Context
A subscription-based mobile application helps people eliminate sleeping issues.
The app was already developed and released to the stores. All money that was left was divided into implementing improvements based on incoming user feedback and user acquisition. The cost of user acquisition was over $3, which was too much for us then, and there was a high risk of spending all the money needed before finding the product market fit.
Problem Identification and Consequences
We knew from previous projects and market benchmarks that finding a product market fit, a state where a stable number of users return to use the app actively, usually takes some time. It could be a couple of months or even years! At the same time, all the projects have a budget, which is a limit we can’t cross. Otherwise, the project will end, and the product will die. Our main strategy for user acquisition was paid campaigns on the most popular social platforms. The CAC steadily grew on these tools, which gradually decreased our runaway. If we did not optimize expenditures quickly, we would run out of money soon.
Solution Implementation
Our goal was to optimize marketing expenditures. Obviously, the marketing team was doing that constantly on their side, but we also thought about how we could play as one team and incorporate our knowledge about the product and its users into their activities.
We started by checking the most popular app opening hours for new users. It turned out that there were two picks during the day, one in the morning and the second one in the evening. Then, we took the data about experiencing the AHA moment, which was a specific action taken by the user that allowed the user to feel the value offered by the app - crucial for maintaining retention. When we looked into the retention chart, the users who experienced the aha moment had 40% retention in D-10; which was 25% points better than the users who didn’t experience that particular ‘Aha’ moment. When we combined those two data, it turned out that users who opened the app during the second pick (evening one) had 4 times more chances to experience the AHA moment than during the first-morning pick. This suggested we narrow the campaigns’ launch hours to only 4 hours during the evening to stream users to the hours with the highest % conversion to Aha moment.
Business and Product Gains
Targeting the exact hours during the day increased the number of users who experienced the ‘Aha’ moment four times, which allowed us to increase retention and extend the runaway for another couple of months.
