The Kano Model: Prioritize Features by How Users Actually Feel
Five categories. Two survey questions. One classification per feature — and a clear verdict on whether to ship, invest, or kill it.
Noriaki Kano published the model in 1984 to explain a frustration every product team still lives with: not all features matter the same way. Some are invisible until they break. Others scale linearly with effort. A small handful create outsized delight disproportionate to their cost. The Kano Model gives you a repeatable way to tell them apart — using a two-question survey, a 5×5 matrix, and a verdict you can take to your roadmap.
Interactive
Kano Classifier
Pick one feature. Imagine a representative user. Answer both questions — the model classifies the feature for you.
Classification
One-dimensional (Performance)
Invest. Satisfaction scales linearly with how well you execute it.
The five categories
- Must-be (Basic)
- Table stakes. Their absence creates dissatisfaction. Their presence doesn't move the needle. Login that works, search that returns results, an invoice that arrives.
- Performance (One-dimensional)
- Linear satisfiers. The better you do them, the happier users are. Speed, battery life, accuracy, price. Investment here pays back proportionally.
- Excitement (Attractive)
- Unspoken delighters. Users don't expect them and won't complain when missing — but their presence creates outsized loyalty. Source of competitive moats.
- Indifferent
- Features users genuinely do not care about. The classic backlog killer: built because someone in the room loved it, shipped to crickets.
- Reverse
- Anti-features. Their presence actively reduces satisfaction (think aggressive notifications, forced AI features, dark patterns). Remove on sight.
The two-question survey
For each candidate feature, ask the same user two questions: a functional form ("If this feature were present, how would you feel?") and a dysfunctional form ("If this feature were missing, how would you feel?"). Use the standard five-point scale: I like it · I expect it · I'm neutral · I can tolerate it · I dislike it. Run the pair across 20–30 users — the classifier above shows you the resulting category in real time.
When Kano beats RICE and FVI
RICE and FVI assume every initiative on the backlog is a legitimate candidate — they rank what's already in the list. Kano is upstream: it tells you whether a feature belongs in the list at all. Use Kano early, when the question is "is this even worth scoring?" Use RICE for weekly grooming of the survivors. Use FVI when finance needs the dollar number behind the rank. The three frameworks compose; they don't compete.
Common failure modes
- Surveying the wrong users. Kano results from power users will overclassify Excitement features. Sample the segment you actually serve.
- Forgetting the decay curve. Today's Excitement becomes tomorrow's Performance and eventually a Must-be. Re-run Kano annually on long-lived features.
- Building too many Excitements. A roadmap of nothing but delighters means the basics are quietly breaking. Keep at least 60% of capacity on Must-be and Performance.