From Keywords to Prompts: Unlocking Hidden AI Intent in Google Search Console Data
Date : November 28, 2025 By
Contents
Why Traditional Keyword Analysis No Longer Reflects User Intent
Traditional keyword research methods are failing to capture the reality of modern search behavior. The shift toward AI-powered search experiences has fundamentally altered how users interact with Google via Google AI Overviews and AI Mode. Google Search Console is the only tool I know that reveals conversational query patterns which traditional keyword research tools cannot detect or analyse effectively. The evolution of search behavior demands new approaches to understanding user intent through comprehensive AI intent analysis.
The Evolution from Short Keywords to Conversational Queries
Search behavior has undergone a dramatic transformation in recent years. The traditional approach to keyword research focused on short, fragmented terms that users typed into search boxes. Modern searchers now input complete questions, request detailed comparisons and specific instructions that resemble natural conversation patterns.
Long-tail queries represent the new standard for search interaction. These extended search phrases, which often contain 15 or more words, provide marketers with unprecedented insight into user intent and search methodology. The queries reveal specific pain points, top-funnel research and decision-making processes, as well as informational needs that were previously hidden within abbreviated keyword data.
Conversational search patterns have increased significantly since the introduction of AI-powered search features, namely AI Mode and AI Overviews in Google. Users feel comfortable expressing complete thoughts and complex questions, which creates opportunities for businesses that understand how to analyse and respond to these detailed search behaviors.
The Data Gap in Traditional Keyword Research Tools
Most prompt research tools tend to provide synthetic, AI-generated data that does not reflect actual search behavior. These platforms generate keyword suggestions using algorithmic predictions rather than real search queries that users type into Google or other search engines. The gap between predicted (simulated) keywords and actual Google Search Console data creates a number of significant blind spots in content strategy and GEO efforts. The limitation extends to AI systems like ChatGPT, Gemini or Perplexity. These LLMs don’t provide real-time search data or any other insights into actual user queries.
Search Console provides access to actual queries that drive traffic to websites, but most marketers only examine the top-performing terms. The Search Console API enables extraction of comprehensive query data, including the detailed long-tail queries that contain the most valuable intent signals. It provides insight into user needs, questions and search patterns that synthetic keyword tools and AI platforms cannot replicate or predict accurately. The authentic query data reveals how users actually phrase their questions when they need specific information or solutions.
The Experiment — Exporting 25,000 Keywords via Google Search Console API
The traditional approach to Google Search Console data analysis limits marketers to surface-level insights that miss the most valuable search patterns. The standard keyword export method fails to capture the depth of modern search behavior, because the user is limited to 1,000 queries only. This doesn’t allow the user to uncover conversational prompts that have 15+ words in them or only 1 search impression.
Why the Standard Search Console Interface Falls Short
The default Google Search Console interface restricts data export to 1,000 query rows per report, which represents a fraction of actual search prompts. This limitation prevents marketers from accessing the detailed long-tail queries that contain the most specific conversational prompts. The interface displays only the highest-volume search terms, which often consist of broad keywords that provide limited insight into user needs and interests.
Keyword research derived from the standard interface focuses on popular terms rather than the conversational queries. The restricted dataset excludes the detailed questions and specific searches that users input when they have clear purchase intent or information needs. These extended queries, which typically contain 15 or more words and have only 1 search impression, reveal the exact language and phrasing that users employ when they search for solutions. Search Console export via API provides access to comprehensive query datasets with up to 50,000 queries.
Setting Up API Extraction and Filtering for Conversational Prompts
Mixed Analytics provides a straightforward API Connector that simplifies the data extraction process without requiring complex coding or technical implementation. The tool connects directly to Google Search Console and exports comprehensive data in spreadsheet format for analysis.
API Connector is available on the Google Workspace Marketplace. To install it, open Google Sheets, click on “Extensions” in the top menu, select “Add-ons” then “Get add-ons,” search for “API Connector” in the marketplace, and click the “Install” button. Once installed, you can access it by clicking Extensions > API Connector > Open to launch the sidebar.
Setting up the extraction process requires authentication through a Google account connected to this Search Console instance.
1․ Accessing Google Search API via the built-in integration of API Connector is the most convenient way. To begin, open the API Connector and create a new request.
2. Select “Google Search Console” from the presented list of applications or use the built-in search function to find it. You will be asked to allow API Connector access to your Google Search Console data in order to proceed.
Once the confirmation is obtained, you will be returned to Google Sheets and would be able to verify whether the connection is active.
3. In order to begin pulling data into Sheets, we need to fill in several parameters. First, select “Get search analytics” under Endpoint (/sites/{siteUrl}/searchAnalytics/query on the following screenshot). Select an available site under siteUrl and fill in other parameters such as date range.
4. Google Search Console sends 1,000 records by default. The rowLimit parameter can be used to change the number of rows to return (from 1 to 25,000). Once this and the other necessary parameters are configured, you can initiate the export (select a destination sheet, name your request, and hit “Run”) and receive the results in the same file.
The screenshot below shows the extracted Google Search Console data, which includes search queries (keywords), along with their associated metrics such as word count, clicks, impressions, CTR, and average position.
To make sense of this large volume of unstructured keyword data, I used GPT to analyse and cluster the queries based on their underlying intent and thematic similarities. The AI model identified patterns across thousands of queries and grouped them into 10 distinct clusters, each representing a specific user need or search behavior related to procurement, accounting and business process automation.
Long-tail queries or prompts emerge when marketers filter the exported data for search terms containing 15 or more words. Clustering techniques focus on identifying queries that demonstrate various search intents: product discovery, competitive comparison, feature evaluation, mapping desired requirements on the product features and others. For instance, in the example I gave earlier the top 3 clusters included the following:
Cluster 1 — AI, Automation, and Procurement Analytics
Examples:
- how does ai-based demand forecasting impact procurement strategy?
- how do procurement platforms support compliance with company policies?
- how to visualise supplier pricing trends in procurement dashboards?
- looking for procurement platforms that automate vendor onboarding and certification tracking
Cluster 2 — Purchase Orders and Invoice Basics
Examples:
- difference between an invoice and a purchase order
- how to make purchase order in erp
- how to send purchase order by email
- difference between a purchase order and an invoice
Cluster 3 — “Evaluate the Company…” (Comparison Queries)
Examples:
- evaluate the b2b e-commerce company office depot business solutions on procurement contract
- evaluate the fintech company vendorful on uncontrolled employee spending
- evaluate the office supply retail company staples on how to make office management effortless
The results reveal a stark contrast between traditional keyword research and actual user behavior. Where classic keyword data shows fragmented terms like “procurement software” or “vendor management,” the filtered long-tail queries contain complete sentences such as “how to evaluate procurement software vendors for manufacturing companies with multiple locations“. These extended searches demonstrate specific context, industry requirements and decision-making criteria that traditional keyword analysis cannot capture.
What Long Queries Tell Us About Generative Search and AI Overviews
Modern search behavior reflects a fundamental shift in how users interact with search engines and expect to receive information. Long-tail queries demonstrate the impact of Google’s AI Overviews & AI Mode on user search methodology and information-seeking behavior. The evolution toward conversational search creates opportunities for businesses that understand how to analyse and optimise for detailed user intent signals.
How Google AI Overviews Reshape Query Structure
Google AI Overviews have fundamentally altered user expectations about search interaction. Users now feel comfortable inputting complete human-like questions. This encourages searchers to provide comprehensive context and specific requirements that marketers can study both for general marketing and generative engine optimisation.
Many of such searches include comparative language, specific criteria and detailed context, when researching for products or services. Users type queries such as “compare procurement software solutions for mid-size manufacturing companies with international suppliers” instead of only using simple terms like “procurement software comparison.”
Real-World Prompt Patterns and Intent Mapping
Analysis of raw Google Search Console data reveals distinct patterns in how users structure their extended search queries. The patterns demonstrate specific intent types that correspond to different stages of the customer journey and decision-making process. Prompts based on real user behavior show clear categories of information-seeking, comparison shopping and solution evaluation.
Research across multiple industries indicates that long-tail queries cluster into predictable intent categories that include:
- problem identification
- solution research
- vendor evaluation & comparison
- implementation planning
Users structure their searches to include specific context about their industry, company size, geographic location and technical requirements. The detailed nature of these queries provides marketers with direct insight into user needs and decision-making criteria.
Use Cases for Agencies and Clients
The raw Google Search Console data extracted through API methods provides agencies and clients with actionable insights that transform traditional SEO & GEO strategies.
GEO Visibility Tracking
This analysis enables agencies to monitor brand mentions across the complete spectrum of real, not machine synthesis based, user prompts. The current GEO optimisation problem is connected with the fact that we don’t always know what prompts we should track and optimise for.
This method of extracting and analyzing raw Google Search Console data provides the solution by revealing the actual conversational queries users are entering. Instead of guessing which AI prompts to monitor, with this data you can use tools like Otterly AI to track the exact long-form queries already driving traffic to your site.
Entity Graph Development
Entity graph development requires understanding the full range of topics, angles, and contexts that relate to specific business areas or product categories. Google Search Console raw data clusterisation reveals the complete landscape of user questions and information needs that surround particular industries or solutions.
For example, in the example about procurement above, we discovered through the GSC data that users were searching in multiple languages, including queries like “ap ar trong kế toán là gì” (Vietnamese for “what is AP AR in accounting”). This insight revealed that potential customers were seeking procurement information in their native languages.
As a result, we will be adding multilingual support and localised content to the product pages, creating language-specific resources that directly address these international search patterns.
Creating Data-Driven Content Briefs
Prompts derived from raw data provide content creators with precise direction for article development. The detailed nature of long-tail queries reveals specific questions, concerns, and information needs that users have about particular topics or solutions. Content briefs based on real search behavior address authentic user intent rather than hypothetical keyword scenarios.
For example, analysing the Google Search Console data reveals that users are searching for procurement solutions specific to certain industries. Queries like “a manufacturing company is looking to purchase order management software” or “procurement platforms for manufacturing industry” indicate that potential customers are seeking industry-specific information rather than generic procurement content.
Based on this insight, you could create targeted content pages such as “Procurement Software for Manufacturing Companies” that directly address the unique procurement challenges in manufacturing, including supply chain complexity, vendor management for raw materials, and compliance with industry regulations.
Conclusion
The transition from traditional keywords to conversational prompts represents a fundamental shift in how marketers must approach content creation for both SEO and GEO. Google Search Console raw data extraction through API methods provides access to authentic user search behavior that reveals genuine intent patterns and information needs. The analysis of comprehensive query datasets enables businesses to understand and respond to the conversational search patterns that define modern search behavior.
Organisations that adapt to this approach will gain significant competitive advantages through improved content relevance and user engagement, as well as better visibility in AI search engines. In addition, the ability to analyse thousands of authentic prompts enables precise targeting for ad campaigns.
