Discovering AI-Era Conversational Queries in Search Console: Transform Your Content Strategy

Uncover AI conversational queries in Search Console using simple RegEx. Find the long-tail searches driving 4x higher-value traffic.

The rules have changed, and most SEO professionals are still playing the old game.

While artificial intelligence rapidly transforms our world, the way people use search engines is undergoing a fundamental shift. Users no longer type short keywords like “what is SEO” into Google. Instead, they ask detailed, conversational questions as if talking to a friend: “What are the most effective SEO strategies for small businesses in 2025?”

This shift isn’t just affecting our search habits; it’s also impacting our online behaviour. It’s completely reshaping the data we extract from Google Search Console, our SEO strategies, and our content production processes.

Here’s what this means for you: Research shows that a visitor coming from large language models (such as ChatGPT, Gemini, etc.) can be four times more valuable than a visitor from traditional search results. The reason is simple—users now make most, sometimes all, of their purchase decisions on these AI platforms. When they find the right answer, they don’t even need to visit another site.

This new reality is pushing us toward what we call “AI-focused SEO” strategy. But how do we capture this changing search behaviour? How do we understand what users are asking? This is where we harness the power of Google Search Console.

How to Find Conversational Queries in Google Search Console
We now know that users interact with search engines almost as if they were having a conversation. To find these long, conversational queries in Google Search Console, we’ll use a RegEx (regular expression) method shared by SEO expert Vijay Chauhan. This technique lets you see exactly what problems users are trying to solve when they come to your site.

Here’s how to find these long conversational queries step by step:

Step 1: Open Search Console
Log in to your Google Search Console account.

Step 2: Navigate to Performance Report
Click on “Search Results” under the “Performance” section in the left menu.

Step 3: Add Query Filter
Find the “+ New” or “+ Add Filter” button at the top of the performance report and select “Query.”

Step 4: Use Custom RegEx
Select “Custom RegEx” from the dropdown menu.

Step 5: Paste the RegEx Code
Paste this code into the box that appears: ([^” “]*\s){32,}?

This code tells Google Search Console, “show me queries with at least 32 words.” Thanks to Vijay Chauhan’s method, you can capture truly long, conversational queries.

Pro Tip: For broader analysis, you can lower 32 to 15. This way, you’ll encounter queries written like genuine questions to a person, such as “which smart bidding strategy optimises value using X feature?” or “you’re an expert market researcher, determine if the company has been involved with SEO in the last six months.”

These types of queries provide valuable clues about how users reach your content through AI search engines. With Google now adding AI Mode interaction data to Search Console, we can assume these long queries likely come from AI-powered searches.

The Importance of Conversational Queries for Content Revision

Finding these long, conversational queries is just the beginning. What matters is how we use this valuable data to adapt our content to the AI-focused world.

Creating general content like traditional SEO is no longer sufficient. Now, content that can answer ultra-specific questions like “What’s the best AI for my 10-year-old manufacturing company in New Jersey?” is worth its weight in gold.

When revising your content and creating new pieces, pay attention to these key areas:

1. Creating Ultra-Specific Content
The conversational queries you find in Google Search Console clearly show what users are looking for. Create content that provides direct, specific answers to these queries. Focus on unique insights rather than general information.

2. Using Original Data
Integrate your unique data into your content. Your customer data, case studies, or market research will differentiate your content from competitors and make it a more reliable source for AI models. For example, if you have data showing that a New Jersey manufacturing company using your product increased their sales closing rates by 3x, definitely use that information.

3. Prioritising Conversion-Focused Content
It makes sense to focus on bottom-of-funnel content. Prioritise queries where users are directly in the purchasing phase or looking for specific details about your product or service to solve a particular problem. While we used to focus on informational content, now content directly related to products—”why we’re the best” type content—has become more critical.

4. Visibility on Third-Party Platforms
Here’s a crucial point: When AI models generate responses, they only link directly to brand sites 9% of the time. A massive 91% of the time, they reference third-party sites like Reddit, Quora, forums, and industry publications.

This completely changes the game rules. You now need to ensure your brand appears positively and accurately, not just on your site, but on other platforms where your brand is discussed. These citations become valuable signals for AI.

5. Understanding New Metrics
While traditional traffic metrics lose importance, metrics like AI visibility and share of voice are coming to the forefront. How much space you occupy in AI responses on specific topics is now critical. Tools like X-Funnel help measure this new visibility. Visit count alone isn’t enough—that “final visit” leading to ultimate conversion has become much more important.

6. Opportunities for New Sites
Good news: AI results may not be as dependent on authority as traditional SEO, so newly established sites can rise faster. This presents a major opportunity for new players.