Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Patrick O'Shaughnessy, Greg Stewart
[00:00:00] S0: You said before, don't listen to investors on product. Can you say more about that? [00:00:00] S1: Well, I think one, don't listen to any one person on product. Everyone has a unique use case, persona, problems, solutions that they're looking for. So no one person is the source of truth. If you get one piece of feedback, that's just one piece of feedback. So when we talk about investors, I remember earlier meetings, it would be like really prescriptive advice saying, you should go build this. And it's like, maybe. I don't know. That's what you want. And then we would go test that with our user base and ask them. And they would say, no, I don't want that. [00:00:00] S0: That's an extremely empirical way of building. [00:00:00] S1: It is just empirical. And it doesn't have to be completely precise. But it becomes very clear what the big, chunky buckets are and everything else on the fringe. It's like, that's not that important. But what is important becomes very, very obvious, even if it's in line with what an investor is looking for or nothing. [00:00:00] S0: At various points through this business, you and I have had a call. And it will be some period where you describe him having gone into like a cave-like process to go study some new topic. Some new thing has become the bottleneck for the business. I can think of several occasions when he was studying X, Y, or Z. Describe from your perspective this cave-like process that you've seen him go through to unlock these bottlenecks.