Kano Model Template
A Kano Model analysis template for categorising product features into Must-Haves, Performance features, and Delighters — with a survey question guide and prioritisation output. Free to copy, download, and use. No signup required.
# Kano Model Analysis **Product:** [Name] **Analyst:** [PM Name] **Date:** [Date] **Features evaluated:** [N] **Research method:** [Kano survey / customer interviews / team workshop] --- ## What Is the Kano Model? The Kano Model (Noriaki Kano, 1984) categorises features by how they affect customer satisfaction: | Category | Description | Example | |---|---|---| | **Must-Have (Basic)** | Expected features. Their absence causes dissatisfaction; their presence is neutral. | Login, data security, core workflow | | **Performance (Linear)** | More = more satisfied. Less = less satisfied. Directly tied to value. | Speed, accuracy, breadth of integrations | | **Delighter (Excitement)** | Unexpected. Their absence is fine; their presence creates delight. | A surprise shortcut, a magical moment | | **Indifferent** | Nobody cares either way. | Most rarely-used settings | | **Reverse** | Some users want it; others are annoyed by it. | AI auto-suggestions | --- ## Feature List | # | Feature | Description | |---|---|---| | 1 | [Feature name] | [Brief description] | | 2 | [Feature name] | [Brief description] | | 3 | [Feature name] | [Brief description] | | 4 | [Feature name] | [Brief description] | | 5 | [Feature name] | [Brief description] | --- ## Kano Survey Questions For each feature, ask two questions: **Functional question:** "If [feature] were present, how would you feel?" **Dysfunctional question:** "If [feature] were absent, how would you feel?" **Response scale:** - I would like it - I expect it - I am neutral - I can tolerate it - I dislike it --- ## Kano Classification Matrix | Dysfunctional → | Dislike | Tolerate | Neutral | Expect | Like | |---|---|---|---|---|---| | **Functional ↓** | | | | | | | **Like** | Q (Questionable) | D (Delighter) | D | D | P (Performance) | | **Expect** | R (Reverse) | I (Indifferent) | I | I | M (Must-Have) | | **Neutral** | R | I | I | I | M | | **Tolerate** | R | I | I | I | M | | **Dislike** | R | R | R | R | Q | --- ## Analysis Results | # | Feature | Category | Satisfaction if present | Dissatisfaction if absent | Priority | |---|---|---|---|---|---| | 1 | [Feature] | Must-Have / Performance / Delighter / Indifferent | +[score] | -[score] | | | 2 | [Feature] | | | | | | 3 | [Feature] | | | | | **Satisfaction coefficient:** (Like + Expect) / (Like + Expect + Neutral + Dislike) **Dissatisfaction coefficient:** (Expect + Dislike) / (Like + Expect + Neutral + Dislike) × -1 --- ## Prioritisation Output ### Must-Have — Fix or ship immediately > These features cause active dissatisfaction if absent. No amount of Delighters compensates for missing Must-Haves. - [Feature] - [Feature] ### Performance — Invest proportionally to competitive gap > Features where being better than alternatives directly converts to more satisfaction and willingness to pay. - [Feature] — currently [X% behind / ahead of] competitors - [Feature] ### Delighters — Ship one per quarter to drive word-of-mouth > These create moments that get talked about. One well-chosen Delighter does more for NPS than 10 Performance improvements. - [Feature] ### Indifferent — Deprioritise or delete > Engineering time spent here is wasted. - [Feature] --- ## Strategic Implications | Finding | Action | |---|---| | [Key insight from Kano analysis] | [What to build, kill, or reframe] |
How to use this Kano Model template
Survey at least 20 customers per segment
Kano analysis is statistical — you need enough responses to see clear patterns. 20 is the minimum; 50+ gives you confidence to make major investment decisions based on the results.
Fix Must-Haves before shipping Delighters
A product with missing Must-Haves cannot be saved by exciting new features. If customers expect something and it's absent, no amount of delight compensates. Run the Kano analysis before your next sprint to check you're not building on a cracked foundation.
Use Delighters strategically — one per quarter
Delighters are most powerful when rare and surprising. A product that constantly surprises users builds cult followings. One unexpected feature per quarter is more memorable than 10 incremental improvements.
Re-run the analysis annually
Delighters become Must-Haves over time as customer expectations rise. A feature that delighted users in 2022 (e.g. dark mode) is expected today. Run the Kano analysis annually to watch features move between categories.
Want a Kano Model grounded in your actual customer data?
PMRead ingests your customer interviews, feedback, and Slack threads — and generates PRDs backed by real evidence, not guesses.
Frequently asked questions
Do I need a formal survey to use the Kano Model?
A formal Kano survey is most rigorous, but you can do a lightweight version in customer interviews by asking the functional/dysfunctional question pair verbally. This gives you directional insight without needing statistical significance. For high-stakes prioritisation decisions, invest in the full survey.
What's the difference between Performance and Delighter features?
Performance features are expected in your category — customers know they want them, and more is always better (faster search, more integrations, higher accuracy). Delighters are unexpected — customers didn't ask for them and would be fine without them, but they produce disproportionate positive emotion when present.
How does the Kano Model compare to RICE scoring?
RICE scores features by business impact (Reach, Impact, Confidence, Effort). Kano scores them by how they affect customer satisfaction. They're complementary: use Kano to understand whether a feature is table-stakes (Must-Have), a differentiator (Performance), or a loyalty driver (Delighter), then use RICE to prioritise within each category.
Can I use the Kano Model for enterprise products?
Yes, but segment by role. What's a Delighter for an end-user might be a Must-Have for an IT admin (e.g. SSO, audit logs). Survey both buyers and users separately and build your priority list with both in mind.
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