Frequently Asked Questions

What is KanoSurveys.com?

A simple online platform for creating, running, and analysing Kano surveys.

Before KanoSurveys.com, teams had to create surveys manually in other standard survey tools and then copy / paste the raw results into a custom-built spreadsheet with complex formulas, or even get a software engineer to do the analysis. It was hard work!

So we built a self-serve tool to automate the hard stuff, so you can spend time on what's valuable to you - the insight.

KanoSurveys.com is so easy to use, everything is just point and click, and the analysis is all automated and in real-time.

How much does KanoSurveys.com cost?

We have a free tier which will always be available to try the product.

We also have a paid Pro tier for larger or more complex projects, or where you need particular features. This is a simple monthly subscription that covers your entire account - all your surveys get Pro features for as long as you're subscribed. Cancel any time.

We take payment via Stripe which is safe and easy, and you can pay in several currencies.

View the full details of pricing here.

How do I use KanoSurveys.com?

1. Register a free account in 30 seconds

2. Create a survey - give it a name, a description, and list the features you want feedback on

3. Share the custom link with your participants so they can fill in the survey

4. Wait for the responses to come in

5. View the automated analysis to see how each feature is categorised, and the level of confidence

6. Use the insights to inform your roadmap, share with stakeholders etc.

Simple! See How it works for more detail.

How do I analyse my own results?

If you've collected Kano survey responses yourself, through another survey platform or even offline in focus groups or a live event, you can upload these responses into the platform to get automated analysis.

Just put the responses into an Excel spreadsheet (XLSX format), with one complete survey response in each row, and our handy import wizard will help you do the rest.

Go to your Dashboard and click the "Import responses" button at the top to get started.

What is a Kano model survey?

The Kano model is a technique invented by Noriaki Kano for categorising product features according to customer satisfaction.

It's now used as a user research tool to inform prioritisation of product roadmaps.

You can find out what your users think about existing or potential features for your product - maybe they are indifferent to those features so you can save time by not building them, or maybe they are a 'must have' so you had better prioritise them before you launch.

The insight you can get from Kano can transform the way you prioritise your backlog.

Read this article for more information about Kano.

Who created the Kano Model?

The Kano Model was created by Noriaki Kano, a professor at the Tokyo University of Science. He published his theory in a landmark 1984 paper titled Attractive Quality and Must-Be Quality.

Noriaki Kano was working in the field of quality management when he observed that not all features contribute equally to customer satisfaction. Some are simply expected as a baseline, some scale satisfaction linearly with quality, and others delight customers in unexpected ways.

The model has since become a widely adopted tool in product management, UX research, and quality management worldwide.

Learn more about the Kano model.

What is the Kano Model used for?

The Kano Model is used in product management, UX research, and service design to understand how product features influence customer satisfaction.

Teams use it to make more informed prioritisation decisions - rather than building features based on gut feel or stakeholder opinion, the Kano model grounds decisions in real customer feedback.

Common use cases include:

• Prioritising a product roadmap or feature backlog

• Validating whether a new feature idea is worth building

• Identifying which features are table stakes vs differentiators

• Informing go-to-market positioning and messaging

• Supporting quarterly planning and OKR setting

See real-world Kano model use cases.

What are the Kano Model categories?

Kano defines 5 categories in the model:

1. Must-have - sometimes called "basic expectations" or "must-be", features that customers cannot live without

2. Performance - sometimes called "linear" or "one-dimensional", features where customer satisfaction increases in line with the quality of the feature.

3. Delighters - sometimes called "attractive", these are things that people don't expect and are surprised and delighted to see.

4. Indifferent - sometimes called "neutral", where people don't really care if they exist or not.

5. Reverse - features which are actively disliked by customers so their satisfaction will go down if the feature is present.

Learn more about the Kano model categories.

What is the difference between must-have and performance features?

The key difference between must-have and performance features in the Kano model lies in how their presence and absence affect customer satisfaction.

Must-have features (also called "basic" or "must-be" features) are expected as a baseline. Their absence causes serious dissatisfaction, but their presence alone does not delight - customers simply expect them to be there. For example, a car must have brakes.

Performance features (also called "one-dimensional" or "linear" features) scale directly with customer satisfaction. The better the feature is, the more satisfied customers are. If it's poor, satisfaction drops. Fuel efficiency in a car is a classic example - more miles per gallon means higher satisfaction.

In practical terms: you must build all must-haves to be competitive, then you can choose how much to invest in performance features to differentiate yourself.

Read more about Kano model categories.

What are Satisfiers in the Kano model?

Satisfiers are features which would improve the level of customer satisfaction for people using your product.

In the Kano model, this would be must-haves, performance and delighter features.

With performance features you can choose how advanced or rich the implementation is to push satisfaction up even further.

Dissatisfiers lower overall customer satisfaction - these are indifferent or reverse features. Or, must-haves that are missing.

What is category drift in the Kano Model?

Category drift in the Kano Model refers to the phenomenon where a feature's category changes over time as customer expectations evolve.

The most common pattern is that delighter features become performance features, and performance features eventually become must-haves. This happens because what surprises and delights customers today becomes expected tomorrow as the market matures and competitors adopt the same features.

A classic example is GPS navigation in cars - once a premium delighter, it became a performance differentiator, and is now a basic must-have expectation.

This means Kano surveys should be repeated periodically, especially in fast-moving markets, to keep your understanding of customer expectations current.

Read more about Kano category drift.

What are the advantages and disadvantages of the Kano Model?

The Kano Model has several advantages as a product management and research tool:

• Customer-centric - decisions are grounded in actual customer feedback rather than assumptions

• Nuanced - it distinguishes between different types of value, unlike simple importance ratings

• Actionable - the output directly informs what to build, improve, or cut

• Quantitative - results are based on data, making them easier to communicate to stakeholders

Disadvantages and limitations include:

• Doesn't account for cost or effort - a must-have feature could be prohibitively expensive to build

• Results are time-bound - customer expectations change, so surveys need to be repeated

• Requires sufficient sample size - small samples can produce unreliable results

• Survey fatigue - long feature lists make surveys tiring for participants

Learn more about the Kano model.

What are the limitations of the Kano Model?

While the Kano Model is a powerful research technique, it has several limitations to be aware of:

• No cost or effort dimension - the model tells you what customers want, not what is feasible or affordable to build. You must combine it with effort estimation to make truly informed prioritisation decisions.

• Categories change over time - as markets mature, delighters become must-haves. Results from a Kano study conducted 12–18 months ago may no longer reflect current customer expectations.

• Cultural and demographic variation - different audience segments may categorise the same feature very differently. Running separate surveys per segment is more accurate but more resource-intensive.

• Survey length constraints - each feature requires two questions, so long feature lists create participant fatigue and reduce data quality.

• Self-report bias - what customers say they want doesn't always match what they actually value in practice.

Learn more about the Kano model.

Can you use the Kano Model for services?

Yes, the Kano Model works well for services as well as physical products and software.

Any offering with discrete, evaluable features - whether it's a consulting service, a bank account, a hotel stay, or a healthcare experience - can be analysed using the Kano method. You simply define the features of the service (e.g. "24/7 phone support", "same-day delivery", "personalised recommendations") and survey customers in the usual way.

The Kano Model is widely used in service quality research and is closely related to the SERVQUAL framework, which also examines expectations versus perceptions in service contexts.

See examples of the Kano model in action.

Can you use the Kano Model for UX research?

Yes, the Kano Model is an excellent tool for UX research. It helps UX teams move beyond subjective opinions and base feature decisions on structured customer data.

In UX, Kano surveys are used to evaluate proposed features, UI improvements, or design directions before committing to development. This can save significant time and resource by identifying which improvements will genuinely delight users and which will go unnoticed.

The model is particularly useful in discovery and concept testing phases, where teams are deciding which ideas to take forward into design and development.

Kano analysis pairs well with usability testing and other UX methods - the Kano model tells you what to build, while usability testing tells you how well it works.

Can you use the Kano Model for SaaS products?

Yes, the Kano Model is particularly well-suited to SaaS product management, where rapid iteration and customer retention are critical.

SaaS teams use Kano surveys to:

• Evaluate which features in the backlog are worth prioritising for the next release

• Identify must-have features that may be missing and causing churn

• Find delighter opportunities to differentiate from competitors

• Validate new feature ideas before investing in design and engineering

Because SaaS markets move fast and customer expectations shift quickly, it's good practice to repeat Kano surveys periodically - perhaps quarterly or at major planning milestones - to keep your understanding up to date.

Learn more about how the Kano model works.

What are the questions used in a Kano survey?

Each feature is tested with a combination of 2 questions: 'how would you feel if the feature was present?' and 'how would you feel if the feature was absent?'.

The list of answers for each question is the same 5 options: 'I like it', 'I expect it', 'I don't care', 'I can tolerate it', 'I don't like it'.

You can play with the wording of these to suit your audience, but make sure the meaning stays the same so your analysis of the results is accurate.

What are functional and dysfunctional Kano questions?

The two questions asked for each feature in a Kano survey are called the functional and dysfunctional questions.

The functional question asks how the respondent would feel if the feature were present - e.g. "How would you feel if this product had offline mode?"

The dysfunctional question asks how the respondent would feel if the feature were absent - e.g. "How would you feel if this product did not have offline mode?"

By combining the answers to both questions, you can categorise each response into a Kano category (must-have, performance, delighter, indifferent, or reverse) using an evaluation table.

Learn more about the Kano questionnaire format.

What are the 5 Kano response options?

Each functional and dysfunctional question in a Kano survey uses the same 5 response options:

1. I like it - the feature actively pleases the respondent

2. I expect it - the respondent takes it for granted and would notice its absence

3. I don't care - the feature makes no difference to the respondent

4. I can tolerate it - the respondent dislikes it slightly but can live with it

5. I don't like it - the feature actively displeases the respondent

The exact wording can be adjusted to suit your audience and context, but the meaning of each option must stay the same to ensure the analysis is accurate.

Learn more about Kano survey questions.

How many features should you include in a Kano survey?

As a rule of thumb, Kano survey best practices suggest keeping the feature list to around 5–15 features per survey.

Because every feature requires two questions (functional and dysfunctional), a 10-feature survey already has 20 Kano questions, plus any screener or demographic questions. Beyond 15 features, participants start to experience survey fatigue, which reduces the quality of responses.

If you have a large backlog to evaluate, consider splitting it across multiple surveys targeted at different participant segments, or running surveys sequentially over time to cover more features without overwhelming any individual respondent.

Read the Kano survey design report for more best practices.

How many responses do you need for a Kano survey?

For reliable Kano survey results, a minimum sample size of around 30 responses is typically cited as a baseline, though 50–100 responses will give you much greater confidence in the findings.

The right sample size depends on a few factors:

• Homogeneity of your audience - if all respondents are very similar (e.g. all power users of the same tool), a smaller sample can be sufficient. If your audience is diverse, you need more responses to account for variation.

• Number of segments - if you want to compare results across segments (e.g. by role, company size, or geography), each segment needs enough responses to be meaningful independently.

• Level of confidence required - for major strategic decisions, aim for a larger sample. For a quick directional check, 30 responses may be enough.

Read more about Kano survey design.

What is a questionable Kano response?

A questionable response in the Kano model occurs when a respondent gives logically contradictory answers to the functional and dysfunctional questions for a feature - for example, saying they would both like it if the feature were present and like it if the feature were absent.

These responses indicate that the participant did not understand the question, was not paying attention, or found the question ambiguous. They cannot be meaningfully categorised into a Kano category.

Questionable responses are typically excluded from the analysis. A high rate of questionable responses for a particular feature may suggest the feature description needs to be reworded to be clearer.

How do you do a Kano model analysis?

The easiest approach is to categorise every response to a feature and then look for the most common category across all the responses. That becomes the primary category for that feature. But this means the data from the other responses is essentially ignored.

So a more complex and nuanced approach was developed by researcher Bill DuMouchel, where each response is first converted onto a numerical scale with more weight attached to positive end of the scale, and then the features averaged together and visualised by being drawn on a graph.

Learn more about Kano model analysis.

How do I make Kano models in Excel?

Create a sheet with all the raw responses in, with one complete response per row. Each column should be the answer to a question - whether the feature is present or absent, for all features.

In another sheet, create another table with one row per response but only a one column per feature - in that column, add a formula to calculate the Kano model category based on the raw response in the first sheet.

Now in a third sheet you can count up which category has the most 'votes' per feature, and that your primary category for that feature.

Kano model template free download.

How do you read Kano model results?

First you have to crunch the numbers to produce the primary category for each feature. Do this by categorising each response and then counting up which one has the most 'votes'.

The raw Kano results won't mean much by themselves, it's hard to see any pattern from just the individual scores.

Once you've got the categories you can see which features to focus on - include all the must-haves, sprinkle in a few delighters, and ignore all the reverse!

How to analyse Kano survey results.

What is a Kano evaluation table?

A Kano model evaluation table (also called a Kano categorisation matrix) is a lookup grid that maps the combination of a respondent's functional and dysfunctional answers to a Kano category.

The table has the 5 functional responses as rows and the 5 dysfunctional responses as columns. Each cell in the grid contains the resulting Kano category - must-have, performance, delighter, indifferent, reverse, or questionable - for that combination of answers.

For example, if a respondent says they would like it if the feature were present (functional) and don't like it if absent (dysfunctional), the evaluation table maps this to a performance feature.

See the full Kano evaluation table.

What is a satisfaction coefficient?

The Kano satisfaction coefficient (also called the Berger coefficient, after researcher Chuck Berger) is a numerical measure that quantifies how much a feature influences customer satisfaction.

Two coefficients are calculated for each feature:

• Satisfaction coefficient (SI) - measures how much customer satisfaction increases if the feature is present. Values range from 0 to 1; a higher value means a bigger positive impact.

• Dissatisfaction coefficient (DSI) - measures how much customer satisfaction decreases if the feature is absent. Values range from -1 to 0; a more negative value means a bigger negative impact.

Together they let you compare features across categories on a single scale, making it easier to prioritise. A feature with a high SI and a strongly negative DSI is a strong must-have or performance candidate.

Learn more about Kano analysis methods.

What is the difference between discrete and continuous Kano analysis?

There are two main Kano analysis methods, which differ in how they handle individual responses:

Discrete analysis (the traditional approach) categorises each response individually using the evaluation table and then counts which category received the most votes for each feature. The winning category becomes the feature's primary classification. It's simple and intuitive, but discards nuance - a feature that is 40% must-have and 38% delighter would just be labelled "must-have".

Continuous analysis (the DuMouchel method) converts each response to a numerical score on a weighted scale and then calculates an average score for each feature. Features are then plotted on a graph, showing their position across the full Kano spectrum rather than a single discrete category. This retains far more information and gives a richer picture of where features sit relative to each other.

KanoSurveys.com uses the continuous method to provide a more accurate and actionable analysis.

Learn more about Kano model analysis methods.

How do you use Kano prioritization?

Start with the must-haves, these are features your customers can't live without. You need to build all of these.

Then choose some performance features that will differentiate you from your competition - your features could be better somehow: faster, richer, smoother, easier to use and so on.

Lastly choose a few delighter features to surprise your users.

Look for features within those categories that have low effort to implement, and will have a big impact on your users.

Learn about Kano model prioritisation.

How do you prioritise must-have features?

Must-have features in the Kano model are non-negotiable - their absence causes significant dissatisfaction, so you must build all of them to be competitive. There is no prioritisation choice to be made: if it's a must-have, it must be built.

The prioritisation question for must-haves is about sequencing and quality level. Consider:

• Which are currently missing? These should be addressed first, as they are actively causing dissatisfaction or churn.

• Which are present but of poor quality? A must-have implemented badly can still drive dissatisfaction - prioritise improving these.

• What is the minimum acceptable quality? Must-haves just need to be good enough - over-investing in them beyond that threshold yields diminishing returns.

Once all must-haves are covered to an acceptable standard, shift investment to performance and delighter features.

Learn more about Kano model prioritisation.

Should you build all delighter features?

Not necessarily. While Kano delighters (also called "attractive" features) are the features that surprise and delight customers when present, that doesn't mean every delighter is worth building.

Because customers don't expect delighters, their absence causes no dissatisfaction - so they are always optional. You should select delighter features based on:

• Effort vs impact - favour low-effort delighters with broad appeal

• Strategic fit - choose delighters that reinforce your positioning or brand

• Differentiation - a delighter your competitors lack is especially valuable

Also keep in mind that delighters can become must-haves over time as customer expectations rise, so revisit your analysis periodically.

Learn about Kano model prioritisation.

What should you do with indifferent features?

Indifferent features in the Kano model are features that customers don't care about - their presence or absence makes no meaningful difference to satisfaction.

The recommended approach is to deprioritise or cut them entirely. Building indifferent features wastes engineering time and can add complexity to your product without adding value.

However, before cutting a feature entirely, consider:

• Segment differences - an overall indifferent result could mask a segment of users who do care. Check if any user group has a stronger reaction.

• Low-effort exceptions - if a feature is trivial to build and has no cost, it may still be worth including for completeness.

• Existing features - if an indifferent feature already exists, removing it may cause disruption even if users don't actively value it.

What are reverse features?

Reverse features in the Kano model are features that actively reduce customer satisfaction when present. The majority of respondents dislike the feature and would prefer not to have it.

This can happen for several reasons:

• The feature adds unwanted complexity or clutter to the product

• It conflicts with how users prefer to work

• It has negative connotations for the target audience

• The feature description was misunderstood by respondents

If a feature is categorised as reverse, the clear recommendation is not to build it (or to remove it if it already exists). It's worth verifying the result with qualitative research to understand why users dislike the feature before making a final call.

Learn more about Kano model categories.

How is Kano different from RICE?

Kano and RICE are both used in product prioritisation, but they work very differently:

RICE (Reach, Impact, Confidence, Effort) is an internal scoring framework. Teams assign numerical estimates to each dimension and calculate a score to rank features against each other. It relies heavily on team judgment and estimation, and doesn't directly incorporate customer input.

Kano is a customer research method. It collects structured data directly from customers and categorises features based on how they influence satisfaction. The output tells you what kind of value a feature provides, not just how impactful it is.

The two methods complement each other well. Kano tells you which features customers care about and how, while RICE helps you sequence them based on effort and reach. Running a Kano survey first gives you better inputs for RICE scoring.

How is Kano different from MoSCoW?

Both Kano and MoSCoW are used for feature prioritisation, but they differ fundamentally in who makes the prioritisation decisions:

MoSCoW (Must have, Should have, Could have, Won't have) is a team and stakeholder-driven framework. Features are classified by internal discussion and consensus. It's fast and practical but based on opinion rather than data.

Kano is data-driven and customer-facing. Features are categorised based on structured survey responses from real customers. It takes more time to run but grounds decisions in actual customer preferences.

A common and effective approach is to use Kano research to inform and validate your MoSCoW categorisation - replacing internal opinions with customer evidence.

How is Kano different from conjoint analysis?

Kano and conjoint analysis are both customer research techniques used in product decisions, but they answer different questions:

Conjoint analysis measures how customers make trade-offs between features or attributes when choosing between options. It's particularly powerful for pricing research and understanding relative feature value when customers can't have everything.

Kano analysis categorises individual features by the type of satisfaction they provide - must-have, performance, delighter, indifferent, or reverse. It doesn't test trade-offs; it evaluates each feature independently.

Kano is generally simpler to run and analyse, and is better suited to early-stage product discovery. Conjoint is more statistically complex but gives richer data on willingness to pay and feature trade-offs. Many teams use Kano first to shortlist features and then use conjoint to fine-tune pricing and packaging decisions.

What are some examples of the Kano model in action?

Here are some real Kano model case studies, including:

- A recipe website redesign

- Netflix

- A science company making hardware products