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Invest Like the Best with Patrick O'Shaughnessy

Tom Digan & Greg Stewart - Building the World’s Best Fitness App - [Invest Like the Best, EP.454]

74 min episode · 2 min read
·
Greg Stewart,Tom Digan

Episode

74 min

Read time

2 min

Topics

Career Growth, Productivity, Health & Wellness

AI-Generated Summary

Key Takeaways

  • Customer research methodology: Read thousands of app store reviews manually, color-coding themes into 100-page documents. Survey users with 200+ questions taking 50 minutes average to complete. Talk directly to members weekly to identify pain points, not features. This empirical approach reveals what actually drives workout completion versus investor opinions.
  • TikTok growth strategy: Started coach accounts from zero, analyzed every viral video element (hook words, clothing, gym setting, movements). Created content in-house with full-time creators rather than agencies to enable rapid iteration. Moved ad budgets 7-10 times daily, ignoring Facebook-based rules that TikTok reps recommended, treating it like active trading.
  • Ruthless prioritization framework: Half the team works on workout completion, half on trial acquisition from TikTok. Every feature must prove it increases workout completions before building. Delayed Android app for years despite pressure because it would split focus and serve lower-revenue users. Earned each level before expanding to next product category.
  • Survival fundraising tactics: Negotiated with creditors at 20 cents on dollar. Led inside rounds personally while selling any liquid assets. Called existing limited partners to buy out positions at discounts during market crashes. Raised money from anyone willing, focusing on conviction and skin-in-game rather than terms or valuation during desperate periods.
  • AI implementation phases: First used for synthesizing 5,000 survey responses and creating TikTok hooks from user data. Built custom tools like Maeve AI handling 90% of support tickets and Ladder Pulse analyzing chat conversations to tell coaches exactly what content to create. Team stayed at 30 people (excluding coaches) during hypergrowth because AI replaced hiring needs.

What It Covers

Tom Deegan and Greg Stewart explain how Ladder grew from near bankruptcy to approaching $100M ARR as the number one strength training app, scaling from 9,000 to 300,000 paying members through empirical product development and TikTok mastery.

Key Questions Answered

  • Customer research methodology: Read thousands of app store reviews manually, color-coding themes into 100-page documents. Survey users with 200+ questions taking 50 minutes average to complete. Talk directly to members weekly to identify pain points, not features. This empirical approach reveals what actually drives workout completion versus investor opinions.
  • TikTok growth strategy: Started coach accounts from zero, analyzed every viral video element (hook words, clothing, gym setting, movements). Created content in-house with full-time creators rather than agencies to enable rapid iteration. Moved ad budgets 7-10 times daily, ignoring Facebook-based rules that TikTok reps recommended, treating it like active trading.
  • Ruthless prioritization framework: Half the team works on workout completion, half on trial acquisition from TikTok. Every feature must prove it increases workout completions before building. Delayed Android app for years despite pressure because it would split focus and serve lower-revenue users. Earned each level before expanding to next product category.
  • Survival fundraising tactics: Negotiated with creditors at 20 cents on dollar. Led inside rounds personally while selling any liquid assets. Called existing limited partners to buy out positions at discounts during market crashes. Raised money from anyone willing, focusing on conviction and skin-in-game rather than terms or valuation during desperate periods.
  • AI implementation phases: First used for synthesizing 5,000 survey responses and creating TikTok hooks from user data. Built custom tools like Maeve AI handling 90% of support tickets and Ladder Pulse analyzing chat conversations to tell coaches exactly what content to create. Team stayed at 30 people (excluding coaches) during hypergrowth because AI replaced hiring needs.

Notable Moment

During the 2020 Texas freeze, Stewart spent days isolated reading Crossing the Chasm and emerged with a 100-page deck identifying their exact customer persona. This focus on fitness enthusiasts seeking strength training programming, not casual exercisers, became the foundation for all subsequent product and marketing decisions that drove their explosive growth.

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Episode Transcript

Here's an interesting question to think about. If your finance team suddenly had an extra week every month, what would you have them work on? Most CFOs don't know because their finance teams are grinding it out on lost expense reports, invoice coding, and tracking down receipts until the last possible minute. That's exactly the problem that Ramp set out to solve. Looking at the parts of finance everyone quietly hates and asking why are humans doing any of this? Turns out they don't need to. Ramp's AI handles 85% of expense reviews automatically with 99% accuracy, which means your finance team stops being the department that processes stuff and starts being the team that thinks about stuff. Here's the real shift. Companies using Ramp aren't just saving time, they're reallocating it. While competitors spend two weeks closing their books, you're already planning next quarter. While they're cleaning up spreadsheets, you're thinking about new pricing strategy, new markets, and where the next dollar of ROI comes from. That difference compounds. Go to ramp.com/invest to try Ramp and see how much leverage your team gains when the work you have to do stops getting in the way of the work that you want to do. Investing is hard. It's an apprenticeship industry with messy data, complicated workflows, and decisions that demand judgment. Investing needs specialized AI, and that's why I'm so excited about Rogo. Rogo is an AI platform purpose built for Wall Street, not a generic chatbot, but a suite of agents designed around how bankers and investors actually work, from sourcing, diligence, and modeling to turning analysis into deliverables. Finance requires deep domain expertise far beyond your average chatbot. As listeners of this podcast know, every investment firm is unique with its own thesis, internal notes, templates, and ways of investing. Generic AI can be impressive, but it doesn't actually understand your process, and that's where the advantage lives. For me, three things set Rogo apart. One, it connects directly to your system so it can work with your actual data internal and external. Two, it understands your workflows, how work really happens across a deal or an investment. And three, it runs end to end and produces real outputs in the way that your best people do. Auditable spreadsheets, investment memos, diligence materials, and slide decks that match your standards. Rogo is built by a deeply technical AI team with real finance DNA, large language models for finance professionals by finance professionals, and it's already being adopted by some of the most demanding institutions in the world. The teams that get this right early won't just move faster, they'll compound better decisions, train their own AI analyst, and the gap will widen. The Rogo team's vision is distinct. Make the most ambitious investors even better, and make finance an AI native industry. I'm fully bought into that vision, and I think their work will fundamentally reshape investing. Learn more at rogo.ai/invest. If you're a long time listener of …

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