In several of my previous blogs, I stressed the importance of statistical thinking in interpreting data. For many people, statistics is probably one of the hardest subjects to learn. In contrast, voice of the customer, often qualitative in nature, may appear simpler. But identifying the right customers and their needs, in my opinion, is exceedingly challenging in practice.

In a recent HBR article “Think Customers Hate Waiting? Not So Fast…” the authors illustrated what they called the labor illusion: “Customers find waiting more tolerable when they can see the work being done on their behalf — and they tend to value the service more.” I found the approach intriguing because I see it representing at least three common challenges in VOC.

1. Identification of unconventional needs

Development of fresh insight in customer needs is the first step to innovative and differentiated solutions. The challenge is not only to see the obvious needs but to discover the hidden ones in the same context. In cases where customers are waiting for their results, what are their needs or Critical to Quality characteristics (CTQs)? In a typical LSS project, people would say it is service lead time. Simple enough. Let’s reduce the lead time to reduce customer waiting. Is that all? Their research showed that “many customers who endure waits but see a running tally of tasks end up happier than those who don’t have to wait at all.” Apparently some customers also need to be informed of the process or progress.

2. Interpretation of customer comments

A related challenge in VOC is that customers rarely articulate their needs in terms of distinct CTQs. How do you interpret a customer’s reaction “What’s taking so long?” Are they complaining about the lead time (“so long”), expressing the need for information (“what”), or both? Interpretation of customer comments depends on the synthesis of information beyond what is presented, i.e. experience of the interpreter, and therefore is subjective. To bring some objectivity, one can perform experiments to support their interpretation, as the authors did. Such experimentation underscores the fact that understanding VOC is not a one-time exercise but continued learning.

3. Prioritization of CTQ

As obvious as it may sound, customer segmentation is often a missing part of VOC. In one of their experiments, they concluded that “A majority preferred the transparent — and slower — site.” What is the significance of “the majority?” Remember the 80/20 rule? Is this majority of customers who value transparency also the ones who contribute the most to your profitability? To answer such questions, we will have to design additional experiments with carefully segmented customers. Without knowing which customers are the most critical to our business success, we cannot prioritize our improvement effort to generate the maximal return. A granular level of understanding of each customer segment is required not only to prioritize CTQs, but also to inform business decisions on choosing the right market to compete in, i.e. strategy.

Do you find VOC challenging? What are the challenges you have faced, and how did you deal with them?

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