At Taleemabad (An Ed-tech company), I was working on a project where our main goal was to reduce the number of users who churned, or stopped using our app. Through our analysis, we found that a significant percentage of users who churned never actually watched the video (product adoption metric), which is the most important metric in our app. With this insight, we decided to take action and increase engagement among our new users.
The first step was to understand why users were not watching the video. Was it because they were not interested, or was it because they didn’t know how to access it? After conducting surveys and user testing, we found that the latter was the case. It was a mixed feedback that many users simply didn’t know how to access the video and were not prompted to do so and many didn’t know about what does this app do.
So at first, we thought of changing our UI which we didn’t agree upon. This was the main menu of our app.
To solve this issue, we decided to create an in-app prompt that would show up when a user logged into the app for the first time and didn’t watch the video within 13 seconds.
The choice of 13 seconds for showing the in-app prompt was a data-driven approach, informed by our analysis of user behavior. We calculated the median time that new users took to watch their first video on the app, and found that 13 seconds was the median time. This meant that by showing the prompt at that specific time, we were able to reach users before they lost interest and moved on to something else.
(Prompt ↑). The data-driven approach of selecting the time for the in-app prompt, combined with the clear call-to-action, was key to the success of the experiment and the resulting increase in engagement among our new users.
The selection of the video for the in-app prompt was also based on a data-driven approach. After a user installed the app, they would select their grade. Based on the selected grade, we showed the prompt of the most watched video in that particular grade. This was a strategic decision that allowed us to provide a relevant and personalized experience for each user.
We worked with our developers to add the in-app prompt feature to the app and conducted extensive testing to ensure it worked as intended.
Once the in-app prompt was live, we monitored the results closely. The results were clear: our engagement increased by 11% among new users. This may not seem like a lot, but for a growing app with thousands of daily users, it was a significant improvement.
This experiment demonstrated the power of using data and user insights to drive improvements. By understanding why users were not watching the video, we were able to develop a simple solution that significantly increased engagement.
This is a prime example of how small changes can have a big impact. By understanding user behavior and providing a clear call-to-action, we were able to increase engagement and reduce churn among our users.
In conclusion, it’s important to continuously analyze user behavior and use that information to drive improvements. By understanding why users were not engaging with our app, we were able to develop a solution that increased engagement and reduced churn. Whether you’re a data analyst, product manager, or developer, this is a valuable lesson that can be applied to any app or product.
😃 On a lighter note: Why did the customer get lost?
Because, he took a wrong churn!