AfterMVP
How Prop GPT grew

Prop GPT growth strategy

Sports betting analytics app using ML to surface high-confidence picks daily

apps.apple.com Founded 2024 By Eyal, Yali
MRR $30k
Users 3.0k
Stage Growing
Category Consumer SaaS
Starter Story How We Built It: $30K/month Mobile App
Growth roadmap

7 moves, in order

  1. Pre launch / Build
    App store launch

    Spent 5 months building the first version of Prop GPT in React Native, launching mid-NFL season to catch peak sports betting demand.

    App live on App Store; initial download activity began
  2. Version 1 – Growth Attempt
    Influencer marketing

    Spent a few thousand dollars on influencer sponsorships to drive app downloads. Selected creators whose audiences overlapped with sports bettors.

    ~20 downloads/day; 5–10 trial starts/day; 45% download-to-trial rate
    MRR $2.0k
  3. Version 1 – Diagnosis
    Product analytics

    Used PostHog to track in-app behavior and identified that 45% of users converted to trial but only 13% converted to paid. Spotted Super Bowl spike in trial starts with near-zero retention after — confirming a product problem, not a distribution problem.

    Identified core UX failure: app required too much manual effort from users who just wanted pre-analyzed picks
    MRR $2.0k
  4. Rebuild – 4 Months Off Market
    Product rebuild

    Shut down all marketing spend for 4 months and fully rebuilt the app from scratch. Redesigned the core experience so users are served pre-analyzed picks directly rather than having to manually evaluate their own bets.

    New version launched April 15th at $1,700 MRR (~$15/day revenue)
    MRR $1.7k
  5. Version 2 – Relaunch
    Influencer marketing

    Relaunched marketing aggressively to coincide with the NBA playoffs, timing the spend to peak sports betting demand with the rebuilt product.

    Trial-to-paid conversion jumped to over 50%; MRR began rapid ascent
  6. Version 2 – Viral Spike
    Organic social video

    Continued publishing influencer/social media videos consistently (~70 videos total). The 70th video went viral with 600,000 views, driving a massive single-event revenue spike.

    ARR jumped from ~$8K to ~$38K in roughly 3 days; 2,000 downloads in a single day at peak
    MRR $40k
  7. Steady State / Scaling
    Influencer marketing

    Maintained ~$10,000/month in ongoing influencer marketing spend. Tracked revenue-per-download ($3.30) and LTV metrics via RevenueCat and Superwall to optimize spend efficiency.

    $30,000 MRR sustained; 40,000+ total downloads; 3,000+ paying customers; 48% conversion to trial; ~50% profit margins
    MRR $30k Users 3.0k users
MRR progression — $2.0k → $40k

How Prop GPT got its first 100 users

Eyal and Yali launched Prop GPT mid-NFL season and immediately leaned on influencer marketing to drive downloads. They spent a few thousand dollars on influencer sponsorships, which generated roughly 20 downloads per day. Of those, 5–10 per day converted to free trials, giving them a strong download-to-trial rate early on. This gave them their first paying users and got them to ~$1,000–$2,000 MRR relatively quickly. However, despite healthy download and trial numbers, conversion from trial to paid was extremely low (only ~13%). They diagnosed this through analytics tools (PostHog) and user behavior data: on high-traffic moments like the Super Bowl, they saw spikes in trial starts but almost zero retention after trial. This was the signal that their first-user playbook was working, but the product itself was failing to deliver on its promise.

Prop GPT's unfair advantage

Eyal had prior experience working with successful app founders and had already observed the influencer marketing playbook in action before building Prop GPT. This gave them a ready-made distribution channel from day one — most first-time founders spend months figuring out what they already knew.

The channel that scaled Prop GPT

influencer_marketing

What didn't work for Prop GPT

The original version of the app required users to manually input their bets and check them against the model — users wanted to simply be given the best picks directly. This UX mismatch caused trial-to-paid conversion to stagnate at ~13%, capping MRR at $1,000–$2,000 despite healthy download volumes. All marketing was shut down during the 4-month rebuild because pouring spend into a broken product was pointless.

Watch the original

How We Built It: $30K/month Mobile App

Starter Story

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