# Glossary (en) ## Search journey The order of searches a person makes before and after a keyword. Search volume tells you how many; the journey shows which questions led there and what people check next. ListeningMind returns aggregated paths built from real search sequences. ## Search intent The purpose behind a query. Two people searching 'dehumidifier' may be comparing prices or worried about mould in the rainy season. Related searches, the searches before and after, and the result page together let you separate intents with evidence. ## Intent knowledge graph (IKG) A graph of what people look for about a topic, why, and how they choose, organised as the 15 intent elements and their relations. Every element carries search evidence and an evidence grade, so people and AI agents answer from the same evidence. Reading a stored graph again costs no Lima. ## 15 intent elements Eight decision elements that make up a purchase decision (CEP, occasion, nano intent, KBF, constraint, threshold, avoidance, RTB) and seven knowledge elements that make up the common sense of a field (heuristic, convention, expectation, pitfall, trade-off, benchmark, stance). Each is defined in the table below. ## Keyword cluster A group of queries that are searched together and lead to similar result pages. Clusters show how people mentally divide a category, and they are the unit for segmenting demand or choosing content topics. ## Search volume and 48-month trend The average monthly number of searches for a query and its month-by-month change over the last 48 months. For Korea, Naver and Google are given separately and combined, with the gender and age share of searchers when that data exists. It separates seasonality, surges and long declines. ## Search result page The whole page shown for a query. Which sites rank where, and which blocks appear (shopping, video, question boxes), tell you the intent of the query. ## AI Overview The generative AI summary Google shows at the top of some result pages. It lists the sources it cites, so you can see which sites and brands AI answers rely on. ## People Also Ask (PAA) The 'People also ask' box on the result page. These are questions people actually have, so they ground FAQs, content outlines and the prompts people put to AI. ## GEO (generative engine optimization) Shaping content and structure so that generative AI (ChatGPT, Gemini, Google AI Overviews) reads your pages and brand correctly and cites them. Unlike SEO, which aims at rankings, the goal is to be used as evidence inside AI answers. ## AEO (answer engine optimization) Writing clear questions and answers so that features that answer directly (featured snippets, People Also Ask, voice answers) pick your page's sentences as the answer. FAQ structure and structured data are central. ## Agentic commerce Buying in which an AI shopping agent finds, compares, adds to cart or orders on a person's behalf. A product makes the shortlist only if the agent can read its price, stock, options and delivery terms in machine-readable form. ## Agent operability How far an AI agent can get real tasks done on a website, such as finding a price, contacting someone or checking the refund policy. It can be measured in four stages: discovery (sitemap, llms.txt), access (robots.txt, blocking), navigation and completion. ## Entity A 'thing' that search engines and AI recognise as one object: a brand, product, person or place. AI describes a brand correctly only when its name, attributes and relations to other entities are stated consistently across pages. ## Distinctive brand assets (DBA) Unique cues, such as a logo, colour, character, phrase or sound, that make people think of a brand at once. Whether those cues appear together with the brand in searches, result pages and AI Overviews indicates how strong the assets are. ## Synthetic consumer panel A group of virtual respondents generated by a language model instead of real people. ListeningMind samples them from the distribution of intent states seen in search (CEP, choice criteria, constraints, avoidance) rather than demographics, to test hypotheses before real fieldwork. ## Gender and age segment A group of searchers by gender and age band. The same query can have a different share and growth in each segment, which shows what is rising for whom. The gender and age split exists for Korean data only. ## Demand seasonality The pattern of demand rising and falling in the same months every year. From 48 months of monthly volume you find peak months and preparation months, forecast the next 3-6 months and time launches and campaigns. ## Taste tribe A group of people who choose a product with the same sensory and emotional words. Tribes are separated by how words such as 'rich', 'fresh' or 'cosy' appear together in related searches, result pages and AI Overviews. ## AI visibility and managed prompts A measure of how often a brand appears when ChatGPT or Gemini answers a fixed set of questions consumers would actually ask (managed prompts). The questions should come from search data (CEP, choice criteria, People Also Ask) so that they represent real demand. ## Smart Store product optimisation Fitting a store's product range, titles and detail pages to search data so that products are found and chosen on Naver Shopping: which products demand flows to, the words people search them with, and what they check before choosing decide which products to grow and how to rewrite names and descriptions. ## Lima The unit ListeningMind apps and the Data API use for the cost of a run. 1 Lima is KRW 100, and 1 Data API credit is KRW 1.3; each call is rounded up to a whole Lima. Reading a result that already exists costs no Lima, and each member has a monthly limit. ## MCP Model Context Protocol, an open standard that lets AI agents call external tools and data in one way. The ListeningMind Data API and each app expose an MCP server, so agents can query search data and fetch results directly. ## WebMCP A way for a web page to tell an AI agent in the browser what can be done on the page, as tools. The agent calls the tools the page defines instead of guessing by clicking around. ## CEP · Buying situation The trigger, situation, problem or moment that creates the need ## Occasion When, where, how often and with what it is used after purchase ## Nano intent The specific result or task wanted in that situation ## KBF · Choice criteria Criteria for comparing and choosing (must-have, preferred, situational) ## Constraint Conditions that narrow the choice (budget, time, eligibility, rules, compatibility, channel, skill) ## Threshold Recurring consumer cut-off values, always with a number and unit ## Avoidance Fears, complaints and risks that cause rejection or drop-off ## RTB · Reason to believe Claim, evidence, source and confidence chain ## Heuristic Practical rules in the form "if this, then do that" ## Convention Rules repeated across brands and documents; may be folk belief, so review it ## Expectation The level consumers take for granted; falling short causes complaints ## Pitfall Conditions that cost you if unknown: counterexamples, exceptions, hidden costs ## Trade-off Gaining one thing means losing another ## Benchmark A measured difference between two options ## Stance Consumption tendencies and buying attitudes observed in that market