Why measure customer satisfaction? So you can improve it!
The benefits of strong satisfaction are well documented in terms of business success. So the important thing is not just measuring customer satisfaction, but developing programs that will drive that satisfaction level higher.
But how do you know what to do? If your customer satisfaction measurement program has been well-designed, you have good metrics for overall satisfaction, loyalty, repeat purchase and likelihood to recommend to others. Additionally, you have satisfaction measures on selected benefits and features delivered by your product and service.
For example, you know that 67% of your customers are very or somewhat satisfied, and you also know that 45% rate their satisfaction with your technical support as “very satisfied”. Further, 52% say they are very satisfied with product reliability. Does that mean that you need to work on your technical support? Or is it possible that improving customer satisfaction with product reliability will deliver a bigger bang for your buck in improving overall satisfaction and loyalty metrics? How can you tell?
Infosurv Research recommends conducting additional analysis on your customer satisfaction research data to “tease out” the relationships between the various benefits and features you are measuring and their potential impact on your overall measures. This additional analysis provides the “derived importance” of any factor. Derived importance analyzes the trends in customer behavior to understand the underlying factors influencing that behavior.
While we could ask respondents how important each factor is, that takes more time (increasing research costs) and also is less reliable than determining importance through derived importance analysis. Customers can’t always tell you what is really influencing their satisfaction or loyalty. And, when asked directly, customers tend to rate everything as important. For example, if you asked airline passengers to rate the importance of Safety as an attribute for airlines, almost all would rate Safety as an important attribute. After all, all flyers want to get to their destinations safely. But, if you conduct a derived importance analysis on the factors that drive choice of airline, the safety attribute has little impact. Why? Because nearly all airlines are considered “safe”, so safety is not a discriminating factor. Think of “derived importance” as a way to read you customers’ minds: the analysis will tell you what your customers might not be able to tell you even if you asked the question directly!
In derived importance, we are looking for relationships between variables and we have no need to understand whether causality exists between variables, only that the states of selected variables influence the state of customer satisfaction, and to what degree. For this we use either correlation analysis or regression analysis, specifically Product Moment Correlation or Bivariate or Multiple Regression.
Derived importance analysis can help give you the answers to operations and marketing questions such as:
- What programs should I fund in my customer satisfaction improvement plan that would have the strongest impact on customer satisfaction and/or loyalty?
- Will I improve customer satisfaction more by investing in better customer support or better product quality?
- Can a change in customer satisfaction be explained by a change in customer service?
- How is customer satisfaction related to repeat purchase? Customer loyalty?
- What can I do to get customers to recommend us to others more frequently?
You can’t fix everything, as least not in the short run. And guessing what is important is not a good tactic. Measuring derived importance can lead to far more targeted, effective and impactful customer satisfaction improvement programs and a more efficient use of your budget. And while derived importance does rely on more advanced analytical techniques than other approaches, the impact on customer satisfaction and cost savings more than make up for that additional effort.
