How Fitbod Uses AI to Hyperpersonalize Strength Workouts

I I'd always been working out. I knew my way around the gym, but when it came to knowing what to do, I kind of made up on the spot or just guessed or took whatever was open in the gym. And so, I felt like something had to be able to tell me what to do. And that was kind of the the spawn of the idea. There's an infinite number of ways in which people work out. There's so many different ways. There's so many nuances. There's so many differences in how they work out. And what's great is now we have these foundational models and LLMs. They came in right at the perfect time.

Someone was telling me how they had joined a gym. They paid for a gym. They stayed in their car and they're crying because they're too afraid to go to the gym. And so while they were in the car, they they downloaded FitBot. Fitbot told them what to do, gave them the confidence, were able to go to the gym, and that's really how they started the fitness [music] journey. Welcome to Subversive, a podcast dedicated to sharing stories from the best consumer subscription apps [music] in the world.

We'll bring you lessons for how to grow your consumer subscription business, including insights and inflection points that led [music] to exponential growth from leaders at category defining companies and innovative startups. >> [music] >> Let's get into the show. My guest today is Alan Chen, the co-founder and CEO of FitBody, a strength training app that first launched in 2015 and has since grown to more than 100 million workouts and 38 million in ARR. In this episode, I talked to Alan about why he and his co-founder spent many years focused on core product and subscriber retention for raising outside capital to accelerate growth, about the specific growth frameworks, strategies, and tactics that have fueled FitBot success, and about how Allen expects AI to reshape the fitness category over the next few years. All right, welcome Alan to the Subversive podcast. I'm really excited to talk about your app, FitBod,

today. It's one I've been using for many years now along with Ladder in the strength training space. But before we

get into the FitBod story, let's just hear a little bit more about you and how you started your career and and what ultimately inspired you to start this company. >> Yeah, absolutely. Sounds great. I'm a big fan of the content, Phil. So, uh we're really happy to be here and yeah, my my background I studied computer science as an undergrad. I was down at UCLA and after I graduated, I went out to New York and worked in finance. uh I did high frequency trading and uh I wrote portfolio trading algorithms there which is really good really interesting.

I graduated in in 2008 so that was uh uh you know interesting time in finance I would say and um was able to start my career and build my career out um in in in quantitative trading. Um and on the flip side of uh of everything that came together for FitBot uh I I also have been working out my whole life. Uh, I played sports all through, you know, all through my, you know, all through, you know, grade school, high school. I played rugby in college and, uh, I did, uh, I did CrossFit after after I stopped playing rugby for a little bit. And, and so the the fitness and the training realm was there, the, you know, algorithmic and, you know, automated programming was there as well. And so, it was actually a nice fit in terms of how how that all came together.

>> Yeah. Well, first of all, I'll say I also graduated college in 2008 and I didn't go into finance. Uh, I actually turned down an offer at JP Morgan and went to Bane to do consulting instead, but I remember those days and it was it was brutal trying to break into finance uh during the Great Recession. So, cool that you started in high frequency trading. We just had Nick Canler on the podcast uh about a month ago and talked a little bit about how there's this sort of crossover between finance and performance marketing, but it's a whole different ballgame to be founding your own tech company. And so what was it

that inspired you to pivot what sounded like a very successful early career in finance and go allin on founding FitBot uh in 2015 with your co-founder Jesse. And what did those early days look like for you? >> Yeah. Yeah. Uh absolutely. So So you're totally right. I was building my career in finance. I was I was actually going going to grad school at the time for um uh mathematics of finance to continue to further my career in in finance. I had taken a break. I was going uh in between jobs. I was going to go to a hedge fund uh later that year and in between jobs uh you know I I put together this logic that I was like hey like this is something that I would be interested in the gym. Um well like I said earlier uh I I'd always been working out. I knew my way around the gym. Um but when it came to knowing what to do I kind of either made it up or made up on the spot or just guessed or took whatever was open

in the gym. And so I felt like something had to be able to tell me what to do. I should be able to program my phone to tell me, hey, like, you know, these muscle groups you worked out yesterday, they're sore. These other muscle groups are fresh. Here's a here's a set of things that you should be able to do when you go to the gym. And uh and and that was kind of the the spawn of the idea. Um got together with Jesse. So Jesse and I went to uh UCLA together. Uh we we met freshman year, second year, we were roommates. And you know, he's one of my best friends coming out of college. So we got together. Uh I I showed him, you know, a very ugly prototype in terms of, you know, what I was thinking and uh Jesse being a a brilliant product and designed mind just took it and ran with it, designed it, made it, you know, you know, really, you know, really welldesigned, really strong UX and and was able to put together

something that was, you know, you know, pretty awesome. Back to me where, you know, I've been computer science, I was able to write write the program. uh the combination of us of of a designer and a developer I think was all we needed to put an app on the app store. So we got together worked on it. It was a fun project. It was something that we wanted for ourselves put in the app store. And um you know the great thing about building it for yourselves is uh we got to product market fit pretty quickly, right? So it was exactly what I wanted, exactly what we wanted. And the great thing is it turns out that it was you know a ton of other people wanted the same thing. and and so we were able to really you know put in the app store get some traction pretty quickly and build off of that. So a lot of fun to be part of that that that early days and and really it was you know um you know you know solving for a problem that we want

to solve for ourselves. >> Well so much of what you just said there I feel like is part and parcel of combinators recruiting strategy when they look at promising teams to add to their incubator. It's like number one, have you worked with your co-founder or your co-founding team before? Because what kills most early stage startups is founder issues and number two, are you building a product that solves your own need? Because that's such an unfair advantage when you know exactly what the gap is in the market that you are solving for and why it's something you personally want to improve your own life. It kind of gives you a de facto product roadmap you can work against.

And so I guess I'll build on that. What

was the early product roadmap for you guys? You launched in 2015. It sounds like you got some initial traction pretty quickly, but then you and Jesse famously spent years continuing to iterate on the core product experience, improve subscriber retention curves before you really took on outside capital and hit the gas on growth. Why did you take that approach and what were some of the benefits to that sort of slow and steady um first couple innings?

>> Yeah, absolutely. You know, in the early days, we were just building something we wanted and uh you know, like I said, we put in the app store, it got traction right away, which is pretty awesome to see. Um, in many ways, we got lucky. Um, and we ranked pretty highly for, you know, people searching for for strength training in the app store. So, that was all great to see. Now, um, you know, in the early days, Jesse had been, you know, had a different job. He was at another, you know, tech company in San Francisco. I was still in school and planning to go back to my job. And so we weren't really we didn't set out to say hey like let's make a you know a venture scale you know venture scale business we we just you know we were just building and in many ways we didn't know what the venture landscape looked like and so we we built this we put in the app store and it just made sense to us that um you

know the next step would be to say hey like can can we make it can we make a little bit of money off of this right so initially the product was free uh completely free put it out there was great and um the awesome thing about this was uh uh the There was a certain delight factor in 2015 where uh people would download it, check it out and it would be able to calculate exactly what they needed. And so it it was it was almost like imagine opening Google maps for the first time. You put in point A to point B and you have exactly what you need to get there and nothing like this had existed before. So uh this is a pretty awesome delight factor. We were free at the beginning, got a lot of users, got a lot of really good feedback, and we're able to continue iterate based on what users were telling us. At some point, we said, "Hey, you know, should we put a subscription on this?" Um, and those those are the early

days of Apple subscriptions. And so, we're like, "Hey, can we make some money off of this?" Right? And, uh, you know, would this be able to drive any sort of money? And as as that started to grow and scale, then it was like, okay, like can we um you know, c can can this potentially be my job, right? Can can I can can I actually, you know, make enough money such that this could be my job? >> And so uh we we went to the app store in late 2015. We worked on it all the way through 2016. Mid 2016, we started to monetize and we're premium, so we only monetized the the very top uh users who wanted to pay for the ancillary features. And um by the end of 2016 uh we had decided that hey you know this is something that we were going to go all in on and try to do. At that point we had joined an incubator. Uh it was called launch accelerator and it was run by Jason Calcanis. And so we went to the incubator that came with a a little bit

of uh money in terms of it it was a 25k check that the incubator came with. And so we were able to put that in marketing. Uh we we we did some ads and then we also hired our first outside team members and uh you know having a strong funnel that we had with you know strong retention, good word of mouth referrals and and people converting to subscribers um you know it was almost like fill on the fire right away and and so that that was you know just awesome to see a lot of fun to be part of and we're able to grow grow on scale to the end of 2017 which is when we closed our essentially our our pre seed round is what we closed at the end of 2017.

>> Got it. Yeah. So, a lot of different

directions we could go here, but one I want to ask about is in those early days as you start to get your first thousand users, it can be difficult to know who to listen to and who not to. So, how did you how did you focus your early user research efforts on the most valuable users who are the right ones to be taking feedback from in order to get the product market fit flywheel going? Did you just focus on the subscribers or were there other metrics you looked at to say like, okay, these are the users we want to be talking to.

>> I didn't really have a huge system around this. Um, obviously we were two people so I was answering every, you know, every email. Um, and so I had a very good idea of, uh, the sentiment and the feedback that people were giving us. And the great thing about that sentiment at the time was, um, a lot of it went towards, hey, I love your product. You know, Fitlot is so great. I wish we could also do, you know, this, this, and that. And so that feedback to me was a very strong signal of saying hey like there's a ton of opportunity here you know based on what we had created at the outset there's much more to work on and and to try to organize that into thoughts you know I don't know I don't really have a system in terms of how I organized the feedback I will say that you know I was using the product you know you know almost every single day and so you know feedback would turn into hey let's you know write this in the

code and let's go ahead and take this to the gym to see how it works so I think Maybe um for better or worse, the way I organize the feedback is how it applied to the way Jesse and I would actually utilize it in the gym, right? So, it's almost like um it was almost like, you know, first-person feedback and and and dog fooding into really a product that we, you know, we wanted to use ourselves. Well, it goes back to why building for your own need is such an unfair advantage. Because if you're iterating in a vacuum, then it's sort of you're doing your best to filter all this feedback from all these users and and figure out what your northstar product vision and strategy is going to look like. But if you're building for yourself, then you can sort of listen to a hundred ideas and be like, well, these 10 are great and these other 90 like maybe it's for someone else, but it's not what we want to build into our

product. So, it's one of many reasons why that's such an unfair advantage. Um,

at what point did you realize that, okay, this is working well enough that it does make sense for us to go beyond the launch incubator and go raise our first round of outside capital and and maybe start to spend a little bit more money on paid advertising and other acquisition channels. >> Yeah. So, we went through launch accelerator and that was actually the the point where we were like, hey, like there's an opportunity ahead of us.

Let's go ahead and, you know, take this down the the venture path. So, we raised money. Jason, you know, was and is still our our biggest investor and uh we went down the path to say, hey, like let's raise what the preede round is. Let's raise subsequent rounds with us. We have always been cash flow positive for the entire life of the company. Going back to being the accelerator and so the question is okay like you know where are we going to take this money? Where are we going to use it on? How are we going to scale it with with this money? The paid ads flywheel work incredibly well.

We had a really strong payback. were able to to take the revenue we made from paid ads and put it back into the business which was incredibly great. And so what happened was uh we went to raise these rounds and they ended up being relatively small rounds. Uh we didn't raise a whole lot. Um and at the same time, you know, we didn't uh dilute ourselves a whole lot through these rounds. Um we did actually try to raise the rounds. And I think that um there was just this dynamic of okay like you know you know what is it what what like where is this outcome going to get to?

And uh and I guess what I'll say is that um we raised a little bit of money. We didn't need a whole lot of money because uh you know we're building we're building and growing off of our revenues that if we look back at where we are today, we did not raise those large rounds. Uh we have $ 38 million nearly $40 million million in AR today. We've been around for 10 years. We've had our ups and downs as a company, but we've made over $150 million in the life of the company. And I know that's cumulative numbers. So it's it's almost like a vanity metric, but the reality is is we've been able to invest all of that money back into the business. So we've invested, you know, all that 150 million after Apple fees, of course, back into uh back into the team, back into the platform, back in acquisition. And so uh you know, we tried to raise uh the venture rounds, but really what we ended up at was a really good position where

we still control the company. we're growing at 40% year-over-year, which is uh you know you know at the pace that you know we should be compared to these companies and and we're in a really good position. So I do think that it was good to have gone that down that path. I think uh definitely learned a lot through it all. Uh but I think that you know we we basically landed in a place that that that is exactly where we need to be.

>> Yeah. Well and I think this is increasingly going to be the deacto playbook for consumer subscription app founders. I think we've just come out of a decade where your approach was a little bit more contrarian because we were living in the zer period, the zero interest rate period where venture capital was so cheap and easy to come by and founders were just taking outside capital wherever they could and pumping it into uh Facebook and Instagram and Google and other paid acquisition channels. And sometimes that works, but oftent times as you scale and you get further from your ideal customer profile, the unit economics stop working. the LTB goes down, the CAT goes up, growth grinds to a halt, and everybody's underwater, which is a terrible position to be in. Uh, so, you know, if you can make it work, this sort of bootstrapped or minimalist outside capital approach could be really powerful. Um, but it sounds like despite

that, you've been able to get to some really impressive numbers, close to 40 million in ARR. Uh, I think you're approaching 50 employees. Let's talk about some of the specific growth strategies and tactics that helped to drive that success. And I'd love to start sort of theoretical and then we'll get increasingly practical about it. Um,

on other podcasts I've heard you talk about some of the frameworks that you and Jesse look to for inspiration. So there's Near Iel's book Hooked, uh, which was one of the first books I read on growth many years ago when I was starting my career in tech. Uh, you've also mentioned Eli Learner's Offense versus Defense Framework. Um, how have those and other frameworks informed your growth strategy at a high level? And then we can get down into the specific strategies and tactics you've used.

>> Yeah, absolutely. just to double back on on what we just talked about and you know you talk about how like in the in the prior days this was a contrarian view but I do think that at least in the consumer subscription realm that makes a ton of sense right it doesn't cost a lot of money for a designer and developer to get especially with AI tools now to get started build a product and put it out there and um and also with acquisition like acquisition should be uh a creative it should be ROI positive so you know if a lot of people are asking hey how do I raise a lot of money to spend a lot of money on on paid ads, a funnel or an upside down direction that that you [clears throat] know never quite made sense to me and I think specifically for consumer subscription isn't absolutely necessary. Obviously there's other areas of you know SAS and you know you know Silicon Valley where venture funding is

important piece of it but I think consumer subscription is very easy to get started and put it out there and start to just generate that flywheel that continues to to scale and scale. >> I completely I mean for what it's worth I completely agree with you. I just think there's this misconception because of the fact that the outliers in consumer subscription from the last decade oftentimes didn't take that approach. So I've cited, you know, the Duolingos, the Straas, the uh tenders of the world. It was like raise a lot of VC, focus on building the user base as large as possible, prolong monetization for years in some cases. I mean, Dolingo founded I think in 2011, didn't start monetizing until 2017 with the launch of Dolingo Plus. And that model can work.

But for every Duolingo, there's a thousand failure stories. Uh and and even for Dolingo, I think it worked because we were in a different era where outside capital was easier to raise and the app stores weren't as crowded and so you could just get organic growth much more easily. That's not necessarily the case anymore. Um, and so let's talk specifically and tactically around, you know, how these frameworks have inspired your growth strategy, but then let's get into how you've grown organically, how you've driven viral word of mouth, and then on the paid side, how you've been able to maintain um, paid marketing efficiency as well.

>> Yeah. Yeah. Absolutely. Going going back to the book Hooked. Uh this is a great book and the book Hooked is all about you know building habit forming products and as we're building fit blog we had the book Hooked. We had the two book we actually had two books Hooked and the power of habits and uh the book Hooked is all about identifying the trigger in our case getting someone to go do their workout. Uh identifying the action giving them a great experience while they're working out having them enjoy you know the actual workout the reward.

people are feeling good about their workout after their workout. Obviously, we, you know, give them some, you know, to some, you know, glitter and trophies after they work out and all that information goes into investment that goes into that next trigger. So, we knew all about this as we were building the product and, uh, in terms of building a habit, this is something that, you know, was ingrained in us um, you know, ourselves, right? And the great thing was a few years later Nar came to us and Ne was like hey like this this is a a great way to actually implement this model and we're like yes you know we followed this and so we came together we you know N's a great guy and and subsequently you actually put us in uh in the book as one of the chapters in the book. So I think all all goes hand in hand in terms of if you build build a great product that allows people to build strong positive habits uh that is

very compelling and that keeps people engaged and retained and that's really a good foundation for you know what's what's helped us uh grow to where we are. Uh like you I've read the book multiple times. I think it's one of the best out there when it comes to habit formation and to me one of the most interesting steps is converting the exttrinsic trigger into the intrinsic trigger. And so in the book, Near talks about how you got to start by manufacturing these reminders for users to in your case go to the gym until they've done it enough times that it becomes an intrinsic trigger and every time they want to go work out, they associate that with Fitbot. So what were

some of the ways that you manufactured those early habit loops to get users to the habit moment where they were just doing it on their own? Yeah. So, it it's actually a pretty natural loop in the realm of working out because the the whole thing about working out is you engage your muscles and you work with your muscles and then they take time to recover and by the time the muscles recover, then it's time to engage the muscles again. And so, a big part of the early product was really identifying the different muscle groups and being able to keep track of, okay, here's how long it's been since you engage that muscle group. here's your pattern of how long it takes for your muscles to recover and oh but you know when they're recovered you know let's let's go work out again and so people get into this and the great thing is people were opening the app and I do this sometimes with different apps I'm sure you do it where

you open an app to check you know to check stuff out you're not even working out but you check it out you're saying hey when is when is the next time I'm going to do this and so it's almost like you know did the investment which is your body recovery state going into that excitement which is the trigger to go to your next workout and so we're able which actually ingrain that into the the flow of our product. >> Got it. Okay, cool. So, what I'm hearing you saying, this makes sense. In some categories, habitation is much more difficult because it's more of an episodic use case or there aren't sort of the natural reminders that hey, it's time to go to the gym. In your case, it's like you go do a strength training workout, your muscles are sore for a few days. Once they start to recover, then you know it's time to go back. But you were able to build on that by having whether it's push notifications,

email campaigns, or even just like inapp features that sort of let the user know these are your different muscle groups. These ones are ready to go. You can go to the gym today and do your, you know, do your upper body workout, but your your lower body is still recovering because you did that, you know, that the heavy thigh day earlier in the week and so give it give it more time before you do more squats. Is that kind of how you were thinking about it?

>> Yeah. Yeah, absolutely. and and you know not discounting the fact that um you know this is work right it is hard work to go to the gym and work out but the actual flow of uh you know engaging your muscle groups letting them recover and then going back to the gym that is a pretty natural flow to to strength training >> that makes sense okay so then moving beyond hooked what what are some of the

other frameworks that you guys have thought about you mentioned Eli's offense versus Diva first framework would love to talk about that one and then any others that have been inspiring for you all as as you've developed your growth strategy over the years Yeah. Yeah. So, absolutely. The Office for Defense Framework is something that we we talk about internally all the time. Um, and it's, you know, Eli Learner put this together and essentially it's that uh defense is the the category of items that are incredibly important that we have to do at a sufficient level in order to maintain our our business. Um, now compare that against offense which is um also incredibly important. But these are the ones with upside. These are the ones where if we invest further into if we invest more into that will help us drive further growth and reach and identifying the two different buckets is critically important to

understand hey like this is where we must invest a set of resources versus this is where we actually see the upside and continue to dive deeper and deeper into it. So that that's a great framework that that we look at we talk about and obviously when we have projects we we you know we put up against that to see you know where they fit there. In terms of other frameworks, uh I'm I'm sure there's a lot out there. Um the product market fit framework is definitely one of them. And yeah, the different um I wouldn't necessarily say frameworks, but different knowledge and teaches and content around growth and and growth loops I think are pretty pretty big. And I'm sure you know you you've talked about some of these as well. So there definitely a lot of different areas where we're, you know, following some of the experts in the space and bringing in the best practices in order to you know, run the business

and grow. Well, and one I'll throw in there, you mentioned growth loops, and for me, Reforge and all of the content that Brian Balffor and the team at Reforge have put out over the last decade or so has been really influential on my career as a growth leader. Uh, I've got my my framework, the subscription value loop, which is very influenced by um some of the more general lessons around compounding growth loops that aren't necessarily specific to consumer subscription, but obviously uh influence how I would think about growing a consumer subscription business. But to me, the real breakthrough with loops versus funnels is that loops compound over time and the output of any cycle of the loop becomes the input into the next cycle. And so maybe we could start to get more tactical and talk about some of the specific growth loops that have driven your success at Fitbot through that lens. And let's start on the organic

side. So, one of the benefits of

spending so much time in the early days focusing on building a delightful core product experience is that you've talked about how every subscriber or even every non-subscriber who comes into Fitbod becomes a potential vector for word of mouth to drive free acquisition for your product. Um, how did that look early on? And then to what extent did you invest in product features that amplified your K factor by just nudging more users to share with more people over time?

>> Yeah, absolutely. go going back to the early days, we we knew that people were talking about the product and this is one of those products that people do talk about. It's almost like um uh like like people earned their own social capital by sharing the next great theme with their friends, right? So we knew that this was happening. The challenge is uh this is hard to measure although we knew was happening. We knew that for sure uh for every you know download or subscriber we get that comes with additional you know downloads and subscribers. how we continue to try to build on this we have tried uh it is challenging uh now uh word of mouth is something that people do naturally and so I think that's always a great thing and that has been a big part of our growth uh we have encouraged by you know helping people you know uh gift uh the subscription to each other we've you know had people gift a certain extended

trial to each other you know different ways to foster um you know uh people talking about fit like on the margins that me that that may continue to foster this. Obviously, sharing to, you know, social media, Instagram, people do that a ton. People screenshot our product a ton. People share it through messages a ton. Um, so I think we see this happening now. I think it's almost like I I go back to it. It's like it's like build a great product people want to talk about and they'll talk about it.

Um, and you know, the additional features I think are are definitely add-ons, but I don't think that's really driving the core word of mouth that we have. It's really people just talking about I mean, that's generally been my experience as well, which is the best way to drive word of mouth is often just improving retention because the longer you retain a user, one, if they're retaining for a long time, that means they love the product, which means they're probably more likely to talk about it, but two, the longer your average customer lifetime, the more shots on goal you get in terms of people telling their friends about the product.

I guess I'm also curious with a product like yours, there's sort of a flavor of word of mouth, which is what Reforge calls the casual contact loop. And that is I go to the gym and somebody doesn't even necessarily have to tell me about Fitbot because as I'm going from station to station, I sort of see somebody with their Fitbot app open and I'm like, "Huh, I wonder what that is." And either I ask them about it or I go just check out Fitbot in the app store. To what extent has that been a part of your uh growth growth strategy with Fitbot?

>> Anecdotally we see that a lot uh we see a lot of people are sharing us uh in the gym. Yeah. It's like what what app is that? And um it's almost like um addressing a problem that that is very real, right? I think other people have the same problem of like you know hey like the bench is stake what do I do now? So absolutely that that that is very real. I'll say that we haven't yet invested a ton of resources into building um social sharing features. I would say I would say largely Fitbot is uh is a single player product. Uh we give value for the individual user. Now I think there's a huge opportunity set around bringing in you know multiple sets of people and having multiple individuals you know continue to make that product experience greater. I think that's something that, you know, we're thinking internally on our side, but we haven't yet quite gone to that yet.

>> Got it. Yeah, that makes sense. And it goes back to start with the most important thing. And the most important thing is subscriber retention because that will drive word of mouth in its own right. Um, so in that vein, let's talk more about the key growth metrics that FitBod focuses on. So, what are the most

important growth KPIs that you all look at as a team? And do those remain pretty consistent quarter over quarter or are you sort of changing each quarter, each year, which growth metrics you're more or less focused on based off of how the business evolves? We we thought a lot about KPIs over the years. Um, now for for for the business, the KPIs are are are pretty s uh for pretty standard, right? We're talking about retention.

We're talking about conversion. We're talking about acquisition. Ultimately, total subscribers and you know the business revenue, right? We talked about ARR. So those those are pretty steady over the years those don't change. Um the challenge and where it gets complicated is there's a lot of nuance when you look at KPIs. And further I believe KPIs for the team should be actionable. You should be act on a KPI as opposed to you know hey here's where we ended up right. And so that's kind of where where these things get um you know more nuance and complicated. Um one of the leading indicators is is engagement.

Um we look at uh what we call perk or user retention rate and we borrowed it from we brought it from Dualingo. Um but it's really you know someone who did a workout last week and came came back and did a workout this week, right? How are we retaining people to continue to be engaged with our product and how can we measure that and how can we um build solutions to drive that and AB test optimizing for metrics like engagement that's definitely a big one. Um if you break down acquisition and conversion into different pieces of the funnel uh then those are of course more actionable. Now zoom it out the challenge then becomes every piece of the funnel if you move that metric then that moves other metrics in the funnel right um and so that's where things get really challenging to say hey like how is it that we you know drive the entire business we can focus on different pieces but we also make sure that that's

in the context of the large funnel that's I think where things get complicated and um and so KPIs to answer your question the main ones have remain remain consistent but we really want to make sure that you know we're looking at the ones that we can act and the ones that are driving the business in the right direction and that's where things get, you know, quite quite a bit more complicated. >> That was such a great response. I want to break down a few of the most important points I heard in there. So, one was when you zoom all the way up to the highest level in terms of what matters for the business. It's generally pretty simple, right? It's how many subscribers do we have? What is our subscriber retention rate? Uh what is the LTV versus acquisition cost of those subscribers? What's the conversion rate?

If you're a premium business, it's trial start rate is trial conversion rate, which gets you your overall subscriber conversion rate. Those are fairly table stakes. But you you called out another very important point there, which was especially as your team gets larger, you need there to be actionable upstream, what are often called proxy metrics that are easier to move in the short term, but that are highly correlated with those downstream conversion and retention metrics. And so that might cause you to drill down deeper into something like, okay, subscriber conversion within the first seven days or monthly versus annual subscriber retention rates. All right. And then you might have initiatives in a given quarter that focus very specifically on an area of the product experience that drive that specific proxy metric. We want to get our annual versus monthly subscriber mix higher. And so we're

going to run experiments on the payw wall that nudge users more into annual subscriptions. or we're going to experiment with our uh subscription prices in order to get more people to select annual versus monthly subscription plans. And then I think the other point you called out and this is so important and so overlooked is that growth strategies operate like machines. I almost think of them as like, you know, you're a car mechanic and if you spend all your time solving for one bottleneck. So within the car analogy, let's say you're building a stronger, more powerful engine, that then has implications for other parts of the car, right? The transmission, the tires, everything needs to be upgraded in order to keep up with the more powerful engine or else you're just shifting the bottleneck to a different part of the growth engine. And so I think those are all very important points. You have to

think of it um as a whole, not as a sum of individual parts. And it sounds like the way that you've done that is maintaining a consistent set of topline business KPIs but then shifting what your specific team proxy metrics are in any given quarter to make sure you're focusing on the biggest bottlenecks in the system. Is that right? >> Yeah. Yeah. Absolutely. Absolutely. And um you know the the focus of you know different actual metrics are definitely things that we look at. I think the the the other thing to to understand is that uh KPA are are indicators right? These are these are indicators. these aren't the actual you know picture of the performance of the business or you know an activity that we're doing and so yes we want to we want to measure things right because we want to have visibility on how we're doing but and I think um so Shopify talks about this good heart's law uh the moment the the and I'm

paraphrasing the moment the the the the metric becomes the focus it sees us become you know effective right because if all we're doing is optimizing on a single number then that basically loses sight on what you're saying what the larger engine is doing So I think it's important to keep that in the context and and you know look at the KPIs for how we're able to use them to to benefit and drive what we're trying to do.

>> Yeah, I think that makes a ton of sense. So now let's zoom out a little bit and let's talk about how okay you've got your overarching growth strategy and you've used frameworks like hooked and offense versus defense to inform that. And then you've got your set of growth metrics and KPIs underlying that strategy that sort of give you a sense for what's working well and what's not. But then ultimately where the rubber hits the road is how are you acquiring these subscribers? And so as we think about organic and paid channels, are there particular uh and maybe we spent a little more time talking about paid because we already talked about word of mouth earlier. What sort of paid

acquisition channels have been most important for acquiring subscribers onto Fitbot and how has that evolved over time. So when we go back to talk about growth loops, our our two big growth loops and I'm a believer reforce talks about this that you know every company's going to have one or two major growth loops until they get to a certain size. um our our nation growth are word of mouth and then paid ads and paid ads in a way going back to the early days such that um they should pay for themselves essentially like with a certain period of payback you should be taking in more than you're putting into the paid ads engine and that's really what what it was for us going back to the early days and it was fuel on the fire we already had a really strong funnel we were basically adding more acquisition to the top And that was turning into, you know, uh driving more subscribers for us and and that was great. And um at the

earliest days, um it was almost like um you mentioned your podcast with with Nick from Comm where you know being able to analyze and have that mind towards finding the opportunity. Uh I I've used the word arbitrage with our team. Finding that arbitrage is is actually what we're looking for. And in the early days we did Apple search ads, we did uh Facebook ads and um you know being methodical about it, understanding the numbers, understanding um you know the the different steps of the funnel was all that was needed. So I want to say I I was the one who did um the the the media buying, the account structure, things like that. Jesse of course is our our design. made the creators for us and the combination of that was able to uh get paid ads working for the first I would say two three four years. That's kind of where that was. Obviously the the landscape of paid ads has changed uh since 2017 is when we probably started

that. Um and so meta has gotten more sophisticated. We obviously have Tik Tok in in the in the space as well. um Apple search ads, you know, they're definitely still there, although a lot of the Apple search ads is, you know, showing up on search terms that at this point we already rank for. And then um and of course there's Google, right? So, and of course now there's all different directions that we can go in terms of, you know, do we want to direct them to the app? Do we want to direct them to the web? What type of experience we want them to have? And and also the account structure and, you know, uh creative experimentation. So now it's it's this whole, you know, you know, much more complex area that we continue to double down and we have a full team that continues to focus on this. I'm constantly learning more. We're all constantly learning more. We're learning. We're sharing insights about,

hey, here's the account structure. Here's how we test creatives. Here's how we experiment. Here's how we break it break out different campaign structures. So all of this becomes, you know, you know, a a large space of a really interesting area that I would say the team has really taken to the next level beyond what we started out with. And what we start out with is really like saying, hey, like if we are meth, if we're thoughtful, if we're thoughtful about our creative, if we are smart about our account structure, then we should be able to bring people into the funnel that are that that that are ROI positive, which will then continue to, you know, create on itself. And that's really how we started. And I think that that has continued to where we are today.

>> Yeah. I mean, this is oversimplifying it, but you sort of break down the art and the science of paid acquisition. And in the early days, you don't have the sophistication to get too fancy on the science. And so, it really comes down to let's get some good creative. Let's make sure we know who our target customer is. Let's speak directly to that target customer and let's just rinse and repeat until we find out what's working. As you get larger and more sophisticated, you can spread your bets across multiple channels. You can get much more dialed in in terms of the, you know, the the math of what's working, what's not working. So, let's talk a little bit more about that because at this point, you're doing close to $40 million in revenue. You've got almost 50 employees.

Sounds like you've got a robust user acquisition team, whether that's in-house or uh using outside agencies or probably a combination of both. So, at

this point, what are some of the different paid channels that you're focused on? And is it a relatively even mix of those channels or are there like kind of one or two hero channels that drive the vast majority of the spend? And I guess my last question as part of all this is just are there one or more metrics that you're using to help drive capital allocation decisions? things like DA rorowaz or payback period or or other paid UA metrics that are most important at FitBot.

>> Yep. Yep. Absolutely. Um T question Meta and Meta is just a massive platform and we continue to you know we continue to find opportunity there and um you know the next opportunity is you know to scale is probably still going to be meta. Um so Meta is a big platform. Uh Apple search ads is very performant although there's a finite number of search inquiries. Uh Google is good because Google is also intentbased search. So that definitely a good thing and you know Tik Tok is um you know is definitely a large platform for us to continue to explore. I think there's a different type of user on Tik Tok. Uh but definitely a large platform that I think will be really really interesting.

Um but right now Meta and Meta encompasses you know Instagram, Facebook and and other apps that continues to be the the biggest one for us. Uh and we continue to to double down there. So that's that's kind of where that is. >> And and to the extent there's one metric to rule them all on paid acquisition, like what are what are you focused on to know what's working and what's not and where you want to shift dollars around to optimize your capital allocation.

>> Yeah. Yeah. Good good good question. Uh CAC, we look at CAC. Um obviously the other pieces are important and um the other pieces like we talk about trial starts or like you know um you know day seven or you know on boarding completions those all go into how we understand CAC and how we improve CA um there's other factors that are important things like payback that will drive you know our working capital ability to continue to fund you know fund our paid ads machine. So I think all these pieces are important but ultimately it comes down to um CAC and CAC of course um knowing what LTV is right so LTV to CAC that that's the entire all formula so that's something that we kind of look look to uh collectively and then ultimately then the other variable is budget right because then you know we have a strong LTV to CA then the question is okay what is the amount of addressable market that we can that we

can you know bring in you know at that level of c so um that that's basically how we look at it I think again they all interact and and affect each other. All the metrics affect each other. However, if we kind of want to isolate down, hey, like this is what we're looking at. It's really looking at what budget looks like, making sure that we're in a CAC that we're comfortable with, and then having that LTB to cap formula, be able to drive the a creative uh a creative uh formula that that we have.

>> Yeah, that makes sense. And when you say CAC, I assume you mean subscriber CAC. Um, and then you're comparing that against the LTV of a subscriber, which gives you your LTD over CAC ratio. Typically, you want at least 3x LTV over CAC. And then obviously the further you get above that, the more room you have to just pour more fuel on the fire. That makes a ton of sense and highly consistent with what I've heard from most other consumer subscription apps that are sophisticated in paid paid acquisition. So let's shift gears and talk a little bit more about the last topic for today, which is AI. So you all

were one of the early movers in using AI uh or or I guess preAI sort of machine learning or call it whatever you want to personalize workout plans. But as more and more fitness apps incorporate AI and we talked about ladder earlier, how does Fitbod plan to maintain its edge and sort of stay ahead of the competition? >> Fitbod was one of the one of the first apps that uh you know made these type of workout recommendations, right? So you can call it the the AI workout app. Now when we first started uh the the algorithms behind the workout recommendations uh it was more of an optimization problem right we were trying to figure out the you know the how to rate the exercises such that the right exercises showed up and then how appropriately make the recommendations we had different machine learning algorithms we had ran random force models that we used as well but largely it was an optimization problem and um

that that that solution that it provided going back to what we talked about earlier was the perfect solution for me. uh I was able to make the perfect workout for me like for for me I I look at the workout this is great this is exactly what I want the great thing kind of like we said before is that when others saw this this was also a great workout for them and it had that delight factor they're like wow this is great so it turns out there's a lot of people that this is a great solution for as we continue to uh grow our user base and the user base got more diverse then we started to say they start to see like hey like all these Different users had different profiles, different dynamics, different preferences, different equipment that they have access to, different goals and all these little different nuances that they wanted that the algo didn't accommodate, >> right? So, you know, someone you know,

you know, wanted to, you know, do strength training and they only have a set of kettle bells and you know, you know, they have 15 minutes a day. Uh, that's a very different workout than let's say the workout I was doing, right? And so in order to accommodate this uh what we did is we had to write you know rule into the algorithm right we had to say hey how do we accommodate this and this is how we can start that work out and so at the very start yes we were an algorith algorithmic recommendation yes in in the previous world you know machine learning and AI that's what that was but AI has gotten much much more sophisticated to where we are today and so what we have done since then after many years of you know you know iterating ing the algorithm to accommodate different users needs. What you can see how there's an infinite number of ways in which people work out.

There's so many different ways. There's so many nuances. There's so many differences in how they work out. Some people like super setting barbells with dumbbells. Some people especially don't want that, right? So there's so many nuances in the way the der work out. And what's great is now we have uh you know these foundational models and LLMs, >> right? So they came in right at the perfect time and our team was our team you know to to our team's credit our data science team were able to jump on it and say hey this is this is exactly what this can accommodate right so so in other words for for a few years now we've been running on a llm based model um that we trained ourselves we put together ourselves on our data 100 million workouts that we have to be able to predict the best workout for anybody given their profile uh given their you know equipment, their preferences, their pattern of workouts, how they like the

workouts, any changes that any adjustments that they made. So we're able to say hey like this is who you are. This is how you line up with the millions of people in a database and 100 million workouts that we have. This is how we predict next best best workout. And so that type of personalization is something that we are we are now doing that we tried to do before but now we can do it at you know just a much better greater and more like more awesome scale that leads to a better experience for more and more people. So that's something that we have currently. Uh we combine that with um you know our own proprietary custom neuron network to be able to say hey this is the this is how much you should be lifted for a given exercise right and we able to take that plug into our you know our data set and say hey like you know you just did x amount for this one exercise barbell incline bench press you're not doing

something else you're doing like flat dumbbell bench press we can probably give you exactly what you need to do for that right because we have all the data together for all of that. So all of this is now like um you know a much more sophisticated set of AI models that we have put together internally that is trained on our 100 million workouts that we have with the data set of you know the the nuance strength training data workout sets reps weight. We compare it against what we recommended so we know your preferences relative to where we are and we're able to do um that type of uh verifiable rewards to say hey this is what we think you should do next. And so I know you asked about like AI and fitness, but and I gave you what we do with AI and fitness, but I just think that um AI is allowing us to be so much more powerful in the way we we personalize workouts to individuals that it is, you know, it is awesome to see

and it's a great thing and um you know, obviously we have the data set to do so, but I think combine that with, you know, the work that the team is doing and I think it's pretty great. >> Yeah. I mean, the word I've been using for that is hyperpersonalization. And what I hear you saying is, okay, even though Fitbuck was one of the first movers in AI powered uh workout personalization, the tools that you had at your disposal when you first got started 10 years ago in 2015 were much more limited. And so it was maybe like, all right, we've got a workout for somebody who's at the gym and a workout for somebody who's at home, or we've got a workout for somebody who's a beginner versus somebody who's advanced, but it was much more limited and rules based.

Whereas now with LLMs, you can hyperpersonalize down to much more granular levels of what somebody wants out of a product like FitBot and you now have a 100 million workouts you can use to train the data set, which goes back to why your first mover advantage in this category is going to continue to yield benefits for for years to come. Um, I want to get to our lightning round quickly, but just sorry just to kind also an aspect of developing AI that includes domain knowledge and and and and a lot of like uh how AI gets developed generally is like you want to have pretty strong controls on what you think the AI is going to do.

>> Yeah. >> And you want to be able to inform the AI, hey, I think you should be doing, you know, A, B, and C. And then gradually, you know, you want to, you know, reduce the supervision that you're you're applying to the AI such that, you know, it's doing what you it's doing a lot of it on its own until ultimately you're able to to to let it run. And so I do think that like it's not only that AI is allowing us to be more powerful.

But I think it's like the 10 years of iterating, domain knowledge, understanding, and being able to to, you know, make that right recommendation to say, "Hey, this person wants barbells, super settled with dumbbells." Do we make that recommendation? Uh I think that that is all grounded in in what we're going to provide. So I think it's it's a unique a pretty pretty fun place for me to be in um just to be able to see see this all come together.

>> Yeah, that makes a ton of sense. I mean the last wave of consumer tech winners were often built on network effects tied to humans, right? So like you have a two-sided marketplace or you have a social network. The more humans using your product, the more powerful that product is for the next marginal user and that creates this compounding flywheel. With AI products, I think there's this new concept of a data network effect where the more people use the product, the more data you have, the more data you have, the better the AI algorithm becomes, the better the AI algorithm becomes, the more people want to come use the product and it and it feeds on itself. And so let's just briefly round out with one more question and then we'll get to our lightning round, which is beyond

hyperpersonalization, which we just talked about, what are some of the other big opportunities for AI in the fitness category and what sort of benefits do you think those will unlock for human health? Yeah, I I think you just talked about it. I think there's a whole ton of stuff in AI around um you know, driving this flywheel and hyperpersonalization. Now, I think there's uh you know, different areas where we look out and we're like, hey, like AI has gotten so good at certain things that um that that that we could we could leverage that, right? And to maybe narrow down to a few things that uh that that we're thinking um the tech is great at text and communication, right? So if we think about like audio that's a very great opportunity. If we think about like video that's a great opportunity image recognition all this is great opportunities where we can say hey like the technology has gone to a place now

where we could actually layer that into our product and create a much better experience with let's say audio cues let's say you know video tracking and form correction take snap a picture of your gym and we we're able to infer all the equipment at the gym right so so much great things that I think weren't there before that I think AI is there now that that are going to be pretty awesome to incorporate um there's a second category and I think you're seeing this a which is um chat with lls, right? Being able to talk to the foundational models and the foundation models will kind of respond to you. Um obviously I think this is maybe a little bit overused in in some areas. Everyone thinks that they can put in a chat chat bot and now that'll be the solution. I think for us it starts with um solving the user needs and being able to figure out okay how best to leverage the LMS for this user needs. And so the example

here is like if you have like an injury, right, you can chat with chat with an LM to actually get information about what to do and then of course we can then adjust the workout accordingly. Uh because you know based on what the LM gives back obviously you know talking to an AI coach is something that that that makes a ton of sense. And then you know again you know being able to adjust the workouts or adjust how the how our models construct the workouts based on you know what you're communicating. I think those are definitely really interesting. And and then finally is just more pieces of data, right? There's so much data out there. Um you I think there's a lot of great stuff out there in terms of health wellness that if we incorporate that into our algorithms, I think they just get much more powerful.

So uh there's a ton of stuff around AI that I think are are coming. Um it's great to, you know, be in this space where we get to we get to look at all the different different great things that are coming out and be able to incorporate that. And I think um I think you know they're definitely going to be great for human health and wellness and longevity to be able to to to to know exactly what you need to do for for your health.

>> Yeah, I love that. And again oversimplifying it, but when we talk about AI on this podcast, often times you get the generic set of answers around personalization or just marginally improving the quality of the LLM's memory or its responses, but then you also have these category specific applications. And I loved a couple of the ones you called out there. I mean, as somebody who goes to the gym often myself, it's like, okay, often times I'm getting recommended exercises that I can't actually do because my gym doesn't have the right equipment. Well, if I can snap a photo of my gym, and it just modifies the workout based out of the equipment that, you know, is at that gym, that's great. Or the other one is form correction. I mean, that's huge in strength training because injuries happen all the time simply because people don't know the right way to be doing uh, you know, doing a squat or a

barrow lift or whatever the exercise might be. And so if you can upload a video of yourself doing an exercise and get pointers on how to do it the proper way, both to improve performance, but also to avoid injury, I think that's that's a huge benefit. So let's get to

our lightning round. I'm going to ask a series of rapid fire questions and you just give me very quick responses. Um the first one I always like to ask is just what is the best part about working at Fitbot and what's one thing that you would change or as the founder are actively working to change? >> Yeah, for for the first part is really just people telling us how FitBot has changed their lives. uh is is been pretty awesome to see and we get it all the time and it's and it's just keep keeps me going, keeps us going. Um I I'll stick to one example that I I've I've had for years, but someone was telling me how um well or someone's giving us feedback on how they had joined a gym, they paid for a gym, uh they stayed in their car because and they're crying because they're too afraid to go to the gym. And so while they were in the car, they they downloaded FitBot. Fitbot told them what to do, gave them the confidence, were

able to go to the gym, and that's really how they started the fitness journey. And so the stories of this are are are just incredible and awesome and keep us going and is is it's pretty amazing that you know you know building a screen training app has resulted in being able to impact these people's lives. Ju just this morning I was looking at feedback in our in our we have a fit bomb community on Facebook and someone was saying how um uh she she has a blind husband and I'm paraphrasing but the blind husband text texted her a message this morning and said hey you know you're so firm and stout and I'm so proud of you. And so it's just like you know people are seeing this. People are recognizing this. we're having an impact on people and to me that's just incredible. And this is a stretch. This is a stretch. But I was thinking recently um not only like the anecdotal and qualitative feedback that we're

getting, but if you think about um the the the science that people are doing around strength and how strength drives longevity. >> I'm sure there's some calculation on like you know if you do x number of workouts this is you know x% long. And so we should be able to quantify 100 million workouts into how much, you know, human life we've added to the world, which is which is just just fun to see, right? So, and it's just like me, but like I think all of this is is just incredible be to be able to be working in this space.

>> Yeah, I love that. I love that. And that first story in particular, I mean, so often with things like fitness, it's just about taking the first step. And so the the image of somebody upset in their car because they can't bring themselves to walk into the gym, but you give them that little bit of extra confidence to get over the hump is is huge. Um, I'm going to keep going here. So besides FitBot, what's another consumer subscription app you love and why?

>> I I I have a pretty boring life. Um I have two two little kids. Uh one's almost two and one's three and a half. And so I think they consume a lot of my life. Uh but but but for this I'll maybe um stay on theme and go go in in the kind of the health and fitness direction. I >> I'm doing the the wearables getting really interested in my numbers. I have an aura. Um >> yeah, I got >> same thing with Yeah. Right here. So same thing with you know smart scales and body composition. I have a whiffing so I'm able to measure my body composition able to see you know all [clears throat] my workouts you know impacts my body composition and then also you know you know exploring you know the these these blood tests and you know you know biometrics and also you know the these body scans right scan. So all of this I I think are definitely really interesting to me and um you know are stuff that I think you know are

going to be part of not only my life but also many others lives. Yeah, I think it's one of the most exciting spaces in consumer tech right now. And we had Max, who's one of the co-founders of Superpower on the podcast, and the stuff he was talking about as far as how the health industry is going to change over the next five years is pretty incredible. So, I couldn't agree more. Um, what's your personal workout routine and how has it changed since you started FitBot?

>> Yeah, I I do FitBop. Um, I I have my profiles in my app and I do the workout. So, I've done fit butter for for the last 10 years and before that, like I said, I've gone through different types of working out with with sports, with CrossFit, with, you know, I did some Olympic lifting. Um, the biggest way it's changed is that as as my life has changed, I've had access to different gyms and through the various gyms, I just plug it in fit and my my my workout routine continues. So, I had a full gym at the start, you know, was doing a lot of different workouts. Then COVID hit and I was at home and I bought a set of adjustable dumbbells. I had a bench and I was getting a great workout for years just just you know 90 pound adjustable dumbbells and a flat bench, right? So that that was great. Um but what was exciting for me more recently is getting back to a gym >> and specifically having a barbell and

now I do a lot of barbell workouts through FitBot and you know th those are great and you know my strength is is doing great. And then right now actually the great thing here is like I'll go to different places. I'll have multiple profiles, multiple gyms. Um I'll go to like an apartment gym or hotel gym and I'll I'll go to just squat profiles and continue my workout routine um you know having access to different gyms. So that's kind of um that that's been my routine is just FitBot and you know like I said you know now that I'm able to track um you know my V2 max my body fat percentage it's working. I'm in top category of all those just continue to do fit. So, uh, so it's a great bonus to see.

>> Well, you've come full circle. The fruits of building for your own need and you know, the the app has generated $150 million in cumulative revenue since you started, but you also you've got the perfect workout now, and you can't you can't put a price on that. Um, two two more here. What's one health benefit of strength training that most people don't know about? I I think strength is is now a little thing. Uh, maybe it's more worth, but I think a lot of people know that there's a massive benefit in in strength and there's a big surge in this area. Um if if you look at the big box gyms uh they are putting in more strength equipment and removing cardio equipment because that's what people want. And if you look at you know various demographics whether it's old, young, male, female, everyone is getting more and more strength. So um >> I'm not sure it's it's something that people know don't know about yet. Um but

but I think it's definitely a big thing. I think it continues to grow and I think that's almost the the correct thing, right? uh Peter Tia you know he wrote the book on on longevity and he talks about the pillars of health you know strength cardio nutrition sleep and uh this is my words but um he he has said that the single biggest factor in health and longevity is strength >> and so [clears throat] obviously all these are important all of them are important to come together but the fact that we are we have been driving strength and it's you know now such a big aspect I think is is it all coming together >> yeah and it's funny because I think you're right that strength training as a key to longevity has been overlooked for a long time. And I'm somebody who, you know, I grew up running track and cross country. I was a soccer player, so I run all the time, but I've only really gotten into strength training more

seriously in the last couple years. And I remember going to my doctor, I turned 40 earlier this year, going to my doctor and asking him, "What's like the number one thing I could do over the next 10 to 20 years to just live a healthier life in an older age?" And he said strength training. Because beyond a certain age, it just it's really hard to maintain muscle mass. And so, you got to sort of be proactive about it. But as long as you're proactive about it, it can have a huge impact on on your health later in life. Um, last question here. What is the most underrated feature of Fitbot that you think more people should use?

Fitbot is very simple at the surface. Simple as relative, but um, at the surface layer, it's simple, easy to use, easy to get into, but as you then dive into certain, you know, screens of the app, certain areas, certain functionality, you see that there's a depth of functionality that that that exists underneath it. Um, and so, you know, for for example, I already talked about the multiple gym profiles. You can go into the app, you can have three different gym profiles, you can have body weight at home, you can have a full gym, you have something else, and wherever you work out, you get a great workout there and it follows through with your routine very nicely. So, multiple gym profiles. Uh, one of the the best things about FitBot is um that we've seen with our users is replacing, adding, removing, making any adjustments to the workout that then feeds the algorithm, right? Right? So the algo

knows, hey, you're removing this, you know, it's responsible for one of many reasons, but you're removing this. So it it knows to actually incorporate that for for future recommendations. Uh we're able to increase and decrease variability. So if you like to stay very consistent, we can give you a consistent workout. But if you otherwise want to try bushing your exercises, we can do that for you, too. And that's saying that you tell us. Uh we have play calculators, we have like videos and instructions that we filmed ourselves and put together ourselves. And then uh one final one, the estimated one rep max uh for every single exercise that I'm able to go in and say, "Hey, like this is the progress I'm making. There's a chart of of where I'm going. Here's how I'm getting stronger over time." Uh is something that, you know, I pay attention to with every single workout.

So they're definitely pretty cool. >> I mean, this is one to me, this is one of the um cornerstones or the hallmarks of a great consumer app is uh if you're a beginner and you just want to use the simple version of the product, you can and it's quick and easy to to learn. But then for power users, there's this whole wealth of additional features underneath the hood that you can dig into. And you know, I'm somebody who uses Fitbod along with ladder on a regular basis and uh half the features you just described I didn't even know were in the app, so I'm going to have to go give it another try.

Well, this has been a pleasure, Allan, as I knew it would be. Any last words to leave the audience with before we wrap up? >> No, no, this is fun. Um, and you know, glad glad glad to be on this podcast. Um, you know, we got a ton of work to do. Uh, and I think, you know, we we'll get to work. Obviously, you know, if people have thoughts or want to reach out or have feedback for me, let me know. We listen to all the feedback, but I know that this is fun.

>> Awesome. Well, good luck with all the work and uh excited to see the the product continue to evolve. Thanks, Alan. >> Sounds great.