kano-model

Noriaki Kano's model classifies candidate features by how their presence or absence drives customer satisfaction, so investment goes where it pays. It catches the mistake of treating all features alike: some only punish you when missing, some reward you in proportion to how well you do them, and a few delight precisely because no one expected them.

The five categories, the paired functional/dysfunctional question, the full 5×5 evaluation table, the satisfaction curves, the decay over time, and the pitfalls live in references/kano.md. Load that file before classifying.

When to use

The user is deciding which features to build, prioritize, cut, or how to position them, and wants to know which features actually matter to satisfaction and why. The unit of analysis is a set of candidate features evaluated for one named customer segment. Pairs with rice-prioritization (Kano says which features matter and why; RICE then sequences the backlog by score), jobs-to-be-done (which jobs the features serve), and value-proposition-canvas (how the features map to customer pains and gains). Use Kano when the question is "what kind of feature is this and how much should we invest," not "in what order do we ship the backlog."

Language

Conduct the session and write the report in the language the user is writing in. The reference file is English; keep the framework keyed to it regardless of the output language.

Inputs

• SEGMENT (required): the target customer segment to classify for. Categories vary by segment, so one classification serves one segment. If the user names several segments, classify the primary one and flag where features likely differ, or run the analysis once per segment.
• FEATURES (required): the candidate feature list to classify. Push for concrete, distinct features, not vague themes. If the list is a wishlist of dozens, narrow to the set the decision actually turns on.
• CONTEXT (optional): the product, its market and competitors, the decision the analysis should inform (roadmap, MVP scope, cut list, positioning), and any real customer data available. This shapes the synthesis and confidence, not the categories themselves.