Kano Model Feature Prioritization

Template Overview

Building features without understanding whether customers view them as basic table stakes, linear satisfaction drivers, or unexpected delighters leads to bloated roadmaps and wasted engineering cycles. The Kano Model Feature Prioritization template provides product managers with a rigorous analytical matrix to evaluate candidate features across functional and dysfunctional customer responses. It maps features into Must-be, One-dimensional, Attractive, and Indifferent quadrants to guide high-conviction product roadmaps.

Markdown Template Structure

Copy the block below or click Download .md File to add this template directly to your personal vault:

---
title: "Kano Prioritization: [Product / Initiative Name]"
date: [YYYY-MM-DD]
product_manager: "[[Lead Product Manager]]"
survey_respondents_count: [Number]
target_quarter: "[e.g., Q1 Roadmap Planning]"
tags: [product-management, kano-model, prioritization, roadmap, customer-satisfaction]
---

# Kano Model Feature Prioritization — [Initiative Name]

## 1. Methodology & Survey Structure
- **Functional Question:** How would you feel if the product had this feature?
- **Dysfunctional Question:** How would you feel if the product did *not* have this feature?
- **Response Scale:** 1) I like it, 2) I expect it, 3) I am neutral, 4) I can tolerate it, 5) I dislike it.
- **Classification Categories:**
  - **M (Must-be):** Taken for granted; absence causes severe dissatisfaction.
  - **O (One-dimensional / Performance):** The more, the better; linear customer satisfaction.
  - **A (Attractive / Delighter):** Unexpected delight; absence does not cause complaints.
  - **I (Indifferent):** Users do not care either way; avoid building.
  - **R (Reverse):** Feature causes dissatisfaction when present.

## 2. Kano Evaluation Survey Matrix
| Feature Candidate | Functional Mode | Dysfunctional Mode | Kano Category | Better Coeff (%) | Worse Coeff (%) | Strategic Recommendation |
|---|---|---|---|---|---|---|
| Zero-Cloud Local Processing | I expect it | I dislike it | **Must-Be (M)** | +12% | -82% | Non-negotiable foundation; table stakes for core privacy users |
| Instant Whisper Transcription | I like it | I dislike it | **Performance (O)** | +74% | -68% | Primary competitive differentiator; invest continuously in speed |
| Automatic Meeting Markdown Export | I like it | I can tolerate it | **Attractive (A)** | +68% | -14% | High delighter; low build cost; prioritized for next sprint |
| Social Media Sharing Buttons | I am neutral | I am neutral | **Indifferent (I)** | +4% | -2% | Eliminate from roadmap; users explicitly do not want social sharing |
| In-App AI Cloud Chatbot | I dislike it | I expect it | **Reverse (R)** | -18% | +35% | Destructive to brand trust; strictly avoid cloud AI integrations |

## 3. Customer Satisfaction & Dissatisfaction Coefficients
- **Better Coefficient (Delight Potential):** `(A + O) / (A + O + M + I)`
- **Worse Coefficient (Dissatisfaction Risk):** `-1 * (O + M) / (A + O + M + I)`
- **Core Insight:** Features with extreme negative worse coefficients (Must-Be) must be stabilized before introducing attractive delighters.

## 4. Prioritized Feature Roadmap Allocation
- **P0 — Must-Be Foundations:** Harden local encryption, secure filesystem sandboxing, and background daemon reliability.
- **P1 — Performance Accelerators:** Accelerate Apple Neural Engine throughput for Whisper large-v3 models.
- **P2 — Attractive Delighters:** Ship curated Markdown meeting templates and automatic Obsidian vault auto-tagging.
- **Deprecate / Backlog Drop:** Remove social sharing, third-party cloud auth, and analytics telemetry.

## 5. Next Steps & Engineering Handoff
- [ ] Draft PRDs for top-scoring Attractive delighter features.
- [ ] Brief sales and marketing teams on the high Worse-Coefficient risks of cloud-sync features.

Filled Real-World Example

Here is a realistic example demonstrating how this template captures actual deliberations:

---
title: "Kano Prioritization: Oyma Desktop Roadmap"
date: 2026-10-09
product_manager: "[[Kavita Patel]]"
survey_respondents_count: 145
target_quarter: "Strategic Product Roadmap"
tags: [product-management, kano-model, prioritization, roadmap, customer-satisfaction]
---

# Kano Model Feature Prioritization — Oyma Desktop Roadmap

## 1. Methodology & Survey Structure
- Survey sent to 145 active users across legal, academic, engineering, and medical domains.
- Evaluated 8 major feature requests to establish engineering resource allocations for the upcoming year.

## 2. Kano Evaluation Survey Matrix
| Feature Candidate | Kano Category | Better Coeff (%) | Worse Coeff (%) | Strategic Decision |
|---|---|---|---|---|
| Complete Offline Mode | **Must-Be (M)** | +15% | -88% | Table stakes; must never break offline guarantees |
| Faster Whisper Transcription | **Performance (O)** | +82% | -74% | Core engineering focus; optimize Core ML execution |
| Obsidian Markdown Auto-Sync | **Attractive (A)** | +71% | -18% | Prioritized for Q1 release; massive delight driver |
| Third-Party Cloud Backups | **Reverse (R)** | -24% | +45% | Rejected; violates privacy expectations of core users |
| Custom Audio Vocabulary Dictionaries | **Performance (O)** | +64% | -52% | High value for legal and medical professionals |

## 3. Key Findings
- Offline data sovereignty is an absolute Must-Be requirement for 92% of respondents.
- Adding third-party cloud connections actually *decreases* overall customer satisfaction (Reverse feature).
- Direct vault export to Markdown scored as the single highest Attractive delighter with minimal technical overhead.

## 4. Strategic Next Steps
- [ ] Allocate 60% of Q1 sprint capacity to Whisper speed optimizations (Performance).
- [ ] Allocate 30% to Obsidian and Logseq template automation (Attractive).
- [ ] Formally cancel all cloud-sync initiative tickets in Jira.

When to Use and When to Avoid

When to Use

• Use during annual or quarterly roadmap planning to prevent building features that users are indifferent toward.
• Use when customer feedback is contradictory and requires structured statistical categorization.
• Use to protect product focus against feature creep requested by vocal minority customers.

When to Avoid

• Avoid using when evaluating small visual UI tweaks or simple copy edits.
• Avoid using when survey sample size is under 30 respondents (insufficient statistical validity).

On-Device AI Integration

Summarize your recording in Oyma with this local instruction:

Analyze the Kano model prioritization matrix:
1. Calculate Better and Worse coefficients for each surveyed feature candidate.
2. Group features into Must-be, Performance, Attractive, and Indifferent tiers.
3. Produce a clear, non-technical executive summary advising leadership on roadmap allocation.
Output in structured Markdown.

Your data remains securely on your Mac with zero cloud exposure.