How to use OpenAI’s Deep Research for smarter SEO strategies

SEO is evolving faster than a fruit fly colony in a genetics lab – constantly adapting, mutating, and surprising even the experts.

One day, long-form content reigns supreme; the next, AI-generated summaries are stealing the spotlight. Staying ahead requires smarter, data-driven insights.

AI-powered tools like OpenAI’s Deep Research are reshaping how marketers approach content strategy, competitive analysis, and SERP optimization.

Unlike traditional AI models that rely on pre-existing training data, Deep Research can pull real-time insights from external sources, making it a game-changer for SEO professionals. 

But how does it compare to standard ChatGPT, and how can marketers use it to outperform competitors and create better content? Let’s dive in.

Deep Research vs. ‘regular’ ChatGPT

Until February, Deep Research was only available to OpenAI’s $200/month Pro+ users. 

Thankfully, regular $20/month users now have access to this tool that can pull real-time insights from external sources, making it a potential game-changer for research of all kinds. 

Whether you’re working on SEO strategies or conducting competitive analysis, real-time, sourced information with thorough citations is invaluable. 

Discovering Deep Research is like switching from gloppy rubber cement to spray adhesive for science fair projects – suddenly, everything is faster, cleaner, and prettier – just like how spray adhesive ensures a smooth, uniform finish without bubbles or unevenness. 

Deep Research speeds up the process and delivers polished and well-organized results right out of the box, saving time and effort while improving overall quality.

So, before diving into SEO applications, let’s examine how Deep Research differs from traditional ChatGPT responses.

Regular ChatGPT (GPT-4o, etc.)

• Generates responses based on its internal knowledge and general training data.
• Can provide SEO guidance, competitive research, and content ideas but does not cite external sources in real-time.
• Responses are based on historical knowledge rather than up-to-date, sourced insights.

Deep Research

• Pulls real-time insights from external sources, synthesizing multiple perspectives and providing links to supporting materials.
• More powerful for research-heavy SEO tasks, such as:
  • Evaluating competitors.
  • Validating E-E-A-T signals.
  • Ensuring factual accuracy in content.
• Unlike ChatGPT, Deep Research includes thorough citations and footnotes, making it easier to verify and trust the information.
• Helps SEOs assess the credibility, relevance, and quality of insights by showing exactly where the data comes from.
• Can also be a valuable tool for discovering new thought leaders, publications, and authoritative sources in the industry.

Example of ChatGPT vs. Deep Research in SEO

Let’s say you want to understand how Google’s latest core update is impacting search rankings.

• ChatGPT prompt: “What are the key ranking changes from Google’s latest core update?”
  • ChatGPT will provide insights based on its training data, which may not include the latest updates.
• Deep Research prompt: “Summarize the latest analysis of Google’s December 2024 core update from industry experts, including changes in ranking factors and who has been affected.”
  • ChatGPT might provide a general summary of past updates but lacks real-time data and direct citations.
  • Deep Research, on the other hand, retrieves insights straight from authoritative sources.
  • For example, when testing this prompt, Deep Research returned a 1,068-word analysis (not counting the list of 13 citations with links). Here’s an excerpt:

“Google’s December 2024 update rewards content-rich, trustworthy sites and raises the bar against spam or subpar content[^1]. SEO analysts noted that Google placed even greater emphasis on high-quality, original content demonstrating E-E-A-T[^2]. Sites with thin or duplicate content, especially in YMYL categories, saw declines[^3]. AI-generated content was scrutinized more heavily, with low-quality, auto-generated text being devalued[^4].”

The footnotes and citations in Deep Research’s response allow SEOs to see exactly who said what and in which publication, making it easier to evaluate the credibility of the insights and make informed decisions.

SEO use cases for OpenAI’s Deep Research

1. Competitive analysis and SERP research

One of the most practical applications of Deep Research in SEO is real-time analysis of competitors and search engine results pages (SERPs).

Example: Identifying content gaps

• Imagine you’re optimizing a blog for a keyword like “best AI SEO tools 2025”. Using Deep Research, you can prompt:

Prompt: “Provide a comparison of the top five AI SEO tools as of 2025, summarizing their features, pricing, and pros/cons with links to sources.”

Instead of relying on outdated or generalized knowledge, Deep Research pulls current information from multiple sources, allowing you to craft more comprehensive and up-to-date content than your competitors.

2. Content ideation and topic research

Creating unique, high-quality content that ranks well requires more than just keyword research.

SEOs often need to find trending topics, authoritative sources, and expert insights to craft engaging content.

Example: Finding trending and evergreen topics

Prompt: “What are the emerging trends in AI-powered search optimization in 2025? Provide references to industry reports or expert opinions.”

Deep Research helps ensure your content is timely, relevant, and backed by authoritative sources, improving both E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) and engagement.

Dig deeper: AI optimization: How to optimize your content for AI search and agents

3. E-E-A-T and link building research

Google increasingly prioritizes content that demonstrates E-E-A-T.

With Deep Research, SEOs can efficiently:

• Find reputable sources to cite for stronger credibility.
• Discover link building opportunities by identifying authoritative industry sites that accept guest contributions.
• Locate credible experts whose insights can add weight to an article.

Example: Strengthening content credibility

Prompt: “Find peer-reviewed studies or expert analysis on the impact of AI-generated content on SEO rankings.”

(Seriously, try this. The footnotes alone are *chef’s kiss*)

By embedding sourced insights directly into your content, you enhance trust and authority, which can contribute to higher rankings.

4. Automating SEO research tasks

SEO professionals spend a significant amount of time manually reviewing sources, extracting insights, and analyzing SERP trends. Deep Research can automate much of this work, freeing up time for strategy and execution.

Example: Generating a content brief

Prompt: “Generate a content brief for a 2,000-word article on ‘How AI is Changing SEO in 2025,’ including H2s, key takeaways, and supporting statistics with sources.”

This allows SEO teams to move faster and maintain consistency in content quality and depth.

Dig deeper: Improving content quality at scale with AI

Reevaluating the role of schema in SEO

While structured data has long been considered a key element of technical SEO, recent advancements in AI-driven search have lessened its importance for many types of content. 

In a December 2024 article for Search Engine Land, I discussed how schema markup is not as critical as it once was, with the exception of certain structured data types, such as product schema.

Deep Research can still assist SEOs by:

• Identifying the most relevant schema types for products, events, or structured content that still benefit from markup.
• Finding industry-specific examples of where structured data is still impactful.

Example: Schema relevance in AI search

Prompt: “Analyze the role of schema markup in AI-driven search results and identify which schema types still provide ranking benefits.”

By leveraging Deep Research, SEOs can avoid unnecessary implementation efforts and focus on structured data that truly matters in today’s search landscape.

Why Deep Research feels like an SEO superpower

SEO’s evolution is starting to feel like a sci-fi experiment gone rogue – constantly mutating and throwing unexpected changes our way. 

OpenAI’s Deep Research helps SEOs track these mutations in real time, ensuring they aren’t optimizing for outdated strategies.

While traditional ChatGPT responses provide helpful general guidance, Deep Research enables SEOs to produce more accurate, authoritative, and competitive content – a necessity in AI-driven search. 

Integrating Deep Research into your workflows can improve competitive analysis, content ideation, E-E-A-T optimization, and automation efforts, ultimately leading to higher rankings and stronger organic performance.

Deep Research shifts the balance of AI-assisted SEO from guesswork to precision. This tool is a game changer for SEOs who thrive on data-backed decisions.