Intro to Analytics

The Analytics dashboard in Leanplum shows the results of all of your A/B tests, messages, and lifecycle campaigns.

At the highest level, raw usage data from your app, tests, campaigns, and messages is displayed in four main reporting categories on the left panel in the Analytics dashboard.

  1. General (User activity)
  2. A/B Tests
  3. Lifecycle Campaigns
  4. Messages

If you do not have A/B tests, lifecycle campaigns, or messages, then these categories may be hidden.

User data is broken into User Activity, Userbase, and Developer Activity.

  • User Activity shows analytics detailing all in-app activity related to specific user dimensions by day. The User Activity report is based on Production data only.
  • Userbase shows analytics based on specific metrics or events seen across the entire unique user base. This section of analytics does not have any dates associated with it; instead, it presents an aggregated view of your entire user base and their complete history within Leanplum.
  • Developer Activity tracks activity from registered development devices so that your test data and production data remain separate and stats are not skewed.


For each of these categories, data is displayed as metrics (e.g. returning users, total purchases, average session-length, opens or accepts). In addition to out-of-the-box metrics, you can also build your own custom metrics.

Metrics are the primary form of data in the Analytics dashboard. Each metric can be viewed as a report, and saved as a favorite. You can select a metric using the dropdown above the main Analytics chart, or by selecting a tile below it.


The favorited metric tiles below the chart show the total value (or average) for that metric in the selected date range. Clicking a tile will toggle the Analytics chart to show data for that metric. This gives you a quick way to view summaries of high-level data and move between reports.

You can narrow and refine metrics data down to get a closer, more detailed, look with filters, groups, and cohorts.


Filters narrow your data down to a subset based on criteria you select. This is the first level of data manipulation, so any data that does not match your filter criteria will not appear in subsequent groups or cohorts.

Many filters are fairly straightforward, filtering by user location, last session start, or device info (OS, version, etc.). Filters let you take a closer look at a metric for a very specific subset of your users. You can also use the current user filter, which will filter for users that match criteria currently, rather than include any users who met that criteria at any point in your date range.

Read more about Filters.


You can also group your data. This lets you view your data grouped into subsets of users based on another criteria you select. This works well for comparing and summing your data.

See more about Grouping data.


Cohorts work very much like Groups, with data combined based on shared criteria. However, rather than show a single total for each cohort for the entire date range like Groups do, a cohort will display a stacked graph of the data over time with totals for each day (hour, week, or month).

Cohorts will show your data as a stacked line graph over time; whereas, Groups will always show a simple bar chart with totals.

See more about Cohorts.

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