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Benchmarking against the average value

  • 17 March 2021
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Benchmarking against the average value
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When you measure activity or performance, it can be important to look not only at the absolute values but also to compare them to a benchmark.

A benchmark can be something as simple as an average value. For example, you can measure how many support tickets your agents answer every month.

Whatever your data model is, you start with a core metric # Answered Tickets.

When you slice this metric by Agent, you can get something like this:

 

Agent

# Answered Tickets

Alice

812

Bob

320

Charlie

12

David

940

 

The numbers are for all time, so they can really vary (maybe Charlie started very recently). We better slice it by month, too. The last two months can look like this:

 

Month

February 2021

March 2021

Agent

# Answered Tickets

# Answered Tickets

Alice

132

75

Bob

150

73

Charlie

 

12

David

96

60

 

The average number of answered tickets in February is (132+150+96)/3 = 126 (Charlie didn’t work in February) and the average in March is (75+73+12+60)/4 = 55

The metric will use the BY ALL keyword to not get sliced by agents:

Benchmark # Answered Tickets: SELECT (SELECT # Answered Tickets BY ALL Agent)/(SELECT COUNT(Agent) BY ALL Agent)

Both nested metric will still get sliced by Month because it is used in the insight.

Now we can create our final metric that will compare the numbers for each agent to the benchmark for the month.

Benchmark Delta: SELECT # Answered Tickets - Benchmark # Answered Tickets
 

Month

February 2021

March 2021

Agent

Benchmark Delta

Benchmark Delta

Alice

+6

+20

Bob

+24

+18

Charlie

 

-43

David

-30

+5

 

Learn more about MAQL and metrics at GoodData University


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