Why Product Is Better Than Distribution: $30K/Month Mobile App | Starter Story
12m 19s
Ayol and Yali, two college students, developed PropGBT, a sports betting analytics app. Initially, despite effective influencer marketing generating about 20 downloads daily, the app struggled with user retention, stagnating at $1,000-$2,000 in monthly recurring revenue (MRR). They identified the core issue: the product was overly complex, requiring users to manually analyze bets rather than providing direct insights. Halting all marketing, they spent four months rebuilding the app from scratch to simplify the experience. Upon relaunch, combined with strategic marketing during the NBA playoffs, conversion rates soared over 50%, leading to $30,000 MRR within 10 weeks and peaks of 40,000 MRR. Their success underscores that distribution is ineffective without a strong product. Key advice includes leveraging data analytics to understand user behavior, focusing on product-market fit before scaling, and using influencer marketing for growth. Their tech stack involves React Native, Python for machine learning, and various analytics tools, resulting in approximately 50% profit margins. The story highlights the necessity of balancing product excellence with distribution strategies for sustainable success.
This is Ayol and Yali, two college students who built an app that at first crushed it with downloads. We were averaging 20 downloads a day right off the bat. But they had a problem. Almost nobody stayed after the trial ended, their conversion was horrible. If your product is shit, nobody's gonna buy it. They were stuck at $2,000 a month and they had no idea what to do. So they decided to scrap everything and go back into the cave. So we shut down all marketing and spent four months completely rebuilding from scratch. After doing this, on their second try, they hit $30,000 MRR in just 10 weeks. And I brought Ayol and Yali onto the channel to share with me exactly what they changed. In this video, we'll dive into why distribution does not matter if your product sucks, how to identify if your product is actually doomed from the start, and Ayol and Yali's exact playbook for building a product that actually makes money in 2025. This one is a must watch for anyone building apps right now. On that walls, and this is Starter Story. Yali and Ayol, welcome to the channel. Tell me about who you are, what you built, and what's your story. Hey, I'm Ayol, I'm Yali, and together, Ayol and I built an app that makes $30,000 a month. We launched it around a year ago. We got stuck at $1,000 to $2,000 MRR. We took four months to rebuild it, and then it skyrocketed. Okay, before we get into what you guys changed that skyrocketed your app, I do need to understand what is your app, and what does it do? We built PropGBT, which is basically a sports betting and analytics platform that uses our machine learning algorithm to hit the most picks. So users get access to our models and get to analyze their own picks, or look through all the pre-analyzed bets that we scan every single day. We do about $30,000 a month, and we've had over $40,000 downloads, and over $3,000 paying customers. We have a 48% conversion to trial rate. And for every user that downloads PropGBT, we make around $3.30. What did the build process look like before you went and got out of the app store? We spent like five months building the first version of PropGBT. It was taking a while to build, particularly because ChatGBT was still new. Eventually, in the middle of the NFL season last year, that's when we started launching, and that's when we sort of figured out that our product market fit wasn't exactly what we wanted. All right, guys. Black Friday isn't just a couple days. And as you might already know, we are doing something huge at StarterStory. I do not want you to miss out on one of the best deals we've ever done. Because last year, our Black Friday deal sold out in just two hours. And once people hear about this year's deal, I'm positive it's going to sell out even faster. So if you don't want to miss out on that, just go to StarterStory.com/BlackFriday or hit the first link in the description and we'll notify you as soon as the deal is live. Spots are going to be very limited for this, so what are you waiting for? All right, let's dive into the interview. Okay, so you get this idea up on the app store. You're ready to go. How do you actually get users for this app? How many users did you get? And kind of where did you get this app to up to that point? The moment we got on the app store, we had a really good base of influencer marketing. I had worked with other really successful app founders before. And then I kind of saw the rise of the initial influencer marketing hype. We started spending a few thousand dollars on influencer marketing to see how many downloads we would get for the money spent. And we were able to get around 20 downloads a day, which converted around five to 10 users a day. Because we had really high conversion from downloads to free trials. And we couldn't really push past 1 to 2K MRR, so we kind of noticed that there was something critically wrong with our app, despite seeing a lot of hype around it. That's one of the reasons why I wanted to bring you guys on the channel, which I think is super interesting. You kind of had the distribution figure it out. But you noticed that even though you kind of knew how to crack this code, you really couldn't get the app past 1 to 2K MRR. How did you solve that problem? How did you eventually build an app that's making over 30,000 dollars a month? We realized that what our customers actually wanted was just to be given the right answers to the test. And not necessarily go through the sportsbooks and input their bets and check for themselves if it was a good, better not. So we took four months to rebuild the app, no marketing, just engineering a design work. And we launched the new version on April 15th, where we were at $1,700 MRR and around 15 downloads a day. This is after a few months of no marketing. The next month, we go all in on marketing again for the NBA playoffs. And all of a sudden, our conversion rate to paid was over 50%. Two and a half months later, we hit our peak of 40K MRR in 2,000 downloads in a single day. See, even though AYAL and Yali had the distribution figured out from day one, they still had a huge problem. The product simply was not good enough. So they spent four months rebuilding it, and that's when everything changed. This is exactly why learning how to build with AI is so powerful. When you know how to build and what to change, you can iterate fast and optimize based on what your users actually need. And this is exactly what we teach inside Starter Story Build. Starter Story Build is our live program where you will learn how to build and launch your project using AI tools in just a few weeks. You'll learn how to think like a developer and how to use AI to build real working products just like AYAL and Yali. Our next accelerator is starting soon, so if you're ready to start building, if you're ready to get off the sidelines, head to the first link in the description to claim your spot. All right, now let's get back to the video. Okay, so you guys realize that no offense, there's a better way of saying it, but you had a bad product or the product wasn't simple enough, but it took you guys a while to realize this. For anyone that's watching this right now, who's built something or thinking about building something, what would be your advice on how to figure out how to build a great product? What are the actual things you should be doing every day to avoid this mistake or at least fix it if it happens? The most important thing is to have an extremely humble attitude about your product. If your product is shit, then simply it's not going to work, like nobody will buy it. Using analytics platforms like post-hog and simply talking to enough users, you see that there's a pattern of human behavior that doesn't align with how the app is designed. For example, on Superwall, we saw that a lot of people were converting to the trial, but that nobody was staying after. Like, what does that really say about your users? What it means is that your users think this is a great idea. They think that this could add a lot of value to their life, but when it actually comes to using the app, it's not the experience they were hoping for. These signals is what a good product manager should do, and it's going to mean that your business model will work a lot better when you make sure that your product actually best fits your users. Based on all you guys' learnings, I would love you guys gave me sort of the playbook for how to build a great product, specifically how to build a great app in 2025. Can you break that down for me? Step one, really understand who exactly you're building for and what their pain points are. Step two is listen to your data. We became obsessed with the numbers. Again, two numbers that stuck out for us, 45% conversion to trial, but then only 13% conversion from trial to paid. What does that mean? Everybody, wonder what we were selling, but the product was shit. Step three, obsess over analytics. So if you know your users are falling off during onboarding, then you know which screens aren't selling your app well enough. If you track all of the feature clicks on your app, you can identify your app's most compelling value proposition and then communicate that in your marketing. Anyway, that you can see where your customers is actually doing is a massive advantage. Step four, once your app is built, the real challenge is scaling it effectively. The smartest way to grow today is through influencer marketing. Their followers engage deeply and convert faster when they see someone that they admire using your product. Another great advantage of organic social media marketing is the chance to go viral. So for example, probably our 70th video to ever come out, hit 600,000 views, and that raised our ARR from around 8K to 38K and about three days. And that's really the power of influencer marketing. One video can seriously change your entire business. Okay, cool. Well, thanks for sharing that demo. That was awesome. On a similar note, I'm curious. What's the tech stack behind this app? How did you build it? And then also, like, what tools do you use on a daily basis? We built this on a React Native repo. We mostly use a lot of typescript and Python for our machine learning algorithms, along with our automated fetching for all of the data. Additionally, we have a giant database which we store on neon in tools for analytics. We use revenue cat and super wall for revenue dashboards and paywalling. What are the costs of the profit margin around running an app like this? Yeah, so the costs are basically 20 cents per conversion to super wall, typically 1% to revenue cat. We pay for a lot of data APIs to get all the real-time sports data every single day. And that costs about $100 a month. We also spend about $10 a month on our neon database. Make sure we have the bandwidth for all the calls that we make. LLAM costs cost about $20 a month and the cost is constantly going down. Marketing is about $10,000 a month. All in all, we have roughly 50% margins at the end of the day. Okay, cool. Well, thank you for sharing that being transparent around the numbers. A lot of people who watch us really appreciate that. The last question that I have are both you guys. A lot of people watching this want to build apps like you want to learn about distribution and product. What would be your advice to people that are starting out right now trying to do this thing online, build apps and make money? Biggest piece of advice I could give is have a co-founder who you will be able to lean on and that will be able to lean on you when things get hard. As an entrepreneur, be honest with yourself and scientific about whether what you're working on has enough demand. And if you prove it to yourself that it's worth the time investment that you will put in struggling, it will be an order of magnitude easier to prove it to others, to both investors, and future team members. Beautiful. Well, thank you, Ayal, and Yali, for coming on. What you built is awesome. Congratulations. You guys are awesome. Pat, thanks so much. We absolutely love Starter Story. It's so cool to get on. Appreciate it, Pat. We had a great time. What I thought was interesting about their story is usually you get the product right, but you don't have the distribution figured out. For them, they had the distribution figured out, but they didn't get the product right. And I think that's cool because it really goes to show that you have to have both figured out if you actually want to build something that can last. This is exactly why we launched Starter Story Build, where we will help you take your idea and turn into a real app using only AI tools. So if you're ready to finally build that idea, get it out of your head and get it into the real world. We'll head to the first link in the description right below to check out Starter Story Build. All right, guys, that's it for this episode. Thank you for watching. I hope you enjoyed it. We'll see you in the next one. Peace.
Podcast Summary
Key Points:
Ayol and Yali initially built a sports betting analytics app (PropGBT) that gained downloads but had poor user retention and low revenue ($1,000-$2,000 MRR) due to a subpar product experience.
They halted marketing for four months to completely rebuild the app, focusing on simplifying the user experience to provide direct betting insights rather than requiring manual analysis.
After relaunching with improved product-market fit, they achieved rapid growth, reaching $30,000 MRR in 10 weeks, with high conversion rates and effective influencer marketing driving scalability.
Key lessons include prioritizing product quality over distribution, using analytics to identify user behavior patterns, and adopting a humble, data-driven approach to iteration.
Their tech stack includes React Native, TypeScript, Python for machine learning, and tools like RevenueCat and Superwall, with a business model yielding about 50% profit margins.
Summary:
Ayol and Yali, two college students, developed PropGBT, a sports betting analytics app. Initially, despite effective influencer marketing generating about 20 downloads daily, the app struggled with user retention, stagnating at $1,000-$2,000 in monthly recurring revenue (MRR). They identified the core issue: the product was overly complex, requiring users to manually analyze bets rather than providing direct insights.
Halting all marketing, they spent four months rebuilding the app from scratch to simplify the experience. Upon relaunch, combined with strategic marketing during the NBA playoffs, conversion rates soared over 50%, leading to $30,000 MRR within 10 weeks and peaks of 40,000 MRR. Their success underscores that distribution is ineffective without a strong product.
Key advice includes leveraging data analytics to understand user behavior, focusing on product-market fit before scaling, and using influencer marketing for growth. Their tech stack involves React Native, Python for machine learning, and various analytics tools, resulting in approximately 50% profit margins. The story highlights the necessity of balancing product excellence with distribution strategies for sustainable success.
FAQs
PropGBT is a sports betting and analytics platform that uses machine learning algorithms to provide users with analyzed picks and pre-scanned bets daily.
They used influencer marketing, spending a few thousand dollars to gain around 20 downloads a day, which converted to 5-10 free trial users daily.
They realized the product wasn't meeting user needs, with high trial conversions but low paid conversions, indicating the app experience was not valuable enough.
They tracked a 45% conversion to trial but only 13% conversion from trial to paid, showing users were interested but the product fell short.
Step 1: Understand your target audience and their pain points. Step 2: Listen to data and analytics. Step 3: Obsess over user behavior metrics. Step 4: Scale effectively through influencer marketing.
They built it with React Native, TypeScript, and Python for machine learning, using tools like RevenueCat and Superwall for monetization, and Neon for database management.
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