Understanding how users interact with your product’s features is crucial to building a better user experience and driving growth. After shipping a new feature, the next logical question is: how do I measure its success? Thankfully, tools like Amplitude provide powerful yet easy-to-use mechanisms to help product teams track feature engagement in just a few steps.
One such tool is Amplitude’s Engagement Matrix, which offers a clear and comprehensive way to visualize how different features are adopted across your user base. Here’s a breakdown of the six essential clicks to effectively gauge feature engagement using this method.
1. Select Relevant Events for Analysis
The first step is to identify the product events you want to analyze. Events represent user actions tied to specific features, such as clicking a button, completing a tutorial, or making a purchase. Within Amplitude’s events module, you can choose up to 20 types of events to include in your matrix.
For quicker insights, you can also select from default options like:
- Top Events: The 50 most frequently performed events.
- Bottom Events: The 50 least performed events.
- Top and Bottom Events: A combination of both to give you a broad overview contrasting frequently and infrequently used features.
Choosing “Top and Bottom Events” can provide a bird’s-eye view of which features users love and which may need attention.
2. Set Your Timeframe and Interval
Next, use the date picker in the metrics module to define the analysis window. You have control over both the timeframe (e.g., last 30 days, last 3 months) and the interval granularity (daily, weekly, or monthly).
Your choice of interval affects the available active user metrics you can analyze. For instance, if you choose a monthly interval, you’ll view metrics relative to monthly active users.
3. Choose User Engagement Metrics
How do you want to view engagement data? You can select whether to show results as a percentage of your daily, weekly, or monthly active users. This lets you understand the feature adoption intensity according to the cadence that best fits your product and goals.
4. Define Engagement Dimensions: Average Days vs. Average Times Performed
To analyze feature usage frequency, choose between:
- Average Days Performed: Shows how many days, on average, within the chosen interval a particular event was performed. This metric helps see how consistently users engage with a feature.
- Average Times Performed: Displays the average number of times the event was triggered per interval unit, highlighting the absolute frequency.
These metrics determine how the features are plotted along the Y-axis of the matrix.
5. Interpret the Engagement Matrix Quadrants
The Engagement Matrix is broken into four quadrants, each offering insights on different feature performance patterns:
- Top Right (High Frequency, High User Count): Core features heavily used by many users. These are your flagship functionalities driving engagement.
- Top Left (High Frequency, Low User Count): Power features loved by a small set of users. Consider enhancing discoverability to broaden usage.
- Bottom Right (Low Frequency, High User Count): Features many users try but use infrequently, like onboarding steps or account creation.
- Bottom Left (Low Frequency, Low User Count): Underperforming features that might need improvement or removal.
Using these quadrants helps prioritize which features to invest in, improve, or potentially deprecate.
6. Zoom In, Analyze Details, and Export Data
To drill down, simply drag to zoom into clusters of points within the matrix, uncovering specific events and their engagement stats. You can reset zoom anytime to regain the full view.
Below the matrix, a data table summarizes the details. You can perform operations directly on this table, toggle views between average times or days performed, and export the data as a CSV file for further analysis or sharing.
From Insight to Action
With these six clicks, you can confidently assess which features resonate most with your users and identify areas for improvement. Features performing poorly might be candidates for iteration or deprecation, while moderately performing features could be optimized to boost engagement.
By focusing your efforts on the insights gained from the Engagement Matrix, you improve your product more efficiently and accelerate user satisfaction growth.
Ready to unlock powerful feature engagement insights in minutes? Explore the Engagement Matrix and start measuring smarter at amplitude.com/6clicks.
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