Two ways to split a feature list into buckets. Kano sorts by how each feature affects user satisfaction. MoSCoW sorts by stakeholder priority within a fixed timebox.
Last updated: 2026-04-01
Developed in the 1980s by Professor Noriaki Kano. Each feature lands in one of five categories from a paired survey: must-be, performance, attractive, indifferent, or reverse. Data comes from users, not the team.
Best for product teams that want a research-backed view of what to build, drop, or invest more in.
Created in 1994 by Dai Clegg at Oracle and later adopted by DSDM. Items go into Must have, Should have, Could have, and Won't have (this time). The buckets reflect what the business needs to ship in a fixed timebox. The Agile Business Consortium explains the category rules and 60% effort guidance.
Best for release planning, sprint scoping, and stakeholder negotiations under a deadline.
Paired survey: "How would you feel if X were present?" + "How would you feel if X were absent?"Classify each paired response, then examine the category distribution by feature and segment. Conflicting answers can be marked questionable.
No formula. Place each item in one of the four buckets, given a fixed timeframe.DSDM recommends typically no more than 60% of delivery effort for Must Haves, leaving room to adjust lower-priority scope.
| Criteria | Kano | MoSCoW |
|---|---|---|
| Origin | Noriaki Kano, 1980s, Japan | Dai Clegg at Oracle, 1994 |
| Categories | Must-be, Performance, Attractive, Indifferent, Reverse | Must have, Should have, Could have, Won't have |
| Input source | User survey responses | Stakeholder and team judgment |
| Best fit | Strategy and feature investment decisions | Release planning under a fixed deadline |
| Time to apply | Days to weeks (survey + analysis) | One workshop session |
| Data needs | Relevant respondents; sample size depends on needed precision | User needs, business obligations, and delivery estimates |
| Output | Feature category assignment | Bucket placement against a timebox |
| Stability | Review as user expectations change | Buckets reset each release |
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Use your own inputs to explore the calculations and compare results.
Yes. Kano helps explain how features affect user satisfaction. MoSCoW uses that research alongside business needs and delivery constraints to agree release scope. A Kano must-be category does not automatically make an item a Must Have for every release.
PM Toolkit has a Kano calculator that classifies paired survey responses. It does not currently have a dedicated MoSCoW tool.
Choose the sample based on the user segments and precision needed. Report counts and category distributions. There is no universal 100-response cutoff that makes a classification reliable.
It records what is out of scope for this timeframe. That gives the team a clear reference when an excluded item is requested again.
MoSCoW can help agree launch scope. Kano can inform research with prospective users if they understand the feature being tested. Neither replaces checking whether the underlying problem matters to those users.