client at the end of the advice interaction.
MH: I think the industry research would suggest somewhere in the vicinity of six to eight hours.
JR: Yep, that's right. And that's just document writing time. So that's-
MH: Ignoring strategy.
JR: Strategy, modelling, all that stuff comes before that. So you add that to that as your document writing time. And we just thought that was fundamentally crazy.
JR: So we were looking at the problem, we were trying to break down and speak to customers about how they were approaching it, why it was taking so long, what were they doing. There was a lot of deleting, a lot of updating, a lot of reformatting, a lot of adding content post-financial planning, so there was all that sort of behaviour going on. And that was ultimately the stuff that we wanted to take over and automate.
JR: And so our approach to automating that was to say, "Let's get a really clear idea of what data is associated with this piece of content. Let's learn what that is and then let's reproduce that particular piece of content when that data appears again in a new client scenario."
MH: And just to qualify. Data. When you're talking about data in this capacity, it's not necessarily numbers. It could be a word.
JR: Yeah, it could be anything, so it could be, often it comes from the client fact find. So what is it about the client scenario? What is it about their goals? What is it about their financial situation, what is it about the recommendations that are being made? What is it about the product that's been recommended? All those things. That's a very high-level snapshot of data, but obviously underneath each of those categories, there's probably an infinite amount of possible permutations and we are using data points like that to drive content into the document and in a way that minimises or removes entirely document editing and yeah, we can do that across SOAs, ROAs, yeah, advice about Lego. If you want to do a document about Lego, whatever you need. So yeah, that's the approach we've always taken.
I think the other part that's really interesting about how we do things is the idea that the template isn't actually a template. It's a living, breathing thing. It should get better over time. It shouldn't stay the same. And I think one of the most, I speak to paraplanners and advisors all the time, and one of the most annoying things about their roles is the fact that they have to do the same edits every day. And that's purely because the templates that sits in a financial planning tool is a very static thing. It's coded, they sit there, if you want to get an update to it, it takes a month.
Whereas our platform actually allows you to learn from the edits that you're making. So if you get a document out and you need to add a piece of content about that client scenario that doesn't yet exist, you add that in our editor and save it back to essentially your content library with all the data associated with that particular piece of content. So the next time that scenario appears, you're not doing that same edit again. So your document generation actually gets better over time.
MH: So with machine learning or AI, a big part of it is actually training the engine, if you like. And the more data that you can put into the engine, the more predictable or likely you are to get the right outcome.
JR: For sure.
MH: When you're training the engine in your particular case, is it at the practise level that they're learning from historic SOAs? Are they learning from the community? What's the sort of the crossover between?
JR: Yeah, I think any data-driven projects should look at data across multiple levels. So if you think about... Maybe just taking it out of the financial planning for a second, if you think about Netflix, right? Netflix is they've got a really good recommendation system and that recommendation system has to be already personalised to you. But there is absolutely no doubt at all that they're taking some of the data from your personal viewing habits to recommend the next movie. But they're also taking, if we both watched James Bond as a movie, they would absolutely take the fact that you'd watch James Bond and I'd watched James Bond and looked at all the other movies that you'd watched and all the other movies that I've watched and kind of correlate those two things. So that's how Netflix would be making their recommendation system work.
I think at Nod, we do the same thing. We're looking at data at the individual user level, at the practise level, and at the Nod level, to see how best to produce the next piece of content.
MH: So is that to define strategies or just to actually write the document? Say for example, is it going to look at someone that looks like me from an age-salary demographic perspective and create strategies off the back of that? Or where is it at, where is that crossover?
JR: The line? Yeah, so we are very clear on the fact that we are a document automation system, not an advice automation system. So that's the particular problem we're looking to solve at this point in time, which is just the six to eight hours of document writing that happens post-advice formulation, we don't think should exist or needs to exist. And we'd like to go in there and solve that problem. Yeah.
MH: Okay. So tech is probably the number one problem outside of hiring and retaining staff through advisors. What are some of the trends that you're seeing when you're in advice practises and talking to them? Are they pulling the stack apart? Are they trying to create their own stack and how do you see AI actually influencing that stack in the future?
JR: Yeah, it's interesting. So there's definitely a mix. So I think self-licenced, independent businesses we're seeing, I would suggest, as a cohort, are generally looking to pull the stack apart. I think not all of them, but I think generally, that's a trend there.
MH: So that is CRM-modelling tools.
JR: Yeah. So CRM-modelling tools, fact-finding administration, portfolio admin documents. Yeah. Pulling, having a best-of-breed technology for each of those things, I think is a real trend there.
MH: And how do you see them integrating those various parts? Because at the moment you've got a number of very disparate systems that don't necessarily talk to each other but might be very good in their own right.
JR: Yeah. So Nod is being built with an open architecture in mind. So make it really easy to get data in and out. We don't want to solve all parts of the financial planning ecosystem. We are not a financial planning tool. We're a document automation system. So it makes it really important for us to be able to make it really easy to get data in and out because we know that data from our system needs to get into a CRM. We know that we want to pull data from a financial planning tool to get the document done, all those sorts of things. And I think ultimately the industry will move that way because it has to. Those that are open will create their own gravity and that gravity will eventually catch up to those that aren't open.
MH: So you left the corporate job and started your startup or version one point something. How did you go about building a team and what are some of the sort of lessons that you've learned the way, because you've gone from effectively a very, well, you, to a team of something, is it 25 or 30 staff now?
JR: Yeah. Yeah. So it's lots of lessons learned along the way there. Do we have time?
MH: We do.
JR: Good. We've been through multiple rounds of funding so I'll talk about that, how do we afford a team first. So we didn't, we chose not to bootstrap the company. We chose to get venture investment in, scale the company, build a product through venture investment, that's not the only way to do it. You can certainly bootstrap a team, there are lots of amazing companies that were bootstrapped like including I think this one, Netwealth. So yeah, multiple ways to go about funding your team.
In terms of building a team, we've always had a really strong focus on product. And we're a digital software as a service company. So we've always had more engineers than sales folk and we've gone through, I think once you get above about the 15 person mark, you need to have some semblance of an organisational structure. I found the tipping point between about 12 and 15 people to be the point where communication got hard. So communication before that point was really easy. You ran one company meeting at the start of every day, everyone was in the circle. We call it the stand up. I think you guys run stand ups as well.
JR: And it's easy to communicate. I think once you get past that point where you need to have two stand ups going or two different teams, communication all of a sudden becomes very, very difficult. So-
MH: Even in a single location and a relatively small-
JR: Even in a single location, doesn't matter. Just 12 people seems to be the tipping point where communication just gets more difficult.
MH: Just in those early days. H2 Ventures was one of the early founders.
MH: They've got a really interesting model, but I think you're also based in, was it the Stone and Chalk?
JR: Yeah. So Stone and Chalk is a coworking space, kind of like WeWork, if people know the brand WeWork. H2 Ventures is an accelerator programme that sits with, that basically ran it out of that space. And so we were put into a cohort of about eight different startups all trying to solve different problems, but solving kind of the same business challenges around the same time. And that's actually a really good way to learn, to be honest.
MH: From other founders or particularly from groups like H2. Did they have a lot of input?
JR: No, I found the learning from other founders to be, all respect to Ben. Ben's awesome and Ben still provides me with daily mentoring. But I think the learning from other founders, because for example, founder one, business one might be solving go-to-market challenges now, you're not quite there yet. You're still trying to solve product. By the time you get around to go to market, they've already been through a whole lot of the stuff you're about to go through and you can just shortcut a lot of that learning that you have to do. So that's how accelerator programmes work. And that's why I think they're actually really good. Yeah.
MH: Yeah. So communication was a big issue when you got to about 12 people?
JR: So you have to work out a structure. You have to work out how to communicate. You have to have leaders all of a sudden, leaders and leaders and teams, you as a CEO and a founder, you go from coaching everyone to coaching a smaller subset of leaders who are then coaching teams. And that's, yeah, the up-and-down challenge. You end up having monthly town halls as a way to make sure everyone gets the same message at the same time.
So your rhythm as a company just completely changes. If you hire well, your culture doesn't have to, I don't think. If you kind of make sure you hire the right people that are values-aligned, I think your culture can actually be enhanced with the more people you get.
MH: And that was really going to be a question as well. When you are a startup, how do you attract talent? You've effectively got a product that is blue sky, doesn't exist yet. You've got financials that have a long way to go and you've got a lot of people competing for talent, particularly in the AI space.
JR: Yeah. I think there's two different motivations for people to join a startup. One of them is mission. So we have some amazing people in our team who, when we interviewed them, they ranted at me about why the mission that we were trying to solve was important, which for us, our mission is to build amazing technology that empowers experts to help more people.
Our PM, Celeste, I'm sure she won't mind me talking about her, actually she probably will mind, but that's okay, I'm going to do it anyway. I remember really clearly her first interview, she ranted at me for an hour and a half about, actually about access to legal advice and why that was a challenge and why that's why she wanted to come and solve the kind of document automation problem was that her hope was that we would get through financial planning and end up going into legal and a few other places as well.
MH: Because of a personal issue she'd had? Or just as an observation?
JR: No, I think she'd worked in a legal aid office as part of her uni degree and kind of seeing the challenges that people face around access to good legal advice when they need it. And so I think when attracting talent as a business owner in a, particularly as a startup founder, you have to lean on mission, you have to lean on scope of the big hairy audacious goal.
You have to lean on those things and say, "I think this is my vision of the future. This is where I think we're going to go. Do you want to come and help me create it?"
MH: I think certainly for a lot of our clients who are small businesses, recruitment, retention is a almost top three problems that they have. And I imagine that very few of them would actually go into an interview and start with-
MH: ... their mission and purpose as a way to try and sort of understand if values are aligned.
JR: Having a point of view is very magnetic, right? Like, if you've got a really clear point of view and you're uncompromising on that and you believe wholeheartedly that this is the way the future should be, that's very magnetic to the right people. The right people will be attracted to that, I think. So I think it's a really good point is whether you're a tech startup or not, you can still lead with mission. I think that's really important.
MH: Salary versus equity or combination. Did that come up a lot?
JR: Yeah, we've always done a combination. So we've always done, I fundamentally believe in all people in our team having an ownership in the outcome and we've always done a combination of both.
MH: And how does that work where you're not going to get every hire right all the time? Is it basically equity that vests over a period of time or what's the typical base package look like?
JR: Yeah, so for small businesses it's, the government provides essentially a tax-effective options scheme. It's called an employee share option scheme that allows anyone who's got less than nine, 10% ownership of the company in terms of options to essentially receive those options tax-free. And what we do is we vest those over a four year period, so they vest over four years with a one-year cliff. So you own none of the company in terms of options in that first year. As soon as you get to that year one, then your first 25% vests and then it vests monthly after that until your four years are up. So yeah, that's how we've always done employee share options.
MH: And is that sweat equity or is it more as a additional incentive to join?
JR: I think it's an additional incentive. I think generally, we've probably paid below market on salaries, but we've obviously added the equity portion as a top up and there is a genuine war for talent out there and particularly technical talent. So you have to compete a little bit on salary. But I think the ESOP is a really good way to augment that.
MH: Yeah. Fundraising. Favourite topic for most founders, I'm sure. How hard... I've heard it described differently before with more explicits. How did you find the whole process? What sort of guidance did you get? How did you deal with knockbacks? It's pretty hard to get up in the morning, presumably, and explain what you're passionate about to a whole room of people and just hear, "No," regularly.
JR: Yeah, for sure. So I personally have always actually not hated fundraising. I actually quite like telling our story and what we're trying to achieve. And I think that's essentially what fundraising is. You're asking in kind of the same way you're asking a prospective hire to believe in you and in your mission. I believe that it's a very similar conversation with investors.
In fact, I actually often, when I'm hiring someone, I'll often run a hire through a very similar presentation as I would an investor because I think essentially you're trying to get people to believe in your mission and what you're trying to achieve and the team and all that sort of stuff. So I think that they're kind of pretty similar conversations.
Rejection is absolutely par for the course with running a new business. Not everyone's going to believe in your version of the future. That's cool. Not everyone's going to believe that your team is the right one to pull it off. That's cool. You don't need everyone to believe it. You just need enough and then you need to prove it. And I think, it's a pretty binary outcome when it comes to start-up businesses. You either prove it or you don't. And it's life on the high seas, right? You stand and you say, "We're heading that direction. We're going to get there." You set a course and then you're definitely going to zigzag your way there. But if you continue to prove and continue to prove that you can achieve things and get traction and all those things, the fundraising part is a bit of an end of the process, of all of that as opposed to like the beginning of it in my view.
MH: And presumably, it's important to get the right shareholders on board too, that have got realistic expectations around timeframes and what you're actually going to achieve.
JR: Yeah, absolutely. And I think good communication too. So for us we do a quarterly update around the businesses, as I'm sure you know, where we're pretty transparent about our challenges. Like our update is always things that were going well, things that we're still working on, things that are expletive, something to that regard. And we're really, we're not backwards in coming forwards and asking for help from our investors to help us grow the business as well. So it's really important to have the right investors on the train. We're very lucky in that in the investor group that we have, all have been helpful in some regard, whether that's in business advice or helping us find customers or helping us solve technical challenges or steering, helping us to see an error that we've made that maybe others haven't. Yeah, we're very lucky with the investor group that we've got.
MH: You recently went over to America to start shaking the can over there. How did you find the difference between the Australian market, the type of investors that you're speaking to and the way that they do business, if you like, over in the States?
JR: Yeah, American investors are generally, have got a slightly different view of risk to Australian investors, I would say. So there is a larger capital pool over there. There's a much deeper culture of investing in stuff that doesn't yet exist and that doesn't yet have traction-
MH: So they'll invest in the idea.
MH: And the people presumably.
JR: Yeah, yeah, absolutely. I don't think anyone really, really invested in the idea. I think it's always idea plus person, I don't think-
MH: Or person plus idea perhaps.
JR: Probably in that order. Yeah. So yeah, I found that for sure.
I found the conversations to be very similar, to be honest, in terms of DD and the questions that get asked around traction and product and team and all that sort of stuff is very similar. So the process is pretty similar.
But the appetite for risk. We had investors, even to the extent of the right investors, particularly ones that had come from product background, actually saying, "You could do this, this and this with the company. I can see how that would kind of work." And that's probably a bit of a different conversation here in Australia where it's kind of on you to prove it. We found that the American audience is probably more open to being convinced and to taking a bet on a person plus an idea.
And so for us, we'll raise a Series A, hopefully in 2020 and the US is very much an option for us there.
MH: One of the things that we've seen over in the States happen quite often is that founders or startups raise a huge amount of money, in some cases an eye-watering amount of money and then tend to blow it pretty quickly. How do you keep discipline around making sure that the money that you do raise gets used effectively and that you're not constantly having to go back to market?
JR: Yeah. And this is probably a lesson we've learned, you asked me for some lessons we learned around team. I was hoping I'd get to this one.
I think in terms of staying disciplined as a small business around your cash and how you use capital, I think it's two things. One is keeping a really close eye on your unit economics, so are you spending more money than you're actually getting back from customers? I think is a really clear one. I'm going to talk a little bit about that in the webinar later on today when I talk about the unit economics of robo-advice, which are really interesting and I think it's a concept that not many, particularly financial planning practises, would have thought about too much historically, I think. So-
MH: And now having to.
JR: And are now having to.
JR: So there's that and also I think scale your business in response to product metrics as opposed to sales metrics. I think that's another one. So-
MH: What do you mean by that?
JR: Well, I think scaling your business, particularly a technology startup, if your usage is up and to the right and your unit economics make sense, you can scale your business. I think where some businesses and some founders go wrong is you have a hot product, hot market, you sell a whole bunch of stuff upfront and then you kind of scale with that. And I think that you need to make sure your product's ready and that you're ready to scale.
MH: I think that's probably the biggest difference between what I see in Australia versus in the States. In the States, it's about raise as much money as possible and try and build out the perfect product and then worry about sales as opposed to I think Australia where the mentality's more let's build the product, get some revenue, and then invest the revenue over time. And somewhere I think in the middle, probably makes sense for a business like yours.
JR: Yep. I think that's true too. So I think yeah, you're always balancing the two things. And there are absolutely trade-offs and it's probably the hardest thing about being a startup founder and CEO is managing those trade-offs. When do we invest in growth? When do we invest in products? How do we know? All those sorts of questions are really hard when you've got pretty limited data, to be honest, in the early days.
JR: And one of the only ways to figure it out is to do it. And I think it's about small. If you can find the cheapest, fastest way to answer the question. It's not a bad principle to live by.
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MH: Robo, you've touched on a couple of times. Are you a believer?
JR: I'm not. No. I'm politely anti-robo, so I thought it was really interesting to see AMP and IOOF announce their digital strategies. I'm respectfully worried about them, only from the sense that, and I'll talk about this in the webinar a little bit more, but historically the unit economics of robo-advice haven't made a great deal of sense. We've seen lots of robo-advice companies come and go, mainly because the cost of customer acquisition outweighs the lifetime value of the customer. That's the core challenge that robo-advice has found.
I did a really interesting, I spoke at the Financial Services Council Summit the other day and did a quick poll of the room. Hands up, how many of you would use robo-advice? 80% of the hands went up. Hands up, how many of you actually use robo-advice today? Opposite, right? 80% was no, 20% was yes. And I think that's the general challenge around robo-advice. Everyone thinks it's a fantastic idea in theory. The actual cost of getting those highly... This is a highly motivated room, by the way. This is-
MH: With existing clients.
JR: Yep. It's not just about getting that existing client book onto digital advice. It's about usage because the cost of getting an active, adding those customers is real. And in my view those unit economics will be worse in a legacy business with large amounts of costs already sitting there, with large technology stacks that need to be integrated. All those things. I think the unit economics will be much worse in a large advice business compared to a scrappy cheap startup robo-advisor.
MH: Because there is a view that you're sitting on a huge amount of data already despite the fact you've really only been in market for-
JR: Two years.
MH: Yeah, just under two years. And that with a nice user interface, you could actually move into that segment.
JR: Yeah. I'm yet to be convinced it's the right idea and only because I haven't yet seen the consumer appetite to engage with digital and robo.
MH: I agree with you by the way. 100%.
JR: Yeah. Yeah. I think-
MH: There's a long way to go. And I think part of the problem is we can't actually define what robo is.
MH: Is it an investment service or is it a strategic solution?
JR: But even before that, we haven't yet defined whether people actually want it. And I think that's, before you do anything, work out whether people actually want it. And I think we've seen enough data now on let's say call it digital investment advice and the lack of good unit economics and the lack of good uptake there. Lack of virality. There's no virality there at all, for that to certainly give me pause and I don't think we'll end up going there. I think we're more likely to become a large global document automation system than we are to become a large global robo-advisor.
MH: Yeah, I think that's it. That's an interesting point as well. We've become insulated and we're focused on the document generation and the issues and the time it takes to produce an SOA.
JR: For sure.
MH: But the reality is if you look at the other verticals, they've got equally the same problem and over in, I think it might be the state's revenue in Australia now, doctors are moving towards a situation where they'll have to provide an SOA-like document to actually provide medical advice.
JR: Yeah. Which is terrible for them. But awesome for me.
JR: And so I know for a fact that, in the Obamacare regime in the US, they have to produce quite a thick treatment plan to get paid. We have bulk billing in Australia. The equivalent of that in the US actually requires a rather large document to get the doctor paid. So there's lots of places where the document requirement is proliferating rather than reducing. Yeah.
MH: Yeah. So we're almost about to run out of time, but probably not a possible outcome, but a couple of sort of final views on AI and where it's heading.
JR: Yeah, I think, I was reflecting on this... I was at another conference in Melbourne two weeks ago and I was reflecting on this question and I think the main, one of our, and certainly one of our main learnings as a product is it's impossible to have artificial intelligence working out there in the wild, having good business outcomes without design intelligence. The two need to go together.
MH: And for our listeners, do you want to just define what you mean?
JR: Design intelligence, yeah. So I think, an artificial intelligence product with great technology is only as good as the ability for a human being to reason with it, to get use out of it, to get value out of it, all those things. So I think a lot of people are working on really great artificial intelligence technology, machine learning, deep learning, that kind of deep tech stuff is really exciting and great.
I don't believe there are that many great use cases out there at the moment where it's working out there in the wild with a human being. And I think it's the next phase is we'll find some really amazing designers come along that have worked out how to make that AI technology, that deep technology actually work for a human being. And I think that's probably the next interesting phase.
JR: And I'll talk a little bit about that today in my webinar, I'm going to talk about kind of humanoid robots and a few different things. So we'll talk a bit about that. Yeah.
MH: Joel's referred to the webinar that he's doing later today.
JR: I'm really plugging it, aren't I?
MH: You are indeed. For those that are interested, I'm sure that we can make a version of that or a copy of that available on our website for anyone that wants to get into the detail of AI and where it's heading.
JR: Sounds good.
MH: Joel, thanks so much for your time.
JR: Thanks mate.
MH: Good luck with the future and congratulations on building a fantastic business.
JR: Cheers. Thank you.