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 Korean, as used in this market.
Audience Taste — the taste structure of consumers searching '복분자'. Based on measured query voice and searcher demographics.
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.
Concentrate · Extract — undiluted extract or concentrate
This node exists because expressions like '원액' were actually found. Sources: Related query 7 · Organic snippet 6 · AI Overview (AIO) 1.
Co-occurring terms (sentences): Sweet 2
Value region Conservation · Brand personality (Aaker) Sincerity
Sweet — sweet fruit sugars
This node exists because expressions like '단맛' were actually found. Sources: AI Overview (AIO) 10 · Organic snippet 2 · AIO source / PAA answer 2.
Co-occurring terms (sentences): Rich · Full-bodied 5, Light · Mild 2, Concentrate · Extract 2
Value region Hedonism · Brand personality (Aaker) Sincerity
Pulp · Texture — pulp and thickness
This node exists because expressions like '과육이' were actually found. Sources: Organic snippet 4 · AI Overview (AIO) 1.
Co-occurring terms (sentences): Light · Mild 2, Calm 2
Value region Hedonism · Brand personality (Aaker) Sincerity
Tangy · Citrus — tangy orange, grapefruit and lemon
This node exists because expressions like '시원한' were actually found. Sources: AI Overview (AIO) 1 · AIO source / PAA answer 1 · Result title 1.
Tangy · Citrus is a sensory attribute; consumer research convention reads it as impressions such as Clean · Crisp. The link is that mapping, not a measurement.
Value region Hedonism · Brand personality (Aaker) Excitement
Floral — floral aroma in the cup
This node exists because expressions like '장미과' were actually found. Sources: Organic snippet 1.
Value region Self-transcendence · Brand personality (Aaker) Sincerity
Rich · Full-bodied — rich, full-bodied fruit taste
This node exists because expressions like '진한' were actually found. Sources: Organic snippet 7 · AI Overview (AIO) 4 · AIO source / PAA answer 1 · Result title 1.
Co-occurring terms (sentences): Sweet 5
Value region Self-enhancement · Brand personality (Aaker) Ruggedness
Clean · Crisp — crisp, not cloying
This node exists because expressions like '깨끗이' were actually found. Sources: Organic snippet 4 · AI Overview (AIO) 3 · AIO source / PAA answer 2.
The impression Clean · Crisp leads to the emotion Refreshed, and on to the value Conservation — the upward path of a means-end chain.
Value region Conservation · Brand personality (Aaker) Competence
Premium · Luxurious — premium fruit and a luxurious bottle
This node exists because expressions like '명품' were actually found. Sources: Organic snippet 6 · Result title 4 · AIO source / PAA answer 1.
Co-occurring terms (sentences): Reassured 2
Value region Self-enhancement · Brand personality (Aaker) Sophistication
Smooth — goes down smooth
This node exists because expressions like '부드럽게' were actually found. Sources: AI Overview (AIO) 2 · Organic snippet 2 · AIO source / PAA answer 1.
The impression Smooth leads to the emotion Warm · Comforted, and on to the value Conservation — the upward path of a means-end chain.
Value region Conservation · Brand personality (Aaker) Sincerity
Light · Mild — light, mild or watery taste
This node exists because expressions like '연하고' were actually found. Sources: Organic snippet 5 · AI Overview (AIO) 1.
Co-occurring terms (sentences): Sweet 2, Pulp · Texture 2
Value region Conservation · Brand personality (Aaker) Sincerity
Reassured — the emotional payoff consumers expect from this taste.
Detected words: 신뢰 · 안심되 · 안심하고 · 안심이 · 믿을 · 믿고 · 검증된 · 검증됐 · 걱정없 · 든든 · 확실 · 보장 · 사후관리 · 믿음직 · 정직한 · 믿고마시 · 믿음이가
Curious · Intrigued — the emotional payoff consumers expect from this taste.
Detected words: 호기심 · 신기 · 새로운자극 · 색다른경험 · 처음느껴 · 놀라운 · 반전매력 · 궁금증 · 궁금해서 · 신기해서
Calm — the emotional payoff consumers expect from this taste.
Detected words: 느긋 · 차분해지 · 마음이잔잔 · 고요 · 평온 · 긴장이풀 · 무념무상 · 멍때리 · 기분이가라앉 · 마음이차분 · 평화로 · 명상
The impression Light · Mild feeds into Calm.
Connected — the emotional payoff consumers expect from this taste.
Detected words: 함께하는 · 함께해서 · 가족과함께 · 유대감 · 공감 · 소속감 · 정이가 · 정겨 · 다같이 · 같이마시 · 나눠마시 · 함께마시
Joyful · Fun — the emotional payoff consumers expect from this taste.
Detected words: 즐거 · 재미있 · 재밌 · 웃음이 · 행복해지 · 행복 · 기분좋아지 · 기분업 · 기쁨 · 기뻐 · 신이나 · 흥겨 · 빠져드 · 도파민 · 기분이좋아 · 즐거워
Power · Status: status and prestige. It sits in the ‘Self-enhancement’ group of the Schwartz value circle; the opposite group is ‘Self-transcendence’.
Rich · Full-bodied · Premium · Luxurious group into the value Power · Status.
Conformity: fitting in, not standing out. It sits in the ‘Conservation’ group of the Schwartz value circle; the opposite group is ‘Openness’.
Clean · Crisp · Light · Mild 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’.
Concentrate · Extract · Smooth group into the value Security.
Hedonism: sensory pleasure. It sits in the ‘Hedonism’ group of the Schwartz value circle; the opposite group is ‘Conservation’.
Sweet · Pulp · Texture · Tangy · Citrus 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’.
Floral group into the value Benevolence.
For people searching '복분자', 11 taste signals were detected, and the strongest is Concentrate · Extract (16,436). That single family accounts for 31% of all taste weight and is 1.4x the runner-up Sweet (12,040). In consumer language, the people behind this search respond to "undiluted extract or concentrate".
The top three come to 67%, a band where neither concentration nor dispersion can be claimed. There are 5 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 (sensory fact) peaks at Concentrate · Extract (16,436) and the perception layer (social meaning) at Rich · Full-bodied (6,580). The heavier attribute layer means consumers are still exploring what it actually tastes, smells or feels like. At that stage concrete product description outperforms abstract brand narrative.
The ladder converges on the value 'Conservation', with Reassured 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 Concentrate · Extract the spine of the message and place Sweet and Rich · Full-bodied as supporting appeals. Product pages that start at the attribute layer (Concentrate · Extract), rise to perception (Rich · Full-bodied) and close on value (Conservation) 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.
81% 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 …').
Concentrate · Extract pulls people in; Sweet pushes them away. The avoidance evidence is searches like "'너무 달지 않을까?". Against a liking total of 16,436, avoidance totals 1,400, with the two forces attached to different attributes inside the same audience.
Lead copy and product pages with Concentrate · Extract and deliberately reduce Sweet associations. It is worth auditing whether visual elements or adjectives implying Sweet 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.
Preference Segments
Pick gender/age to see how strongly that group over-indexes on each taste (lift).
This section crosses taste with demographics. Pick a gender and an age band and each card re-ranks to show how many times more that group leans into each taste (the preference index). Above 1.15 is over-indexed, below 0.85 under-indexed.
Demographic-only segments are easy to reach but explain nothing about why people buy; taste-only segments explain well but cannot be reached. The numbers in this cross-tab are the bridge. Use them to check that the demographic target of the media plan and the taste target of the creative are not pointing in different directions.
Measured demographics for this run are 47.1% male / 52.9% female, concentrated in 50+. Any taste whose own distribution departs sharply from this baseline is where segmentation has something to offer.
The four lines on each card answer different questions. Wants is what they like; Acts is where in the purchase journey that taste sits; Avoids is what causes drop-off; Spreads is which adjacent category the taste extends into. For cross-sell and collaboration candidates, read Spreads.
Tastes marked as having no demographic sample come from SERP text only. They are valid as tastes but should not carry demographic interpretation. The preference index is also approximated from the product of marginals, so genuine interactions such as a spike specific to women in their twenties need separate verification.
Context × Taste
Taste is situational, not fixed (Person × CEP × Attribute). Cells = measured co-occurrence of context and taste words in the same query/snippet.
| Concentrat | Sweet | Rich · Ful | Clean · Cr | Premium · | Smooth | |
|---|---|---|---|---|---|---|
| Home · Solo | 700 | |||||
| Gift | 350 | 210 | ||||
| Daily · Office | 210 |
In the "Home · Solo" context, Concentrate · Extract 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 "Conservation". 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 "Conservation" means that is the value consumers express through taste in this category. Openness 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: ‘Concentrate · Extract’ sits furthest top-right at 70% consumer voice and 75% 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: Concentrate · Extract · Sweet · Clean · Crisp. 3 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 '복분자' stands for in the public mind. Based on measured market description language (AIO·snippets) and reference patterns ('복분자-style', 'like 복분자').
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 '복분자'. 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.
2 collected paths drifted into a different meaning of the same spelling and were removed. This analysis refers to: "복분자는 장미과 산딸기속에 속하는 열매로, 주로 건강 효능과 함께 복분자주, 복분자즙, 복분자청 등 다양한 가공식품의 원료로 사용됩니다.". 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 '복분자' 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 '복분자' as Sweet·Rich · Full-bodied. 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.
Statistics
Taste × demographic association via Cramér's V. weak<0.1 · med 0.1–0.3 · clear 0.3–0.5 · strong>0.5.
This index is not computed on a probability sample; it converts search-volume weights into cell counts. The conventional Cramér's V thresholds (0.1/0.3/0.5) are therefore not applied. Use it only to compare axes within this one report.
| 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