Interpreting Kano survey results

The Kano Model relies on structured surveys to capture how customers feel about potential features. Each feature is usually tested with two questions: how users feel if the feature is present and how they feel if it is absent. Responses are collected on a scale such as “like it,” “expect it,” “don’t care,” “can live with,” and “dislike.” This dual questioning helps distinguish between essentials, performance drivers, and delighters.

For example, if most users say they “expect” a feature and “dislike” its absence, it is classified as a Basic. If satisfaction rises as the feature improves, it is a Performance. If many users say they “like it” when present but “don’t care” if absent, it is likely a Delighter.

Interpreting results requires careful grouping of responses. Teams can use discrete analysis, which categorizes answers into tables and counts the frequency of each feature type. A more advanced approach is continuous analysis, where each response is given a score on a satisfaction scale ranging from negative to positive values. This yields a more nuanced view of which features create delight and which risk frustration. By analyzing results in this way, product managers move beyond assumptions and gain a clear map of what customers value most, allowing prioritization to reflect real user needs.^[1]