For an interactive course on how to mesaure the success of a feature post-launch, check out our Heap Use Cases | Measuring Feature Success course in Heap University.
A good way to see feature use is to Graph it. You can view event counts and unique users over time by adjusting the date range, granularity, and toggling the unique checkbox. You can adjust your analysis to focus on properties captured automatically or any custom event properties you’ve defined by either filtering for or grouping by the property name.
For example, if you defined a Search event, and captured the search term using Snapshots, you can view the most frequently searched words by:
Step 1: Graphing the Count of the Search Event
Step 2: Grouping by search_term
Step 3: Adjusting your view to a table
Once you build out a cohort based on behaviors, you can also analyze an event for a specific subset of users by applying a filter for In Segment X. Explore this segment of users further by including a Group by In Segment X statement to your graph. This will allow you to compare the actions of a particular group of users with the actions of others.
You can also analyze who a feature is important to based on user-level properties. For example, you can pass unique user-level properties such as plan type via our addUserProperties API. Group by plan type in the graph to see how many of your highest-paying customers are interacting with an element. If your largest customers are using a feature, it might make sense to bump the priority.
