Pre build validation
Market research app store and google trends
Used Sensor Tower to analyze competitor quit-smoking/quit-vaping apps and confirm they were generating significant revenue. Cross-referenced with Google Trends to verify vaping was a rising search topic. Scanned TikTok to confirm vape-related content was going viral, validating organic distribution potential.
→ Confirmed market demand before writing a line of spec or spending on development
Pre launch build
99designs plus upwork
Sketched app wireframes on paper, uploaded to 99designs to crowdsource 50–70 professional UI designs. Selected a design and hired an Eastern European developer on Upwork on a per-project (not hourly) basis. Built MVP for under $5,000.
→ Functional MVP shipped to App Store
Launch – Months 1–6
TikTok — organic
Researched the most viral vaping videos on TikTok, saved them in a spreadsheet, and replicated their formats. Created entertainment-first videos (e.g. 'what's inside a vape') with a 2-second CTA at the end. One video hit 8.3M views and drove tens of thousands of downloads.
→ Tens of thousands of downloads; however, $0 meaningful revenue for first 4–6 months due to weak monetization
Revenue unlock – Paywall overhaul
Product monetization hard paywall
Switched from a soft/skippable paywall to a hard, unskippable paywall requiring users to commit to a free trial before accessing any features. Combined with an extensive onboarding flow that walked users through their own problem (vaping habit), priming them emotionally before the paywall appeared.
→ Conversion rate jumped to 20–25%; described as 'changed my business overnight'
Optimization – Pricing AB tests
Superwall ab testing
Used Superwall to remotely configure and A/B test paywall pricing from $4 up to $12/month without pushing App Store updates. Optimized for highest LTV price point rather than highest conversion rate.
→ Identified optimal price point; contributed to scaling MRR
Scaling – Paid ads
Tiktok ads and facebook ads
Took top-performing organic TikTok creatives and ran them as paid ads on TikTok Ads and Facebook Ads. Used AppsFlyer as MMP to send attribution data back to ad platforms. Logic: if a video performed organically, it was proven creative — the algorithm would then find paying customers at scale.
→ Scaled installs and revenue; app reached $40K MRR (last 30 days $43K, last 90 days $112K+)
MRR $40k
Scaling – Influencer content
Influencer ugc tiktok
Sourced 'diamond in the rough' creators on TikTok to produce low-cost UGC-style content. Used this content as additional paid ad creatives to supplement in-house videos.
→ Mixed results — most influencers demanded high fees with no performance accountability, but select cheap creators provided scalable content
Ongoing – Analytics and iteration
Revenuecat mixpanel amplitude
Used RevenueCat to track subscriber LTV and understand how much could be spent on acquisition. Used Mixpanel/Amplitude to analyze in-app behavior of paying users. Iterated on onboarding based on data showing longer onboarding increased paywall conversion.
→ Data-driven product iteration; maintained customer acquisition cost below LTV
MRR $40k