Difference between Cumulative, Average and Point in Time input measures

When using/ creating measures, it is important to consider and understand how time impacts the base input measure.

Point in Time allows users to understand a number for a particular point in time (i.e. a snapshot of what the organization looked like). Point in time measures traditionally count / sum data as of a particular date. Point in time measures can be the start of a time period, end of a time period or any point in between. 

Point in time measures allow customers to answer key questions such as

For example, if a user was requesting the number of Applications for an organization for a particular date, the measure/ data returned would bring back all Applications that were currently open/ active as of that date. The results wouldn’t include those just opened on that date, but instead all active applications that have yet to be closed.

Cumulative or Span of Time is the most common way time is used in a measure. Traditionally Cumulative measures sum data across a span of time.

Cumulative measures allow users to answer key questions, such as:

For example, if a user was requesting the number of Applications for an organization for a particular calendar year, the measure/ data returned would bring back all Applications from January 1 of that year to the current date. The results would be the sum all of the applications submitted for that time period. Cumulative measures can sum data across years, for a particular quarter, month or week.

Average is similar to a Cumulative measure. Instead of summing data across a span of time, an Average measure will average the data across a span of time. These measures are rarely used as independent measures and most commonly used as part of a calculated measure. 

The most common Average time measure is Average Headcount. This input measure is used a denominator in many calculated measures (e,g. Termination Rate, Hire Rate, Promotion Rate). It is important to use an average in these measures, rather than Point in Time because Point in Time are more likely to overstate or understate the final result measure results.

These measures are rarely used to answer key business questions on their own. 

For example, if a user was requesting the average number of Applications for an organization for a particular calendar year, the measure/ data returned would average the days an application was active from January 1 of that year to the current date. The results would be the average of applications for that time period. Average measures can average data across years, for a particular quarter, month or week.

Example Results for Different Measures

In the data example below, if I was creating the 3 different time measure types for March 31. I would expect to see the following results:

EOP Applications:  2  
Includes: Candidates 1234 and 1240
Reason: These are the only ones that still haven’t been Hired/ Rejected as of March 31

Total Applications: 7
Includes: All Candidates
Reason: This is a cumulative count of all Applications for the year. Note, since this is a count of Applications and not Applicants/ Candidates, we count both positions that candidate 1236 applied for.

Average Applications: 2.38
Includes: Average Candidates per Month
Reason: Similar to Average Headcount this is rolling daily average an Application is open. Honestly this metric doesn’t mean much on its own or even in combination with other Recruiting metrics, but I've provided the example so you can see the different results.