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You Need To Be Careful How You Measure Your Processes

Spurious Correlations

Everyone knows the saying that you can lie with statistics. One of the themes around the responsible use of statistics is that correlation does not imply causation. For example, the above graph from the Spurious correlations book illustrates how ridiculously unrelated things can be correlated.

Another problem that is less frequently mentioned is that you get what you measure. This is the inverse take on the popular “you can’t know what you don’t measure” and hints at the fact that the way you measure influences your results.

To understand the you get what you measure problem take a look at the following process from a customer service department at a large Internet company. It shows the contact moments that customers had with the support team over various channels (phone, web, email, chat).

The key metric that was used in the team to monitor the service performance was the First Contact Resolution Rate (FCR). The FCR measures how many of the customer problems the team could solve within the first contact with the customer, for example, without the customer having to call back again. In the process map below you can see that out of 21,304 inbound calls only 540 resulted in repeat calls. The overall FCR was an impressive 98%.

Customer Service Process with Service Request as Case ID (click to enlarge)

However, the process mining analysis was done based on the Service Request number as a Case ID. The Service Request ID is a unique identifier that is automatically…

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