How to calculate DAU/MAU with BigQuery (engagement)

Question

DAU and MAU (daily active users and monthly active users) are an established way of measuring user engagement.

How can I get these numbers using SQL and Google BigQ

Answer 1

2019 standard SQL update:

• https://stackoverflow.com/a/49866033/132438

---

(to understand the utility of DAU/MAU see articles like http://blog.compariscope.wefi.com/mobile-app-usage-dau-mau)

Let's play with the reddit comments data stored in BigQuery. We want to find out the dau/mau ratio for the 'AskReddit' subreddit during September on a daily rolling basis:

SELECT day, dau, mau, INTEGER(100*dau/mau) daumau
FROM (
  SELECT day, EXACT_COUNT_DISTINCT(author) dau, FIRST(mau) mau
  FROM (
    SELECT DATE(SEC_TO_TIMESTAMP(created_utc)) day, author
    FROM [fh-bigquery:reddit_comments.2015_09]
    WHERE subreddit='AskReddit') a
  JOIN (
    SELECT stopday, EXACT_COUNT_DISTINCT(author) mau
    FROM (SELECT created_utc, subreddit, author FROM [fh-bigquery:reddit_comments.2015_09], [fh-bigquery:reddit_comments.2015_08]) a
    CROSS JOIN (
      SELECT DATE(SEC_TO_TIMESTAMP(created_utc)) stopday
      FROM [fh-bigquery:reddit_comments.2015_09]
      GROUP BY 1
    ) b
    WHERE subreddit='AskReddit'
    AND SEC_TO_TIMESTAMP(created_utc) BETWEEN DATE_ADD(stopday, -30, 'day') AND TIMESTAMP(stopday)
    GROUP BY 1
  ) b
  ON a.day=b.stopday
  GROUP BY 1
)
ORDER BY 1

This query gets DAU for each day in September, and looks also into August data to get the MAU for each 30 day period ending in each DAU day. That takes a lot of processing (30x), and we can get almost equivalent results if we only calculate one MAU for September, and proceed to use that value as the denominator:

SELECT day, dau, mau, INTEGER(100*dau/mau) daumau
FROM (
  SELECT DATE(SEC_TO_TIMESTAMP(created_utc)) day, EXACT_COUNT_DISTINCT(author) dau, FIRST(mau) mau
  FROM [fh-bigquery:reddit_comments.2015_09] a
  CROSS JOIN (
    SELECT EXACT_COUNT_DISTINCT(author) mau
    FROM [fh-bigquery:reddit_comments.2015_09]
    WHERE subreddit='AskReddit'
  ) b
  WHERE subreddit='AskReddit'
  GROUP BY 1
)
ORDER BY 1

That's a much simpler query that brings us almost equivalent results much faster.

Now to get an average value for this subreddit for the month:

SELECT ROUND(100*AVG(dau/mau), 2) daumau
FROM (
  SELECT DATE(SEC_TO_TIMESTAMP(created_utc)) day, EXACT_COUNT_DISTINCT(author) dau, FIRST(mau) mau
  FROM [fh-bigquery:reddit_comments.2015_09] a
  CROSS JOIN (
    SELECT EXACT_COUNT_DISTINCT(author) mau
    FROM [fh-bigquery:reddit_comments.2015_09]
    WHERE subreddit='AskReddit'
  ) b
  WHERE subreddit='AskReddit'
  GROUP BY 1
)

This tells us that 'AskReddit' had an engagement of 8.95% during September.

Last stop, how to compare engagement within various subreddits:

SELECT ROUND(100*AVG(dau)/MAX(mau), 2) avg_daumau, MAX(mau) mau, subreddit
FROM (
  SELECT a.subreddit, DATE(SEC_TO_TIMESTAMP(created_utc)) day,
         EXACT_COUNT_DISTINCT(author) dau, FIRST(mau) mau
  FROM [fh-bigquery:reddit_comments.2015_09] a
  JOIN (
    SELECT EXACT_COUNT_DISTINCT(author) mau, subreddit
    FROM [fh-bigquery:reddit_comments.2015_09]
    GROUP BY 2
  ) b
  ON a.subreddit=b.subreddit
  WHERE mau>50000
  GROUP BY 1, 2
)

GROUP BY subreddit
ORDER BY 1