Intro to Segments
To better understand a particular group of users, you can define a segment to bring into your analysis. A segment in Heap is any subset of users based on a particular criteria defined by you. Examples of popular types of segments include:
- Frequent buyers where users have made more than a certain amount of purchases (ex. three purchases per month)
- Korra’s accounts where the Customer Success Manager of those accounts is Korra
- Internal users where you group together your employees based on email or IP address to exclude them from analytics tracking
- Canadian visitors where the users’ initial country is Canada
Segments only give you access to user-level properties. However, you can create and filter for an event to use as your segment.
Segment daily, weekly, or monthly active users
Heap comes with a set of predefined user segments for daily, weekly, and monthly active users. To learn more about how Heap measures these segments and how to set started with them, see How does Heap measure daily, weekly, and monthly active users?
Segment by users who started, stopped, or re-engaged with an event
Heap offers a suggested report that allows you to define user segments based on whether users have just started, consistently, stopped, never or re-engaged with a certain event. See our Active Usage Analysis guide to learn more about what these segments measure and how to define them.
To create a segment, navigate to Definitions > Segments within Heap and click the + New Segment button. On the page that appears, define the name, category, and filters for your segment.
A segment can be defined based on any combination of behavioral actions and user-level properties. See the examples below of popular types of segments you can create in Heap.
Big Spenders (high purchase volume)
You could define a segment called Big Spenders for all users with greater than 10 purchases.
Power Users (completed an event multiple times)
You can track power users by defining a segment for all users who have done the event more than, say, 10 times per month.
My Accounts (account by CSM/AM)
You can segment users based on the Customer Success/Account Manager that owns those accounts via properties that come with our Salesforce Integration.
Internal Users (your employees)
We recommend creating a segment of your internal users to filter out of analysis so your results will be as accurate as possible. Here’s how we have that segment defined in Heap.
The Identity property in the screenshot above is only available if you configure our Identify API to capture an identity for your users. See our Using Identify guide for fulls steps to manage identity in Heap.
When conducting analysis, you can limit the results to users who are in any of these segments, or directly compare the behavior of one of these segments to users who do not fall into the segment. You can also directly compare multiple segments.
Time-bounded segments are a type of segment that allow you to define a segment in terms of whether a user has done a specific event within the last day, week, or month. This means that the segment’s membership changes every day. These type of segments can be graphed using a Number of Users query, or used as filters for other queries.
Graphing Number of Users in a Segment
In a Number of Users graph, the size of a segment is calculated at midnight each day. If your segment finds users who have logged on in the past 7 days, each day’s data point will represent the number of users who logged on at least once in the 7 days leading up to that point. In prior day, it’s the past 24 hours.
The segment’s behavior changes when it is used as a filter. For example, if you set up a segment to filter activity from over the past 2 weeks, a filter is applied to the event being analyzed, which is calculated as the prior 14 days x 24 hours (336 hours) from the moment that the query is run.
So if you are running a query for the Count of Sessions in the past month, filtered by your past 2 weeks segment, the results will answer the question “Of all the users who have logged in at least once in the past 336 hours, which users had sessions each day in the past month?”