Too Much Customer Voice? Or Enough to Make Good Decisions?
posted by: Nick Roman on 3/11/2010
I read a very interesting post on the Harvard Business Review site last week, entitled “Do You Need All That Data?” In the article, the author examines business decision making and asks a number of sensible questions challenging companies to look at the data they acquire, the metrics derived from them, and whether they provide any actual value to the business.
It’s quite good, and the author asked four questions to help businesses break through that mountain of data. Those questions are:
• “Are we asking the right questions?”
• “Does our data tell a story?”
• “Does our data help us look ahead, rather than behind?”
• “Do we have a good mix of qualitative and quantitative data?”
Although the author is, for the most part, referring to transaction data in the article, this question set got me to thinking about how companies who create Voice of the Customer programs should think about the data they’re about to acquire and how it will help them to make sound business decisions.
Let’s have a look at each question:
• “Are we asking the right questions?”
This is something that’s hotly debated within the Customer Feedback and Market Research industries, and I won’t pretend to have the answer here. But I would argue that the answer partly depends on the purpose for asking the question.
For Customer Feedback surveys, in which the point is to ask about specific customer experiences, the questions are likely to be more operational in nature. I tend to tell customers that there are three questions to ask here: “Did you like it?” “Would you do it again?” and “Would you tell your friends to do it?” These questions get to the heart of customer satisfaction, customer loyalty, and advocacy.
• “Does our data tell a story?”
This is where we can add the “why” to the three questions mentioned above. In some cases this is merely an opportunity to add a comment: “Why did you give us the answer you gave?” In other cases it’s an opportunity to
correlate customer attitudes with, for example, customer satisfaction. It’s not enough merely to know what your score is; you’ll want to know what affects it and how to improve it.
• “Does our data help us look ahead, rather than behind?”
The maxim “changes in attitude precede changes in behavior” not only makes intuitive sense, it’s backed by science, notably in the
Theory of Planned Behavior. This indicates that attitudinal data, that is the responses to customer feedback surveys, is actually forward looking. This means that a change in your CSAT number or your Customer Loyalty Index could well indicate a change in business results. We’re referred to these as
Key Attitudinal Indicators, which you can match to your Key Performance Indicators.
By definition, such data is forward looking.
• “Do we have a good mix of qualitative and quantitative data?”
It’s important to have both. It’s also important to understand when qualitative data is representative and when it’s merely anecdotal. It’s not without reason that the squeaky wheel always gets the grease. Sentiment analysis performed by companies such as
Clarabridge can play a key role in quantifying the qualitative data and complementing the quantitative data acquired directly.
What seems interesting is that the questions apply no matter which type of data you’re looking at: transactional business data that forms part of your balanced scorecard KPIs, or the attitudinal data that forms your KAI scorecard.
The trick is in matching the two together so that your Voice of the Customer program is tied to business performance. That helps you determine the ROI of that program so that not only does it maintain its funding but also its expansion across the various “moments of truth” in your customer lifecycle.