As CCC’s resident benchmarking expert, I thoroughly enjoy looking at customer data.  I find it to be one of the most interesting parts of what we do in the service environment.  Of course, the challenge here is how to analyze the data and come up with conclusions that match with true customer needs.

I believe we have a tendency to look at individual data points in relative isolation (for example, tracking higher handle times or lower resolution rates) which can lead to a myopic point of view that doesn’t take into consideration the context of that data point, nor connect with what the customer is actually experiencing.

Instead, I’ve found that every data point has a rich story behind it – a story that better explains what customers actually need to have a positive customer experience (as opposed to what they may say at any singular time).

The key is to recognize the difference here – and to dig a little deeper to get a true understanding of the customer.

Here are a few examples of a data that says one thing when simply taken at face value, but something very different once we figure out the story behind it:

• High Web site satisfaction scores and click through rates: These figures are observable and easy to collect, but they only measure what a customer actually does on your customer service Web site, not what they were trying to do.  Higher scores here might initially indicate a successful customer service web site, but actually doesn’t capture if the customer’s issue was resolved.Additional questions about the customer’s intention (and if they were successful) provides a fuller picture of their experience – and identify possible web failure points that could drive the customer to other, more expensive channels to ultimately resolve their issue.
• High customer-reported FCR rates: Asking the customer at the end of the interaction “Was your issue resolved?” captures a part of issue resolution – the customer’s perception about their experience.  It can lead service organizations to feel comfortable in their issue resolution performance if most customers feel as though they got the issue resolution they need. But, the customer simply doesn’t know what they don’t know.  Maybe the check takes two weeks to clear, or maybe they customer will have to call back about a related, downstream issue in a few days.  Tracking callback rates along with customer-reported data (and even QA analysis) can triangulate a more accurate issue resolution rate.
• Higher average handle time and lower first contact resolution rates: Together, even these two data points sound like our service organization isn’t performing well against their goals – a reasonable conclusion from this data is the center is less efficient and provides a lower customer experience.But, what if the call mix is changing as well?  If easier calls are successfully resolved in self-service, the live phone channel will end up only getting the hardest of the hard issues.  Naturally, these would lead to longer calls with lower resolution rates.  By looking at self-service data and channel usage (as well as these phone metrics), service organizations can get a more complete picture of their performance.

So, the moral of the story?  Don’t just a single data point by itself, in isolation of changing customer preferences and needs.  Take some time to connect that data point to the larger customer experience – and you might be surprised with what you find.  Have you ever gone through a similar exercise in your organizations – and what have you found?

For CCC members interested in learning more on measuring the customer experience, visit our resource page on this topic.

Related posts:

1. FCR: How Accurate Is Your Data?
2. How to Become More Customer Centric on a Shoestring Budget
3. Give Colleagues the VOC Data They NEED
4. Putting E-mail in my Delete Box
5. Are You Using the Right Channel to Survey Customers? (Part 1 of 2)