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Kano Model

A theory of product development that classifies features into categories based on how they affect customer satisfaction: basic needs, performance needs, and delight factors. It reveals that not all features contribute equally to user happiness.

The Kano Model helps teams understand that customer satisfaction is not linear. Basic needs (like reliability) cause dissatisfaction when absent but do not create delight when present. Performance needs (like speed) scale linearly with satisfaction. Delight factors (like unexpected personalization) create disproportionate satisfaction but are not expected. Over time, delighters become basic expectations as competitors adopt them.

This framework is highly relevant to AI product strategy. Many AI features start as delighters: an unexpected smart suggestion that saves time feels magical. But as AI becomes ubiquitous, these features shift to performance needs or even basic expectations. Growth teams should use Kano analysis to identify which AI capabilities still create genuine delight versus which have become table stakes. This informs both feature development and marketing messaging. Investing in AI features that are already basic expectations across your competitive set prevents churn, while identifying new delight opportunities drives acquisition and word-of-mouth growth.

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