Kano Model Survey Tool: The 2026 Guide to Feature Classification for Indian Brands

The Kano Model is a product development and customer satisfaction framework developed by Professor Noriaki Kano at Tokyo University in 1984. The model classifies product features into five categories based on how they affect customer satisfaction.

The Five Kano Categories.

The Kano Survey Methodology. For each feature, respondents answer two questions:

1. Functional question: 'If [product] HAS [feature], how would you feel?' (Positive: 'I would like it', 'I expect it', 'I am neutral', 'I can tolerate it', 'I dislike it')
2. Dysfunctional question: 'If [product] DOES NOT HAVE [feature], how would you feel?' (Negative: 'I like it', 'I expect it', 'I am neutral', 'I can tolerate it', 'I dislike it')

The combination of functional and dysfunctional answers classifies each respondent's perception of the feature into one of the Kano categories. The overall classification for the feature is the modal category across respondents, often with a category strength (e.g., 'Must-Be with strength 0.65, One-Dimensional with strength 0.20').

Why Kano Matters. Without Kano analysis, product teams often:

• Invest in features that don't move satisfaction (Indifferent features)
• Miss features that would create delight (Attractive features)
• Fail to maintain Must-Be features (causing churn)
• Compete on features that are One-Dimensional without realising they're commoditised

Kano gives you the strategic framework to invest in the right features.

The Kano Model is uniquely valuable for Indian product development because Indian consumers have a distinctive feature hierarchy that differs from Western consumers. Understanding the Kano categories for your features — and the segment-level differences across Indian demographics — is critical for prioritising product development investments.

Indian Must-Be Features. Indian consumers have specific must-be features that are not always obvious to Western product teams. For FMCG: 'safe to consume', 'good value for money', 'available widely', 'no side effects'. For fintech: 'RBI regulated', 'money safety guarantee', 'easy withdrawals'. For automotive: 'good mileage', 'low maintenance', 'spare parts availability'. For e-commerce: 'genuine products', 'easy returns', 'cash on delivery'. The Kano analysis reveals which features are non-negotiable for your category.

Attractive Features in India. Attractive features vary by segment. For Tier 1 urban consumers: smart features, premium design, sustainability claims. For Tier 2/3 consumers: family-friendly features, value-add services, vernacular language support. For senior consumers: simple UX, large fonts, voice-based interaction. The Kano analysis with cross-segment breakdowns reveals which Attractive features to invest in for each segment.

One-Dimensional Quality in India. For many Indian product categories, One-Dimensional features include quality, reliability, customer service, and after-sales support. These are competitive battlegrounds — invest in excellence but understand that your competitors are investing too. The Kano analysis tells you which One-Dimensional features matter most.

Indifferent Features to Remove. Kano often reveals that features the product team thought were important are actually Indifferent — customers don't care. Common examples: complex technical specifications, obscure certifications, design elements that don't affect functionality. Removing these can simplify the product, reduce cost, and focus the team's energy on the Must-Be and Attractive features.

Multilingual Kano. The Kano Model works well across Indian languages because the functional/dysfunctional structure is conceptually simple. The Poseidon AI on Hercules Works runs Kano analysis in English, Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati, Kannada, and Malayalam. A multi-language Kano study can reveal segment-level differences in feature preferences that drive differentiated product strategies.

Kano is one of several methodologies for understanding feature importance. Here's how it compares.

Kano Model. Best for: classifying features into strategic categories (Must-Be, One-Dimensional, Attractive, Indifferent). The output is a categorical classification with strategic implications. Methodology: two questions per feature (functional and dysfunctional). Sample size: 200-500 respondents for a national study. Best for: product development prioritisation, feature roadmap decisions, competitive positioning.

MaxDiff (BWS). Best for: relative preference ranking of features. The output is a ranking with preference shares. Methodology: best-worst choices from small subsets. Sample size: 200-500 respondents. Best for: feature prioritisation when you need to know the relative preference ranking, especially for long lists of features. See maxdiff survey tool.

Conjoint Analysis (CBC). Best for: understanding trade-offs between features and price. The output is utility scores, importance weights, and price elasticity. Methodology: choice-based product profiles. Sample size: 300-500+ respondents. Best for: complex product decisions with multiple features, willingness-to-pay research. See conjoint analysis India.

Rating Scales (Likert). Best for: quick directional reads on feature importance. The output is ordinal ratings. Methodology: rate each feature on a 1-5 or 1-7 scale. Sample size: any. Best for: internal research, quick pilots, when the depth of analysis isn't critical.

Van Westendorp PSM. Best for: pricing research, not feature prioritisation. The output is a price range and optimal price point. Methodology: four price-elicitation questions. See van westendorp price sensitivity survey.

The Right Approach. Use Kano first to classify features into strategic categories. Then use MaxDiff to rank the Attractive and One-Dimensional features by preference. Then use Conjoint if you need to understand trade-offs with price. The Poseidon AI on Hercules Works supports all these methodologies and can combine them in a single study for comprehensive product research.