activity_engagement plugin

Calculates activity engagement ratio based on ID column over a sliding timeline window.

activity_engagement plugin can be used for calculating DAU/WAU/MAU (daily/weekly/monthly activities).

T | evaluate activity_engagement(id, datetime_column, 1d, 30d)

Syntax

T | evaluate activity_engagement(IdColumn, TimelineColumn, [Start, End,] InnerActivityWindow, OuterActivityWindow [, dim1, dim2, ...])

Arguments

Name	Type	Required	Description
T	tabular expression	✓	The input tabular expression.
IdCoumn	string	✓	The name of the column with ID values that represent user activity.
TimelineColumn	string	✓	The name of the column that represents timeline.
Start	datetime		Scalar with value of the analysis start period.
End	datetime		Scalar with value of the analysis end period.
InnerActivityWindow	timespan	✓	Value of the inner-scope analysis window period.
OuterActivityWindow	timespan	✓	Value of the outer-scope analysis window period.
dim1, dim2, ...	table array		List of the dimensions columns that slice the activity metrics calculation.

Returns

Returns a table that has a distinct count of ID values inside an inner-scope window, inside an outer-scope window, and the activity ratio for each inner-scope window period for each existing dimensions combination.

Output table schema is:

TimelineColumn	dcount_activities_inner	dcount_activities_outer	activity_ratio	dim1	..	dim_n
type: as of TimelineColumn	long	long	double	..	..	..

Examples

DAU/WAU calculation

The following example calculates DAU/WAU (Daily Active Users / Weekly Active Users ratio) over a randomly generated data.

// Generate random data of user activities
let _start = datetime(2017-01-01);
let _end = datetime(2017-01-31);
range _day from _start to _end  step 1d
| extend d = tolong((_day - _start)/1d)
| extend r = rand()+1
| extend _users=range(tolong(d*50*r), tolong(d*50*r+100*r-1), 1) 
| mv-expand id=_users to typeof(long) limit 1000000
// Calculate DAU/WAU ratio
| evaluate activity_engagement(['id'], _day, _start, _end, 1d, 7d)
| project _day, Dau_Wau=activity_ratio*100 
| render timechart 

DAU/MAU calculation

The following example calculates DAU/WAU (Daily Active Users / Weekly Active Users ratio) over a randomly generated data.

// Generate random data of user activities
let _start = datetime(2017-01-01);
let _end = datetime(2017-05-31);
range _day from _start to _end  step 1d
| extend d = tolong((_day - _start)/1d)
| extend r = rand()+1
| extend _users=range(tolong(d*50*r), tolong(d*50*r+100*r-1), 1) 
| mv-expand id=_users to typeof(long) limit 1000000
// Calculate DAU/MAU ratio
| evaluate activity_engagement(['id'], _day, _start, _end, 1d, 30d)
| project _day, Dau_Mau=activity_ratio*100 
| render timechart 

DAU/MAU calculation with additional dimensions

The following example calculates DAU/WAU (Daily Active Users / Weekly Active Users ratio) over a randomly generated data with additional dimension (mod3).

// Generate random data of user activities
let _start = datetime(2017-01-01);
let _end = datetime(2017-05-31);
range _day from _start to _end  step 1d
| extend d = tolong((_day - _start)/1d)
| extend r = rand()+1
| extend _users=range(tolong(d*50*r), tolong(d*50*r+100*r-1), 1) 
| mv-expand id=_users to typeof(long) limit 1000000
| extend mod3 = strcat("mod3=", id % 3)
// Calculate DAU/MAU ratio
| evaluate activity_engagement(['id'], _day, _start, _end, 1d, 30d, mod3)
| project _day, Dau_Mau=activity_ratio*100, mod3 
| render timechart