Chris acquired his first 10 users for his productivity app by leveraging a waitlist and personal connections, then focused on improving onboarding, stability, and user experience through engaging illustrations, technical fixes, and new features like quick add and offline mode. He also implemented analytics and a feedback board to prioritize development based on user data and feedback, preparing to scale effectively for the next 200 users.
In this video, Chris shares his journey of acquiring the first 10 users for his new productivity app and the steps he took afterward to improve the app before onboarding the next 200 users. He emphasizes the importance of having a waitlist from the very beginning and actively promoting the app on social media platforms like TikTok, Twitter, and Instagram to attract initial users. For his first 10 users, he combined signups from the waitlist with people he knew who could provide valuable feedback, even if they weren’t the exact target audience.
One of the main issues users reported was feeling overwhelmed due to the lack of onboarding. Chris decided to invest significantly more time in creating a longer, engaging onboarding experience, inspired by other calorie tracking apps that found longer onboarding increased user retention. To make the process enjoyable, he collaborated with his fiancée to create custom hand-drawn and animated illustrations of a character named Amy the cat, which users found delightful and motivating to go through.
Stability was another major concern, with users experiencing crashes and data not saving properly. Chris addressed these issues by rebuilding core technologies and adding features like offline mode, which allows users to log food even without internet access, and time zone support to ensure food entries appear on the correct day when users travel. Tackling these technical challenges early with a small user base helped avoid more complicated fixes later when the app scales.
To reduce friction and improve retention, Chris added a quick add feature that lets users save and instantly re-add frequently eaten meals, saving time and reducing costs associated with AI processing. He also implemented an analytics system using PostHog to track key metrics like retention, core user actions, and AI costs. This data-driven approach helps him prioritize features and optimize the app experience, such as encouraging users to enable reminders to log their meals.
Finally, Chris introduced a feedback board integrated into the app using User Jot, allowing users to leave and upvote feature requests. This tool provides clear signals on what users want most, helping him prioritize development effectively. He expresses gratitude to his initial testers and looks forward to repeating this iterative process with the next 200 users, continuously improving the app based on real user feedback and data.