This section contains definitions and information about standard metrics used at Mozilla. You may wish to refer to the terminology section while reading this document, in case a particular concept is not clear.

Table of Contents

Daily Active Users (DAU)


Daily Active Users or DAU counts the number of unique profiles active in the product on each day. This is intended to approximate the number of people using the product each day.


DAU counts unique profiles. Keep in mind that a profile is not necessarily a user.

The standard concept of active varies by product, but generally, active users are defined as unique profiles that have sent a main ping (on desktop) or a baseline ping (on mobile). The precise criteria are defined in the usage criterion section of this documentation.

We can also restrict the metric to alternative usage criteria. It's critical to clearly state any non-standard usage criteria on the metric. The metrics team suggest the following format: DAU(usage criterion). For example, we might be interested in the count of profiles that have viewed more than 5 URIs in a day. We'd denote that metric as DAU(URI > 5).

Some common alternative usage criteria documented in the usage criterion section.


If the number of users stays constant, but the average number of active profiles per user increases, this metric will tend to increase. For more details on the relationship between users and profiles, see the profiles vs users section in analysis gotchas.


This metric is available on the standard Growth and Usage Dashboard (GUD) for most products and with some slicing available.


DAU can easily be calculated from the Exact MAU tables; for example:

  SUM(dau) AS dau
  -- You define your slice using additional filters here.
  country IN ('US', 'GB', 'CA', 'FR', 'DE')


You can run this query on STMO#72012.

Day 2-7 Activation


This measure attempts to tell us whether, after creating a profile, the user returned at any point in the next 6 days. This is meant to measure whether a product is successful in engaging users early on: at present, many users churn after the first run and we want to measure whether efforts to make them stick around are succeeding.

This metric is used for all mobile applications, excluding Fenix and Firefox Preview, and is a top-level OKR inside Mozilla for 2020. It can apply both on the level of a specific product (e.g. Firefox iOS, Lockwise for Android) as well as an aggregate measure across all mobile products and devices. This metric is not used for Firefox desktop.


The day 2-7 activation metric is calculated as:

\[ \frac{\text{Activated New Profiles (day 2-7)}}{\text{New Profiles (day 1-7)}} \]

Activated New Profiles (day 2-7): Unique count of client ids who use the product at any point starting the day after they created a profile up to 6 days after.

New Profiles: Unique count of client ids with a given profile creation date. As not all initial pings are received exactly on the day of profile creation, we wait for 7 days after the profile creation date before establishing the New Profile cohort to ensure the data is complete.

E.g. For a cohort acquired (with a profile creation date) of Mar 1, they are considered activated in day 2-7 if they show up in DAU at any time between Mar 2nd and Mar 7th.


The non-desktop day 2-7 dashboard tracks this measure.


You can calculate this measure via the firefox_nondesktop_day_2_7_activation table in BigQuery. Here is a sample query:

  SUM(day_2_7_activated) as day_2_7_activated,
  SUM(new_profiles) as new_profiles,
  SAFE_DIVIDE(SUM(day_2_7_activated), SUM(new_profiles)) as day_2_7_activation
  cohort_date = "2020-03-01"


Reference below.

{{Metric name}}

TL;DR: two sentence max.

E.g: MAU counts the number of distinct users we see over a 28-day period. Desktop and Mobile MAU are both corporate KPIs for 2020.

  • Overview:
  • What the metric measures
  • Calculation:
  • Definitions for both Mobile and Desktop, if applicable.
  • What is the easiest way to calculate this metric? E.g. MAU over clients_last_seen.
  • At least one working definition
  • Link to a scheduled Redash query (link with stmocli?)
  • Possibly an API-linked graph from STMO
  • If it’s non-obvious, examples for how to stratify. E.g. calculating MAU from clients_daily
  • Common Issues: Failures and Gotchas
  • Resources
  • Link to the STMO query from Definition section
  • Notable dashboards for the metric
  • Similar metrics

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