Driver Analytics is a sub-field within the analytics’ space, where effects of different drivers are estimated. Typical questions answered are “what is the effect of shorter customer service queues on our customer satisfaction” or “what is the effect on our churn rate, if the customer comes back for a 2nd buy”.

Do you wrestle with understanding the effects of different measures taken? You are certainly not alone. Often companies do something, like an A/B test, and the results are reported – typically in a histogram. But quantifying effects begs for more advanced methods.

One example: The CX department at a multinational client was urged to help prioritize the technical development – something at the organization’s heart. Quite a task, right? So how to start?

By identifying CX factors as drivers, such as customer service willingness to help, loading time, 2nd purchase rate, and several other drivers, we used statistical models to quantify the effects of each driver on customer satisfaction.

The result? A super-clear answer to where to spend the money. Shorten the loading time? The customer service queues? Or always, in each and every call, make sure the customer was left with the feeling that its problem was understood? Now, our client knows.

Insights lite those above are presented as quantified effects on the target variable. For example, it can be shown that customer satisfaction (CSAT, 1-5)) on average is improved by 0.9 units by shortening the customer service queues by 1 minute. Or, that the churn rate is lowered by 15% by converting customers to a 2nd buy.

Magic? No. Statistics.

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