Do Scores Matter if You Don't Know What is Critical to Your Customers?

Last week, I wrote about the importance of knowing what drives customer dissatisfaction, which dealt with data interpretation and the need to dive more deeply into the business issues as well as the data.

There are exploratory ways to interpret data and more analytical methods as well. The key to correct interpretation is knowing which approach will deliver insights to your business. As I said in my last post, coming to the wrong conclusions even with good data is a possibility without using the right tools. In the case of customer loyalty, you could be investing in programs that have little or no impact on the customer’s intention to buy again or you could be ignoring “dissatisfiers” that diminish a customer’s perception of your critical attributes.

So, there are a few initial questions to ask:

• What do you believe are the attributes that contribute to your customers’ loyalty?
• Are you measuring those attributes specifically in any data gathering exercise including social media monitoring?
• Do you classify these attributes in terms of their impact on the customer or importance?

The example below is from an article titled Guests’ Perceptions on Factors Influencing Customer Loyalty, which appeared in the March 2010 issue of the International Journal of Contemporary Hospitality Management.

Customer Service : Dissatisfier

Cleanliness : Neutral

Room quality : Dissatisfier

Value for money : Critical

Quality of food : Dissatisfier

Family friendliness : Neutral

The authors selected typical product and service attributes for a guest at a hotel and designed questions around those.

Using simple regression, they did something very interesting and in my view very revealing about the data they collected. The usual loyalty question was asked (intention to return) and all other questions were tested against this one. Then, questions above a median score were tested individually and those below the median also were tested in the same way. The results were plotted against agreed criteria from Critical to Neutral.

Critical Attributes were significant in both tests and would be considered a driver of loyalty as well as a reason to switch. These have high compliments and high complaints. Performing well in other areas won't compensate for low performance here.

Dissatisfiers were significant in testing low performance but not when high performance was tested. So, if customer service is bad, it influences a decision to switch but an average experience doesn’t critically drive loyalty. These are the attributes that should be maintained but not at the expense of more critical ones.

Neutrals generally may not be noticed by customers and although bad performance would reduce perceptions of quality, it would not be to the point where quality is considered poor.

There are some lessons to be learned, I think, from this type of data interpretation:

• Simple statistical tests are used in a way to deliver insight that would be difficult to obtain with exploring low and high scores alone.
• Knowing whether a key attribute delivers the loyalty factor; whether it has no affect or whether it destroys loyalty is so valuable in terms of designing the customer experience and making the right investments.
• This is the kind of "I know" insight that is compelling when reporting on Voice of the Customer issues.

How are you measuring customer loyalty? Do you know what is critical; which areas need only a minimum performance to maintain loyalty and which attributes have no impact on loyalty at all?