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The headline measure of overall customer satisfaction that organisations monitor from one period to the next does not benefit from one widely understood and universally adopted methodology.

The three most commonly used techniques are:
• a simple overall satisfaction question
• a composite index based on a number of components of satisfaction
• a weighted index based on the relative importance of its component elements.

Statistically the single question measure is by far the worst option due to a phenomenon that is variously labelled random, observation or measurement error. In 1632, Galileo first propounded that measurement errors are symmetrical (ie equally prone to under- or overestimation). This enabled 18th-century scientists such as Thomas Simpson to demonstrate the advantage of using the mean compared with a single observation in astronomy, the instances of over- and underestimation effectively cancelling each other.

The use of a composite index also conforms with theories about how customers make satisfaction judgements’ based on multiple aspects of the customer experience rather than one overall impression. The unsuitability of a single overall satisfaction question as the trackable measure has also been supported elsewhere in literature.

Weighting the index is advocated on the grounds that the relative importance of customers’ requirements will differ across sectors/geography and from one individual to another. This means that measures of importance as well as satisfaction need to be collected or calculated. Some people say that statistically derived importance rather than stated importance measures should be used, although any mathematical derivation of “relative importance” is something quite different from asking the customers to score factors for importance.

It’s therefore better to use both stated and derived importance measures for a fully rounded analysis of customer satisfaction data, but to use stated importance measures to produce the weighting factors for the customer satisfaction index. This is because these most accurately reflect the actual importance of the requirements to the customer.

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