As the developer of Puff Counter AI, I hit my first $1K in revenue within a month. But this is just the beginning—I’ve been refining the product using AI, behavioral science, and data-driven monetization strategies. Here’s what worked for me and how I’m scaling further:
How I Reached $1K in a Month with My Puff Counter AI App
1. Solving a Real Problem with AI & Behavioral Science
Most vaping trackers simply log usage, but they don’t help users change behavior.
I designed AI-powered reduction plans based on BJ Fogg’s behavior model, ensuring users reduce puffs gradually and sustainably.
The app personalizes recommendations using prompt engineering, analyzing the user’s patterns to provide tailored reduction strategies.
Instead of generic tips, every user gets a dynamic AI-generated plan based on their onboarding data.
Use Apple Shortcuts and Siri to add new puff, Even app is close you can add puff just click back off device (People love that easy life trick use them !)
2. Leveraging Subscription Model & A/B Testing with RevenueCat
Instead of a one-time purchase, I launched a freemium model with RevenueCat v2 paywalls.
The free tier provides core tracking, while premium unlocks AI-powered insights, personalized reduction plans, and savings calculations.
A/B testing with RevenueCat helped me fine-tune the best-performing paywall design. Early results? A 20% increase in trial-to-paid conversions.
And you can see, I have many A/B Test not one!
3. Optimizing ASO & Targeted Growth Strategies
Keyword Strategy: Focused on not really high search terms like “quit vaping,” “puff tracker,”
Tiktok & Twitter Marketing: Targeted content around real user progress stories brought in engaged users at a low CAC.
4. Reimagining Onboarding for Long-Term Engagement
Instead of rushing users into the app, I designed a longer, in-depth onboarding process that captures their habits, motivations, and reduction goals.
This is where AI meets behavioral science:
Users input their smoking habits →
The AI constructs a personalized behavior-change plan →
Dynamic messaging adjusts based on user progress
The result? Users stay longer and actually engage with the reduction plans.
5. Automating Analytics & Scaling for Growth
Firebase & serverless functions handle tracking, engagement analysis, and personalized recommendations.
Stripe API + RevenueCat seamlessly manages subscriptions.
Automated feedback collection helps continuously refine features based on real user insights.
I use for mixpanel for eventing. Events are reaaly matters ! COLLECT EVENTS !
What’s Next?
Now that I have a solid monetization foundation, my next steps include:
Expanding A/B testing on pricing models and paywall variations
Enhancing AI-driven coaching based on more advanced habit-forming techniques
Experimenting with longer-term engagement strategies (gamification, deeper analytics)
As the developer of Puff Counter AI, I hit my first $1K in revenue within a month. But this is just the beginning—I’ve been refining the product using AI, behavioral science, and data-driven monetization strategies. Here’s what worked for me and how I’m scaling further:
How I Reached $1K in a Month with My Puff Counter AI App
1. Solving a Real Problem with AI & Behavioral Science Most vaping trackers simply log usage, but they don’t help users change behavior.
I designed AI-powered reduction plans based on BJ Fogg’s behavior model, ensuring users reduce puffs gradually and sustainably.
The app personalizes recommendations using prompt engineering, analyzing the user’s patterns to provide tailored reduction strategies.
Instead of generic tips, every user gets a dynamic AI-generated plan based on their onboarding data.
Use Apple Shortcuts and Siri to add new puff, Even app is close you can add puff just click back off device (People love that easy life trick use them !)
2. Leveraging Subscription Model & A/B Testing with RevenueCat Instead of a one-time purchase, I launched a freemium model with RevenueCat v2 paywalls.
The free tier provides core tracking, while premium unlocks AI-powered insights, personalized reduction plans, and savings calculations.
A/B testing with RevenueCat helped me fine-tune the best-performing paywall design. Early results? A 20% increase in trial-to-paid conversions.
And you can see, I have many A/B Test not one!
3. Optimizing ASO & Targeted Growth Strategies Keyword Strategy: Focused on not really high search terms like “quit vaping,” “puff tracker,”
Tiktok & Twitter Marketing: Targeted content around real user progress stories brought in engaged users at a low CAC.
4. Reimagining Onboarding for Long-Term Engagement Instead of rushing users into the app, I designed a longer, in-depth onboarding process that captures their habits, motivations, and reduction goals.
This is where AI meets behavioral science:
Users input their smoking habits →
The AI constructs a personalized behavior-change plan →
Dynamic messaging adjusts based on user progress
The result? Users stay longer and actually engage with the reduction plans.
5. Automating Analytics & Scaling for Growth Firebase & serverless functions handle tracking, engagement analysis, and personalized recommendations.
Stripe API + RevenueCat seamlessly manages subscriptions.
Automated feedback collection helps continuously refine features based on real user insights.
I use for mixpanel for eventing. Events are reaaly matters ! COLLECT EVENTS !
What’s Next? Now that I have a solid monetization foundation, my next steps include: Expanding A/B testing on pricing models and paywall variations Enhancing AI-driven coaching based on more advanced habit-forming techniques Experimenting with longer-term engagement strategies (gamification, deeper analytics)
If you are here please I really need your feedbacks. Thanks :) https://apps.apple.com/tr/app/puff-counter-ai-track-vaping/i...
NEXT GOAL IS $2K
This is just the beginning—if you’re an indie dev, my biggest advice is: Solve a niche problem, experiment fast, and leverage data to optimize!