TASTE INTELLIGENCE

Consumer Taste Report — 'ウイスキー'

Attribute → Perception → Emotion → Value taste structure, measured from search-behavior data.

Live · lmpipe + DaaS + KGData 2026-10-15Generated 2026-10-16 15:55Corpus 1496DaaS SERP · AIO 67 · PAA/related 108 · snippets 183 · titles/sources 209actual basis for taste extraction (community·blog·social) 8%Seeds: ウイスキー + ウイスキー おすすめ + ウイスキーおすすめ + ウイスキー 人気ランキング + ウイスキー 種類

Quotes, search terms, purchase-situation names and knowledge-graph items are shown in Japanese, as used in this market.

Monthly searches
264,333
Trend · maturity
-24% · 0.50
Consumer words
100% · queries · community · social · blogs
Taste signals
2
Avoidance found
2
Lifecycle
declining · flat year-round
A

People who search 'ウイスキー'

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.

Weight basisSentence evidence (default)Combined (mixed units)Search demand only1% of this report's weight comes from search volume (demand) and 99% from search-result sentences (evidence). The volume share is small, so the combined and evidence views are nearly the same. Now: search volume of related queries only.

Means-End Chain

'ウイスキー' · Taste ladder — Attribute → Perception → Emotion → Value

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.

measurednode weights = search volumemeasuredsolid links = same-sentence measurementderiveddashed links = convention

How this is analyzed

Taste ladder — Means-End Chain (MEC) and Hierarchical Value Map (HVM)

What this section is computed from, and how far it can be trusted.

Theory & framework

Gutman (1982) argued that consumers choose a concrete product attribute because of the consequence it delivers, and want that consequence because of a personal value. Reynolds & Gutman's (1988) laddering interview repeatedly asks "why does that matter to you?" to recover A→C→V chains, then aggregates them across respondents into an implication matrix that is drawn as a Hierarchical Value Map. Taste Finder splits the consequence layer into perception and emotion, giving a four-level ladder: attribute → perception → emotion → value. In sensory categories "smooth" (a perceived impression) and "comforting" (an evoked emotion) are genuinely different levels.

How we re-read it from search data

Laddering requires one-on-one depth interviews, so samples are typically a few dozen people. Here, instead of interviews, attribute and perception terms are extracted by lexicon matching from queries, AI Overview text and organic snippets, and each node is sized by measured search volume. In other words, "how many people voiced this attribute" is measured as search demand rather than respondent count. The links between layers follow conventional mappings from sensory science and the fragrance industry; that part alone is not measured, so it is labelled as derived inference.

What it is for

It gives you a ladder for deciding what to say in copy, packaging and product pages. If you only claim attributes, differentiation evaporates the moment a competitor claims the same one; knowing which emotion and value an attribute ladders up to lets you position at a higher level. Useful for concept boards, brand essence definition, and structuring an advertising message hierarchy.

Limits & caveats

The inter-layer links are convention-based and can differ by category. Search captures only tastes that have already been put into words, so preferences people feel but never type stay invisible. And volume is the size of attention, not the intensity of liking.

References
  • Gutman, J. (1982) A Means-End Chain Model Based on Consumer Categorization Processes. Journal of Marketing
  • Reynolds, T. & Gutman, J. (1988) Laddering Theory, Method, Analysis, and Interpretation. Journal of Advertising Research
  • Olson, J. & Reynolds, T. (2001) Understanding Consumer Decision Making: The Means-End Approach

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.

View
AttributePerceptionEmotionValueNo attribute evidencenone found in this runAged · Premium378consumer 100%iSatisfied1,400consumer 55%iRefreshed1,050consumer 33%iSpecial · Pamper1,050consumer 0%derived 24,948iConfident700consumer 0%iPower · Statusi

Perception

Aged · Premium378 · consumer 100%→ Confident

Emotion

Satisfied1,400 · consumer 55%
Refreshed1,050 · consumer 33%
Special · Pampered1,050 · consumer 0% · derived 24,948
Confident700 · consumer 0%→ Power · Status

Value

Power · Status

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).

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.

Brand-manager note · auto-generated from measured values

For people searching 'ウイスキー', 1 taste signals were detected, and the strongest is Aged · Premium (378). That single family accounts for 100% of all taste weight and is —x the runner-up Aged · Premium (0). In consumer language, the people behind this search respond to "long-aged, premium bottlings".

Only 1 taste groups were detected, so concentration is not judged. With few groups the top-three sum rises mechanically, so this figure must not be read as a concentrated market. Widen the seeds, gather more material, and look again.

Only the perception layer of social meaning was detected this time (Aged · Premium 378 and so on); no attribute layer of sensory fact surfaced. The consumer language of this category stops at how it feels and never descends to texture, taste or scent. This is common in categories with little sensory vocabulary, such as cars or appliances, and it marks an empty space for content that translates specs into sensory language.

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 Aged · Premium and Aged · Premium as supporting appeals. Since only one rung of the ladder was detected here, ordering priorities inside that rung is more realistic than designing a message that climbs between layers.

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.

Show the source data behind this reading 75 rows
The figures above are the sum of these rows’ weights.
Aged · PremiumTop 10 of 75 rows · weight sum 378
熟成AI Overview (AIO)700
ウイスキーは、穀物を原料に発酵・蒸留したのち、木樽で熟成させて造られる蒸留酒です。
プレミアムAI Overview (AIO)AI Overview (AIO)700
日本で人気のウイスキーをランキング形式でご紹介します。定番の銘柄からプレミアムなボトルまで、多くの支持を集める代表的なジャパニーズウイスキーをまとめました。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
熟成AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの人気・評価ランキング(代表銘柄)
熟成AI Overview (AIO)AI Overview (AIO)700
熟成年数(エイジ)
高級AIO source / PAA answer420
高級ウイスキーやシングルモルト(一つの蒸溜所の原酒のみ)では、以下の銘柄が特に有名です。
プレミアムRelated query350
プレミアム 角瓶
高級Related query350
サントリーウイスキー高級
高級Related query350
ウイスキー銘柄 高級

Who said it

'ウイスキー' · Source mix — 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.

measuredsource class = measured from URLderivedvoice/description split = class mapping
Queries83%718,819
Other8%66,185biccamera.com · my-best.com · syurui.co.jp
AI Overview text5%46,900
Commerce2%18,620kakaku.com · amazon.co.jp · ranking.rakuten.co.jp
Social1%6,580youtube.com
Personal blog1%4,830joylab.jp · sakesen.com · web.hh-online.jp
Press0%4,095asahibeer.co.jp · moov.ooo · sakidori.co
Brand official0%2,240sakehack.com · nta.go.jp · kakuyasu.co.jp
Community0%560detail.chiebukuro.yahoo.co.jp
Reference0%350wikipedia.org

82% of this analysis came from consumer voice (queries + community, social, blogs).

Brand-manager note · auto-generated from measured values

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

'ウイスキー' · Drivers of liking & avoidance barriers

The same attribute can be wanted or avoided (sentiment ≠ preference). Right = liking volume, left = measured negation/JAR patterns ('not too …').

measuredliking/avoid = measured negation patterns

How this is analyzed

Drivers of Liking — with JAR scales and penalty analysis

What this section is computed from, and how far it can be trusted.

Theory & framework

In sensory and consumer science, Drivers of Liking separates which sensory attributes raise overall liking from which ones depress it. JAR (Just-About-Right) scales and penalty analysis go further, treating both 'too little' and 'too much' as penalties: the same attribute becomes an avoidance driver the moment it leaves its acceptable band. The premise of this section is that sentiment and preference are not the same thing — describing an attribute positively and actually wanting it are separate events.

How we re-read it from search data

Negation and excess expressions are detected in queries and sentences using directional windows, with markers for each language: Japanese 「〜ない」「〜すぎ」「苦手」, English 'not', 'too', 'without', Korean 않/없·너무 (e.g. 'not too sweet', 'too heavy'). Praise such as 'no heaviness' is not counted as avoidance. Weights caught this way form the avoidance side (left); expressions with no negation marker form the liking side (right), placed on the same axis. When one attribute shows up large on both sides, it is a question of intensity (just-about-right), not direction.

What it is for

It tells you what to remove. Most brand research only asks what people like, and so misses the reasons they leave. Useful for renewal decisions, validating claims (finding where emphasis starts to backfire), and designing an intensity spectrum across a product line.

Limits & caveats

Only negation that reaches a search box is captured, so avoidance is structurally under-measured — people tend not to search for what they dislike. Negative phrasing may also target the category rather than the brand, so attribution needs care.

References
  • Moskowitz, H. (1972) Subjective Ideals and Sensory Optimization
  • Rothman, L. & Parker, M. (2009) Just-About-Right (JAR) Scales: Design, Usage, Benefits and Risks. ASTM MNL63
  • Popper, R. et al. Penalty analysis in consumer testing
AvoidLiking
Aged · Premium
378
97
Sweet · Sherried
0
Brand-manager note · auto-generated from measured values

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 378, avoidance totals 97, 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.

Show the source data behind this reading 94 rows
The figures above are the sum of these rows’ weights.
Aged · PremiumTop 10 of 75 rows · weight sum 378
熟成AI Overview (AIO)700
ウイスキーは、穀物を原料に発酵・蒸留したのち、木樽で熟成させて造られる蒸留酒です。
プレミアムAI Overview (AIO)AI Overview (AIO)700
日本で人気のウイスキーをランキング形式でご紹介します。定番の銘柄からプレミアムなボトルまで、多くの支持を集める代表的なジャパニーズウイスキーをまとめました。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
熟成AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの人気・評価ランキング(代表銘柄)
熟成AI Overview (AIO)AI Overview (AIO)700
熟成年数(エイジ)
高級AIO source / PAA answer420
高級ウイスキーやシングルモルト(一つの蒸溜所の原酒のみ)では、以下の銘柄が特に有名です。
プレミアムRelated query350
プレミアム 角瓶
高級Related query350
サントリーウイスキー高級
高級Related query350
ウイスキー銘柄 高級
Sweet · SherriedTop 10 of 19 rows · weight sum 97
甘く · avoidanceRelated query350
甘く ないウイスキー
甘く · avoidanceRelated query350
ウイスキー 甘く ない
甘さ · avoidancebrand kakuyasu.co.jpOrganic snippet350
2024/02/01 — オールドパーの中でも定番の「オールドパー 12年」より甘さ控えめで飲みやすく、価格も抑えられている「オールドパー シルバー」は、雪の中に置き忘れた樽 ...
甘いAI Overview (AIO)AI Overview (AIO)700
サントリー 角瓶:甘い香りと厚みのあるコク。ハイボールにすると爽やかで美味しい定番。約1,600円〜。
バニラAI Overview (AIO)AI Overview (AIO)700
シーバスリーガル 12年:ハチミツやバニラの芳醇な香りが特徴の飲みやすいスコッチ。約2,500円〜。
バニラAI Overview (AIO)AI Overview (AIO)700
アメリカン: アメリカ産。原料にトウモロコシを主としたバーボン・ウイスキーなどが有名で、バニラのような甘い香りがします。
甘いAI Overview (AIO)AI Overview (AIO)700
アメリカン: アメリカ産。原料にトウモロコシを主としたバーボン・ウイスキーなどが有名で、バニラのような甘い香りがします。
甘いRelated query350
ウィスキー 甘いなぜ
甘くてother dearwhisky.comOrganic snippet350
2024/03/29 — 女性にも人気の甘くて飲みやすいウイスキーのおすすめ銘柄【スコッチウイスキー】 · バランタイン 7年 · グレンモーレンジィ オリジナル · グレングラント ...
甘くother kixdutyfree.jpOrganic snippet350
サントリーウイスキーローヤル 700mは甘く華やかな香りで、柔らかな口当たりです。すっきりとした心地よい余韻が特徴です。また、シングルモルト嘉之助 The Mellow Bar ...

Context × Taste

'ウイスキー' · Context × Taste — when each taste switches on (curated buckets)

Taste is situational, not fixed (Person × CEP × Attribute). Cells = measured co-occurrence of context and taste words in the same query/snippet.

measuredco-occurrence strength = measuredderivedcontext lexicon = curated

How this is analyzed

Context × taste — situational consumer behaviour and Category Entry Points

What this section is computed from, and how far it can be trusted.

Theory & framework

Belk (1975) showed that the same person's preferences shift systematically with situation. The Category Entry Point view (Romaniuk & Sharp) likewise holds that a brand is retrieved from memory when it is linked to specific situational cues. Treating taste as something switched on and off by context, rather than as a fixed trait, predicts better.

How we re-read it from search data

Co-occurrence between context terms (season, time of day, place, occasion, company) and taste terms is measured across the search corpus to build a context × taste matrix. Each cell is the summed weight of both vocabularies appearing in the same text, with a per-cell cap so that one very high-volume text cannot dominate the matrix. The context lexicon is curated, so it counts as derived inference.

What it is for

It feeds campaign timing and creative tone directly. If the taste that switches on for "summer, alone, after work" differs from the one for "winter, gift", that is an instruction to write different messages by season. A good input for seasonal calendars and CEP mapping.

Limits & caveats

Co-occurrence is not causation. Two vocabularies appearing in the same text does not guarantee that the taste is actually expressed in that situation, and low-volume cells are statistically unstable, so read the top cells.

References
  • Belk, R. (1975) Situational Variables and Consumer Behavior. Journal of Consumer Research
  • Romaniuk, J. & Sharp, B. (2016) How Brands Grow Part 2 — Category Entry Points
Aged · Pre
Beginner700
Gift2.7k
Evening · Occasion350
Brand-manager note · auto-generated from measured values

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.

Show the source data behind this reading 75 rows
The figures above are the sum of these rows’ weights.
Aged · PremiumTop 10 of 75 rows · weight sum 378
熟成AI Overview (AIO)700
ウイスキーは、穀物を原料に発酵・蒸留したのち、木樽で熟成させて造られる蒸留酒です。
プレミアムAI Overview (AIO)AI Overview (AIO)700
日本で人気のウイスキーをランキング形式でご紹介します。定番の銘柄からプレミアムなボトルまで、多くの支持を集める代表的なジャパニーズウイスキーをまとめました。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
熟成AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの人気・評価ランキング(代表銘柄)
熟成AI Overview (AIO)AI Overview (AIO)700
熟成年数(エイジ)
高級AIO source / PAA answer420
高級ウイスキーやシングルモルト(一つの蒸溜所の原酒のみ)では、以下の銘柄が特に有名です。
プレミアムRelated query350
プレミアム 角瓶
高級Related query350
サントリーウイスキー高級
高級Related query350
ウイスキー銘柄 高級

Perceptual Map

'ウイスキー' · Taste map — which values it sits closest to

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.

measuredbubble size = search volumederivedcoordinates = Schwartz mapping

How this is analyzed

Perceptual map — Schwartz value circumplex and Kansei engineering

What this section is computed from, and how far it can be trusted.

Theory & framework

Schwartz (1992) found that ten basic value types, replicated across cultures, form a circumplex on two axes: self-transcendence ↔ self-enhancement and conservation ↔ openness. Adjacent values are compatible; opposing values conflict. Kansei engineering (Nagamachi 1995) translates physical product attributes into consumers' affective vocabulary as a design language. Overlaying the two lets you see, in one space, which value a taste is a means of expressing. Aaker's (1997) brand personality scale is used as a secondary coordinate for translating each taste into brand language.

How we re-read it from search data

Taste families are defined as clusters of affective terms, then projected onto coordinates on the Schwartz circumplex. Bubble size is measured search volume; the coordinates themselves are derived inference from the value mapping. Opposing quadrants are conflicting values, so a brand claiming both ends of a diagonal can be read as cancelling its own message.

What it is for

It shows the centre of gravity of your positioning and the empty space around it on a single page. Putting competitors on the same map reveals where differentiation is available, and it separates 'adjacent moves' (feasible) from 'diagonal moves' (expensive) when considering a taste extension.

Limits & caveats

Coordinates are theoretical mapping, not measurement, so read relative arrangement rather than absolute position. Schwartz values are an individual-level theory, so applying them to category tastes involves analogy. The map shows the size of a taste, not its profitability.

References
  • Schwartz, S. (1992) Universals in the Content and Structure of Values. Advances in Experimental Social Psychology
  • Nagamachi, M. (1995) Kansei Engineering. International Journal of Industrial Ergonomics
  • Aaker, J. (1997) Dimensions of Brand Personality. Journal of Marketing Research
Toward othersToward myselfSeeks the newKeeps the familiarcare · harmonystatus · achievementchange · adventurestability · traditionAged · Premiumvolume37813238

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-manager note · auto-generated from measured values

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.

Show the source data behind this reading 75 rows
The figures above are the sum of these rows’ weights.
Aged · PremiumTop 10 of 75 rows · weight sum 378
熟成AI Overview (AIO)700
ウイスキーは、穀物を原料に発酵・蒸留したのち、木樽で熟成させて造られる蒸留酒です。
プレミアムAI Overview (AIO)AI Overview (AIO)700
日本で人気のウイスキーをランキング形式でご紹介します。定番の銘柄からプレミアムなボトルまで、多くの支持を集める代表的なジャパニーズウイスキーをまとめました。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
熟成AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの代表的な人気銘柄は、国内外問わず入手困難な長期熟成品や希少価値の高いブランドが上位にランクインしています。
高級AI Overview (AIO)AI Overview (AIO)700
高級ウイスキーの人気・評価ランキング(代表銘柄)
熟成AI Overview (AIO)AI Overview (AIO)700
熟成年数(エイジ)
高級AIO source / PAA answer420
高級ウイスキーやシングルモルト(一つの蒸溜所の原酒のみ)では、以下の銘柄が特に有名です。
プレミアムRelated query350
プレミアム 角瓶
高級Related query350
サントリーウイスキー高級
高級Related query350
ウイスキー銘柄 高級

Preference → Choice

'ウイスキー' · Choice map — where it appears, and in whose words

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.

measuredx (funnel) = stage markers in the source textmeasuredy (voice) = queries vs description textmeasuredno stage marker means no coordinate

How this is analyzed

Choice map — funnel position × consumer voice

What this section is computed from, and how far it can be trusted.

Theory & framework

Search intent taxonomies (Broder 2002; Rose & Levinson 2004) split queries into navigational, informational and transactional, and that split maps reasonably onto purchase-journey stages. Crossing it with the openness ↔ conservation axis of taste produces a choice map: which stage of consumer holds which taste disposition. The premise is that the same taste needs different messages during exploration versus just before purchase.

How we re-read it from search data

Both axes are measured per family. The horizontal axis is a weighted average of the funnel-stage markers present in the source texts where the taste word actually appeared: 'meaning, types, how-to' scores 0.12, 'review, compare, pros and cons' 0.50, 'price, cheapest, buy, quote' 0.88. The vertical axis is the share of the family's weight that came from search queries (qw) versus description text (dw). Bubble size is search volume. Top-right is what consumers themselves say right before buying, which is ad-copy material; bottom-left is what the market says during exploration, which is content and SEO material. Families with no stage marker at all get no coordinate and are listed separately as unmeasured.

What it is for

It lets you cut funnel-stage creative and landing design by taste at the same time. Directly applicable to keyword grouping in performance advertising, branching retargeting messages, and ordering information on a product page.

Limits & caveats

The horizontal axis depends on stage-marker vocabulary, so source texts without a marker drop out of the calculation. Read a bubble's position loosely when its stage-signal count is small. The vertical axis is a ratio between two different units (search volume, i.e. people, and document weight), so read relative positions between families rather than absolute values. A long unmeasured list is itself a signal that purchase language has not formed around this keyword yet.

References
  • Broder, A. (2002) A Taxonomy of Web Search. ACM SIGIR Forum
  • Rose, D. & Levinson, D. (2004) Understanding User Goals in Web Search. WWW '04
Words consumers useWords the market appliesqueries · community · blogsAI overview / snippets← ExploringBuying →Aged · Premiumvolume37813238

Reading: ‘Aged · Premium’ sits furthest top-right at 26% consumer voice and 50% funnel. Consumers actually use it and close to the buying stage, so it is the first candidate for ad copy. 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.

B

What 'ウイスキー' symbolizes

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

'ウイスキー' · Search journey: what gets weighed alongside

Measured search sequences (Path Finder). What people search before and after this keyword is the consideration set and the entry path.

measuredpaths & frequency = measuredderivedconsideration set = derived
Alternatives weighed (appearances in paths)
ウィスキー 山崎 工場 2山崎 18年 1
Paths leading into this keyword
ジンジャーエール ウイスキー→ウイスキーバックとは→ウイスキー
Paths leading out of this keyword
ウイスキー→ウイスキー山崎→ウイスキー山崎定価
ウイスキー→ウイスキー山崎→ウイスキー山崎 12年
ウイスキー→ウイスキー山崎→ウイスキー山崎 12年→山崎 18年
ウイスキー→ウイスキー おすすめ→安いウイスキーおすすめ
ウイスキー→ウイスキー おすすめ→ウイスキープレゼントおすすめ
ウイスキー→ウイスキー おすすめ→ウイスキーおすすめ 日本
Brand-manager note · auto-generated from measured values

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

'ウイスキー' · Symbol profile — the taste language the market uses for this keyword

Taste words extracted from market description language (AIO bodies · organic snippets), not from queries — 'what this keyword is described as', not 'what people want'.

measureddescription & reference = measuredderived'symbol' synthesis = derived

How this is analyzed

Symbol profile — Brand Concept Maps and aspect-based sentiment analysis

What this section is computed from, and how far it can be trusted.

Theory & framework

Brand Concept Maps (John, Loken, Kim & Monga 2006) reconstruct consumers' association networks as a map, and Keller's (1993) brand knowledge structure explains brand equity through the strength, favourability and uniqueness of associations. Aspect-based sentiment analysis separates what an evaluation is about, aspect by aspect. The operational definition of a brand having become a symbol of a taste is this: people use it as the yardstick when describing something else.

How we re-read it from search data

Two things are kept apart. First, description language: taste vocabulary extracted from AI Overview text and organic snippets, showing how the market describes this keyword. Second, reference patterns: real searches of the form '{keyword}-esque', 'like {keyword}', '{keyword} style', which are direct evidence that consumers use the brand as a benchmark. Where description exists but references do not, the report says the keyword is described but not yet symbolic.

What it is for

It judges, with evidence, whether the brand has become shorthand for a taste inside its category. Used for assessing extension headroom, choosing licensing and collaboration partners, and diagnosing the starting point for repositioning.

Limits & caveats

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.

References
  • Keller, K. L. (1993) Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. Journal of Marketing
  • John, D. R., Loken, B., Kim, K. & Monga, A. (2006) Brand Concept Maps. Journal of Marketing Research
  • Pontiki, M. et al. (2016) SemEval Task 5: Aspect Based Sentiment Analysis

Not enough taste signal in market description language.

Reference patterns (real searches using it as a benchmark)

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.

Brand-manager note · auto-generated from measured values

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.

Methodology · academic grounding · sources · limits
Gutman (1982) · Reynolds & GutmanMeans-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 ScienceDrivers of Liking — sensory attributes → acceptance.L4c
Nagamachi — Kansei EngineeringConsumer feeling → design specification.L3·L5
John, Loken et al. (2006) JMRBrand 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
L0 corpus = search relations + SERP + DaaS /serp direct (AIO ×2 weight · PAA · organic snippets). L1 layered taste words (attribute/perception/emotion) + negation/JAR window for avoidance. L2 HVM ladder (nodes measured, links curated). L3 Schwartz·Aaker. L4 segments · context×taste · DOL · brand positions. L5 Cramér's V·lift.
Limits (disclosed) · This market's data has no gender or age, so no demographic analysis is made. HVM links are measured only where the words co-occur in a sentence (solid); the rest follow convention (dashed). Context×taste is co-occurrence, not causation. Brand positions require KG brands.
Summary · 「ウイスキー」は月間264Kの関心度で下降トレンド(-24% annualized linear trend)にあります. 関心パターンは下降型です. 主な検索動機は「行動(購入等)」です. SERPにAI Overview, PAA(4), 関連検索(0件), 画像, ナレッジパネルを含む. ドメイン分散度100%(上位: syurui.co.jp, likaman.net, amazon.co.jp). 1544個の関連概念が10個のクラスターで構成されています. 最大クラスターは「サントリー·ウイスキー」(210個)でハブ概念は「サントリーウイスキー」です. この概念は探索の起点の役割を果たしています. 流入経路は「情報探索 流入」パターンが最も頻繁です.

Taste Finder v2.0 · Taste Intelligence Framework v5

Sections left out of this report, and why

Taste Finder at a glance

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)

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