The Right Way to Analyze Feature Adoption by User

Quick Overview

The best way to view feature engagement per individual user in Amplitude, especially when tracking 30 features, is to use the Data Table visualization, grouping by Event Name first and then by User ID, which allows users to see the total event counts and specific user IDs associated with feature usage.

Key Points: The primary recommendation for viewing feature engagement per individual user is to utilize the Data Table visualization in Amplitude, rather than graphical representations. To achieve the desired breakdown, users should first group by 'Event Name' and subsequently group by 'User ID' to see which specific users triggered which events. When grouping by Event Name, the resulting view shows the total count of events and the percentage breakdown, helping identify the most popular features (e.g., 'Send Message' being one of the top events shown). The initial query should pull 'Any Active Event' and then be segmented by users to ensure all relevant user interactions are included. The Data Table offers flexibility to filter the results based on specific features (events) or properties, allowing analysts to narrow down the scope from all 30 features to a relevant subset. While the Engagement Matrix can show feature popularity, the Data Table provides the necessary granularity by listing individual User IDs and their corresponding event counts, which is crucial for individual user analysis.

Context: The video addresses a user query from 'Lora' who is tracking 30 features and needs the best method within Amplitude to view engagement metrics broken down by individual user IDs. The discussion involves two Amplitude experts clarifying how to configure reports in the Data Table to move beyond high-level popularity metrics (like those seen in graphs or basic Engagement Matrix views) to see concrete user-level engagement.

Detailed Analysis

The optimal approach for viewing feature engagement broken down by individual users when tracking 30 features involves leveraging the Data Table visualization in Amplitude. The initial step is to create a query that pulls 'Any Active Event' and segments the results by 'All Users'. The key to achieving the desired granularity is through multi-level grouping: first, group the results by 'Event Name' to see which features are generally most used (e.g., 'Send Message' is highlighted as popular), and then add a secondary grouping by 'User ID'. This setup reveals a detailed table where each row shows a specific event, the User ID that triggered it, and the total count of that event for that user. While graphical views or the Engagement Matrix are useful for seeing general popularity, the Data Table is necessary for seeing the 'who' behind the actions. Furthermore, analysts can refine this view by filtering the events/features they care about, as the initial grouping by all events can result in a messy table listing all 30 features, including those the user does not care about.