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Kano Model Feature Classification

Table output Has example

Separate basics from delighters.

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Classify features with the Kano model. Features under consideration: [FEATURES]. Customer segments: [SEGMENTS]. Survey responses to functional and dysfunctional questions (if available): [KANO_RESPONSES].

Explain the categories briefly: must-be, performance, attractive, indifferent, reverse. For each feature write the pair of survey questions (how would you feel if the feature were present? how would you feel if it were absent?) with the five standard answers. If responses are given, classify each respondent's answers using the standard evaluation table, count the categories per feature, pick the most frequent category, and compute satisfaction and dissatisfaction coefficients (Better = (attractive + performance) / total; Worse = -(must-be + performance) / total) with arithmetic.

Return a table and recommendations: what to build first, what to leave, and where to invest in delight. Add cautions: attitudes change over time, small samples are unreliable, and segments differ.

Present the standard evaluation grid in text so I can reproduce the classification, show the tally for each feature as counts and percentages, and flag features where no category reaches 40 percent as 'mixed, discuss with the segment'.

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Feature: saved filters. 40 respondents. Functional question: "If saved filters were present, how would you feel?" Dysfunctional: "If absent?"
Counts: attractive 12; performance 14; must-be 4; indifferent 9; questionable 1.
Most frequent: performance (35 percent).
Better = (attractive + performance) / total = (12 + 14) / 40 = 0.65.
Worse = -(must-be + performance) / total = -(4 + 14) / 40 = -0.45.
Feature: dark mode: attractive 9; performance 3; must-be 1; indifferent 25; reverse 2. Most frequent: indifferent (62.5 percent).
Recommendation: build saved filters first. Caution: 40 responses; segments may differ; attitudes change.

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