How to Use ChatGPT Deep Research Function for Market Research

ChatGPT’s deep research feature is an AI tool designed to automate complex research tasks. It searches the web, gathers data, and compiles structured, well-cited reports. Instead of manually reviewing multiple sources, users receive a consolidated analysis in one place.

But is it actually reliable? Can it replace manual research? And how do you get the best results without wasting your limited queries?

Here’s what we’ll cover:

• How Deep Research works and when it’s worth using.

• Strategies for crafting prompts that get useful insights.

• Use cases across industries

• Limitations of Deep Research (and AI) and how you can overcome them

P.S. If you’re after real-time market intelligence, ValidateMySaaS pulls fresh competitor data, analyzes trends, and eliminates guesswork**.** So if you need more than just AI-generated research, it’s worth checking out.

TL;DR How to Use ChatGPT's Deep Research for Market Research

• Deep Research automates market analysis by scanning multiple sources, extracting insights, and compiling detailed reports, saving hours of manual work.

• Step 1 Enable Deep Research before starting a query to ensure it follows a multi-step research process rather than providing a quick AI-generated response.

• Step 2 Craft a precise prompt using the C.P.R. framework, defining the context, prioritising key factors, and refining output structure for actionable insights.

• Step 3 Answer Deep Research’s clarification questions with specificity to fine-tune its search scope and ensure relevant, high-quality findings.

• Step 4 Monitor the research process to verify sources, correct any misinterpretations, and keep the focus on high-value data.

• Step 5 Review and refine the report by organising insights, checking for missing details, and restructuring key takeaways into a digestible format.

• Step 6 Expand research as needed by running follow-up prompts to dive deeper into specific insights, compare findings, or validate trends with additional data.

Keep reading to be able to use Deep Research effortlessly.

Regular ChatGPT (GPT4o) vs Deep Research: What's the difference?

ChatGPT is great for quick answers, brainstorming, and general assistance, but when it comes to in-depth research, it has limitations. Standard ChatGPT relies on its pre-trained knowledge and, if browsing is enabled, can pull surface-level summaries from search results. It’s fast but not built for deep analysis of complex topics.

Deep Research: An AI That Works Like a Researcher

Deep Research takes things further. Instead of just pulling top-ranked links, this AI agent autonomously scans multiple sources, extracts key insights, and synthesizes the data into a structured, well-cited report. It follows a multi-step research process that mirrors how a human would gather and analyze information.

How the o3 Model Powers Deep Research

OpenAI's deep research is built on a specialized version of OpenAI’s o3 model, optimized for web browsing and data analysis. Unlike standard AI models that rely on a single query-response approach, the o3 model:

• Breaks down complex questions into subtopics for deeper insights.

• Searches across multiple sources instead of relying on a few top results.

• Cross-references and verifies information to reduce misinformation.

How Deep Research Works

Once a research task is submitted, Deep Research:

1. Clarifies the request by asking follow-up questions (if needed).

2. Searches the web for multiple sources, not just the first results.

3. Analyzes text, PDFs, and other documents to extract key points.

4. Synthesizes information into a well-structured report with citations.

5. Presents findings in a format that’s easy to read and reference.

Unlike standard browsing, which just fetches links, Deep Research acts like a researcher, compiling and organizing data into a clear, usable report.

Why Use ChatGPT's Deep Research for Market Research?

Traditional market research is known to be messy. You’re jumping between reports, reading blog posts, and trying to piece together insights from multiple sources. Even with standard AI tools, you still need to fact-check and structure the data yourself.

ChatGPT’s Deep Research flips the script. Instead of surface-level answers, it runs a multi-step research process, analyzing multiple sources and structuring insights into a single, well-cited report that provides reliable information.

What Makes It Useful for Market Research?

🔹 Saves Time: Instead of manually searching and analyzing data, Deep Research automates the process and delivers a structured report in a far more timely manner.

🔹 Structured, Well-Cited Reports: No more scattered insights. Deep Research organizes findings into a clear, easy-to-read format, with citations for verification.

🔹Multiple Data Sources at Once: Unlike traditional search, which pulls from a few top-ranking results, Deep Research scans multiple sources simultaneously for a more complete market analysis.

🔹Finds patterns and gaps: Whether it’s spotting rising trends or pinpointing overlooked market opportunities, it connects the dots for you.

This makes it especially valuable for founders, marketers, investors, and enterprise users who need to make high volume decisions backed on data.

How to Use ChatGPT's Deep Research Step-by-Step

In this section, we’ll go over how to get the most out of Deep Research with precise prompts and follow-ups to expand access to specialized data sources and subscription-based resources.

Since Deep Research scans a huge breadth of data, vague prompts will only return generic, surface-level insights. If you want actionable results—whether for competitor analysis, industry trends, or customer sentiment—you’ll need to guide it properly.

Here’s how to do it, step by step.

Step 1: Select ‘Deep Research' Before You Start

Before typing anything, make sure Deep Research is enabled. This tells ChatGPT to run a multi-step research process instead of giving you a quick AI-generated response.

How to enable Deep Research:Click on the ‘Deep Research’ mode in the query bar. Start typing your market research request (see step 2).

Step 2: Write a Prompt That Gets High-Value Insights (C.P.R. Framework)

Deep Research is not a mind reader—it follows your lead. If you give it a messy, vague prompt, it’ll return a messy, vague report. When exploring options for crafting effective prompts, use our C.P.R. framework to get structured, high-quality insights.

C.P.R. = Context → Prioritize → Refine

Example Prompts for Different Use Cases

Now, let’s apply the C.P.R. framework to specific market research scenarios with a deep dive into each area.

Wrapping up step 2

Be SURE to use the C.P.R framework religiously. Specificity is king in the prompt engineering world.

By using the C.P.R. Framework, you'll ensure that Deep Research gives you:

• Actionable insights, not fluff.

• Structured reports, not messy data dumps.

• Competitive advantages, not outdated general knowledge.

Bonus Tip: If you want real-time competitor data before running Deep Research, ValidateMySaaS can help. It tracks SEO traffic, customer sentiment, and pricing models—so you can refine your research before you even start.

Step 3: Answer Deep Research's Clarification Questions the Right Way

Once you submit your query, Deep Research may ask follow-up questions to refine its approach. How you respond determines the accuracy of your final report.

How to Respond to Deep Research's Questions

• Be specific → If asked about timeframe, scope, or sources, give a clear, precise answer.

• Adjust based on research needs → If you realize something is missing, refine your request.

• Think about CPR again → If your original request was too broad, now's your chance to tighten it up.

Example Interaction Between a User & Deep Research

User's Original Prompt:\ "Analyze the top 5 competitors in the AI SaaS space. Compare pricing, features, and customer sentiment. Identify market gaps and summarize in a table."

Deep Research's Clarification:\ "Would you like a focus on B2B or B2C AI SaaS competitors? Also, should I prioritize review sites like TrustPilot or financial data sources like Crunchbase?"

Best User Response:\ "Focus on B2B AI SaaS companies. Prioritize financial data from Crunchbase and funding reports, but also include customer sentiment from TrustPilot for insights into user experience."

Step 4: Watch Deep Research Work & Guide It in Real-Time

Once you've launched your query, Deep Research starts scanning sources, analyzing data, and structuring the report. But this isn't a set-it-and-forget-it process. You should be reviewing its progress and adjusting if necessary.

What to Watch For

• Are the sources relevant? If you see low-quality sources, adjust your prompt (CPR) and add a new request:

  • "Focus only on sources from Gartner, McKinsey, and industry reports. Exclude personal blogs."

• Is it staying on track? If Deep Research starts pulling irrelevant info, redirect it before the report is finalized.

• How is it connecting insights? If the thought process doesn't seem logical, ask it to clarify or refine.

By stepping in early, you prevent wasted time and ensure the final report is useful.

Step 5: Break Down the Detailed Reports & Extract What's Important

Deep Research delivers a structured report, but it's up to you to refine it into actionable insights.

How to Review & Improve the Report

1. Check for structure. If the report is cluttered, ask:

   • "Reorganize this into sections: competitors, pricing strategies, and customer sentiment."

2. Assess source quality. Are insights from trusted industry sources or random blogs? If needed, reapply CPR and specify better data sources.

3. Look for actionable insights. If the findings feel too general, push for specifics:

   • "Provide 3 case studies of companies successfully leveraging this strategy."

4. Fill in missing gaps. If something crucial is missing, go back and refine:

   • "Expand this section with competitor funding data from 2023-2024."

Step 6: Expand & Refine for a Complete Picture

Your first Deep Research report is just the start. The real advantage comes from layering follow-up research on top of it.

How to Expand on Findings

• Deepen a key insight:

  • "Expand on [competitor name]'s acquisition strategy—what marketing channels drove their growth?"

• Compare across different segments:

  • "Compare how [trend] is affecting enterprise SaaS vs. startup SaaS companies."

• Get structured data:

  • "Summarize in a table comparing key pricing strategies across competitors."

This is where you reapply CPR again - but instead of a brand-new prompt, you refine based on the first report:

1. Highlight what's missing or unclear.

2. List specific questions that will add depth.

3. Use CPR again to tighten your next query.

For example, if your original request was: "Analyze AI adoption in healthcare over the past two years."

Your follow-up could be: "Compare AI adoption in hospitals vs. private practices. Focus on patient outcomes, cost efficiency, and regulatory challenges using 2023-2024 case studies."

This iterative process IS time consuming, but it's ultimately what separates a generic AI response that doesn't do you any good and a top tier market report comparable to the ones people hire market research specialists for.

For real-time competitive intelligence, tools like ValidateMySaaS fill in the gaps by tracking:

• Live competitor moves

• SEO & traffic insights

• Pricing models & launch strategies

Using Deep Research + ValidateMySaaS together gives you both historical and live market insights which will help you make data-backed decisions that actually drive results. Find out more here

Best Practices for getting the most out of Deep Research

AI can do a lot of heavy lifting, but it’s not a silver bullet. Deep Research can pull data, organize insights, and save hours of manual searching but the real value comes from how you use it.

To make sure your research is actually useful (and not just a pile of AI-generated reports collecting dust), follow these best practices. Additionally, keep an eye on OpenAI plans to introduce features like embedded images, data visualizations, and other analytic outputs, which aim to improve user experience and make reports more informative and user-friendly.

1. Double-Check AI Findings With Other Tools

AI models don't always get things right. They can misinterpret data, pull from unreliable sources, or miss key insights. That's why cross-checking matters.

How do you do that? Use multiple tools.

• Compare results across different AI research tools like Perplexity AI, Claude 3, or Google Gemini. If they all point to the same conclusion, you know you're onto something.

• Verify business data manually—AI might misread funding numbers or market trends. Check Crunchbase, SEC filings, and industry reports to be sure.

• Look at customer sentiment directly—if Deep Research says people love or hate a product, dig into real reviews on TrustPilot, Reddit, or G2 to see if it holds up.

This makes sure you're working with verified data

2. Think Beyond the Data—Apply It

A long, detailed report might look impressive, but data without action is useless. AI gives you the facts and then you turn them into a strategy. This means thinking deeper, questioning assumptions, and spotting angles most people overlook.

Look for Patterns, Not Just Numbers

Let's say Deep Research finds that your competitors are raising prices. That doesn't automatically mean you should do the same.

Ask yourself:

• Why are they increasing prices? Is it due to higher costs, demand, or a strategic move to reposition as a premium brand?

• Will customers actually pay more? Are they tolerating the price hike, or are they looking for alternatives?

• Does raising prices align with our brand image? If your competitive edge is affordability, increasing prices could push customers away.

• How did they roll out the higher pricing? Did they justify it with improved features, better customer service, or a strong PR push?

• Are there hidden factors? Are they struggling financially and using this as a short-term fix?

Understand the trends and then make your own decision.

Challenge AI Predictions

Deep Research might tell you a trend is gaining momentum, but trends don't always mean opportunity.

Think about:

• Who actually benefits from this trend? Does it favor startups, enterprise players, or niche businesses?

• Is this trend just hype, or does it have staying power? Early AI-generated artwork was exciting, but did it replace real designers? Not really.

• What happens if I wait? Is this a "now or never" situation, or would jumping in later be less risky and more strategic?

• Is the market already saturated? If the trend is big enough for Deep Research to flag it, you're probably not the first to notice. Is there still room to win?

Use AI Insights to Fine-Tune Execution

AI can highlight market gaps and customer complaints, but not all problems are worth solving.

Ask:

• Are these gaps actually profitable? Some gaps exist because they're too expensive or complex to solve at scale.

• Do customers even know they have this problem? Some pain points seem obvious but aren't urgent enough for people to pay for a solution.

AI is a Research Assistant, Not the Decision Maker

Deep Research is an incredible tool, gathering and organising information faster than any person could. But it's not a replacement for business intuition, strategy, or critical thinking.

Be sure to question the data and think through the courses of action you can take. AI insights can be helpful, but it will never beat human ingenuity. Keep that in mind.

How Different Industries Can Use Deep Research

Deep Research uncovers insights in minutes that would take weeks manually. Here’s how marketers, investors, and startups can put it to work and stay updated on recent developments, modernizations, and initiatives in their respective industries.

E-Commerce: Spot Trends Before They Go Mainstream

The e-commerce world moves fast—what's hot today could be forgotten next month. Deep Research helps sellers stay ahead of shifting consumer behavior by identifying emerging trends, pricing strategies, and gaps in the market.

How to use it:\ ✔ Track best-selling products on Amazon, Shopify, and Etsy to see what's gaining traction. ✔ Analyze customer reviews to uncover recurring complaints and feature requests. ✔ Study how competitors adjust pricing, discounts, and promotions across different regions.

Example Prompt Using C.P.R.:\ "Analyze trending product categories in e-commerce over the last 6 months. Compare consumer sentiment, return rates, and price sensitivity across Amazon, Shopify, and eBay. Identify emerging demand patterns and summarize insights with supporting data."

Finance: Stay Ahead of Market Trends & Investment Shifts

Timing is everything in finance. Deep Research helps investors and analysts track funding trends, economic shifts, and stock sentiment—without sifting through endless reports.

How to use it:\ ✔ Monitor VC and private equity investment trends across different industries. ✔ Compare stock sentiment across financial news, analyst reports, and earnings call transcripts. ✔ Identify macro trends like inflation rates, consumer spending shifts, and regulatory changes.

Example Prompt Using C.P.R.:\ "Analyze venture capital funding trends in AI startups from 2023-2024. Identify the fastest-growing sectors, average deal sizes, and notable investor movements. Compare funding trends to public market performance in AI-related stocks. Summarize insights with sources and data points."

Tech & SaaS: Scout Competitors & Find Market Gaps

In tech, staying ahead means knowing what your competitors are building, what customers are asking for, and where the biggest gaps are. Deep Research can map this out in minutes.

How to use it:\ ✔ Compare feature sets and pricing models of your biggest competitors. ✔ Analyze customer reviews to pinpoint frustrations and feature requests. ✔ Track funding rounds and hiring trends to predict where competitors are expanding next.

Example Prompt Using C.P.R.:\ "Compare the top 5 SaaS competitors in the project management space. Analyze pricing, user sentiment, and product roadmap changes from 2023-2024. Identify the biggest feature gaps and market opportunities. Summarize findings in a comparison table with citations."

Healthcare: Research Breakthroughs Without the Noise

Medical research is complex and constantly evolving. Deep Research helps cut through the clutter to track new treatments, policy changes, and expert analyses.

How to use it:\ ✔ Monitor emerging drug treatments and compare clinical trial results. ✔ Analyze public health trends like disease outbreaks or shifts in healthcare policies. ✔ Track medical device advancements and their adoption rates in different regions.

Example Prompt Using C.P.R.:\ "Analyze recent advancements in non-invasive diabetes treatments. Compare clinical trial outcomes, FDA approvals, and adoption rates in the U.S. and Europe. Identify key players, research gaps, and future predictions. Summarize findings with citations from peer-reviewed sources."

Common Challenges and Limitations of Deep Research (And How to Overcome Them)

Deep Research is a powerful AI-driven tool, but like any technology, it comes with limitations. While it can scan massive amounts of data and generate well-structured reports, it's not infallible - misinterpretations, outdated information, and blind spots can still occur. The key is knowing where AI falls short and how to compensate for those weaknesses to get the most reliable market insights.

1. AI Hallucinations: When Deep Research Provides Incorrect or Outdated Information

AI is only as good as the data it pulls from. Deep Research doesn't “know” anything. It predicts responses based on patterns in the data it finds. This means that sometimes, it will confidently generate false or misleading information because it lacks the ability to critically evaluate sources the way a human would.

Why This Happens

• AI aggregates data but doesn't inherently verify it. If misinformation is widespread online, Deep Research may treat it as factual.

• Some sources may be outdated, and Deep Research doesn't always indicate when the last update occurred.

• AI can misinterpret information, especially when complex or nuanced topics are involved.

How to Prevent AI Hallucinations

• Cross-check findings with trusted sources. If Deep Research gives a surprising or bold claim, verify it against industry reports, expert analyses, and firsthand data from companies and institutions.

• Use precise prompts. If you don't specify that you need information from 2023-2024, AI might pull older, irrelevant data. The CPR framework helps force AI to focus on the most recent and authoritative sources.

• Look for citations. Deep Research provides links to its sources. Click through and see if the original data backs up what AI is saying.

2. No Access to Private or Subscription-Based Data

Deep Research doesn't have access to everything on the web. It can only pull from publicly available sources, meaning it can't retrieve:

• Paywalled reports (e.g., Bloomberg, Forrester, Gartner).

• Private financial filings or competitor sales numbers.

• Internal company data or confidential research.

This can lead to gaps in market research, especially when analyzing industries where the most valuable insights are locked behind subscriptions or proprietary platforms.

How to Work Around This Limitation

• Supplement with human research. If AI can't access a report, try searching for publicly available summaries, executive interviews, or investor calls where key insights may be discussed.

• Leverage your own data. If you have access to proprietary datasets (e.g., sales performance, customer feedback, internal research), combine them with AI-generated reports for a more complete market analysis.

3. AI Can't Replace Human Expertise and Judgment

Deep Research provides information, but it doesn't think critically or make strategic decisions - that's still on you. AI can identify trends, compile competitor insights, and summarize consumer sentiment, but it doesn't understand context, emotions, or long-term business strategy.

Where Human Judgment Is Still Essential

• Competitive Strategy → AI can highlight feature gaps, but you need to decide if those gaps are worth filling or if they align with your company's goals.

• Investment and Financial Decisions → AI can analyze funding trends, but relying solely on its findings without independent verification could be risky.

• Emerging Industry Trends → Just because AI identifies a trend doesn't mean it's worth pursuing. Is it long-term growth or short-term hype? AI won't know.

How to Stay in Control of AI-Generated Research

• Use AI as a guide, not a decision-maker. It can save time, but final calls should always be based on your own experience, expertise, and additional verification.

• Refine and iterate. If Deep Research provides vague or overly broad insights, go back and refine your prompt using the CPR framework to dig deeper.

• Context matters. AI doesn't understand brand positioning, business priorities, or customer psychology—that's where human judgment remains essential.

How ValidateMySaaS Reduces AI Research Risks

While Deep Research can be a valuable starting point, relying on AI alone can introduce risks. That's where ValidateMySaaS comes in. VMS bridges AI's gaps by pulling real competitor insights from verified sources and tracking industry shifts in real-time. Read more on it here

Why This Matters:

• AI makes assumptions based on online data, but ValidateMySaaS pulls actual market data.

• Deep Research scans information, but ValidateMySaaS analyzes competitor strategies, product launches, and pricing models to give you actionable insights.

• AI can misinterpret trends, but ValidateMySaaS cross-validates data, so you don't act on bad information.

The Bottom Line

Deep Research is a great tool for efficient, large-scale data collection, but it's not perfect. By combining AI insights with critical thinking, independent research, and tools like ValidateMySaaS, you can minimize risks and maximize the reliability of your market research.

The Future of AI in Market Research: What's Next?

AI-powered research tools like Deep Research are just getting started. As AI models advance, they're reshaping how businesses gather insights, analyze competition, and forecast trends. But where is all this heading?

Here's what to expect from AI-driven market research in the near future and how tools like ValidateMySaaS will continue to evolve alongside it.

How AI-Driven Research Is Evolving

AI is slowly becoming a strategic asset for businesses that want to make faster, data-backed decisions.

Where things are heading:

• Better Contextual Understanding → AI is improving at understanding industry nuances, competitive landscapes, and real-world decision-making instead of just pulling raw data.

• Automated Cross-Validation → Future AI tools may automatically verify findings across multiple sources to reduce misinformation and conflicting insights.

• Predictive Market Research → Instead of just summarizing trends, AI will be able to forecast market shifts based on historical data and emerging consumer behavior.

As AI models like OpenAI's o3 continue to improve, the quality, accuracy, and depth of AI-generated research will only get better.

AI's Expanding Role in Business Intelligence

Right now, AI research tools still require human oversight, but in the future, they'll become more autonomous and actionable.

Here's where AI-driven market research is likely headed:

• Personalized Business Insights → Instead of one-size-fits-all reports, AI could generate custom research based on a company's specific needs, competitors, and market positioning.

• Deeper Sentiment Analysis → AI will analyze not just what customers are saying, but how they feel

  • detecting emotional cues in product reviews, social media conversations, and support tickets to uncover hidden frustrations and unmet needs.

• Real-Time Competitive Intelligence → AI will track and analyze competitors as they make moves, from price changes to feature launches to shifts in customer sentiment (all without needing manual research)..

AI isn't here to take your place. It's here to help you do your actual work better

You were never meant to spend time doing market research. It was merely a means to an end. Instead of drowning in open tabs and scattered notes, you get clear, structured insights in minutes so you can focus on what actually matters: strategy, execution, and making the right calls.

Here's why it's worth trying:

• Less time digging, more time deciding. AI pulls together competitor data, market trends, and customer insights—so you're not stuck piecing everything together manually.

• It spots patterns you might overlook. AI crunches thousands of data points, helping you see trends, gaps, and opportunities before they become obvious.

• Better insights mean better decisions. Whether you're launching a product, adjusting pricing, or scoping out the competition, you're working with real data, not assumptions.

At the end of the day, AI is just a tool. Your judgment still matters.

With Deep Research and ValidateMySaaS, you're not replacing human thinking - you're giving it an edge. Try it, test it, and see what insights you've been missing.

ValidateMySaaS Roadmap: The Future of Competitive Intelligence

At ValidateMySaaS, we're building a system that integrates AI advancements (like DeepResearch) to create a marketing intelligence software that goes beyond what AI alone can do.

Here's What's Coming:

• More In-Depth Competitor Intelligence → We're expanding our real-time market tracking to pull insights from more data sources - including hidden industry trends, investor reports, and product launches. Our goal is to create a dashboard with everything you'd ever need to know about all of your competitors in one place

• Deeper Customer Sentiment Tracking → We're enhancing our ability to analyze what customers love and hate about competing products, giving you a real-world advantage when positioning your business.

As AI progresses, market research changes from being about data to being about getting the right insights, at the right time, with full confidence in their accuracy.

With ValidateMySaaS, that's exactly what we're building.