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#measure engagement
FOCUS: DAU / MAU – THE WHAT, WHY, AND HOW
MEASURECAMPNL 2016-04-09, JULES STUIFBERGEN
Some metrics
Page metrics
 time on page
 exit rate
 scroll-to-bottom
Session metrics
 visit duration
 pageviews per visit
User metrics
 sessions per user
 # multi-session users
More compound metrics
 Return visits within x days
 DAU/MAU and Average Active Days
What is DAU/MAU ?
Definition:
DAU (Daily Active Users)
MAU (Monthly Active Users)
Hence:
DAU / MAU = percentage of monthly active users that we see active this day
What does it say?
https://mixpanel.com/help/platform/tutorials/dau-mau
 DAU/MAU is a common metric used to measure stickiness (i.e. how engaged your users are).
It's derived by dividing DAU (Daily Active Users) by MAU (Monthly Active Users).
http://www.startupdefinition.com/dau-over-mau
 The ratio of Daily Active Users to Monthly Active Users. This metric is used to measure the
depth of engagement of a company's users.
High = good (mostly)
Same users visit often
 Loyal visitors
 Sticky Site
For games, content-sites, community sites, auction sites.. when you want habit or loyalty, you
want high DAU/MAU rates.
Low DAU/MAU rates?
Customer service pages, .. ? ..
Average DAU/MAU and ”AAD”
http://techcrunch.com/2009/10/29/how-to-measure-the-true-stickiness-and-success-of-a-facebook-app/
 [An Average] [..] DAU/MAU ratio of 50% would mean that the average user of your app is
using it 15 out of 30 days that month. [ correction mine ]
The sum of all DAU/MAU ratio’s in a period: Let’s call that “Average Active Days”: AAD
AAD / [number of days] = Average DAU/MAU
sum(DAU day 1, .., DAU day n) / MAU = 15
sum(DAU day 1 / MAU, … DAU day n / MAU) = 15
sum(DAU day 1 / MAU, … DAU day n / MAU) / n = 15/30 = 50%
So, how to calculate in Google Analytics?
Static (once for a period)
period = 28 days (4 whole weeks)
Query 1: users per day (28 rows)
Query 2: users per period (1 row)
DAU / MAU for each day = Q1 day 1 / Q2 … Q1 day n / Q2
AAD = sum(Q1) / Q2
So, how to calculate in Google Analytics?
Rolling (useful for segmented traffic (e.g. New Users), or to correct for period before peaks:
period = 28 days (4 whole weeks)
Query 1 28x (for every day in period): users per day (28 rows per query)
use only 1 row per query
Query 2 28x (for every day in period): users per period (1 row per query)
Automate it (in R)
library(rga)
i <- "12345" # Google Analytics View ID
mau <- ga$getData(i, start.date = s, end.date = e,
metrics = "ga:users", dimensions = "",
samplingLevel = "HIGHER_PRECISION")
dau <- ga$getData(i, start.date = s, end.date = e,
metrics = "ga:users", dimensions = ”ga:date",
samplingLevel = "HIGHER_PRECISION")
Automation is useful for rolling..
dau.rolling <- data.frame()
for (day in seq(from = period.end – 27, to = period.end, by=1) {
q <- ga$getData(i, start.date = day, end.date = day + 27,
metrics = "ga:users", dimensions = "ga:date"
segment = "gaid:-101") #multi-session users (built-in)
# add the row we want
dau.rolling <- rbind(dau.rolling, q[ which(q$date == day),])
}
Plot it in ggplot
Multi-Session users compared
to ALL traffic.
Logically, this differs.
Also plotted: the Average
DAU/MAU (which is AAD)
Source: not-so-sticky website
Missing: absolute numbers
All sessions vs Customers: Big difference
(Same site, all users vs. UID view)
Static vs Rolling: see the pre-peak difference
(AAD higher)
Comments / Discussion
 Would YOU use DAU/MAU? Is it useful?
 How would you communicate what it means?
 Useful segments to compare?
BTW: script available for R-lovers, ask me  @zjuul

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#Measure Engagement - DAU / MAU and AAD

  • 1. #measure engagement FOCUS: DAU / MAU – THE WHAT, WHY, AND HOW MEASURECAMPNL 2016-04-09, JULES STUIFBERGEN
  • 2. Some metrics Page metrics  time on page  exit rate  scroll-to-bottom Session metrics  visit duration  pageviews per visit User metrics  sessions per user  # multi-session users More compound metrics  Return visits within x days  DAU/MAU and Average Active Days
  • 3. What is DAU/MAU ? Definition: DAU (Daily Active Users) MAU (Monthly Active Users) Hence: DAU / MAU = percentage of monthly active users that we see active this day
  • 4. What does it say? https://mixpanel.com/help/platform/tutorials/dau-mau  DAU/MAU is a common metric used to measure stickiness (i.e. how engaged your users are). It's derived by dividing DAU (Daily Active Users) by MAU (Monthly Active Users). http://www.startupdefinition.com/dau-over-mau  The ratio of Daily Active Users to Monthly Active Users. This metric is used to measure the depth of engagement of a company's users.
  • 5. High = good (mostly) Same users visit often  Loyal visitors  Sticky Site For games, content-sites, community sites, auction sites.. when you want habit or loyalty, you want high DAU/MAU rates. Low DAU/MAU rates? Customer service pages, .. ? ..
  • 6. Average DAU/MAU and ”AAD” http://techcrunch.com/2009/10/29/how-to-measure-the-true-stickiness-and-success-of-a-facebook-app/  [An Average] [..] DAU/MAU ratio of 50% would mean that the average user of your app is using it 15 out of 30 days that month. [ correction mine ] The sum of all DAU/MAU ratio’s in a period: Let’s call that “Average Active Days”: AAD AAD / [number of days] = Average DAU/MAU sum(DAU day 1, .., DAU day n) / MAU = 15 sum(DAU day 1 / MAU, … DAU day n / MAU) = 15 sum(DAU day 1 / MAU, … DAU day n / MAU) / n = 15/30 = 50%
  • 7. So, how to calculate in Google Analytics? Static (once for a period) period = 28 days (4 whole weeks) Query 1: users per day (28 rows) Query 2: users per period (1 row) DAU / MAU for each day = Q1 day 1 / Q2 … Q1 day n / Q2 AAD = sum(Q1) / Q2
  • 8. So, how to calculate in Google Analytics? Rolling (useful for segmented traffic (e.g. New Users), or to correct for period before peaks: period = 28 days (4 whole weeks) Query 1 28x (for every day in period): users per day (28 rows per query) use only 1 row per query Query 2 28x (for every day in period): users per period (1 row per query)
  • 9. Automate it (in R) library(rga) i <- "12345" # Google Analytics View ID mau <- ga$getData(i, start.date = s, end.date = e, metrics = "ga:users", dimensions = "", samplingLevel = "HIGHER_PRECISION") dau <- ga$getData(i, start.date = s, end.date = e, metrics = "ga:users", dimensions = ”ga:date", samplingLevel = "HIGHER_PRECISION")
  • 10. Automation is useful for rolling.. dau.rolling <- data.frame() for (day in seq(from = period.end – 27, to = period.end, by=1) { q <- ga$getData(i, start.date = day, end.date = day + 27, metrics = "ga:users", dimensions = "ga:date" segment = "gaid:-101") #multi-session users (built-in) # add the row we want dau.rolling <- rbind(dau.rolling, q[ which(q$date == day),]) }
  • 11. Plot it in ggplot Multi-Session users compared to ALL traffic. Logically, this differs. Also plotted: the Average DAU/MAU (which is AAD) Source: not-so-sticky website Missing: absolute numbers
  • 12. All sessions vs Customers: Big difference (Same site, all users vs. UID view)
  • 13. Static vs Rolling: see the pre-peak difference (AAD higher)
  • 14. Comments / Discussion  Would YOU use DAU/MAU? Is it useful?  How would you communicate what it means?  Useful segments to compare? BTW: script available for R-lovers, ask me  @zjuul