16 ListeningMind apps

CEP Finder

In which situations do people start looking for this category (category entry point map)

Give one keyword (category, product, service or brand) and it finds category entry points (CEPs) from real search paths, related searches, search volumes, search results and AI Overviews, then ranks them in a report. Marketers and brand managers use it to choose campaign situations and messages. Data comes from ListeningMind search behavior data and Google results for Korea, Japan and the US.

Key ideasCEP · Buying situationSearch journeySearch volume and 48-month trendSearch result pageAI Overview

Works well withBrand Naming AgentCreative FinderFunnel Leak Finder

Taste Finder

In what sensory and emotional words do people choose this (consumer taste report from search data)

Give a keyword (category, product or brand) and it extracts sensory and emotional words from related searches, search results, 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 search data for Korea, the US and Japan. Reports can be compared.

Key ideasTaste tribePeople Also Ask (PAA)AI OverviewSearch result page

Works well withCEP FinderBrand FinderBrand Naming Agent

Brand Finder

Which brands lead search by generation or field, and what their brand assets are

A brand databank that rebuilds Japan's Brand Data Bank (bdb) from ListeningMind search data. The Korean edition is organised by generation, the Japanese and US editions by field, across 65 genres (64 in the US), with brand search volume, change, rising brands and 3-year change, refreshed every two months. Opening a brand builds and stores a distinctive brand asset (DBA) profile grounded on search volume, search results and AI Overview. Used by brand and ad planners and researchers.

Key ideasDistinctive brand assets (DBA)Gender and age segmentSearch volume and 48-month trendAI Overview

Works well withPulse FinderBrand Naming AgentCEP Finder

Pulse Finder

What is rising in search for a gender and age segment right now

ListeningMind Audience Pulse. It harvests candidate keywords from seed keywords or categories, measures gender and age share, 48-month search volume and search results and AI Overviews presence, and ranks the things that are surging, new or over-indexing in the chosen segment. Marketers, merchandisers and content planners use it to find next-quarter openings and risks. Data comes from ListeningMind search data. Markets: Korea, Japan, US; the gender and age split exists only for Korea.

Key ideasGender and age segmentSearch volume and 48-month trendKeyword clusterAI Overview

Works well withBrand FinderBrand Naming AgentCEP Finder

Funnel Leak Finder

Where category search journeys leak to competitors

Search journey leak finder. It samples real search paths that start from category entry keywords, classifies every node as a brand, generic, compare, exit or channel query, and shows where journeys switch to competitor brands and where your brand captures or loses them. Brand marketers and search / content teams use it to pick points to defend or attack. Data comes from ListeningMind search data. Markets: Korea, Japan, US.

Key ideasSearch journeySearch intentCEP · Buying situationSearch result page

Works well withCEP FinderCreative FinderGEO Audit

Demand Forecast

What category demand looks like in the next 3-6 months and when to launch or campaign

Demand forecast and season calendar. From category seed keywords it collects related queries, groups them into segments, forecasts the next 3 or 6 months and the change versus last year from 48 months of monthly search volume, and derives prep, campaign, launch and peak months. Product planners, merchandisers and campaign owners use it to set season timing. Data comes from ListeningMind search data. Markets: Korea, Japan, US.

Key ideasDemand seasonalitySearch volume and 48-month trendKeyword cluster

Works well withPulse FinderSmart Store SuiteBrand Finder

Creative Finder

Finds purchase situations (CEP) in search data and drafts a creative, an image and a video for each

Give one to three product lines. The app finds the moments when people think of them in ListeningMind DaaS data (related searches, real search paths, monthly volume), grades the evidence in code, and writes a 15-second creative draft with a distinct tone per moment. Images and 8-second vertical videos can be generated per draft. Markets KR, JP, US; UI ko, en, ja.

Key ideasCEP · Buying situationSearch journeySearch volume and 48-month trendOccasionGender and age segment

Works well withCEP FinderDemand ForecastTaste Finder

Brand Naming Agent

Creates and checks brand name candidates grounded in search data

From a business description or category keywords, it builds a strategy brief from ListeningMind search data (volume, journeys, related searches, AI Overviews), generates candidates through several naming routes, then checks whether each name is already in use (search volume), trademark-like similarity (a critic model) and domain availability, and recommends about 8 names. Used by brand managers and founders. Markets: Korea, Japan, US.

Key ideasSearch journeySearch volume and 48-month trendAI OverviewEntity

Works well withCEP FinderBrand FinderCreative Finder

AOO

Measures whether AI agents can get real tasks done on a website

AOO (Agent Operability Optimizer) sends a real LLM agent to a domain with about ten everyday tasks (find the price, contact someone, check the refund policy), records every request and how the agent learned each URL (link, sitemap, llms.txt, web search, model recall and so on), and scores the site 0-100: discovery 20, access 30, navigation 30, completion 20. Site owners and marketers get a fix pack (robots.txt, llms.txt, JSON-LD), apply it through deploy connections and re-measure. It also has a leaderboard, company group audits and monitoring. Sites in any country can be measured; reports are in Korean, English and Japanese.

Key ideasAgent operabilityAgentic commerceMCPWebMCP

Works well withACO

GEO Audit

Checks whether a web page is ready to be read and cited by search engines and AI answer engines

GEO Audit renders 1-5 URLs (or HTML captured in the browser) in a real browser, compares them with the pre-JS HTML and scores SEO, AEO and GEO items with rules. Only the main content (without navigation, footer and recommendation widgets) is judged, and a re-review step drops weakly supported findings. With a topic keyword it checks coverage against ListeningMind Data API search volume, intents and related keywords. Marketers and SEO staff use it for pages in Korea, Japan and the US.

Key ideasGEO (generative engine optimization)AEO (answer engine optimization)Search result pageSearch intent

Works well withACOBrand WikiFunnel Leak Finder

Prompt Builder

Finds what consumers ask AI and builds managed brand prompts with AI visibility

From a brand or category keyword it collects ListeningMind related keywords, People Also Ask questions and Google AI Overviews, extracts CEP, nano context, KBF, constraints, avoidance, reasons to believe and competitors, and generates managed prompts in 6 fixed groups with estimated monthly prompt volume. It can then ask ChatGPT and Gemini those prompts to measure brand visibility, and keeps history and Excel exports. Used by GEO leads and brand managers. Markets: Korea, Japan, US.

Key ideasAI visibility and managed promptsPeople Also Ask (PAA)CEP · Buying situationKBF · Choice criteriaGEO (generative engine optimization)

Works well withICSSACOBrand Wiki

ACO

Diagnoses whether a product detail page can be cited by AI answers and used by shopping agents

ACO (Agentic Commerce Optimizer) scores one product detail page for SEO, AEO, GEO and agentic commerce and produces ready-to-paste fixes (JSON-LD, FAQ, rewritten copy, UCP templates, PIM feed). Search demand and intent come from the ListeningMind Data API, text generation and citation simulation from an LLM, and scores from a rule-based parser. E-commerce operators and consultants use it for the Korean, US and Japanese markets. It also offers a sitemap batch scan, a domain-level agent readiness scan and per-store product feeds.

Key ideasAgentic commerceGEO (generative engine optimization)AEO (answer engine optimization)Search intent

Works well withSmart Store SuiteGEO AuditBrand Wiki

Brand Wiki

Turns a brand website into the entities, wiki pages and evidence AI assistants read

Crawls a brand website to build an entity map, brand wiki, evidence claims and schema, then links them to ListeningMind search demand to produce an action plan, entity gaps, coverage and page signals. GEO/AEO teams use it to fix content so AI answers describe the brand correctly. Demand data exists for Korea, Japan and the US; demand and category context can also be imported from Intent KG runs.

Key ideasEntityGEO (generative engine optimization)AEO (answer engine optimization)Intent knowledge graph (IKG)

Works well withACOGEO AuditBrand Naming Agent

Smart Store Suite

Which Smart Store products to grow, and how to rewrite their titles and detail pages in searchers' words

Smart Store Suite joins three checks for Naver Smart Store sellers into one flow. Step 1, store check, collects products from a store URL (Smart Store, Brand Store, Shopping Window, Naver+) or from product, sales and margin CSVs and combines search demand with margin or reviews and sales to show which products to grow and which to drop. Step 2 rewrites a product title in the words searchers actually use, and step 3 scores how well a detail page covers the buying criteria searchers look for and suggests fixes. Smart Store sellers, brand managers and commerce MDs use it. Data comes from ListeningMind search data; the market is Korea (Naver Shopping).

Key ideasSmart Store product optimisationSearch volume and 48-month trendSearch intentKBF · Choice criteriaKeyword cluster

Works well withACOFunnel Leak FinderDemand Forecast

ICSS

Synthetic consumer panel conditioned on search intent, for surveys and FGDs

ICSS (Intent-Conditioned Silicon Sampling) samples synthetic respondents from intent states observed in search behavior (CEP, nano intent, KBF, constraint, avoidance, consideration, journey stage) instead of demographics, and runs surveys, FGDs and 1:1 chats with them. Marketers and researchers use it to test hypotheses before real fieldwork. Data comes from ListeningMind search data and search results, plus stored CEP Finder and Intent KG runs, for Korea, Japan and the US. It also includes a distinctive brand asset (DBA) recognition test.

Key ideasSynthetic consumer panelSearch intentCEP · Buying situationKBF · Choice criteriaDistinctive brand assets (DBA)

Works well withFunnel Leak FinderPrompt BuilderSmart Store Suite

Intent KG

What people look for about a topic, why, and how they choose (Intent Knowledge Graph)

Give a topic and it builds a graph of intent elements (entry situations, choice criteria, brands, pitfalls, thresholds and more) and their relations, with evidence grades, from ListeningMind search data (related searches, clusters, search journeys, search results and AI Overviews). Used by marketers, product planners and AI agents that need grounded answers. Stored graphs are reused at 0 credits through the v1 and v2 APIs and 21 export formats. Markets: Korea, Japan, US.

Key ideasIntent knowledge graph (IKG)15 intent elementsSearch journeyKeyword clusterCEP · Buying situation

Works well withCEP FinderCreative FinderFunnel Leak Finder