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What Product Analysts Should Know About MAU

How to measure product engagement

Photo by Cookie the Pom on Unsplash
Photo by Cookie the Pom on Unsplash

When I became a product data analyst, I was introduced to MAU (monthly active users), a major KPI (key performance indicator) for Product Engagement and growth. Over time though I realized MAU as a single KPI wasn’t enough to answer questions from product stakeholders. Today, I’m going to cover the issues with MAU and ways to make it more a meaningful KPI to measure product engagement.


Issues

Different Definitions

MAU is defined as the "number of unique users who visit a site within the past month" where month is a 30-day period.

The first problem lies in the definition of a "user". Are these anonymous visitors to the website that are tracked by a cookie ID? Are these limited to users that registered for an account or both?

The second issue is what’s considered an "active" user? Do we count a visitor that saw one page on the website and left? What if the user just logged into the app because they got a push notification and then closed it? One option is to require an active user to perform at least one activity within the core function of your product to count as "active". If you have a gaming app maybe that means the user played a game. If you have a messaging app the user had to send or read a message.

Given that "user" and "active" definitions vary MAU may not be comparable across products and you should be mindful to clarify the definitions when analyzing MAU changes.

Overall MAU may be misleading without looking at the composition

In the example below total MAU is increasing monthly from 3.1 million in January to 3.6 million in September. If product managers were just looking at MAU growth, they would think everything was fine because engagement is up.

However, if we split MAU into three segments using the definitions below, we see a different trend. The majority of the MAU increase is coming from new users and continuing and returning users have actually been declining since January.

  • New users are the ones that signed up this month.
  • Continuing users were active last month and this month.
  • Returning users were active 2+ months ago and were active this month.
Image by author
Image by author

Graphing MAU by segment shows the trend more clearly. The blue new user line is increasing while the orange continuing and grey returning users are gradually declining.

Image by author
Image by author

A stacked bar chart helps us see new users as a percentage of total MAU is increasing while the continuing and returning percentages are decreasing.

Image by author
Image by author

In the example above, the company has a user retention problem. New users are increasing but many aren’t returning. Without segmenting MAU, the product managers don’t realize there’s a user retention issue that needs to be addressed.

Alternatives

Since MAU can’t tell the entire story about product engagement these are a couple of alternatives.

Analyze MAU Using Cohorts

Cohort your users by signup month to analyze retention over time. For the example I used above, cohort analysis can help identify if the retention was always declining or it was due to a product change in 2021 that affected retention. Cohort analysis will help pinpoint when retention started to decline.

Stickiness Ratio

Product stickiness is the "propensity of customers to return to your product or use it more frequently". DAU (daily active users) divided by MAU can be used to measure stickiness and is a popular engagement metric because it was used by Facebook.

Takeaways

In closing, MAU can be useful to track product engagement but it may not tell the entire story. Now that you understand the limitations and have alternatives, I hope this helps this gives you a new perspective the next time you get a question about engagement.


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