ChatGPT Playbook
for Market Research
A Practical Guide for Founders
Contents
Introduction .............................................................................................. 3
Getting Started with ChatGPT ................................................................. 4
What ChatGPT Can and Can’t Do ......................................................................................... 4
Understanding Hallucinations in Large Language Models ..................................................... 6
Data Security ......................................................................................................................... 8
Prompting Fundamentals ........................................................................ 9
Prompting Techniques............................................................................................................ 9
Prompt Library for Market Research .................................................................................... 12
Idea Brainstorming ........................................................................................................... 12
Competitor Analysis .......................................................................................................... 14
Trend Exploration ............................................................................................................. 15
Persona & Customer Profiling .......................................................................................... 16
Voice of Customer / Survey Analysis ................................................................................ 17
Market Sizing & Ecosystem Mapping ............................................................................... 18
GTM Strategy & Positioning ............................................................................................. 20
Creating ChatGPT Projects ................................................................... 22
Building and Using Custom GPTs......................................................... 22
Deep Research and Web Search with ChatGPT ................................... 25
Final Guidelines...................................................................................... 29
References .............................................................................................. 31
2
Introduction
This playbook is designed to help founders use ChatGPT effectively for fast, insightful market
research. It provides clear guidance on how to make the most of ChatGPT’s strengths, such as
its speed, broad knowledge, and ease of use, while avoiding common pitfalls like inaccurate
information. Inside, you’ll find straightforward explanations, useful prompts, and tips to help
you maximize the reliability of your insights and support your business, especially in its early
days.
Remember, while ChatGPT can cut down your manual research time by about 37% to 70%
(according to various reports), it cannot replace thorough research from trusted sources.
Thoughtful input and careful fact-checking are always necessary, especially when using AIgenerated insights for important decisions like investor pitches or business planning. Always
verify any numbers or facts and use this playbook as a guide to keep your research accurate
and valuable.
Before diving into ChatGPT specifically, it’s helpful to understand that there are other AI tools
available for market research each with its own strengths. Depending on your goals (e.g.,
factual precision, trend awareness, document synthesis), you may find value in exploring
additional tools or even combining them:
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Perplexity AI is known for surfacing well-cited information quickly from high-quality
sources. It’s a strong option when trustworthy references are a top priority.
Claude (Anthropic) offers long context windows and a thoughtful, conversational
tone, making it well-suited for in-depth analysis of large documents or nuanced
research tasks.
Google Gemini integrates tightly with Google Search and YouTube, often producing
fresh results and more current web-based insights, especially useful for tracking live
trends.
Grok (xAI) is designed for real-time reasoning and context-aware responses,
particularly when integrated into the X platform (formerly Twitter). It may appeal to
users already embedded in that ecosystem.
DeepSeek is an open-source LLM gaining traction for its transparency and emerging
research capabilities, with growing interest from technical and developer
communities.
While this playbook focuses on ChatGPT due to its wide adoption, flexible tools (like file
uploads, custom GPTs, and Deep Research), and integrated ecosystem, we encourage
founders to explore what works best for their specific use case and not hesitate to combine
tools when needed.
3
Getting Started with ChatGPT
What ChatGPT Can and Can’t Do
ChatGPT is a smart tool that can write and answer questions in a human-like way. It is useful
for quickly summarizing texts, generating creative ideas, clearly explaining topics, and
answering questions. It is also helpful for market research tasks, such as brainstorming,
identifying competitors, spotting trends, creating customer profiles, and analyzing provided
data.
Models and Tools
OpenAI regularly releases new models and features. The default flagship model is generally
the safest, most balanced option for everyday use, but some models are designed to excel at
complex reasoning, while others are more cost-efficient. Today, ChatGPT is increasingly able
to switch between models automatically depending on your task. Because the product evolves
quickly, always check the in-app model picker or release notes to see the latest options.
Beyond the core chat experience, ChatGPT includes specialized tools that expand what it can
do:
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Browsing / Web Search: Allows ChatGPT to search online for the latest
information.
File Uploads / Advanced Data Analysis: Lets users upload files like spreadsheets
or PDFs. ChatGPT can then analyze this data and even create visualizations.
Connectors (like Google Drive or SharePoint): These help ChatGPT directly access
and use documents and messages from other apps you use regularly, making it
more integrated into your workflow.
Deep Research: A newer tool designed for source-cited, multi-step market research.
Unlike standard chat, Deep Research delivers structured reports (in 5–30 minutes)
with citations, charts, and cross-source insights. It's especially useful for tasks like
market sizing, competitor mapping, or regulatory research, where accuracy and
references matter most. Use this mode when decisions require verification or live
data from trusted sources.
→ We dig deeper into how and when to use these features, and how it compares to standard
ChatGPT, later in this playbook.
4
Knowledge Cutoff and Access
AI models are trained on past data and don’t automatically know what’s happening today.
They’re great at reasoning, summarizing, and connecting ideas, but they can’t pull in real-time
updates on their own. If you need current facts, turn on Web Search or Deep Research so the
model can go out, find, and cite fresh information. Pairing these tools with your own uploaded
files or app connectors lets the model check answers against both public and private sources,
making your results more accurate, transparent, and easy to trace.
Reliability, Accuracy, and Hallucinations
Modern models handle language and problem-solving well and can often keep up with or
outperform humans in structured tasks. But that doesn’t mean every detail is right. Sometimes
the model will miss context, over-simplify, or even make up details, especially with stats,
references, or specific numbers. Because it writes in a confident tone, those mistakes can
sound more credible than they are. Think of its output as a strong draft: something that speeds
you up but still needs your judgment.
Founder tip: Use ChatGPT to accelerate your work, but double-check the important stuff,
facts, references, and numbers, before you share it with investors, partners, or customers.
Bias and Coverage Gaps
Like any system trained on human data, AI models carry the biases and blind spots of their
sources. Big, mainstream topics are often well covered, but emerging, niche, or
underrepresented areas may be thinner. For instance, you might get detailed insights on
Western markets but far less depth on smaller or non-English regions. Biases also show up in
subtle ways, like cultural framing or assumptions built into examples.
For founders, this means your research might look more solid in familiar, well-documented
industries but leave gaps in new markets or diverse customer groups. These gaps can shape
how you see opportunities, risks, and customer needs. To balance this out, try asking
questions from multiple angles, combine ChatGPT’s output with your own data, and use
document uploads to add missing perspectives. That way you’re building on a more rounded
view of the market.
5
Understanding Hallucinations in Large Language Models
What Are “Hallucinations”?
In simple terms, a hallucination is when AI makes something up.
For example, it might:
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Assert a statistic it invented
Cite a source that does not exist
Present minor errors like wrong dates or names, or create entire fictional details as
fact
In market research, this could mean a fabricated data point in a report summary, an invented
quote from an industry expert, or a misrepresented trend. These mistakes are easy to miss
because the model presents them with confidence.
Why Do Hallucinations Occur?
Even advanced language models can hallucinate for several reasons:
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Prediction over accuracy: Models are built to predict the next likely word, not
guarantee correctness. When asked about an unfamiliar topic, they often generate a
best guess instead of admitting uncertainty.
Limited or biased training: If certain topics, like niche industry data, are poorly
represented in the training material, the model may combine unrelated facts or
generalize inaccurately.
No real-time fact-checking: Models do not verify information against live sources
or current databases. Without external checks, they fill gaps with text that sounds
credible but may not be accurate.
Outdated information: Since training ends at a specific point in time, models may
share information that is obsolete or assume trends that no longer apply.
Overconfident answers and weak prompts: Fine-tuning can make models eager
to sound helpful, so they may deliver confident but incorrect responses rather than
say “I don’t know.” Prompts that encourage guessing or imagining can also increase
the risk of made-up details.
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Practical Examples in Market Research
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Fake statistic and source:
Q: “What was Company X’s market share in 2023?”
A: “Company X had 15% market share in 2023, per IDC’s July 2023 report.”
Both the number and the IDC citation may be completely fabricated.
Plausible but unsupported analysis: A competitor SWOT might list strengths or
weaknesses that sound reasonable but are not based on actual data.
Benign persona details: For customer personas, the model may invent traits, such
as claiming “Marketing Molly” reads Harvard Business Review daily, without
evidence.
How to Reduce Hallucination Risk
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Ground with real data: Add context like report excerpts or key facts so the model
has reliable anchors.
Connect to trusted sources: Enable browsing or link the model to your company
knowledge base so it uses verified information instead of guessing.
Ask for reasoning: Prompt the model to explain its assumptions and double-check
facts. If a number appears, ask “How do you know?”
Use the latest tools: Newer models hallucinate less and can handle multimodal
inputs such as reading charts. Browsing features help pull in up-to-date info, but
always verify links manually.
Keep human oversight: Treat AI like a junior analyst: fast and helpful, but
imperfect. Always review key numbers, sources, and surprising claims.
Founder takeaway: Hallucinations are part of how large language models work, but they can
be managed. By grounding outputs in real data, using the right tools, and keeping a human in
the loop, you can reduce risk while still getting the speed and creativity benefits AI brings.
7
Data Security
OpenAI’s ChatGPT service, in its default setup for individual users (Free and Plus plans), may
use conversations to help improve its models. This means your prompts and responses can be
collected and analyzed to make the system more accurate, detect misuse, and build new
capabilities. If you would rather not share your data for training, you can opt out anytime under
Settings > Data Controls. You can also use Temporary Chats (which are not stored or used
for training) or delete your chat history. Deleted chats are cleared from OpenAI’s systems
within 30 days.
What This Means for Business Users and Founders
For teams and companies, the rules are different. OpenAI does not use data from ChatGPT
Enterprise, Team, Edu, or the API platform to train its models by default. These models are
trained on public information, licensed content, and data that users explicitly choose to share
(for example, via the API dashboard). This provides stronger security, but founders should still
take a cautious approach with sensitive information.
Here are key practices to keep in mind:
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Know the policies: Enterprise and Team data is not used for training, but it is still
important to review your service agreement and OpenAI’s privacy policy so you are
clear on retention and limitations.
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Protect sensitive information: Even with enterprise-level protections, avoid pasting
confidential, proprietary, or client-specific details into ChatGPT. Stick to a “need-toshare” approach to reduce risk.
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Watch third-party GPTs: OpenAI manages its own products, but not external
plugins or custom GPTs. Do not share sensitive information unless you have vetted
their data practices.
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Set team standards: Establish and share clear AI guidelines for your employees,
including approved use cases, restrictions, and compliance with standards like
GDPR or SOC 2.
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Prioritize security and feedback: Use strong passwords and two-factor
authentication (2FA), manage access with admin controls, and encourage your team
to report biased or unexpected outputs. Stay up to date with OpenAI’s policy
changes and adapt your practices as needed.
Founder takeaway: Enterprise tools give you a stronger security baseline, but data discipline
is still on you. Treat ChatGPT as a partner to speed up your work, not as a place to store or
process your most sensitive information.
8
Prompting Fundamentals
Prompting Techniques
Prompting techniques are structured ways of writing inputs that guide ChatGPT toward more
accurate, relevant, and actionable responses. They help clarify your intent, set the right tone,
provide essential context, and improve reasoning through strategies like role-based prompts,
step-by-step instructions, and examples. Effective prompting is a core skill for founders who
want to get the most value out of ChatGPT-whether for analysis, writing, or decision-making.
Below is a reference table of key prompting techniques with descriptions, best-use scenarios,
and practical examples.
Technique
Description
When to Use
Example
Contextual
prompting
Supply detailed facts,
numbers, or constraints to
anchor the model’s output
When you need
tailored answers
grounded in specifics
"Summarize Tesla’s last 3
quarterly reports with a focus
on EV sales and margins."
Role-based
prompting
Ask the model to adopt a
persona or expert role to
shape tone and depth
When tailoring content
to a stakeholder or
target audience
"Act as a venture capitalist
and evaluate a SaaS startup
pitch deck."
Chain of
thought
Instruct the model to reason
step by step before
producing the final answer
When solving multistep or complex
problems
"Walk through, step by step,
how to size the Canadian
food delivery market."
Start with a broad query,
When refining ideas
Least-to-most
then progressively add detail gradually or layering
prompting
and constraints
nuance
"First list the top EV
competitors, then compare
their pricing strategies."
N-shot
prompting
When you need
Provide multiple examples to
outputs to match a
demonstrate the style, tone,
consistent format or
or structure you want
style
"Here are 3 customer
persona examples. Create a
new one for Gen Z
commuters."
Looking to learn more about prompting techniques? Here’s a helpful resource you can refer to:
Prompt Engineering Guide
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What Makes a Good Prompt
High-quality answers depend on clear, specific communication. Good prompts reduce
ambiguity, provide context, and set expectations for how the output should look.
Key elements of a strong prompt:
• Clear, specific, and contextual: The more detail you provide, the more actionable
the response.
• Structure: Use a simple framework: Role, Task, Context, and Format.
o Role: Define the perspective (e.g., market analyst, VC, research assistant). This
shapes tone and depth.
o Task: Clearly state what you want ChatGPT to do, with specific focus areas.
o Context: Share background, constraints, or relevant data sources.
o Format (optional): Request the desired structure (e.g., bullet points, table,
narrative).
Example Prompt:
“Act as an experienced market analyst (role) and evaluate the competitive landscape of the
electric scooter market (task). Our startup offers premium scooters for urban commuters
(context). Provide a 5-bullet summary comparing our product to key competitors, in a neutral
analytical tone (format).”
Prompting Mistakes to Avoid
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Too vague: Prompts without detail produce broad, less useful answers. For
example, “Tell me about marketing” will yield generic insights, whereas “Summarize
3 B2B SaaS marketing strategies used in the past 12 months” produces something
specific and actionable.
Impossible or risky requests: Avoid asking for confidential, private, or speculative
information. Prompts like “What will my competitor launch next quarter?” cannot be
answered reliably and may produce hallucinated responses. Stick to verifiable, nonsensitive asks.
Overloaded prompts: Too many questions at once cause confusion and partial
answers. Instead of writing one long query with five sub-asks, break it into smaller
steps such as “List main competitors” followed by “Compare their pricing models.”
Missing context or viewpoint: Always specify the audience, perspective, or use
case. A request to “summarize AI trends” might generate a broad list, but
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“summarize AI trends relevant for early-stage Healthtech startups in North America”
will deliver insights that matter to you.
Refining Prompts: Iteration and Follow-ups
Do not treat the first response as final. Think of it as a starting point that can be shaped into
something much stronger. Iteration is where most of the value comes from, because it allows
you to clarify, reframe, and direct the model toward exactly what you need.
Key ways to iterate effectively include:
• Start broad, then layer in detail and constraints. This gives the model space to
explore ideas before narrowing in on what matters most.
• Give explicit feedback. If the response misses the mark, explain what to changeadd depth, examples, restructure, or clarify missing assumptions. Clear feedback
usually produces much better results in the next attempt.
• Break large tasks into smaller steps. Ask for an outline first, then expand each
section. This reduces error and creates a more structured dialogue.
• Use iteration as dialogue. Think of it like working with a junior analyst-you provide
feedback, set standards, and push for higher precision with each round.
• Save and reuse. Document effective prompts and iterations as templates for
yourself or your team. This speeds up future work and creates consistency.
Founder takeaway: Prompting is a skill worth practicing. The clearer and more intentional
your instructions-and the more you refine them through back-and-forth-the more ChatGPT
becomes a reliable partner for research, strategy, and communication.
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Prompt Library for Market Research
This section provides ready-to-use prompt examples for common market research tasks. Each
covers a typical use case, like competitor analysis or trend spotting, with tips on how to ask
and what to expect.
Use these as templates or adapt them to suit your industry, product, or audience.
Idea Brainstorming
ChatGPT can be used to stay competitive by quickly generating, evaluating, and organizing
business ideas with clear, detailed prompts tailored to your goals.
Example Prompt - Generate Ideas:
Act as an innovative startup founder. Suggest the top [number] fresh business ideas in
[YOUR INDUSTRY]. For each idea, please provide:
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A concise, catchy name.
A clear one-sentence description.
The unique market gap or customer pain point it solves.
A short profile of the target customer.
A brief note on how it’s better or different from existing solutions.
Use bullet points or a clean table format for easy reading.
Example Prompt - Test an idea:
You are a veteran startup advisor or investor. Critically assess my business idea:
[DESCRIBE IT]. In your evaluation, please cover:
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Market Analysis: Estimate market size, growth trends, and real demand.
Competitive Landscape: Identify key competitors and compare my idea to them.
SWOT Breakdown: Highlight Strengths, Weaknesses, Opportunities, and
Threats.
Recommendation: Conclude with a clear ‘Go’, ‘Refine’, or ‘No-Go’ verdict, with a
short explanation.
Structure your answer in clear sections or a neat table for clarity.
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Example Prompt - Spot Niches & Gaps:
Act as a seasoned market research analyst. Find [number] promising but underserved
niches within [YOUR SECTOR]. For each niche, please include:
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A clear description of the niche and target audience.
Why it’s currently underserved and what gap exists.
A brief business idea or product concept to tap into this niche.
Present your findings as a bullet list or concise comparison table.
Example Prompt - Organize Ideas:
You are an expert product strategist. Here is my raw list of ideas: [PASTE LIST]. Please
organize them by:
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Grouping related ideas into logical categories.
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Giving each category a clear, descriptive name.
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Providing a 1–2 sentence summary of what unites the ideas in that category.
Present the organized result as a clean list or a simple, well-labeled table.
Tips:
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Narrow the scope for niche research by specifying a region, age group, or subsector for more relevant gaps.
Ask for signs of real demand, such as popular search terms, frequent customer
complaints, or trending topics.
Share your goal for organizing ideas (e.g., investor pitch, roadmap, marketing
plan) so the categories serve a clear purpose.
After organizing or analyzing, ask which items to prioritize and why, this helps turn
ideas into action steps.
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Competitor Analysis
ChatGPT can help quickly analyze competitors by summarizing their products, pricing,
customer sentiment, and strategies to uncover market gaps and insights.
Example Prompt:
You are a market research analyst. Our company [briefly describe your product,
service, or target market here]. Provide an up to date [specify time frame, e.g., 2023–
2025] competitive analysis of the top 5 companies operating in [your industry/region].
For each competitor:
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List their pricing model (specific numbers or tiers if available)
Describe key features and unique selling points
Summarize customer sentiment based on recent reviews from trusted sources
(e.g., G2, Capterra, Trustpilot, or relevant review sites)
State one main strength and one main weakness
Explain their market position relative to our company
Finally, present the findings in a well-formatted summary table and include source
citations for each data point.
Tip:
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Use “search the web” with a clear time frame & ask for citations: e.g. “Fetch
fresh info from 2023–2025 and include source links.”
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Trend Exploration
Use ChatGPT to spot and assess emerging industry and consumer trends by summarizing
insights from news, reports, and online discussions.
Example Prompt:
Identify the top [number] emerging trends in [YOUR INDUSTRY OR TOPIC] from
[TIMEFRAME, e.g., the past 12 months or 2024]. For each trend, do the following:
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Provide a clear description of the trend
Explain why it matters for [TARGET AUDIENCE, e.g., startups, SMEs, or
investors]
Include recent, reputable data points or statistics (from [SPECIFIC SOURCES,
if you have preferences, e.g., McKinsey, Gartner, CB Insights])
Cite recent articles or reports (2024–2025) and provide direct clickable links
Assess the trend’s potential impact on [YOUR AUDIENCE]
Present the response in [FORMAT, e.g., concise bullet points with key stats, or a
table].
Be thorough but avoid repeating information.
Tip:
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Afterward, ask follow-up questions such as:
o “Which trend is most likely to last long-term?”
o “Provide a startup example for each trend.”
o “Add a key statistic for each.”
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Persona & Customer Profiling
Create realistic buyer personas by generating user profiles with demographics, motivations,
pain points, and behaviors based on your input or market trends.
Example Prompt:
Act as an experienced market researcher. Create [number] realistic and diverse
customer personas for [brief product/service description]. For each persona, include
the following details:
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Persona Name & a one-line summary
Demographics: age, role/job title, income range, location
Psychographics: key goals, values, motivations
Primary pain points that our product/service addresses
Buying behavior: how they research and decide to buy, who influences them
A direct quote that captures their mindset
[Optional: Add any extra info relevant to your product, e.g., preferred channels
or marketing messages that resonate.]
Ensure the personas feel believable and reflect realistic attitudes and needs, avoid
stereotypes or generic filler. Present the personas clearly in a structured format, using
bullet points or headings."
Use these as a practical starting point, then enhance them with real data and research for
maximum accuracy.
Tips:
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Be clear and specific about what the product is and who it’s for.
List exactly what you want: name, demographics, goals, pain points, buying
behavior, suggested marketing channels, key product features, and a realistic user
quote or feedback.
Ask for realism and variety. Personas should feel like real, diverse people, not
generic copies.
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Voice of Customer / Survey Analysis
Quickly analyze open-ended feedback by summarizing key themes, sentiment, and quotes
from reviews, surveys, or social posts.
Example Prompt:
Analyze the following set of customer feedback (reviews, survey responses, or
comments). Please:
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Identify the top 3 to 5 recurring themes or topics that appear most frequently in
the feedback.
For each theme, summarize the overall sentiment (positive, negative, or mixed).
Provide a representative customer quote for each theme to illustrate it clearly.
Suggest one practical, actionable recommendation for the team based on each
theme (e.g., product improvement, support process change, marketing focus, or
training need).
Present your findings in a clear, structured format, preferably a table or bullet list, so
they can be easily shared with stakeholders.
Tips:
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For deeper insights, ask for extra details:
o “Estimate how many reviews mention each theme.”
o “What’s the overall sentiment breakdown (% positive vs negative)?”
o “Summarize answers to a specific survey question.”
o “Highlight any outliers or unusual feedback.”
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Market Sizing & Ecosystem Mapping
Estimate market size (TAM/SAM/SOM) and map your industry ecosystem by providing base
figures or asking it to identify key stakeholders and players.
Example Prompt - Market Sizing:
Estimate the market size for [Industry] in [Region].
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Provide Total Addressable Market (TAM) in annual revenue (USD), clearly
citing sources from recent credible reports (last 1–2 years).
Include the projected compound annual growth rate (CAGR) for the next 5
years.
Break down the Serviceable Available Market (SAM) with an explanation of
relevant customer segments and realistic access limitations.
Estimate a realistic Serviceable Obtainable Market (SOM) for a new entrant
like us, considering competitive landscape, go-to-market capacity, and
regulatory factors.
List all key assumptions and explain calculation logic.
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Summarize each figure in bullet points with a brief context.
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Include URLs or report names wherever possible for verification.
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Cross-check figures with at least two trusted sources if possible.
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Example Prompt - Ecosystem Mapping:
Map the [specific industry segment] ecosystem in [specific target market or region].
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Identify the main stakeholder categories relevant to this market (e.g., suppliers,
manufacturers, distributors, service providers, regulators, industry associations,
and key customers).
List 2–3 credible, up-to-date examples under each category, including names
and short descriptions of their role or influence.
Highlight any dominant players, strategic partners, or gatekeepers who strongly
influence market entry or scale-up.
Include links to company websites or credible profiles whenever possible.
Note any emerging players or notable trends (e.g., new entrants, mergers,
regulatory shifts) that affect this ecosystem.
If useful, add a simple diagram or table summarizing the ecosystem structure.
The output gives you a practical starting point to validate, refine, and use in strategic planning,
saving time on early research and framing. Always double-check critical numbers with credible
sources before final use.
Tips:
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Follow up with clarifying questions like:
o “What sources or assumptions did you use?”
o “Can you show the calculation steps?”
o “Who are high-priority partners, gatekeepers, or dominant players?”
o “Are there recent trends, shifts, or risks to note?”
For ecosystem maps, request 2–3 specific examples per stakeholder category,
with names, short descriptions, and links.
For market sizing, ask for sub-segment splits (by product line, customer type, or
region) if relevant, and define the CAGR timeframe (e.g., 5 years).
Break big, broad markets into smaller, realistic segments to get actionable
insights.
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GTM Strategy & Positioning
Craft GTM strategies, positioning statements, and messaging by combining insights on
competitors, customers, and channels.
Example Prompt - GTM Strategy:
Act as a CMO. Develop a Go-To-Market (GTM) strategy for [Product or Service Name]
targeting [specific audience/segment] in [region, if relevant].
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Define 1–2 ideal customer segments with clear pain points.
Craft the core value proposition and key messaging tailored to these segments.
Recommend the best launch and distribution channels, with brief rationale for
each.
List key partners, influencers, or communities to accelerate adoption.
Highlight 2–3 points of differentiation vs. top competitors.
Summarize actionable next steps or quick wins in 4–5 concise bullet points.
If helpful, include any assumptions about budget, timeframe, or market maturity.
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Example Prompt - Positioning:
Act as a brand strategist. Based on our product’s key benefit — [X], the current
competitor positioning — [Y], and our target customer profile — [Z]:
• Write 2–3 clear, compelling positioning statements or taglines that highlight how
we stand out.
• Ensure each option communicates our unique value and addresses the
customer’s biggest need or pain point.
• Keep each statement short, memorable, and suitable for use in marketing copy
or pitch decks.
• Briefly explain the angle or strategic idea behind each one.
If helpful, list any assumptions you made about customer priorities or market trends.
Tips:
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Use follow-ups to refine and adapt:
o “Draft an elevator pitch using this positioning.”
o “Adapt messaging for Instagram vs. LinkedIn.”
o “Which channels or tactics should we prioritize first 90 days vs. later?”
o “What risks should we plan for with this GTM strategy?”
Request clear, actionable output: For positioning, keep statements short,
memorable, and pain-point focused. For GTM, ask for practical next steps or quick
wins in bullet form.
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Creating ChatGPT Projects
A strong first step in any research project is to set up a ChatGPT Project. This creates a
dedicated workspace that keeps your materials, context, and progress in one place, making it
easier to focus your efforts and return to them without losing track.
Projects allow you to:
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Keep related chats organized and easily searchable
Upload reference files (PDFs, spreadsheets, images) to have all sources in one
location
Add custom instructions to shape responses in a way that fits your goals and style
Preserve memory so ChatGPT delivers more relevant, consistent answers over time
Build continuity across tasks, ensuring your research grows rather than starting from
scratch
Whether for strategy, research, or drafting, Projects provide a simple structure to stay
organized, maintain momentum, and streamline collaboration with the assistant as your work
evolves.
To create a Project, go to the left-hand navigation panel in ChatGPT, click Projects, and then
select New Project. From there you can name your project, upload reference files such as
PDFs, spreadsheets, presentations, or images, and add custom instructions. Good instructions
typically include your goals, tone preferences, or specific ways you’d like ChatGPT to analyze
or present information. Once set up, you can begin organizing your work right away.
For further details and the latest updates, visit the official OpenAI guide on Projects in
ChatGPT.
Building and Using Custom GPTs
Custom GPTs are tailored versions of ChatGPT that combine unique instructions, reference
materials, tools, and behaviors to help you automate specific workflows. For founders, that
means creating AI assistants to handle repetitive or strategic startup tasks fast, consistently,
and at scale.
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You can use GPTs for a range of founder tasks, from drafting cold outreach emails to analyzing
market trends, summarizing investor questions, or refining your startup story.
Custom GPTs are:
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No-code: You don’t need a developer to build one.
Flexible: Enable features like web browsing, data analysis, or image generation.
Reusable: Build once and use it as many times as needed or share with your cofounders or team.
When Should you Build a Custom GPT?
Consider building a Custom GPT when:
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You are repeating key tasks like investor updates, competitor analysis, or outreach
and want faster, consistent output.
You find yourself writing long, elaborate prompts over and over again for the same
workflow, and want to save time by embedding those instructions into a reusable
GPT.
You want outputs to follow a very specific format or tone every time, such as
structured tables for market data or polished investor-ready summaries.
How to build a Custom GPT
Building a Custom GPT is straightforward and does not require coding or technical setup.
Think of it like training a personal AI assistant: you provide instructions, upload helpful
resources, and set behaviors.
Practical steps to build one:
1. Open ChatGPT and click on Explore GPTs.
2. Select Create a GPT and follow the guided setup.
3. Add core instructions: write down what the GPT should know, how it should
respond, and the tone it should use.
4. Upload reference files (pitch decks, FAQs, product docs) so it can ground answers
in your content.
5. Configure tools and permissions, like enabling browsing, file uploads, or APIs if
needed.
6. Test with real prompts you use often, refine the instructions, and repeat until results
are consistent.
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7. Save and share the GPT with teammates so they can benefit from the same
workflows.
For detailed, step-by-step instructions, check out the Official Guide to building a CustomGPT.
Tips for Success
Even though it is easy to build, here are key best practices to keep in mind:
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Start with a clear use case: Focus on a specific task you want to automate or
scale, like drafting investor emails or summarizing market research.
Upload only relevant, high-quality files: Keep your GPT’s knowledge base tight
and useful, such as pitch decks or FAQs, not your entire workspace.
Write clear, role-based instructions: Treat it like onboarding a new hire by defining
its tone, purpose, and how it should respond.
Test and improve: Try real prompts, refine as needed, and keep iterating to boost
accuracy and value.
Be mindful of privacy: Only upload what you are comfortable sharing, and review
settings if you are enabling tools like browsing or APIs.
Make it reusable: Add prompt starters and formatting rules so your team can use it
consistently.
Track performance: Check usage, gather feedback, and treat it as a living product
that improves over time.
Using Existing GPTs
You don’t always need to start from zero. The Explore GPTs tab lets you browse GPTs that
other people have already created, tested, and shared. They are grouped into categories like
productivity, research, and writing.
For startup founders doing market research, this library can be especially useful. You’ll find
GPTs built for:
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Competitive analysis: scanning analyst reports and summarizing competitor moves
Trend scanning: surfacing emerging industry themes
Persona development: generating or refining customer personas
And many more!
Once you find the right fit, you can save your favourites for reuse across projects helping you
standardize workflows like investor prep or go-to-market planning. You can also create and
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the GPT Store, verified builders can publish their GPTs and even monetize them based on
usage.
OpenAI designs GPTs with privacy and safety in mind. Conversations are not shared with GPT
builders, and if a GPT uses a third-party API, you decide whether to send data. Best practice is
to avoid uploading sensitive information like customer lists or financial details.
Check out OpenAI’s full overview: Introducing GPTs
Deep Research and Web Search with ChatGPT
When conducting research, providing accurate, source-based information is key. Standard
ChatGPT can sometimes struggle with hallucinations, overconfidence, and outdated data. To
minimize these risks, two integrated tools, Web Search and Deep Research, offer more
reliable, source-backed workflows within ChatGPT.
Web Search
Web Search is built into ChatGPT to provide fast, timely answers. It’s ideal for retrieving facts,
statistics, breaking news, and other current information within seconds. Responses include
source links for easy verification. This tool shines when you need quick lookups, spot-checks,
or to validate a single claim. It complements standard chat by grounding answers in up-to-date
information, though its depth of analysis is limited.
Deep Research
Deep Research is designed for slower but more powerful workflows, functioning almost like a
junior analyst embedded in your workspace. It autonomously explores multiple sources,
synthesizes findings, and delivers structured, cited reports for decision-making. Whereas Web
Search emphasizes speed and immediacy, Deep Research emphasizes accuracy, structure,
and synthesis across diverse perspectives. It can process webpages, PDFs, spreadsheets,
and images, running asynchronously and usually taking 5-30 minutes to complete.
Deep Research relies on advanced reasoning models that plan, test, and refine search steps.
Users can follow this reasoning trace in real time, seeing which sources are being read and
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how conclusions are formed. At key points, you can intervene, clarify, adjust scope, or redirect
focus to ensure the output aligns with your goals.
In addition to reasoning-driven search, Deep Research offers autonomous browsing,
multimodal data handling, and Python-based analysis for charts and visualizations. Outputs
typically include tables, graphs, and bullet points that synthesize insights, highlight patterns,
and provide evidence-backed conclusions. It may also ask clarifying questions upfront to better
define scope, ensuring results are targeted and relevant.
Founders can use:
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Standard ChatGPT for flexible ideation and quick insights.
Web Search for fast fact checks and verification.
Deep Research for major decisions requiring rigorous analysis and citations.
Choosing the right tool balances speed, depth, and reliability.
Summary: Web Search vs. Deep Research
Purpose
Web Search on ChatGPT
Deep Research
Instant retrieval of current info
on demand
In-depth, multi-step research and analysis
Response Time Seconds
~5–30 minutes
Output Format
Short answers, summaries,
links
Structured reports with tables, charts,
citations
Reasoning
Process
Direct lookup and response
Iterative planning, browsing, and
synthesis
Depth of
Analysis
Surface-level facts
Expert-like synthesis of diverse sources
Source
Handling
Links and snippets; user
follows up
Autonomous navigation; inline citations
and reasoning trace
Usability
Context
Quick answers (news,
weather, stats)
Complex subjects (finance, policy, legal,
product comps)
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How to Use Deep Research Mode
Using Deep Research Mode is similar to standard ChatGPT or Web Search, but there are best
practices to maximize results:
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Query Limits: Deep Research can only handle a set number of each month
depending on your plan. As of September 2025, Pro users receive 250 tasks/month,
Team and Enterprise plans get 25, and Free users are allotted only 5 lightweight
tasks monthly.
Craft Clear Prompts: Because queries are limited, avoid vague prompts. Instead,
create precise, well-scoped prompts. You can even ask ChatGPT to refine your
prompt before submitting.
Attach Context: Upload relevant documents (PDFs, spreadsheets, images) to
improve accuracy and relevance.
Answer Clarifications: Deep Research often asks clarifying questions before
running. Answer carefully so the system can focus on what matters most.
Follow the Reasoning Trace: Watch as it evaluates sources and builds
conclusions. This transparency helps you understand and guide the process.
Allow Processing Time: Reports usually take 5–30 minutes. You’ll be notified when
results are ready.
Verify Outputs: Review the report thoroughly, cross-check key data points, and
validate sources before sharing.
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Deep Research Applications in Market Research
Below are key cases that show the types of tasks Deep Research is best suited for:
Use Case
Deep Research Strength
Startup Value
Market Sizing &
Opportunity
Synthesizes recent reports, datasets,
and credible industry sources to
estimate TAM/SAM and highlight
underserved niches
Provides investor-ready market
sizing and sharper go/no-go
decisions based on current,
sourced data
Competitive
Landscape
Mapping
Builds structured comparisons of
competitor features, pricing,
positioning, and traction using
multiple verified sources
Reveals whitespace
opportunities, strengthens
differentiation, and informs
strategic positioning
Mines reviews, forums, surveys, and
uploaded customer data to surface
unmet needs and recurring themes
Improves product-market fit;
aligns product development and
messaging with real user pain
points
Customer
Segments & Pain
Points
Pricing &
Packaging
Comparisons
Collects, verifies, and tabulates
Guides profitable, competitive
competitor pricing models and
pricing strategies grounded in
feature tiers with transparent citations market evidence
Positioning &
Messaging
Trends
Analyzes messaging across digital
campaigns, press releases, and
content for emerging buzzwords and
themes
Helps refine value propositions,
improve resonance, and sharpen
marketing impact
Regulatory
Research
Aggregates and summarizes relevant
laws, standards, and compliance
requirements from authoritative
sources
Reduces compliance risk,
accelerates approvals, and
supports informed operational
decisions
GTM Case Study
Mining
Identifies and synthesizes credible
go-to-market case studies,
benchmarks, and lessons from
comparable firms
Supports execution planning,
highlights best practices, and
avoids common pitfalls
Detects early signals in blogs,
Enables first-mover advantage
Trend Tracking in
forums, patents, and social platforms and better timing for market entry
Niche Markets
to highlight emerging patterns
or product pivots
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Accuracy and Limitations
While Deep Research significantly reduces hallucinations by using real sources, it can still
make errors or cite content that doesn’t fully support its claims. Users should always verify
critical facts, figures, and quotes by checking citations, especially in high-stakes situations.
Source credibility also varies. Polished outputs might mask citations from outdated or lowquality websites.
To improve reliability, users should:
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Ask for reputable domains (like .gov or .edu).
Manually review unfamiliar sources.
Prompt Deep Research to flag disagreements or note confidence levels.
Upload proprietary data when paywalled or private information is needed.
For the best outcomes, use Web Search for quick verification and Deep Research for complex,
multi-step analysis. Together, they offer a complementary toolkit: one for instant fact-checking
and the other for deep, structured research. Human judgment remains essential to interpret,
refine, and validate results.
Final Guidelines
Congratulations! You are now ready to apply ChatGPT in your market research work. The
practices shared so far will help you get started, but you should continue experimenting to see
what works best for your specific needs. Here are some final suggestions:
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Treat AI as a junior research assistant, not the lead analyst. You should have a
clear vision of how to structure your research, the steps in the process, the
objectives at each stage, and how to combine everything into meaningful
recommendations.
Double-check key market stats: AI can highlight trends, but do not assume figures
such as market size, growth rates, or customer counts are fully accurate. Always
cross-check with trusted databases, analyst reports, or government sources.
Track and verify sources: When ChatGPT provides references or data, validate
them by reviewing the original reports or running a quick search. Credible sources
strengthen your research deliverables.
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Add human judgment: ChatGPT can help organize competitor scans or summarize
customer reviews, but your expertise is critical for interpreting how insights apply to
your specific industry or target market.
Keep prompts focused: If a result feels vague, refine your question. For example,
instead of asking “What is the outlook for the fitness industry?”, ask “Summarize
projected growth drivers for boutique fitness studios in Canada between 2023 and
2026.”
Use multiple inputs: Do not rely on ChatGPT alone. Blend AI-generated insights
with interviews, surveys, expert commentary, and industry reports for a complete
view of the market.
Be transparent with your team: Note which sections of your draft came from
ChatGPT and which you verified or expanded. This clarity builds credibility and trust.
Protect client and company data: Never paste proprietary forecasts, pricing
sheets, or customer lists into ChatGPT unless you are using a secure environment.
Use placeholders when testing prompts.
Watch for bias: AI may overemphasize English-language or US-centric sources.
Ensure insights reflect the markets you care about (e.g., Canada, EU, or emerging
markets).
Stay on track: AI outputs can drift off-topic. Guide them back with precise follow-ups
like “Focus only on B2B SaaS trends” or “Exclude consumer products from this
analysis.”
Keep evolving: Market research changes quickly, and so does AI. Share prompt
techniques with your team, test new features, and adjust your approach as tools
improve.
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References
ALLMO.ai. (2025, May 05). A comprehensive list of Large Language Model knowledge cut off
dates. Retrieved from https://www.allmo.ai/articles/list-of-large-language-model-cut-offdates
Netscribes. (2025, January 16). ChatGPT prompt guide: A mini toolkit. Retrieved from
https://www.netscribes.com/chatgpt-prompt-guide-simple-steps-to-smarter-aiinteractions/
OpenAI. (n.d.). Retrieved from OpenAI: https://openai.com/
OpenAI. (2025). What is the ChatGPT model selector? Retrieved from
https://help.openai.com/en/articles/7864572-what-is-the-chatgpt-model-selector
OpenAI. (n.d.). Creating a GPT. Retrieved from OpenAI:
https://help.openai.com/en/articles/8554397-creating-a-gpt
OpenAI. (n.d.). Deep Research FAQ. Retrieved from OpenAI:
https://help.openai.com/en/articles/10500283-deep-research-faq#h_d673aa5a9b
OpenAI. (n.d.). Introducing GPTs. Retrieved from OpenAI:
https://openai.com/index/introducing-gpts/
OpenAI. (n.d.). News. Retrieved from OpenAI: https://openai.com/news/
OpenAI. (n.d.). Privacy Policy. Retrieved from OpenAI: https://openai.com/security-andprivacy/
OpenAI. (n.d.). Projects in ChatGPT. Retrieved from OpenAI:
https://help.openai.com/en/articles/10169521-projects-inchatgpt?utm_source=chatgpt.com
OpenAI. (n.d.). Security & Privacy. Retrieved from OpenAI: https://openai.com/security-andprivacy/
Prompt Engineering Guide. (n.d.). Prompting Techniques. Retrieved from Prompt Engineering
Guide: https://www.promptingguide.ai/techniques
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