The Kano model is a market research framework used to identify how specific product or service features influence customer satisfaction. Kano analysis helps teams distinguish between basic expectations, performance drivers and unexpected benefits, so that product priorities reflect what customers actually value rather than only what they explicitly request.
What is the Kano model?
The Kano model is a method for classifying product or service attributes according to their relationship with customer satisfaction. It was developed by Professor Noriaki Kano and colleagues and introduced in 1984, challenging the assumption that every improvement produces a proportional increase in satisfaction.
In Kano analysis, an attribute is not assessed solely by asking whether customers consider it important. Instead, respondents evaluate their reaction both when a feature is present and when it is absent. This makes it possible to identify features that are expected as a minimum standard, features that improve satisfaction in line with their performance, and features that create positive surprise.
The Kano model commonly distinguishes the following categories:
- Must-be attributes – basic requirements that customers take for granted. Their presence does not usually increase satisfaction significantly, but their absence creates strong dissatisfaction. In online banking, secure transactions and reliable access to account information are typical examples.
- One-dimensional attributes – performance factors for which better delivery produces higher satisfaction and weaker delivery produces dissatisfaction. Examples include delivery speed, product durability, call-centre response time or software loading speed.
- Attractive attributes – unexpected features that can create high satisfaction when present but do not necessarily cause dissatisfaction when absent. A useful onboarding tool in a B2B platform or a personalised recommendation in an e-commerce service may have this role.
- Indifferent attributes – elements that have little or no measurable effect on satisfaction for the studied audience. They may receive attention internally while remaining largely irrelevant to customer choice.
- Reverse attributes – features whose presence reduces satisfaction for some respondents. For example, a highly automated interface may be perceived as convenient by one segment and as a loss of control by another.
- Questionable results – inconsistent response patterns that can indicate misunderstanding, accidental responses or problems with questionnaire design.
The central value of the Kano model lies in recognising that customer expectations change over time. An attractive attribute can become a standard expectation as competitors adopt it and customers become accustomed to it. For this reason, Kano analysis should be treated as a time-sensitive input to product and market decisions rather than a permanent classification of features.
Practical application of the Kano model
The Kano model is used when an organisation needs to decide which product, service or experience improvements should receive priority. It is particularly useful before product launches, redesigns, feature roadmap decisions, service development programmes and customer experience research.
In market research, Kano analysis can support decisions in both B2C and B2B settings. A consumer goods manufacturer may use it to assess packaging features, delivery options or loyalty programme benefits. A SaaS provider may apply the method to reporting functions, integrations, account management tools and security settings. In industrial or professional services markets, it can help differentiate contract terms, technical support models, self-service portals or implementation services.
The method is especially valuable when product teams face a long list of potential improvements and limited development capacity. Rather than prioritising features only by stated importance, the Kano model shows the likely satisfaction consequences of including or excluding each attribute.
Typical business uses of Kano analysis include:
- prioritising product features for a roadmap or minimum viable product;
- identifying hygiene factors that must meet market standards before investment in differentiators;
- finding attractive features that can strengthen positioning or support premium pricing;
- comparing the expectations of customer segments, such as new versus experienced users;
- supporting customer journey improvements at critical touchpoints;
- testing whether proposed innovations are genuinely valued by the target market.
In quantitative studies, the Kano model is usually implemented as a structured survey administered to a defined sample of current customers, prospects or users of competing solutions. In mixed-methods research, qualitative interviews or usability sessions may precede the survey to identify a relevant and understandable list of features. This sequence reduces the risk of testing attributes that are too technical, too broad or poorly aligned with real customer needs.
Kano model and related methods
The Kano model is often combined with other research methods because it classifies the type of satisfaction effect but does not independently determine market size, price sensitivity, technical feasibility or financial return.
Kano analysis differs from a standard importance-performance analysis. Importance-performance research asks customers how important an attribute is and how well a brand performs on it. The Kano model examines the asymmetric relationship between feature presence, feature absence and satisfaction. A must-be attribute, for example, may receive limited recognition when it works properly, even though poor performance on it can strongly damage the customer experience.
The Kano model also differs from Net Promoter Score and other loyalty metrics. NPS measures respondents’ likelihood of recommending a company, product or service, while Kano analysis helps explain which attributes may influence satisfaction and loyalty. Kano findings can therefore inform the interpretation of NPS drivers, provided that the feature list is based on evidence and the sample is relevant to the decision.
Conjoint analysis is another related method. It estimates the trade-offs respondents make between different product configurations, attribute levels and prices. Kano analysis focuses instead on the satisfaction category of an attribute. Conjoint analysis is more suitable when the key question concerns likely choice between offers. The Kano model is more suitable when the aim is to understand whether a feature is expected, performance-related or potentially delighting.
Customer journey mapping, usability testing and qualitative interviews can also strengthen Kano analysis. They help reveal the context in which an attribute matters, the language customers use to describe it and the practical barriers that a survey alone may not capture.
How to run a Kano model survey
To run a Kano model survey, the research team first defines a focused set of product or service attributes. Each attribute should be specific, understandable and actionable. “Better customer service” is too broad, whereas “access to a named account manager during implementation” can be evaluated more reliably.
A standard Kano questionnaire presents two questions for each attribute:
- a functional question asking how the respondent would feel if the feature were available;
- a dysfunctional question asking how the respondent would feel if the feature were not available.
Responses are commonly expressed through options such as “I like it”, “I expect it”, “I am neutral”, “I can live with it” and “I dislike it”. The combination of answers is then interpreted using the Kano evaluation table, which assigns the response to one of the Kano categories.
Sound questionnaire design is essential. Attributes should not overlap, contain several benefits in one statement or imply that a feature is objectively desirable. The survey should also use language appropriate to the respondent’s level of knowledge. In B2B research, this may require separate versions for end users, decision-makers, procurement specialists and technical stakeholders.
After data collection, results can be analysed at total-sample level and by segment. Segment analysis is important because the same feature may be attractive to new users, indifferent to advanced users and reverse for customers who prefer a simpler service. Frequency of Kano categories, open-ended comments and satisfaction coefficients can be used together to establish priorities.
The Kano model should not be applied mechanically. A feature classified as attractive may still be expensive, difficult to implement or relevant only to a small segment. Conversely, a must-be attribute may deserve immediate attention even if it does not create a visible competitive advantage. The strongest decisions emerge when Kano analysis is interpreted alongside customer needs, competitive context, operational constraints and evidence from other market research methods.