Kano Model
Classify features by satisfaction impact
The Kano Model classifies product features into five categories based on their relationship to customer satisfaction: Must-Be (expected, no delight if present), One-Dimensional (more is better), Attractive (delighters, unexpected), Indifferent (doesn't matter), and Reverse (actively disliked by some). The model reveals that not all features contribute equally to customer satisfaction — some are hygiene factors, others are differentiators.
Developed by Professor Noriaki Kano at Tokyo University of Science in the 1980s. Originally applied to quality management; widely adopted in product management for feature prioritisation.
Use Kano Model when
- ✓Prioritising a large backlog when you need to differentiate between hygiene features and delighters
- ✓Product discovery to understand which potential features would most exceed customer expectations
- ✓When customers keep requesting features that wouldn't actually increase satisfaction
- ✓Roadmap planning where you need to communicate why some features are threshold requirements, not differentiators
Avoid it when
- ✗Fast-paced prioritisation where the research overhead of Kano surveys is too high
- ✗Niche B2B products where customer needs are well-understood through close relationships
- ✗When you need a simple rank-order priority list rather than category insights
Key Concepts
Baseline features customers expect. Their absence causes dissatisfaction; their presence is taken for granted (e.g. a mobile app must not crash).
Linear satisfaction: more is better, less is worse. Classic competitive dimensions (e.g. load speed, storage capacity).
Delighters: unexpected features that exceed expectations. Their absence causes no dissatisfaction; their presence creates delight.
Features customers neither notice nor care about. Building these wastes investment.
Features some customers actively dislike. Adding them increases dissatisfaction for part of the user base.
A paired question format: functional form ("How would you feel if this feature was present?") and dysfunctional form ("How would you feel if it was absent?").
How it works
List the features to evaluate. Typically 10–20 at a time — too many creates survey fatigue.
Create paired functional/dysfunctional questions for each feature. Collect responses from representative customers (30+ respondents minimum).
Use the Kano evaluation table to classify each feature based on the distribution of responses.
Ensure Must-Bes are complete. Invest in Attractive features to differentiate. Avoid Indifferent features. Kill Reverse features.
Tools that support Kano Model
Industry standard for software development teams — most PMs will encounter Jira in their career
Exceptionally intuitive and visually clean interface — one of the lowest onboarding friction tools for non-technical teams
Highly visual and intuitive interface with color-coded boards — one of the easiest PM tools for non-technical teams to adopt
All-in-one platform replacing multiple tools — docs, whiteboards, goals, time tracking, chat, and project management in a single workspace
Unmatched flexibility as an all-in-one workspace — combines docs, wikis, databases, and project management in a single tool
Spreadsheet-familiar interface makes adoption easy for teams transitioning from Excel — minimal training needed for basic use
Extremely intuitive drag-and-drop Kanban interface — virtually zero learning curve, new users productive within minutes
Browser-based with no installation required — runs on any OS and enables instant sharing via URL, removing friction for cross-functional collaboration with PMs, engineers, and stakeholders
Frequently Asked Questions
Features migrate categories as the market matures. Yesterday's Attractive feature (e.g. mobile app) becomes today's Must-Be. Monitoring this migration is important for roadmap planning — once a feature becomes a Must-Be, it stops being a differentiator.
30–50 respondents is the minimum for meaningful analysis. For segmented analysis (e.g. enterprise vs consumer users), you need 30+ per segment. More responses give you confidence in borderline classifications.
Yes — use Kano to classify features into categories, then use RICE to prioritise within the Attractive and One-Dimensional categories. Must-Bes don't need RICE analysis — they're required regardless of score.