Facebook continues to use Daily Active Users (DAU) and Monthly Active Users (MAU) to monitor and report growth. The company reported 1.18 billion daily active users on average for September 2016 and 1.79 billion MAU for same month. Mobile growth has been significant, and was reported as 1.09 billion DAU (average for September 2016) and 1.66 billion MAU.
Measuring unique active users over a period helps web and mobile app publishers monitor and report on growth, and compare growth across channels (mobile vs total in above example). The period could be a specific day, specific month, specific week, specific hour, last 7 days, last 30 days, Today, Yesterday, last 24 hours, and so on.
Daily Active Users (DAU)
Daily Active Users (DAU) is the number of unique active users of your app on a specific day.
You can report DAU for a specific day (December 7, 2016) or as an average of days in a quarter. For instance, Facebook reports DAU as an average over the number of days in the reporting quarter.
Monthly Active Users (MAU)
Monthly Active Users (MAU) is the number of unique active users of your app in a specific month. The month could be a specific month “February” or the preferred “previous 30 days” which excludes today.
It is much harder to manage uniques users for an app across “previous 30 days” but the data is much more reliable and not calculated as an estimate.
Weekly Active Users (WAU)
Weekly Active Users (WAU) is the number of unique active users of your app in a specific week. The week could be a specific week number with Monday as the starting day or the preferred “previous 7 days” which excludes today.
A month is a lifetime in an app’s lifecycle. Most product managers look at DAU, WAU and MAU to catch and act on changes early. Facebook monitors but does not report WAU.
MAU and WAU versus DAU
It is important to state that Weekly or Monthly active users is NOT an aggregation of Daily Active Users. Many analytics packages double and triple count daily active users. The right way to count MAU, WAU and MAU numbers is by not counting the same user multiple times. This is how Facebook and linkedIn report their DAU and MAU numbers. Note, that MAU is always less than or equal to DAU.
This is best explained with an example. Consider following users visited a website or used an app. Although obvious, but note, Sam used website or app three times on Tuesday the 9th.
- Three unique users used the app or visited the website on Monday 25th, that is, Sam, Sarah and Kim, so the DAU is 3.
- Kim and Sarah were the two unique users on Wednesday 10th, so the DAU is 2 for that day
- For Week A, the unique users were Sam, Sarah, Kim, Nick and Adrian or 5, assuming week starts on Monday.
- For Month N, the unique users were Sam, Sarah, Kim, Dickey, Charlie, Michelle or MAU for Month N is 6
- On Monday the 1st, the Last 7 D active users would be the number of active users in period Tuesday 26th to Monday 1st would be 5, i.e. Sam, Sarah, Kim, Nick and Adrian. The example assumes zero users from 27th to 31st for simplicity.
Typically, app publishers and growth marketers always look at the following numbers on a daily basis.
- Yesterday Active Users
The number of unique active users of your app yesterday.
- Today Active Users
The number of unique active users of your app today as of now.
- Previous 7 Days Active Users
The number of unique active users of your app in the last 7 days excluding today.
- Previous Week Active Users
The number of unique active users of your app previous week.
- This Week Active Users
The number of unique active users of your app this week as of now.
- Previous 30 Days Active Users
The number of unique active users of your app in the last 30 days excluding today.
- Previous Month Active Users
The number of unique active users of your app previous month.
- This Month Active Users
The number of unique active users of your app this month as of now.
To see how DAU is changing over a month, take a look at Statistics.
The Chess Grandmaster app is shown below along with winsorized mean (middle horizontal dotted line) along with high and low standard deviations (the other two dotted lines). Winsorized Mean, Tri Mean are Comparative Statistics that take temporary surges out of the math.
To see how DAU is categorized over a number of built-in variables see here.
Customer app Chess Grandmaster is shown below with categorization for geo-location for the last 24 days.
Hourly Active Users (HAU)
Hourly Active Users (HAU) is the number of unique active users by the hour.
Here is an analysis of hourly active users compared with the tri mean (an advanced mean that ignored skewness) along with high and low standard deviations, represented by dotted horizontal lines.
We have found two useful scenarios for hourly active users:
- aggregating by hour of day and day of week, gives you an idea of when to mange server upgrades for apps with a global audience.
- aggregating by hour of day and day of week and across timezones gives you an idea what time your app has the most active users globally. In the chart below, most people use the Best Reads book reader app around 8pm-9pm in their timezone and Thursday is the most popular day of the week.
Stickiness is a ratio of DAU and MAU and indicates the depth of engagement. The closer the ratio of DAU and MAU to 1, better the stickiness and it means that more of your users are returning to the site every day.
In overly simplistic terms: Stickiness = (Daily Active User for a specific day) / (unique & active users in a month or week)
However here are some enhancements:
- The numerator should not be the active users for the current day, since active users increase over the duration of the day and you maybe running install campaigns that run at a specific time of the day making your DAU sporadic.
- The denominator should not be calculated for the previous past whole month, so you are using a more recent and accurate number as a divisor.
Also using previous month introduces inconsistencies for months with 28, 29, 30 or 31 days.
It is much harder to manage uniques users for an app across previous 7 and 30 days but the Stickiness Factor is much more reliable, not an estimate, and does not deteriorate at month end.
For instance, it does not make much sense to use Feburary’s MAU number as a divisor on March 26.
7D Stickiness Factor
7 Day Stickiness Factor is ratio of yesterday’s active users versus unique and active users in the previous 7 days (excluding today). In general,
7D Stickiness Factor = (Daily Active User for a specific day) / (Previous 7D unique & active users)
30D Stickiness Factor
30 Day Stickiness Factor is ratio of yesterday’s active users versus unique and active users in the previous 30 days (excluding today). In general,
30D Stickiness Factor = (Daily Active User for a specific day) / (Previous 30D unique & active users)
Measuring change in Stickiness Factor is useful when you make a change to your app with your hypothesis being that it’s going to increase engagement and retention.
Stickiness Factor over days
It is well observed that implementing push notifications for retention marketing (i.e. to retain users) and in-app messages for engagement marketing (i.e. to engage active users) increases both retention and engagement considerably. Stickiness Factor can help determine the factor of growth.
The following shows Previous 7D active users, 7D Stickiness Factor, Previous 30D active users, 30D Stickiness Factor and Daily Active Users (DAU) for previous 60 days.
- DAU is used for reporting growth and comparing growth across channels.
- Stickiness Factor is ratio of daily active users over monthly or weekly active users and is useful to indirectly track growth.
- The metrics have to be calculated correctly for you to rely on them.
- Most analytics packages are configured by default to provide mostly reports on vanity metrics.
- Activations is a much better alternative to Installs as it is actionable.
- Loyalty is a much better alternative to daily active users as it indicates how loyal users are.
We will be covering vanity metrics, Activations and Loyalty in the next few blogs and link them here.