ANALYTICS
Anyone who has ever carried out a survey in which they ask respondents to rate the importance of various aspects of a product or service invariably finds that most people say everything is very important. What this disguises is that while respondents say everything is very important, in reality some things are more important to them than others.
One way of addressing this is use maximum difference scaling which is discussed elsewhere. Another approach is to use correlation analysis which looks at the indirect relationships between variables and can help in objectively assessing the extent to which one variable really influences another.
The starting point for correlation analysis is to identify a ‘dependent variable’ – for example, overall satisfaction – and then to see to what extent the responses given to each of the variables correlates to the responses given to overall satisfaction. This analysis takes place at respondent level and enables us to establish, for each aspect, how closely related it is to the dependent variable – this is measured by the co-efficient of correlation.
The correlation co-efficient is scored between 0 and 1; a score of 1 would mean there was complete correlation between responses, a score of 0 would mean there was none. The higher the co-efficient, the greater the correlation.
