Most businesses want to know how satisfied their customers are, and what can be done to make them even more satisfied. However, if not done right, then customer intercepts, in-store interviews, and mobile surveys used to measure customer satisfaction and loyalty can produce useless or (even worse) misleading results.
Earlier we had made you aware of the danger of "no frame of reference", "no tracking", "poor sampling" and how it can be avoided. Presented below is the second common blunder associated with this type of research – and how you can avoid it.
Fourth Blunder: Flawed attribute lists
Problem: Many customer intercepts, in-store interviews, and mobile surveys ask customers to rate companies, brands, or products on a list of attributes. The problem is that these attribute lists are often too long, incomplete, and/or imbalanced. This can reduce overall data quality (respondent fatigue leading to item non-response, lack of variation, etc.). It can also yield misleading results – for example, if your list is incomplete or skewed, what the data suggests is most important may not be what’s truly most important to your customers.
Solution: Take the time to carefully construct your attribute list. Keep it as short as possible (minimize overlap/redundancy), include a mix of functional and emotional attributes (seeking input from multiple sources), use your customers’ vocabulary (e.g., from open-ended responses, interviews, or focus groups), and be consistent in wording and scaling if making comparisons with other/previous research.
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