Feature adoption is a core concept in product analytics that tracks how users begin using new or existing features in your product. It answers questions about whether users are discovering and finding value in specific functionalities. Understanding feature adoption matters because it directly informs you about the success of product launches, the stickiness of new features, and where to focus improvement efforts. The most important metrics in feature adoption analysis are the first use—the first time a user tries a feature—and the adoption rate—the percentage of users who have used a feature at least once.

To identify when a user first tries a feature, you use the MIN() function to get the earliest event date for each user and feature. By grouping your data with GROUP BY user_id, you ensure you get one row per user, showing the first time they interacted with the feature. This approach works for any feature by adjusting the feature_name filter.

When calculating the adoption rate, you count how many unique users have used a feature at least once and divide by the total number of users. The result is the adoption rate as a percentage. High adoption rates suggest that a feature is widely used and likely valuable, while low rates may indicate discoverability or usability issues. Product teams use these insights to decide where to invest in onboarding, feature improvements, or even sunsetting underused features.