Invest Like the Best with Patrick O'Shaughnessy
· Greg Stewart
[00:00:00] S1: Well, I can tell you is number one is don't listen to investors on product feedback. That is by far number one. But for us, it's just two things. It's prioritization, ruthlessly prioritize. You have to prove to each other that this is going to be additive to the product. And additive to the product means we have a thesis that is going to increase workout completions. That's our North Star. We're solving, not forgetting you to pay one time. We're solving for you to actually complete workouts with Ladder. And what can we go build to go improve the odds of you doing that and staying with us? And how we get to that is we don't guess. We absorb every piece of information that's coming in and it's all driven by our members. And if you ask the right questions and you have people that are talking to you, you can start forming a picture of what are the huge buckets that actually move the needle. And you can do some work to figure out how complex is this to go build and go do it. And don't do 10 other things just because it's interesting or you can do it. Just do the one thing and do it really well and do it again. That's still the case today. We just launched Nutrition. It's the biggest thing we've built since the first version of the product. And people on the outside were like, that should be a separate app or like, that's a different business. And we're like, well, to our customers, not. We did this survey a year ago. We had 5,000 people spending 50 minutes and it was qualitative and quiet. And we were trying to figure out what are the biggest bets that we could go make. We were ready for a big project and we didn't want to guess. And out of that work, it became very clear that it was nutrition. And what we learned was we have a third of our members who are tracking macros. Of that third, 90% are using an app. Of that 90%, most are using MyFitnessPal and they hate it. And they're really tired of managing their problem set in two different spaces. And what we learned through that experience and talking to members was that for the consumer, it's one problem set. I want to lose weight. I want to gain muscle. There are inputs and outputs to that math equation. Outputs is activity and exercise input is what you're eating. And they're managing this in two places. And they know that's not the right way to do it.
· Greg Stewart, Patrick O'Shaughnessy
Because if you worked with a coach, these things are all symbiotic together. But it all started with a kernel from our members. And we had a really clear line of sight that this would move the needle. They were telling us, I want you to use all of these things, motivational mechanics, streaks, badges, celebrations, and help me with the other side of the equation, which is going to dictate success or not. And then once we have that kernel, we do another survey. We go very deep on nutrition, like extremely deep to understand. And we all review it, read everything. We synthesize it and it gives us a blueprint. And when you do that, it becomes obvious what to build. [00:00:00] S0: What does very deep mean?
· Patrick O'Shaughnessy, Greg Stewart
[00:00:00] S0: What does very deep mean? [00:00:00] S1: The nutrition survey, that was probably a couple 100 questions. We read all of them. Now you can put them in ChatGPT. In the beginning of this, I would copy and paste App Store reviews into a Word doc. I would read every single one of them. And I'd organize the words by buckets. I would color code them. And I would do ChatGPT. And I have these documents that are a 100 pages long. And it's all just deconstructed words from our members. And very similar to survey work. In the beginning, we would do that with surveys. We'd dissect the themes. What do people care about? And now you can use ChatGPT to synthesize all of this. What is the biggest bet that we can make on behalf of our members? And then we'll think about what are the building blocks of what this should do? Is this nutrition coaching? Is it macro tracking? What does the feature set do? And you aim questions at uncovering the pain point and what they're looking for. And that becomes the guide on the first version of the feature. [00:00:00] S0: What did you learn there? What do people want on nutrition?
· Greg Stewart
[00:00:00] S1: There are two halves to this from my perspective. It's looking backwards and looking forwards. What they were doing is tracking, looking backwards. Make it really easy to track macros. I want to know if I'm in a surplus or a deficit. I want good visualizations. And I want it to be really easy in terms of how to log. It's very tedious. It's very hard with some of the existing legacy products. So give me an easy way to do this. And so the looking backwards to us was basically the table stakes. We need to give a really easy path to tracking macros, logging your meals, logging your food, and telling you what's happening as a result. And then if we do that, and we do that really, really well, there's going to be a whole other opportunity to tell you what to do. Not what you did, but what to do. Members want to know what to eat specifically. Where should I get it? I'm at a restaurant. Here's the menu. What's the best choice right now based on my goals? They want prescriptive advice, but the looking backwards was what they were using for the apps that they were using at that moment. And it was mostly MyFitnessPal, and there was no brand affinity or love. It was just the tool that existed, that there was trust because they had the biggest database. They had done it for a long time. But there was just a lot of pain in these conversations on how they were doing this stuff. So we made a conscious decision not to charge for it. So we're going to give this part away. We want to go build trust with this group and win over tracking with Ladder. We're not going to convince the whole new group to do this. We're going to go get the people who are already doing it. We're going to do it better. We're going to win their trust, start compiling this data. And there's a million products that we can now build. If you think about what Nutrition does for us, it gives us the whole math equation of inputs and outputs. There's no product in the world that has that. So the picture around the consumer, it's the clearest of any other potential product or service around this consumer. We know exactly what's happening on both sides of the equation. And that's going to unlock a whole bunch of products and services to help our consumer.
· Patrick O'Shaughnessy
[00:00:00] S0: And this would be things like, I could take a picture of my food.
· Greg Stewart
[00:00:00] S1: We already have that. That was table stakes. Take a picture. That's easy. But also tell you what's the level of confidence and accuracy. Because there are trade-offs if you use take a picture versus scanning a barcode or entering very specifically an ingredient or a recipe. So we had to nail that use case, make it super slick, make it very easy to use. And that was the beginning of building trust. Even in that process, how do you figure out what to build? We built the first version. We have a group of 2,000 members who are beta members. And we give it to them. First, we do it as a team. That's the alpha. We're all giving feedback. We have a Slack channel. Everyone's communicating on what's happening. That helps the picture get clear. And then we release to our beta members. And it's in the wild. And they're using it. And you hear it right away. What's wrong? What's the gaps? What do you need? We would survey these users every week. We had a question of how likely are you to switch from your existing app that you're using. And when it started, the number was 20%. Pretty low. Not super exciting for us. And then we would go build and make the picture clear and help fill the gaps of what folks were experiencing. And it kept going up, kept going up, kept going up. And there was a point where you could just keep building the MVP forever. But it was 85% one day. And it was like, we're ready. We're done. This is ready for the wild. And then we released it. And it's been a home run. We're in week six. We're almost 4 million meals logged, which is insane thinking about those early stories where we were literally, we knew all the people in the app. It was our buddies. We knew the faces. So a really amazing experience.