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Give Correlation a Try

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By Sean Mahoney, Manager, EFM Solutions Consulting, Verint® Systems

*This is reposted from http://www.verint.com/verint-blog/

Want to get new insights from your survey data? Or maybe you want a little help prioritizing areas you’ve uncovered for improvements? Give correlation a try.

Correlation is a simple yet powerful technique for teasing new insights out of your survey data. It measures the strength of the relationship between two variables – revealing whether they are independent of one another or strongly “co-related.”

Correlation is an important analytical building block, supporting key driver analysis, factor analysis, estimation of predictive validity and other techniques. And, it’s a powerful tool for helping prioritize areas you’ve identified for improvement.

Example Correlation Coefficients

A correlation coefficient is a number ranging from -1 to 1 that measures the strength of the relationship between two variables. For instance, customer loyalty studies often seek out correlations with likelihood to repurchase. The following chart shows some scatterplots of two variables with some examples of possible correlations.

Here are some correlation coefficient examples:

• 1 – The two variables increase in lockstep. I’ve seen customer satisfaction have a .95 correlation to likelihood to repurchase.
  
  
• 0.8 – The two variables almost increase in lockstep and are practically equivalent at measuring the same underlying construct, as with likelihood to recommend and repurchase likelihood.
  
  
• 0.4 – As the scatterplot shows, the two variables are less in sync, but in many studies the highest correlations are in this range. Since many attributes make up product or service loyalty, it is not surprising that no single attribute will have a much higher correlation than 0.4. In one study, price satisfaction had a 0.4 correlation coefficient to repurchase – many respondents were less satisfied with price than their overall satisfaction. However, this dissatisfaction was mitigated by the service’s unique attributes. In other words, it was moderately expensive but with no direct competition.
  
  
• 0 – Few attributes are completely independent of loyalty. However, for example, I know of one company that wanted to know how much of a long survey was answered to see if more loyal customers completed more of the survey. There was no correlation between likelihood to repurchase and the percent of the survey that was completed.
  
  
• -0.4 – Negative correlation coefficients indicate that as one variable increases, the other variable decreases. This is less common—often, if there’s an example, it’s counterintuitive and related to unique situations for different businesses. One more general finding is that likelihood to switch to a different provider decreases as loyalty increases. The two variables don’t have a higher correlation, because some disloyal customers weren’t going to purchase similar services from any vendor.

You can calculate correlation coefficient using spreadsheets, free online tools and survey software applications. Because correlations above 0.199 indicate a significant relationship (19 times out of 20 at a sample size as small as 100), a common analytical mistake is to ignore the effect of subsample sizes when calculating correlations for filtered results. The above table can be used for a quick reference as to whether or not a correlation is statistically significant.

Correlation Does Not Equal Causation

Sometimes, a correlation may be statistically significant but meaningless. For example, when analyzing a transactional survey against actual subsequent loyalty behavior, the variable with the highest correlation can turn out to be whether or not respondents entered their phone number!

In other words, respondents who requested a call back for an open service request were more likely to be loyal than those who didn’t. What seems to be happening here is that customers who were more loyal anyway are more likely to seek out a personal response rather than an email response.

A Powerful Technique

Correlation is a simple yet powerful technique. Give it a try next time you want to glean some new insights from your hard-won survey data.