Search is rarely a one-step action — it’s a journey that becomes more specific with each query.For example: meal planning → general diet meals → tofu diet meals
To understand this full flow, we need to go beyond individual keywords and analyze the search path itself.
Path Finder traces these sequences of related searches to reveal how consumers move from broad exploration to concrete decisions. By collecting and linking subsequent and related search terms (such as “related searches” or “people also searched for”), it maps out the real search behavior behind each intent.
Unlike simple keyword graphs, Path Finder visualizes the connections between multiple keywords, showing how they form distinct search journeys (Paths).
Data source: Path Finder uses non-personalized search suggestions — the same “related searches” shown to users in organic results, not ads.This ensures a clean, unbiased picture of public search patterns.
Method: It repeatedly identifies the next related search term and connects them to form a directional graph.This visual map reveals both previous and next queries around your input keyword.
Structure:
Next searches show what users looked for after your keyword.
Previous searches show what users searched before, based on connection frequency.
While search behavior can form infinite loops, Path Finder focuses only on meaningful and recurring paths.It uses aggregated data and relevance signals (search volume, CPC, frequency, etc.) to surface the most representative journeys — each with a clear starting and ending point.
If search terms were connected infinitely, an endless loop would be created.However, Path Finder determines frequently appearing follow-up search terms as important keywords and selects only major paths.
This provides a search journey with a defined start and end.
To do so, it connects data such as Google autocomplete and related searches, and comprehensively evaluates search volume and ad cost to display only the most meaningful paths.
Competitor Keyword Analysis: You can see which brands are compared by consumers who search for your brand, and which keywords competitors are focusing on.
Stage-by-Stage Customer Journey Analysis: You can identify keywords used in the early exploration stage, just before purchase, and after purchase, and establish marketing strategies accordingly.
Consumer Purchase Factor Analysis: You can see what factors consumers consider important when making purchase decisions.
Selecting and Optimizing Core Campaign Keywords: Identify which keywords play key roles in users’ search paths (purchase decision process).
Writing Personalized Messages: Use Persona View to identify customer problems and reflect the solutions in your ad messages.
Performance Measurement and Optimization: Use keyword filters by gender and age to measure and improve ad campaign results.
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