TASTE INTELLIGENCE

Consumer Taste Report — 'スーパードライ'

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

Live · lmpipe + DaaS + KGData 2026-09-30Generated 2026-09-30 21:47Corpus 1140DaaS SERP · AIO 41 · PAA/related 131 · snippets 171 · titles/sources 183actual basis for taste extraction (community·blog·social) 16%Seeds: スーパードライ + スーパードライ 味 + スーパードライ 口コミ + スーパードライ 特徴 + スーパードライ 評価

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

Monthly searches
33,100
Trend · maturity
+16% · 0.47
Consumer words
100% · queries · community · social · blogs
Taste signals
5
Avoidance found
3
Lifecycle
mature · Seasonal peak Oct·Jul
A

People who search 'スーパードライ'

Audience Taste: the taste structure of consumers searching 'スーパードライ', 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 only5% of this report's weight comes from search volume (demand) and 95% 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

'スーパードライ' · 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
AttributePerceptionEmotionValueCrisp Finish · D9,082consumer 100%iBitterness · Hop187consumer 100%iDrinkability · R19consumer 100%iHoppy · Citrus11consumer 100%iMalty · Sweet7consumer 100%iRefreshed12,629consumer 52%iSatisfied2,310consumer 0%iConfident700consumer 0%derived 2,450iJoyful · Fun560consumer 0%iHedonismiStimulationi

Attribute

Crisp Finish · Dry9,082 · consumer 100%→ Refreshed · Satisfied
Bitterness · Hops187 · consumer 100%→ Refreshed · Stimulation
Drinkability · Refreshing19 · consumer 100%→ Refreshed · Satisfied · Hedonism
Hoppy · Citrus11 · consumer 100%→ Hedonism
Malty · Sweet7 · consumer 100%→ Hedonism

Emotion

Refreshed12,629 · consumer 52%→ Hedonism · Stimulation
Satisfied2,310 · consumer 0%→ Hedonism
Confident700 · consumer 0% · derived 2,450
Joyful · Fun560 · consumer 0%

Value

Hedonism
Stimulation

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 'スーパードライ', 5 taste signals were detected, and the strongest is Crisp Finish · Dry (9,082). That single family accounts for 98% of all taste weight and is 48.6x the runner-up Bitterness · Hops (187). In consumer language, the people behind this search respond to "a dry, clean-cut finish".

Only 5 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 attribute layer of sensory fact was detected this time (Crisp Finish · Dry 9,082 and so on); no perception layer of social meaning surfaced. Consumers still talk about this category in terms of what it is, not what it looks like it means. A brand that builds the symbolic language first can take the upper layer of perception.

The ladder converges on the value 'Hedonism', with Refreshed 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 Crisp Finish · Dry the spine of the message and place Bitterness · Hops and Drinkability · Refreshing 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 371 rows
The figures above are the sum of these rows’ weights.
Crisp Finish · DryTop 10 of 259 rows · weight sum 9,103
ドライ · avoidanceAI Overview (AIO)AI Overview (AIO)700
刺激の強さ:人によっては炭酸やドライ感が強すぎて、ビールのコクや重みを求める好みには合わない場合がある。
辛口 · avoidanceRelated query350
スーパードライ辛口じゃ ない
辛口Related query3,634
アサヒスーパードライ冷涼 辛口
辛口Related query2,727
スーパードライ 爽快 辛口
ドライRelated query814
ビール ドライ
キレAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
辛口AI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
シャープAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
キレAI Overview (AIO)AI Overview (AIO)700
抜群のキレと喉越し:飲んだ瞬間にガツンとくる刺激があり、後味には雑味がなくスッと消えていきます。
辛口AI Overview (AIO)AI Overview (AIO)700
冷やすことで際立つ旨さ:キンキンに冷やす(「冷え×辛口」)ことで、シャープなキレと麦の飲みごたえが最大限に引き立つように設計されています。
Bitterness · HopsTop 10 of 56 rows · weight sum 187
苦味 · avoidanceAI Overview (AIO)AI Overview (AIO)700
どんな料理にも合う: 苦味が控えめで透明感があるため、和食や洋食など、さまざまな食事を引き立てます。
苦味 · avoidanceAI Overview (AIO)AI Overview (AIO)700
アサヒ スーパードライの口コミは、「キレが良い」「爽快感がある」「食事に合う」と高く評価する声が多い一方、「苦味が物足りない」「リニューアルで味が変わった」という意見もあります。
苦み · avoidanceAIO source / PAA answer420
苦みが控えめビール特有の苦みが少ないため、苦みこそがビールだという人には物足りなく感じられます。
苦く · avoidanceAIO source / PAA answer420
徹底した市場調査:消費者が求めている「苦くない、すっきりしたビール」の潜在ニーズを的確に捉えて開発された。
苦味 · avoidancecommerce amazon.co.jpOrganic snippet350
お客様はこのビールについて、飲みやすくて美味しいと評価しています。発泡酒系の甘ったるさを感じず、飲み口がスッキリしていて苦味が少ないという声が多くあります。
苦味 · avoidancesocial note.comOrganic snippet350
確かにキレのある辛口ですが、嫌な後味や苦味は全くなく、むしろその爽快感がクセになり、どんどん飲み進めてしまうんです。これは、アサヒ スーパー ...
苦味 · avoidanceother late.beerResult title210
アサヒスーパードライのレビュー「1年振りのビールが苦手だと思ったビール。しかし、苦味を感じないとここまで飲みやすいのか!?」
苦味 · avoidancepress asahigroup-holdings.comResult title210
辛口=苦味ではない!新「スーパードライ」の味わいをビールづくりのプロが解説|ニュースルーム
苦味AI Overview (AIO)AI Overview (AIO)700
すっきりしたクリアな口当たり:苦味や重さが抑えられており、軽快で爽快感のある味わいです。
苦味AI Overview (AIO)AI Overview (AIO)700
味の物足りなさ・変化:リニューアルにより以前と比べて印象が変わった、または苦味が軽くなったと感じる人すいる。
Drinkability · RefreshingTop 10 of 56 rows · weight sum 19
喉越しAI Overview (AIO)AI Overview (AIO)700
抜群のキレと喉越し:飲んだ瞬間にガツンとくる刺激があり、後味には雑味がなくスッと消えていきます。
爽快感AI Overview (AIO)AI Overview (AIO)700
すっきりしたクリアな口当たり:苦味や重さが抑えられており、軽快で爽快感のある味わいです。
キンキンAI Overview (AIO)AI Overview (AIO)700
冷やすことで際立つ旨さ:キンキンに冷やす(「冷え×辛口」)ことで、シャープなキレと麦の飲みごたえが最大限に引き立つように設計されています。
のどごしAI Overview (AIO)AI Overview (AIO)700
辛口・キレ味: 雑味を抑えたシャープなのどごしで、後味がすっきりとしています。
爽快感AI Overview (AIO)AI Overview (AIO)700
アサヒ スーパードライの口コミは、「キレが良い」「爽快感がある」「食事に合う」と高く評価する声が多い一方、「苦味が物足りない」「リニューアルで味が変わった」という意見もあります。
爽快感AI Overview (AIO)AI Overview (AIO)700
抜群のキレと爽快感:炭酸が強く、喉越しがスッキリしているので暑い夏や一口目に最適。
喉越しAI Overview (AIO)AI Overview (AIO)700
抜群のキレと爽快感:炭酸が強く、喉越しがスッキリしているので暑い夏や一口目に最適。
ゴクゴクAI Overview (AIO)AI Overview (AIO)700
飲みやすさ:クセが少なく、何杯でもゴクゴク飲める。
冷えたAI Overview (AIO)AI Overview (AIO)700
アサヒスーパードライは、2026年(および2022年)に大きなリニューアルを実施しています。従来のスーパードライが持つ「キレ」の良さはそのままに、麦芽の配合比率を高めることで飲みごたえを向上させ、より「冷えた状態」でおいしく飲める味わいへと
キンキンAI Overview (AIO)AI Overview (AIO)700
「新 辛口×冷え」: キンキンに冷やす ことで、最高の「辛口のうまさ」が引き立つように設計されています。

Who said it

'スーパードライ' · 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
Queries71%301,127
Other8%32,200rocketnews24.com · my-best.com · news.yahoo.co.jp
AI Overview text7%28,700
Press7%28,140asahibeer.co.jp · xtrend.nikkei.com · asahigroup-holdings.com
Social4%16,030youtube.com · note.com · ameblo.jp
Commerce2%8,960amazon.co.jp · review.kakaku.com · miraido-onlineshop.com
Community1%4,480detail.chiebukuro.yahoo.co.jp · rinsh.hatenablog.com
Brand official1%4,480shareview.jp · s.mognavi.jp · monolog.r-n-i.jp
Reference0%1,050wikipedia.org

74% 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

'スーパードライ' · 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
21
Crisp Finish · Dry
9,082
Bitterness · Hops
187
Drinkability · Refreshing
19
Hoppy · Citrus
11
Malty · Sweet
7
Brand-manager note · auto-generated from measured values

Crisp Finish · Dry works in both directions in this search: it pulls people in and pushes them away at the same time. Liking 9,082 and avoidance 21 attach to the same attribute, evidenced by searches like "スーパードライ辛口じゃ ない". This is sentiment diverging from preference, and in sensory science it is a JAR (Just-About-Right) attribute.

The response is dosage, not direction. Dropping the attribute would remove the pull along with the push. Build an intensity spectrum across the line (mild, medium, strong) and state the intensity on the product page so expectation and experience stay close.

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 259 rows
The figures above are the sum of these rows’ weights.
Crisp Finish · DryTop 10 of 259 rows · weight sum 9,103
ドライ · avoidanceAI Overview (AIO)AI Overview (AIO)700
刺激の強さ:人によっては炭酸やドライ感が強すぎて、ビールのコクや重みを求める好みには合わない場合がある。
辛口 · avoidanceRelated query350
スーパードライ辛口じゃ ない
辛口Related query3,634
アサヒスーパードライ冷涼 辛口
辛口Related query2,727
スーパードライ 爽快 辛口
ドライRelated query814
ビール ドライ
キレAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
辛口AI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
シャープAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
キレAI Overview (AIO)AI Overview (AIO)700
抜群のキレと喉越し:飲んだ瞬間にガツンとくる刺激があり、後味には雑味がなくスッと消えていきます。
辛口AI Overview (AIO)AI Overview (AIO)700
冷やすことで際立つ旨さ:キンキンに冷やす(「冷え×辛口」)ことで、シャープなキレと麦の飲みごたえが最大限に引き立つように設計されています。

Context × Taste

'スーパードライ' · 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
Crisp FiniBitternessDrinkabiliHoppy · CiMalty · Sw
Summer1.8k7001.3k
Beginner350
Brand-manager note · auto-generated from measured values

In the "Summer" context, Crisp Finish · Dry 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 259 rows
The figures above are the sum of these rows’ weights.
Crisp Finish · DryTop 10 of 259 rows · weight sum 9,103
ドライ · avoidanceAI Overview (AIO)AI Overview (AIO)700
刺激の強さ:人によっては炭酸やドライ感が強すぎて、ビールのコクや重みを求める好みには合わない場合がある。
辛口 · avoidanceRelated query350
スーパードライ辛口じゃ ない
辛口Related query3,634
アサヒスーパードライ冷涼 辛口
辛口Related query2,727
スーパードライ 爽快 辛口
ドライRelated query814
ビール ドライ
キレAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
辛口AI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
シャープAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
キレAI Overview (AIO)AI Overview (AIO)700
抜群のキレと喉越し:飲んだ瞬間にガツンとくる刺激があり、後味には雑味がなくスッと消えていきます。
辛口AI Overview (AIO)AI Overview (AIO)700
冷やすことで際立つ旨さ:キンキンに冷やす(「冷え×辛口」)ことで、シャープなキレと麦の飲みごたえが最大限に引き立つように設計されています。

Perceptual Map

'スーパードライ' · 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 · traditionCrisp Finish · DryBitterness · HopsDrinkability · RefreshingHoppy · CitrusMalty · Sweetvolume9,0823,179908

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 "Hedonism". 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 "Hedonism" means that is the value consumers express through taste in this category. Conservation 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 331 rows
The figures above are the sum of these rows’ weights.
Crisp Finish · DryTop 10 of 259 rows · weight sum 9,103
ドライ · avoidanceAI Overview (AIO)AI Overview (AIO)700
刺激の強さ:人によっては炭酸やドライ感が強すぎて、ビールのコクや重みを求める好みには合わない場合がある。
辛口 · avoidanceRelated query350
スーパードライ辛口じゃ ない
辛口Related query3,634
アサヒスーパードライ冷涼 辛口
辛口Related query2,727
スーパードライ 爽快 辛口
ドライRelated query814
ビール ドライ
キレAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
辛口AI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
シャープAI Overview (AIO)AI Overview (AIO)700
アサヒ「スーパードライ」は、「さらりとした飲み口、キレ味さえる辛口」が特徴のシャープでクリアな味わいのビールです。
キレAI Overview (AIO)AI Overview (AIO)700
抜群のキレと喉越し:飲んだ瞬間にガツンとくる刺激があり、後味には雑味がなくスッと消えていきます。
辛口AI Overview (AIO)AI Overview (AIO)700
冷やすことで際立つ旨さ:キンキンに冷やす(「冷え×辛口」)ことで、シャープなキレと麦の飲みごたえが最大限に引き立つように設計されています。
Drinkability · RefreshingTop 10 of 56 rows · weight sum 19
喉越しAI Overview (AIO)AI Overview (AIO)700
抜群のキレと喉越し:飲んだ瞬間にガツンとくる刺激があり、後味には雑味がなくスッと消えていきます。
爽快感AI Overview (AIO)AI Overview (AIO)700
すっきりしたクリアな口当たり:苦味や重さが抑えられており、軽快で爽快感のある味わいです。
キンキンAI Overview (AIO)AI Overview (AIO)700
冷やすことで際立つ旨さ:キンキンに冷やす(「冷え×辛口」)ことで、シャープなキレと麦の飲みごたえが最大限に引き立つように設計されています。
のどごしAI Overview (AIO)AI Overview (AIO)700
辛口・キレ味: 雑味を抑えたシャープなのどごしで、後味がすっきりとしています。
爽快感AI Overview (AIO)AI Overview (AIO)700
アサヒ スーパードライの口コミは、「キレが良い」「爽快感がある」「食事に合う」と高く評価する声が多い一方、「苦味が物足りない」「リニューアルで味が変わった」という意見もあります。
爽快感AI Overview (AIO)AI Overview (AIO)700
抜群のキレと爽快感:炭酸が強く、喉越しがスッキリしているので暑い夏や一口目に最適。
喉越しAI Overview (AIO)AI Overview (AIO)700
抜群のキレと爽快感:炭酸が強く、喉越しがスッキリしているので暑い夏や一口目に最適。
ゴクゴクAI Overview (AIO)AI Overview (AIO)700
飲みやすさ:クセが少なく、何杯でもゴクゴク飲める。
冷えたAI Overview (AIO)AI Overview (AIO)700
アサヒスーパードライは、2026年(および2022年)に大きなリニューアルを実施しています。従来のスーパードライが持つ「キレ」の良さはそのままに、麦芽の配合比率を高めることで飲みごたえを向上させ、より「冷えた状態」でおいしく飲める味わいへと
キンキンAI Overview (AIO)AI Overview (AIO)700
「新 辛口×冷え」: キンキンに冷やす ことで、最高の「辛口のうまさ」が引き立つように設計されています。
Hoppy · CitrusTop 3 of 4 rows · weight sum 11
グレープフルーツRelated query350
ビタリスト グレープフルーツ
清涼other my-best.comOrganic snippet350
全体的にすっきりとした味で、雑味はほとんどありません。加えて、のどごしがよく清涼感も強い点が評価を伸ばしたポイントです。
爽やかother rocketnews24.comOrganic snippet350
2023/09/12 — リニューアル後、新スーパードライを飲む機会は何度もあった。より爽やかになったかなという印象だが、そのものが持つ特徴は大きく変わっていないと感じ ...
Malty · SweetTop 10 of 12 rows · weight sum 7
甘いRelated query350
ビタリスト 甘い
甘みsocial instagram.comOrganic snippet350
⚡ キレのある辛口甘みや重さを抑えたシャープでスッキリした味わい。 クリアで軽快雑味が少なく、スムーズな飲み口。 ✨ 後味はスッと消えるこれぞ ...
甘さsocial note.comOrganic snippet350
やはり「甘さ」を感じ、そして後味に「水っぽさ」を感じている。 これを僕は「美味しくなくなった」と形容せざるを得ない。 スーパードライに求めるものは ...
甘みsocial youtube.comOrganic snippet350
一口目のキレがありつつ、温度が上がって行った後には甘みと飲みごたえが感じられた気がして個人的には今度の方が好みでした。 以前のリニューアル前の方 ...
甘みsocial instagram.comOrganic snippet350
⚡ キレのある辛口甘みや重さを抑えたシャープでスッキリした味わい 。 クリアで軽快雑味が少なく、スムーズな飲み口。 ✨ 後味はスッと消えるこれぞ ...
甘さother zero-start.websiteOrganic snippet350
飲んだ感想ですが、やはりキレの良さが際立ちますね!甘さはなく、どちらかというと苦味寄り?香りは弱めだがのどごしの良さが特徴的なビールと思いました。
甘くSERP · PAA350
スーパードライ 甘く感じる
甘みsocial youtube.comOrganic snippet350
... 味として冷たい”スーパードライを実現しています。 実際に飲んでみると、通常のスーパードライにあるモルト由来の甘みや旨味をさらに削ぎ落とし、冷た ...
甘いsocial note.comOrganic snippet350
スーパードライは数年前だったか、突然甘いが強くなり、端的に「美味しくなくなった」と感じるようになった。 それからは黒ラベルと赤星を軸に、キリン ...
甘みother my-best.comOrganic snippet350
比較した商品内の軽い飲み心地のものは甘みや旨味が薄く、飽きやすい傾向にありましたが、麦の風味と旨味をしっかりと感じられ雑味もほとんどありません。試飲したモニター ...

Preference → Choice

'スーパードライ' · 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 →Bitterness · HopsDrinkability · RefreshingHoppy · CitrusMalty · Sweetvolume18765191 not placed (no funnel-stage signal — no coordinate invented)Crisp Finish · Dry

Reading: ‘Malty · Sweet’ sits furthest top-right at 82% consumer voice and 50% funnel. Consumers actually use it and close to the buying stage, so it is the first candidate for ad copy. 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.

B

What 'スーパードライ' symbolizes

Brand/Keyword Symbol — what taste 'スーパードライ' stands for in the public mind. Based on measured market description language (AIO·snippets) and reference patterns ('スーパードライ-style', 'like スーパードライ').

Every figure carries an evidence label — measured = raw data; derived = measured values mapped onto a validated framework.

Symbol Profile

'スーパードライ' · 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 'スーパードライ' 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 'スーパードライ' 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 · 「スーパードライ」は月間33Kの関心度で上昇トレンド(+16% annualized linear trend)にあります. 関心パターンはシーズン型です. 主な検索動機は「行動(購入等)」です. SERPにニュース(13件), PAA(4), PASF(6), 関連検索(6件), ナレッジパネルを含む. ドメイン分散度90%(上位: asahibeer.co.jp, amazon.co.jp, news.yahoo.co.jp). 565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「アサヒ·スーパードライ」(113個)でハブ概念は「アサヒ スーパードライ 値段」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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