Finding the wrong meaning in correlations can be dangerous. Let’s say I find a statistical correlation between the number of drowning victims at a beach and the number of ice cream cones sold at the same beach. I would be in error to claim, “because of the correlation, we believe that ice cream cones cause drowning.” That’s probably not the story; a better explanation would be that on hot days, the number of people at the beach increases, the more ice cream is purchased, and the more people are in the water exposed to drowning risk.

The second problem with correlations is that people seldom address what is causing what. A correlation doesn’t indicate which factor is the “cause” and which is the “effect.” One of our studies showed a positive correlation between marketing a contact center to the rest of the enterprise and lower agent turnover. But, we didn’t do additional work to identify whether “marketing” was the cause and “lower turnover” the effect or the other way around. Sometimes you can “guess” at the direction of the “cause / effect” based on insight, experience, and logic but you never really know for sure without further research.

We should watch the data source and understand how the researchers gathered the data and drew their conclusions. And researchers need to understand the complexity of the call center environment. Without the understanding, they may draw the wrong conclusions.

Once, when I was managing a contact center, a customer complained to my boss about receiving a busy signal when she called. This boss saw a positive correlation between busy signals and customer complaints. He called me in and strongly suggested that I add more lines so no customer would ever get a busy signal again. I told him that I would be glad to, but we would probably need to add more agents. If we did not, we would be trading one problem for another -- the customer would have to wait on hold longer. My boss saw the correlation, but didn’t understand all the contact center linkages and complexities. What seemed to him like a quick fix was much more complex.