Using Measures in Explorer, you can calculate ad hoc metrics from your app’s data using powerful aggregation, filtering and grouping capabilities.
For example, when you run queries with segments, you can determine the popularity of a product, based on a geographical breakdown or other demographic. In so doing, you can analyze product trends for your app or business.
Typically, you can analyze your app’s data by aggregating and grouping metrics by specific dimensions. This type of segmentation analysis is most useful when you need to dig deeper into a particular metric and understand how it breaks down by different dimensions.
The metrics you can analyze in Explorer Segments include:
Average Time per Session
Average Time per User
Event - Occurrences
Event - Unique Users
You can apply various dimensions to these metrics that illustrate how they break down. By adding filters, you can also limit the scope of the data being analyzed in your query.
Build & Run Measures Queries¶
To build and run Segment queries, first navigate to Explorer as described in Getting Started.
Once you are in the Explorer UI, you can run saved queries by selecting one from the Saved Queries dropdown or create a new query by clicking the + New Query button at the right side of the page.
To build and run a new Segment query:
Click the + New Query button.
Select the metric to be segmented under Metrics.
Optional: Specify the first dimension to define the X-axis
Optional: Specify an additional dimension to further segment the metric
Optional: Add Filters to limit the scope of the Segment query
Click Run Query.
Example Use Cases¶
When you segment your audience, you gain valuable insights about the users who have installed your app (and those who haven’t). Segmenting can help your marketing and development teams to further bolster installations in the more responsive segments, or increase the number of installations in the less responsive segments.
If you build a simple Measures query for your dataset, specifying the number of DAU’s by Region, with User metrics, Dimensions defined as Session Date/Time in days, and User Region as All, and run that query, Explorer will report the data similar to the chart shown below.
The DAU count tells you how many users in your install base have used your app at least once on a given day. If a user initiates 10 sessions, the active user session count only increments once for that user on that day.
If your goal is to increase the number of unique users who use your app on a regular basis, you can determine the unique user session count, and work towards increasing the number of users who initiate sessions.
You can show trending data to expose unique user sessions over time. For apps that generate ad revenue, unique user sessions determine the number of unique impressions (the number of unique users who have viewed an advertisement) that you can bill to your advertisers.
Measure DAU by Region¶
Measuring DAU by region provides insights into the location and culture of your users. Knowing your users’ geographical region can help you make decisions about promoting and localizing your app to increase adoption, and targeting in-app advertising.
Analyze Your Events Using Flurry Decoration¶
In this example, we look at the number of texts sent broken down by the Gender of the sender.
The X-Axis can be defined as time axis by selecting either Session Date-Time or Install Date-Time or as a category axis using any of the other available options, including your Event Parameter values.
Breakouts define how the data is split across series. Series can be split by any user attribute and for Event-based metrics, by the parameter of the event.
User filters are those that control which users are to be include in the funnel. For example, if you only wanted to include users in the funnel that had performed a certain event at least 3 times in the past, you would add an Event History filter under User Filters such as:
There are many other filters that can be applied at the User level.
While User filters act on Users, Session filters focus on limiting the sessions that are included in the analysis. For example, if you wanted to only include sessions from version 1.1.1 of your app, you would add the following Session Filter: