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📊Interactive Tool

Kano Model Analyzer

Classify your features using the Kano Model. Answer functional and dysfunctional question pairs to discover which features delight, which are expected, and which don't matter.

Kano Categories

Must-be
Expected basics. Absence causes dissatisfaction, but presence doesn't increase satisfaction.
One-dimensional
Linear satisfaction. The better it is, the more satisfied customers are.
Attractive
Delighters. Unexpected features that create excitement when present.
Indifferent
Customers don't care either way. Low priority.
Reverse
Some customers actively don't want this feature.
Questionable
Contradictory answers — may indicate a confusing question.

Add Features to Analyze

Kano Model Framework Guide

Deep dive into the Kano Model with examples, survey design, and analysis techniques.

Weighted Scoring Calculator

Use custom criteria and weights to score and rank your features.

Continue your workflow

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Quantify Kano categories

Build Persona

Who are these users?

Write PRD

Spec the delighters

What is the Kano Model?

The Kano Model classifies features into five categories based on how they affect customer satisfaction: Must-be (expected, cause dissatisfaction when missing), One-dimensional (satisfaction scales linearly with performance), Attractive (delighters that create outsized satisfaction), Indifferent (nobody cares), and Reverse (actively unwanted). Learn the full methodology in our Kano Model guide.

How to Use This Analyzer

  1. Add your features. Enter the features you're evaluating.
  2. Input survey responses. For each feature, enter how users responded to the functional ("if this feature existed") and dysfunctional ("if this feature didn't exist") questions.
  3. Review classifications. The analyzer categorizes each feature and calculates satisfaction/dissatisfaction coefficients.
  4. Prioritize accordingly. Must-be first, then One-dimensional, then Attractive. Cut Indifferent and Reverse features.

FAQ

How is Kano different from RICE?

Kano tells you what customers want. RICE tells you what to build first given reach and effort constraints. They're complementary: use Kano to identify which features matter, then RICE to sequence them. See our Kano vs RICE comparison for a detailed breakdown.

How many survey responses do I need?

Aim for 100+ responses per segment for statistically meaningful results. For early-stage products, 30-50 responses can still reveal clear patterns. The key is surveying representative users, not just power users who want everything.

For scoring-based prioritization, try RICE, ICE, or weighted scoring. For fast visual sorting, use the value-effort matrix or MoSCoW tool.