TASTE INTELLIGENCE
Attribute → Perception → Emotion → Value taste structure, measured from search-behavior data.
Quotes, search terms, purchase-situation names and knowledge-graph items are shown in Japanese, as used in this market.
Audience Taste: the taste structure of consumers searching 'ウイスキー', measured from queries and consumer sentences.
Every figure carries an evidence label — measured = raw data; derived = measured values mapped onto a validated framework.
Means-End Chain
A Hierarchical Value Map (means-end chain). Node size = the card's weight (per the weight basis above; solid part = consumer words); solid links = measured in the same sentence, dashed = convention where no evidence.
Do not compare across categories — taste labels reuse the same names but point at different things. ‘Sweet’ in whisky means sherry-cask dried fruit and vanilla; in wine it means residual sugar. Never compare the same label's figures between categories.
In each bar, the solid part is consumer words (queries, community, social, blogs) and the pale part is words the market applies (press, commerce, brands, AI Overviews).
The ladder redrawn from consumers' own words only (queries, community, social, blogs): card size, order and solid links all come from those sentences. Write copy from this ladder; read market positioning from the full one.
Attribute: a feature you can see or measure · Perception: words that judge the product · Emotion: how the person feels · Value: the life goal it serves. Tap any card to open its evidence and reading. Selecting a card keeps only the attributes, impressions, emotions and values connected to it; the rest fade. Card size and numbers are measured (search volume). Solid lines are measured links (the two appeared in the same sentence; thicker = more often); dashed lines are first-order inference from convention where no evidence was found. Hover a line to see its basis. Faded cards rest on a single source sentence. Fainter dashes are links that skip a step (emotion or impression) that was not measured in this capture.
Sweet · Sherried — sherry, vanilla, dried fruit
This node exists because expressions like '甘い' were actually found. Sources: Organic snippet 6 · AI Overview (AIO) 4 · Related query 3 · SERP · PAA 1.
Co-occurring terms (sentences): Smooth 6, Pretty · Photogenic 2
Value region Hedonism · Brand personality (Aaker) Sincerity
Full-bodied — weighty body, high proof
This node exists because expressions like 'コク' were actually found. Sources: AI Overview (AIO) 5 · Organic snippet 4.
Co-occurring terms (sentences): Citrus · Dry 3
Value region Self-enhancement · Brand personality (Aaker) Ruggedness
Smooth — goes down smooth
This node exists because expressions like '柔らか' were actually found. Sources: Organic snippet 6 · AI Overview (AIO) 3 · Result title 2.
Co-occurring terms (sentences): Sweet · Sherried 6, Pretty · Photogenic 2
Value region Conservation · Brand personality (Aaker) Sincerity
Citrus · Dry — citrus and dry
This node exists because expressions like 'ドライ' were actually found. Sources: AI Overview (AIO) 2 · Organic snippet 2 · AIO source / PAA answer 1 · Related query 1.
Co-occurring terms (sentences): Full-bodied 3
Value region Hedonism · Brand personality (Aaker) Excitement
Smoky · Peaty — peat smoke
This node exists because expressions like 'ピート' were actually found. Sources: Organic snippet 4 · AI Overview (AIO) 2.
Smoky · Peaty is a sensory attribute; consumer research convention reads it as impressions such as Niche · Cask strength. The link is that mapping, not a measurement.
Value region Openness · Brand personality (Aaker) Ruggedness
Aged · Premium — long-aged, premium bottlings
This node exists because expressions like '高級' were actually found. Sources: AI Overview (AIO) 6 · Related query 4 · Organic snippet 3 · AIO source / PAA answer 1.
Co-occurring terms (sentences): Niche · Cask strength 3, Confident 2
Value region Self-enhancement · Brand personality (Aaker) Sophistication
Niche · Cask strength — limited, cask, independent bottlings
This node exists because expressions like '個性' were actually found. Sources: Organic snippet 6 · AI Overview (AIO) 1.
Co-occurring terms (sentences): Aged · Premium 3, Confident 2
Value region Openness · Brand personality (Aaker) Excitement
Pretty · Photogenic — looks as good as it tastes
This node exists because expressions like '華やか' were actually found. Sources: Organic snippet 3 · AI Overview (AIO) 1 · AIO source / PAA answer 1.
Co-occurring terms (sentences): Sweet · Sherried 2, Smooth 2
Value region Hedonism · Brand personality (Aaker) Sincerity
Light · Easy — light and easy
This node exists because expressions like '気軽' were actually found. Sources: Organic snippet 1 · Result title 1.
The impression Light · Easy leads to the emotion Relaxed, and on to the value Self-transcendence — the upward path of a means-end chain.
Value region Self-transcendence · Brand personality (Aaker) Sincerity
Subtle · Delicate — delicate, gentle
This node exists because expressions like 'ほのか' were actually found. Sources: Organic snippet 1.
The impression Subtle · Delicate leads to the emotion Calm, and on to the value Conservation — the upward path of a means-end chain.
Value region Conservation · Brand personality (Aaker) Sincerity
Satisfied — the emotional payoff consumers expect from this taste.
Detected words: 満足感 · 失敗しない · 間違いない · 後悔しない · 買ってよかった
Refreshed — the emotional payoff consumers expect from this taste.
Detected words: 気分転換 · 爽快 · リフレッシュ · 元気が出
Special · Pampered — the emotional payoff consumers expect from this taste.
Detected words: 特別感 · 贅沢 · ご褒美 · 格が違
The impression Aged · Premium feeds into Special · Pampered.
The measured value is far below the derived one because people rarely type emotions into a search box — they search 'Yamazaki 12 price', not 'Yamazaki makes me happy'. Emotion language lives mostly in reviews and AI Overview text, so read the measured figure as a lower bound.
Confident — the emotional payoff consumers expect from this taste.
Detected words: 自信 · 大人っぽ · 堂々 · 映える · 注目 · センスが · 自分らし
Curious · Intrigued — the emotional payoff consumers expect from this taste.
Detected words: 刺激的 · 驚き · 意外 · 新しい体験 · 好奇心
The impression Niche · Cask strength feeds into Curious · Intrigued.
The measured value is far below the derived one because people rarely type emotions into a search box — they search 'Yamazaki 12 price', not 'Yamazaki makes me happy'. Emotion language lives mostly in reviews and AI Overview text, so read the measured figure as a lower bound.
Power · Status: status and prestige. It sits in the ‘Self-enhancement’ group of the Schwartz value circle; the opposite group is ‘Self-transcendence’.
Full-bodied · Aged · Premium group into the value Power · Status.
Self-direction: choosing and expressing in one's own way. It sits in the ‘Openness’ group of the Schwartz value circle; the opposite group is ‘Conservation’.
Niche · Cask strength group into the value Self-direction.
Hedonism: sensory pleasure. It sits in the ‘Hedonism’ group of the Schwartz value circle; the opposite group is ‘Conservation’.
Sweet · Sherried · Citrus · Dry · Pretty · Photogenic group into the value Hedonism.
Benevolence: caring for people close to you. It sits in the ‘Self-transcendence’ group of the Schwartz value circle; the opposite group is ‘Self-enhancement’.
Light · Easy group into the value Benevolence.
Conformity: fitting in, not standing out. It sits in the ‘Conservation’ group of the Schwartz value circle; the opposite group is ‘Openness’.
Subtle · Delicate group into the value Conformity.
Security: safety and stability. It sits in the ‘Conservation’ group of the Schwartz value circle; the opposite group is ‘Openness’.
Smooth group into the value Security.
Stimulation: new, stimulating experiences. It sits in the ‘Openness’ group of the Schwartz value circle; the opposite group is ‘Conservation’.
Smoky · Peaty group into the value Stimulation.
For people searching 'ウイスキー', 12 taste signals were detected, and the strongest is Aged · Premium (24,570). That single family accounts for 44% of all taste weight and is 3.8x the runner-up Sweet · Sherried (6,510). In consumer language, the people behind this search respond to "long-aged, premium bottlings".
The top three come to 64%, a band where neither concentration nor dispersion can be claimed. There are 7 effective taste groups: one lead message covers about half, and misses the other half. Running one core message plus one secondary message is the safer read.
Read vertically, the attribute layer peaks at Sweet · Sherried (6,510) and the perception layer at Aged · Premium (24,570). The heavier perception layer means consumers are consuming what the product looks like it means, not the sensation itself. Context, scene and symbol persuade better than spec comparison, and price resistance is comparatively lower.
The ladder converges on the value 'Self-enhancement', with Satisfied strongest in the emotion layer just below it. That is what consumers ultimately want confirmed through this category, so closing a campaign in the language of that value, rather than of an attribute, keeps the message aligned with the ladder.
Practically, make Aged · Premium the spine of the message and place Sweet · Sherried and Full-bodied as supporting appeals. Product pages that start at the attribute layer (Sweet · Sherried), rise to perception (Aged · Premium) and close on value (Self-enhancement) follow the path consumers actually walk.
Two cautions. These figures measure how many people put a taste into words, not how strongly they prefer it. Tastes that never reach a search box stay invisible, so absence of signal is not evidence of absence of taste. And these are liking-side figures only; whether the same attribute also repels is settled in the next section.
Who said it
Measured classification of search-result URLs and AI Overview citation URLs. Communities, social and personal blogs are words consumers wrote themselves; press, commerce, brand, reference sites and AI Overview text are the market's words. The same sentence carries different weight depending on where it came from.
82% of this analysis came from consumer voice (queries + community, social, blogs).
To know whether a word is what consumers actually say, look at where it came from. Queries show intent but carry almost no sensory or emotional vocabulary; that language lives in community posts and personal blog reviews. If this mix leans toward commerce and press, the taste words in this report are closer to the market's language than to consumers'.
Watch what the AI Overview cites, too. If it only cites commerce pages, generated answers in this category are filled with selling language — which means there is room for a brand to get in with content.
Drivers of Liking
The same attribute can be wanted or avoided (sentiment ≠ preference). Right = liking volume, left = measured negation/JAR patterns ('not too …').
Aged · Premium pulls people in; Sweet · Sherried pushes them away. The avoidance evidence is searches like "甘く ないウイスキー". Against a liking total of 24,570, avoidance totals 1,050, with the two forces attached to different attributes inside the same audience.
Lead copy and product pages with Aged · Premium and deliberately reduce Sweet · Sherried associations. It is worth auditing whether visual elements or adjectives implying Sweet · Sherried sit in the default imagery. Avoidance often fires just before purchase, so it has to be resolved at the top of the page, not the bottom.
Read the avoidance side as a floor, not a measurement. People search for what they want, rarely for what they dislike, so this axis is structurally under-counted. A small number here is not evidence that avoidance is absent. If any part of this report warrants a quantitative follow-up study, it is this axis.
Context × Taste
Taste is situational, not fixed (Person × CEP × Attribute). Cells = measured co-occurrence of context and taste words in the same query/snippet.
| Aged · Pre | Sweet · Sh | Full-bodie | Smooth | Citrus · D | Niche · Ca | |
|---|---|---|---|---|---|---|
| Beginner | 700 | 350 | 2.0k | 350 | ||
| Gift | 2.7k | |||||
| Evening · Occasion | 350 |
In the "Gift" context, Aged · Premium switches on most strongly. Switching on here means the two vocabularies co-occur in the same search text with the greatest weight.
Treat taste as a fixed trait and each person belongs to one segment; treat it as something context switches on and off and the same person belongs to several. The latter predicts better, which in practice means when to speak is a bigger lever than whom to speak to.
This matrix feeds a seasonal calendar directly. If different contexts switch on different tastes, the same product needs different copy by period; if the signal concentrates in one context, concentrating budget there is the efficient move.
Co-occurrence is not causation, and low-volume cells are statistically unstable. Read the top cells.
Perceptual Map
The horizontal axis asks “toward others or toward myself”, the vertical asks “seeks the new or keeps the familiar”. These are plain-language versions of Schwartz's value circle (self-transcendence↔self-enhancement · conservation↔openness). Bubble size = search volume.
Hover a bubble for its share, search volume, layer, value region and the expression actually found. Only the six largest are labelled by default; press ‘Show all labels’ above the map to see every name.
The centre of gravity sits in "Self-enhancement". The two axes come from the Schwartz value circumplex: self-transcendence versus self-enhancement horizontally, conservation versus openness vertically. Adjacent regions are compatible; opposing regions conflict.
A centre of gravity in "Self-enhancement" means that is the value consumers express through taste in this category. Self-transcendence lies diagonally opposite, so claiming both at once cancels the message. It is worth checking whether the brand is currently saying both.
For extension decisions this map prices the move. Shifting to an adjacent region is cheap because it does not break existing perception; a diagonal move is repositioning in all but name. Where you see empty space, check bubble sizes to tell an opportunity apart from a space that is empty because nobody wants it.
Coordinates are theoretical mapping, not measurement, so read relative arrangement rather than absolute position. Schwartz values are individual-level theory, so applying them to category taste involves analogy. The map shows the size of a taste, never its profitability.
Preference → Choice
Horizontal: whether the source text is exploring or buying (measured). Vertical: whether consumers wrote the word or the market applied it (measured). Top-right = words consumers use right before buying, i.e. ad-copy material. Bottom-left = words the market applies during exploration, i.e. content and SEO material.
Reading: ‘Citrus · Dry’ sits furthest top-right at 11% consumer voice and 88% funnel. Consumers actually use it and close to the buying stage, so it is the first candidate for ad copy. Closest to the buying stage: Full-bodied · Citrus · Dry. 1 were left unplaced for lack of any funnel-stage signal. Hover a bubble for its share, search volume, layer, value region and the expression actually found. Only the six largest are labelled by default; press ‘Show all labels’ above the map to see every name.
Brand/Keyword Symbol — what taste 'ウイスキー' stands for in the public mind. Based on measured market description language (AIO·snippets) and reference patterns ('ウイスキー-style', 'like ウイスキー').
Every figure carries an evidence label — measured = raw data; derived = measured values mapped onto a validated framework.
Search Journey
Measured search sequences (Path Finder). What people search before and after this keyword is the consideration set and the entry path.
These are 12 search paths actually observed around 'ウイスキー'. Related keywords are words that appeared together; a journey is what the same person typed next. The latter is far closer to a consideration set.
1 collected paths drifted into a different meaning of the same spelling and were removed. This analysis refers to: "大麦やライ麦などの穀物を原料とし、発酵・蒸留・熟成させて造られる蒸留酒の一種。". Without this filter, paths from an unrelated category leak in.
Inbound paths show which broader concept this brand is recalled from; outbound paths show where interest goes once confirmed. The first feeds category entry messaging, the second feeds cross-sell and retargeting.
Watch where paths converge. If they end in taste vocabulary such as flavour, reviews or pairings, taste exploration is the real engagement. If they end in price, duty-free or resale, engagement is closer to transaction than taste, and the taste findings in this report should be read in that light.
Symbol Profile
Taste words extracted from market description language (AIO bodies · organic snippets), not from queries — 'what this keyword is described as', not 'what people want'.
No searches using 'ウイスキー' as a taste benchmark were found in this capture — it may not yet be a taste symbol, or the signal is in the long tail.
The market describes 'ウイスキー' as Aged · Premium·Sweet · Sherried. However, no trace was found in this capture of consumers using the keyword as a benchmark for something else. A described taste has formed, but it has not yet reached the stage of a symbol.
At this stage the work is to make the description language consistent. If the vocabulary the market uses to describe this brand differs from the vocabulary the brand uses about itself, that gap is likely what blocks symbolisation. Put the description list above next to your own messaging guide and compare.
Note that the audience taste measured in Part A and the described taste here in Part B are different data. Where they agree, brand perception and consumer expectation are aligned; where they diverge, the gap itself is the positioning task.
Reference patterns live mostly in other categories' search corpora, so a single-keyword capture under-detects them. Zero references is not proof that no symbolism exists. AI Overview text is a generative summary, so the market's language and the model's language may be mixed.
| Gutman (1982) · Reynolds & Gutman | Means-End Chain / Laddering → Hierarchical Value Map. | L2 |
| Schwartz (1992) | Basic human values — circular structure. | L3 |
| Aaker (1997) | Dimensions of Brand Personality, JMR. | L3 |
| Li et al. (2015) J. Food Science | Drivers of Liking — sensory attributes → acceptance. | L4c |
| Nagamachi — Kansei Engineering | Consumer feeling → design specification. | L3·L5 |
| John, Loken et al. (2006) JMR | Brand Concept Maps — brand association networks. | L4d |
| ABSA (IJHM 2026 · MDPI 2025) | Aspect × Sentiment — sentiment ≠ preference. | L1b |
| Hur, Etcoff & Silva (2023) | Preference structure of everyday aesthetic choices. | L4 |
| Bourdieu (1984) · Peterson (1992) | Distinction / Cultural omnivore — taste homology. | L4 |
| Cramér (1946) · Newman (2006) | Effect size · community structure. | L5 |
Taste Finder v2.0 · Taste Intelligence Framework v5
Give a keyword (category, product or brand) and it extracts sensory and emotional words from related searches, SERPs, AI Overviews and People Also Ask, groups them into taste tribes and saves a report. Product planners and marketers use it to choose product language and messages. Data comes from ListeningMind DaaS and Maistro lmpipe for Korea, the US and Japan. Reports can be compared.
Key ideasTaste tribePeople Also Ask (PAA)AI OverviewSearch result page (SERP)
Works well withCEP FinderBrand FinderBrand Naming Agent