Try KanoSurveys.com

Create Kano surveys, collect responses, and analyse the results automatically in one simple tool.

Glossary

A glossary of Kano model terms, survey language, analysis terminology, and related product management concepts.

Core Kano terms

Category terms

Analysis terms

Product and adjacent topics



Core Kano terms

Kano Model

The Kano Model is a product research framework for understanding how features affect customer satisfaction. It helps you separate baseline expectations from features that create real differentiation.

The model classifies features into categories such as must-have, performance, delighter, indifferent, and reverse. Read more in About the Kano model.

Kano analysis

Kano analysis is the process of turning Kano survey responses into categories and using the result to guide product decisions. It is the stage where raw survey answers become something you can actually prioritise from.

People also use it as a shorthand synonym for Kano model analysis. See how Kano analysis works and how it differs from a simple category tally.

Kano survey

A Kano survey is the questionnaire you send to customers to measure how they feel about features being present or absent. It is the practical input that powers the whole Kano method.

Each feature is usually tested with a functional question and a dysfunctional question. For structure and wording, see the Kano questionnaire format.

Functional question

The functional question asks how someone feels if the feature is present. It tells you whether a feature creates delight, expectation, or indifference when it exists.

This is the positive half of each Kano pair. See how Kano questions are phrased in practice.

Dysfunctional question

The dysfunctional question asks how someone feels if the feature is absent. It exposes whether the feature is truly expected or simply nice to have.

This is the negative half of the pair used for each Kano feature. See the questionnaire format guide for the standard wording pattern.

Category terms

Must-have / Must-be feature

A must-have feature, also called a must-be feature, is a baseline expectation. These are the non-negotiables that customers notice most when they are missing.

Its absence causes dissatisfaction; its presence is simply expected. Read more in Must-have features in the Kano model.

Basic expectation

Basic expectation is another common synonym for a must-have feature. It describes the floor your product has to meet before any extra value matters.

If users do not get the basics, they usually do not stick around. See the must-have feature guide for more detail.

Performance feature

A performance feature is a feature where satisfaction rises as the quality, speed, or completeness improves. The stronger the implementation, the better the customer reaction tends to be.

Better execution usually means happier customers. Read Performance features in the Kano model for examples.

One-dimensional quality

One-dimensional quality is an older or more academic label for a performance feature. The term comes from the way customer satisfaction moves in one direction as quality improves.

It is still useful when reading older Kano papers or research notes. See performance feature examples for the modern product wording.

Attractive feature

An attractive feature is a feature that delights when present but is not missed when absent. It is the classic Kano category for unexpected value and surprise.

This is one of the easiest ways to create word of mouth and positive perception. Read Delighters in the Kano model.

Delighter

Delighter is a product-friendly synonym for attractive feature. It usually refers to something surprising, useful, or pleasantly unexpected that lifts the experience beyond the obvious.

Delighters are often small touches that customers remember and recommend. See the delighters guide.

Excitement need

Excitement need is an alternative synonym for an attractive feature. The phrase is useful when you want to frame the category in a more research-oriented or older Kano style.

It still means a feature that creates positive surprise rather than baseline expectation. See delighters in the Kano model for a practical explanation.

Indifferent feature

An indifferent feature is one customers do not care much about. Adding or removing it does not materially affect satisfaction, which makes it weak for prioritisation.

These are often features that are interesting internally but not meaningful externally. Read Indifferent features in the Kano model.

Reverse feature

A reverse feature actively reduces satisfaction for some or most users. It is usually a sign the feature is unwanted, confusing, or misaligned with the audience.

That makes reverse features strong candidates for removal rather than improvement. Read Reverse features in the Kano model.

Analysis terms

Questionable response

A questionable response is an inconsistent answer pattern that cannot be classified reliably. It often means the respondent misunderstood the question, skipped too quickly, or found the wording ambiguous.

Questionable responses are important because they highlight data quality problems rather than feature value. See how Kano analysis handles questionable data.

Evaluation table

An evaluation table maps each combination of functional and dysfunctional answers to a Kano category. It is the core lookup matrix used in discrete Kano analysis and in many spreadsheet templates.

Once you understand the table, the whole model becomes much easier to explain to stakeholders. See the Kano model categories guide.

Discrete analysis

Discrete analysis categorises each response first and then counts the most common category. It is the traditional Kano approach and is often the easiest way to get started.

The downside is that it throws away nuance once the counts are done. Read Kano analysis for the broader context.

Continuous analysis

Continuous analysis converts responses into scores and averages them to show where a feature sits on the Kano spectrum. It keeps more nuance than discrete analysis and is better for comparing features side by side.

This is the approach used when you want more than a single winning category. Read how Kano analysis works.

Satisfaction coefficient

The satisfaction coefficient measures how much a feature can increase satisfaction when it is present. Higher scores point to stronger upside and more attractive opportunities.

It is useful when you want to compare categories using a single numeric scale. See Kano analysis methods for more on the coefficient logic.

Dissatisfaction coefficient

The dissatisfaction coefficient measures how much dissatisfaction a feature can cause when it is missing. More negative scores point to a stronger downside and a more urgent baseline need.

Together with the satisfaction coefficient, it helps show both upside and risk. Read the Kano analysis guide.

Product and adjacent topics

Customer satisfaction

Customer satisfaction is the parent idea behind the Kano Model. Kano studies are a way of learning which features improve or damage it so product teams can make better trade-offs.

If you are trying to connect research to outcomes, this is the central metric to watch. See About the Kano model.

Feature prioritisation

Feature prioritisation is the product decision process the Kano Model supports. The output helps teams decide what to build, improve, delay, or remove in a way that is rooted in customer evidence.

It becomes much easier when Kano results are combined with effort, strategy, and roadmap constraints. Read Kano model prioritisation for product management.

Product roadmap

A product roadmap is the broader plan Kano results feed into. Kano helps teams decide which features deserve space on that roadmap and which can wait.

In practice, Kano is one of the cleanest ways to reduce roadmap debate by grounding it in data. See prioritisation guidance.

Voice of customer

Voice of customer refers to customer feedback collected and analysed to inform decisions. Kano surveys are one structured way to capture it because they turn vague opinions into comparable answers.

It is a broader category than Kano, but the two work well together. Read Kano model use cases for related product research examples.

Quality Function Deployment / QFD

Quality Function Deployment, or QFD, is a related method for translating customer needs into design and product decisions. Kano results are often used as an input because they describe what customers value before design starts.

If you are mapping research into engineering or design work, QFD gives you a more structured follow-through. See About the Kano model for the broader context.