How to Use Gen AI For Market Research in 2025?

Market research is evolving, and generative AI is at the heart of this transformation. Whether you’re trying to understand customer behavior, test a new product idea, or analyze open-ended feedback, Gen AI can help you do it faster and more efficiently. 

It doesn’t just crunch numbers; it helps you think, explore, and create. From writing surveys to summarizing thousands of responses, generative AI is becoming an everyday co-pilot for researchers and marketers. 

In this guide, we’ll walk through how to actually use Gen AI tools in your workflow, with real examples and practical prompts you can try right away.

What is Generative AI in Market Research?

Generative AI in market research refers to the use of AI models, particularly large language models (LLMs), that can generate text, insights, surveys, personas, and trend analyses based on prompts and datasets for marketing research purposes.

Unlike traditional AI, which classifies or predicts, generative AI creates. It mimics human-like reasoning to draft survey questions, synthesise customer feedback, simulate user behaviour, and generate executive summaries instantly.

In market research, this enables faster turnaround and deeper insight generation. For instance, Gen AI tools can:

• Draft surveys tailored to specific demographics

• Analyse open-ended responses at scale

• Build dynamic customer personas.

• Summarise thousands of reviews into clear patterns

• Simulate "what-if" market scenarios using past data

According to a 2024 McKinsey report, 65% of the respondents said they use Gen AI in their organisations. Generative AI is mostly used in marketing and sales. 

They integrated Gen AI into various parts of their research workflow, particularly for tasks such as sentiment analysis, data synthesis, and report generation.

While the application of generative AI is rapidly growing in various research areas, its impact is most evident in high-risk, data-intensive domains like fraud detection.

According to Consainsights, the Generative AI in Fraud Detection market alone was valued at $1.8 billion in 2023 and is expected to grow at a CAGR of 6.3%, reaching $3.37 billion by 2033.

Top sectors adopting Gen AI for research-driven functions include financial services, insurance, retail, and government, where fraud detection and behavioural modelling are critical.

Key Use Cases of Generative AI in Market Research

Generative AI is transforming market research by streamlining complex workflows and producing insights at unprecedented speed. From designing surveys to simulating market trends, its capabilities cover both qualitative and quantitative domains. Here are the most impactful use cases:

1. Survey Design and Optimisation

Generative AI can rapidly draft, test, and refine survey questions tailored to specific audiences and research goals. It reduces bias, improves clarity, and suggests phrasing variations that improve response rates.

2. Persona and Audience Development

By analyzing customer data (e.g., purchase history, CRM logs, or social interactions), generative AI can build detailed personas that include demographic, psychographic, and behavioral insights.

These personas help researchers and marketers understand motivations, barriers, and potential triggers, without manual segmentation.

3. Trend Detection and Market Synthesis

Gen AI tools can scan thousands of online sources, including social media, forums, news sites, and search queries, to identify emerging trends, unmet needs, and shifts in customer sentiment in real-time.

Tools like Perplexity AI and ChatGPT with browsing or plugin access are used for live market pulse checks. This helps researchers spot trends nearly 6–12 months earlier than traditional research methods.

4. Competitive and Product Analysis

AI can generate structured competitor benchmarks by comparing website content, reviews, pricing, and product features, saving hours of manual research.

Tools like Eliotron enable ecommerce teams to automate product tracking and generate AI-written SWOT summaries, helping brands stay agile.

5. Qualitative Data Summarization

Open-ended feedback from surveys, interviews, and focus groups can be difficult to process manually. Generative AI reads and synthesises these responses into clear, actionable summaries.

A 2023 study by McKinsey found that teams using LLMs like ChatGPT for qualitative analysis reduced the time spent on coding responses by up to 70%.

6. Ideation and Content Co-Creation

Generative AI can support early-stage ideation for new products, services, campaigns, or positioning statements. Researchers and marketers can use it to generate naming options, value propositions, customer pain point narratives, or creative concepts based on existing market data.

For example, tools like ChatGPT, Copy.ai, and Jasper are used to co-create product taglines, write UX microcopy, and simulate A/B variations, cutting down creative development time by nearly 30–50%.

7. Report Generation and Visualisation

With data uploaded in the form of CSV, Excel, or PDF files, generative AI tools can summarize findings, create charts, and even draft entire research reports or executive summaries with key insights and recommendations.

Research teams are increasingly using platforms like Julius AI and Notion AI to automatically generate slide decks or visual dashboards from raw research data.

8. Real-Time Q&A and Data Exploration

When connected with internal datasets or web data, generative AI enables researchers to ask ad hoc questions like:

Tools such as Power BI with Copilot, Tableau GPT, or ChatGPT’s Advanced Data Analysis (ADA) enable real-time, conversational analysis, which is especially helpful in time-bound strategy sprints or C-level presentations.

Step-by-Step: How to Use Gen AI for Market Research

Generative AI can serve as a powerful assistant throughout your market research process, from forming hypotheses to delivering final insights. Here’s a practical step-by-step guide on how to actually use Gen AI tools to run market research efficiently

Scenario: Research Why Gen Z Users Uninstall a Fitness App

Step 1: Define the Research Objective

A well-defined research objective acts as the foundation for your entire study. It guides not just the questions you ask but the direction your analysis will take. When using Gen AI tools, clarity at this stage ensures the AI understands the scope and context, helping you get targeted and meaningful outputs.

Without a specific objective, generative AI may deliver surface-level or irrelevant information. A strong goal helps narrow the AI’s focus and produces outputs that align with your business need, whether it's user churn, onboarding drop-off, or feature dissatisfaction.

How to Use Gen AI:

We are using ChatGPT here. 

Prompt:

ChatGPT Output:

Did Gen AI help? Yes. It clarified the problem and suggested language I could directly use in a stakeholder brief.

Would I use Gen AI again for this? Definitely, for quickly framing hypotheses and objectives, it’s incredibly efficient.

Step 2: Generate Survey Data

This step involves generating simulated responses from hypothetical users. By describing the target audience and scenario clearly, you can use Gen AI to create data that mirrors real-life responses. These aren’t just placeholders, they help you test your assumptions and understand the likely pain points from a user’s perspective.

When timelines are tight or real user data isn't available, simulated feedback provides a practical workaround. It lets teams build prototypes, validate assumptions, and run internal demos without waiting weeks for survey collection and analysis.

How to Use Gen AI:

Prompt:

AI Output:

Did Gen AI help? Absolutely. We didn’t need to fabricate examples manually; the tone, language, and variety felt authentic.

Would I use Gen AI again? Yes, especially for training junior researchers or prototyping surveys and analysis workflows.

Step 3: Analyze and Theme the Responses

The process here is about clustering user feedback into common themes, like frustrations with usability, low motivation, or technical bugs. Gen AI can quickly scan responses and group similar ideas, saving hours of manual coding typically required in qualitative research.

This matters because understanding recurring themes is essential to uncovering the “why” behind user behavior. Gen AI speeds this up, offering structured insights instantly. It’s a massive time-saver, especially during early-stage testing or stakeholder presentations.

How to Use Gen AI:

Prompt:

AI Output:

Did Gen AI help? It saved hours. Normally, I’d manually code responses. The AI identified recurring words, grouped them effectively, and prepared them for charts.

Would I use Gen AI again? Yes, for qualitative summarization, it’s a no-brainer.

Step 4: Generate a User Persona

Creating personas allows teams to put a human face to the data. Gen AI can take thematic insights and build a relatable profile, complete with age, name, goals, frustrations, and behavior traits. This persona becomes a storytelling tool to unify design, marketing, and product decisions.

User personas are critical for empathy-driven work. They help teams make user-focused decisions, not guesses. And with Gen AI, you can go from raw data to a fully fleshed-out persona in seconds, ready for design sprints, pitch decks, or product planning.

How to Use Gen AI:

Prompt:

AI Output:

Did Gen AI help? Yes. The persona felt relatable and could be effectively used in a pitch deck or during a product sprint.

Would I use Gen AI again? Yes, for creating quick user stories and journey maps, it’s very practical.

Step 5: Simulate the Customer Journey

Mapping a user’s journey helps uncover key friction points. It reveals when and why users disengage, which is critical for product and marketing teams. 

This step is about mapping the full experience a user has with your product, from initial discovery to the point they drop off. With just a prompt, Gen AI can simulate this flow and identify friction points like poor onboarding or lack of motivation that may not be obvious from analytics alone.

Understanding the customer journey helps product and marketing teams spot exactly where users lose interest. It gives context to behavioral patterns and supports better UX and retention strategies, without needing real-time behavioral data.

How to Use Gen AI:

Prompt:

AI Output:

Did Gen AI help? It illustrated the friction journey better than I could have written manually. Great for UX reviews or design sprints.

Would I use Gen AI again? Yes, for mapping and storytelling around churn triggers.

Step 6: Benchmark Competitors with AI

Secondary research is crucial for market positioning. Gen AI helps gather, compare, and summarise competitor insights from public data. 

Benchmarking involves evaluating how similar apps or services compare across critical user-facing dimensions. Gen AI can summarize competitor features, onboarding processes, pricing models, and more, using publicly available data to generate quick, structured comparisons.

This helps businesses save time on manual research and spot strategic gaps. Whether it’s for internal analysis, investor decks, or go-to-market planning, AI-driven benchmarking lets you stay informed and move faster, without poring over every app store listing or review site.

How to Use Gen AI:

Prompt:

AI Output:

Did Gen AI help? It provided a concise snapshot that I could easily incorporate into a competitor slide. Much faster than manual review.

Would I use Gen AI again? For exploratory research, 100% yes.

Step 7: Summarize Findings and Recommend Actions

Your insights need to be shared. Gen AI helps convert all the work into concise, executive-ready summaries. 

At this stage, you’re turning insights into a compelling narrative. Gen AI takes all the inputs you’ve gathered and distills them into clear summaries, often structured for stakeholders. It can also suggest next steps based on the findings, ensuring your research drives action.

This is where the value of research comes alive. A well-written summary boosts credibility, aids decision-making, and saves hours of drafting. AI makes the wrap-up process feel effortless, allowing you to deliver professional-grade reports even under tight deadlines.

How to Use Gen AI:

Prompt:

AI Output:

Did Gen AI help? It pulled everything together better than a first draft I’d write manually. The tone was polished and shareable.

Would I use Gen AI again? Yes, for presentation prep and final report summaries, it’s extremely effective.

Final Verdict: Is Gen AI Worth Using for Market Research?

Yes. For exploratory research, training, mock presentations, and even early product testing, generative AI is a powerful tool. It accelerates thinking, simulates datasets, and provides structure, especially when real data is delayed or unavailable.

“Gen AI won’t replace skilled researchers, but it will replace slow ones.”

Top Generative AI Tools for Market Research

According to Consainsights, the global Generative AI Tools market is projected to grow from $7.50 billion in 2023 to $51.81 billion by 2033, achieving a robust CAGR of 20.1%. This growth is fueled by rising enterprise demand for content automation, real-time analysis, and insight-driven decision-making.

1. ChatGPT by OpenAI

What it does:

ChatGPT excels at generating human-like text, making it ideal for automating qualitative tasks in market research, such as:

• Writing surveys and research briefs

• Summarizing lengthy reports or transcripts

• Creating buyer personas from unstructured data

• Simulating customer responses for testing

Market Impact:
As text generation tools lead the market, growing from $4.61 billion in 2023 to $31.87 billion by 2033, ChatGPT stands as the flagship solution, particularly among enterprise users, who dominate 84% of the market by deployment preference (cloud-based tools).

2. Google Gemini (formerly Bard)

What it does:
Google’s Gemini is known for integrating live search data with NLP to:

• Analyze trends across time and geography

• Extract real-time insights from forums, SERPs, and datasets

• Draft early-stage market forecasts

Why it matters:
As the Asia-Pacific region surges from $1.40 billion to $9.70 billion by 2033, Gemini’s multilingual capabilities and Google’s strong R&D presence in India and China make it a powerful tool for market intelligence in emerging regions.

3. Adobe Firefly

What it does:
Firefly allows researchers and marketers to:

• Generate realistic mockups for product testing

• Create visual stimuli for focus groups

• Quickly design creative assets for A/B testing

Industry Use Case:
The marketing industry, projected to grow from $1.84 billion to $12.68 billion, will increasingly rely on tools like Firefly to enhance campaign ideation and message testing visually.

4. Jasper AI

What it does:
Jasper AI is a content automation platform ideal for:

• Producing blog posts, whitepapers, and executive summaries

• Turning research findings into client-ready collateral

• Personalizing reports for different stakeholders

Deployment Advantage:
Jasper operates fully in the cloud, aligning with the dominant cloud-based deployment trend, which is expected to reach $43.60 billion by 2033, capturing 84.15% of the market.

5. Synthesia

What it does:
Synthesia converts text into AI-powered video avatars. Researchers use it to:

• Present findings via short videos

• Localize presentations in multiple languages

• Engage decision-makers through visual storytelling

Market Potential:
As Creative Arts functionality grows from $1.69 billion to $11.70 billion, tools like Synthesia will play a key role in transforming how insights are communicated.

6. IBM Watsonx.ai

What it does:
IBM Watson provides robust tools for:

• Analyzing structured and unstructured data

• Creating compliance-ready documents

• Powering large-scale industry-specific AI applications

Enterprise Edge:
Watson is preferred in sectors such as healthcare and finance, where data privacy and AI governance are crucial. With healthcare alone growing from $1.52B to $6.61B, Watson’s role in regulated research is expanding rapidly.

7. Microsoft Azure AI (Copilot)

What it does:
Azure AI integrates with Microsoft tools like Excel, Word, and Power BI, making it ideal for:

• Auto-generating data visualizations

• Building slide decks from raw data

• Enabling self-service research for business users

Regional Driver:
In North America, where the market will grow from $2.56 billion to $17.71 billion, Azure AI benefits from Microsoft’s enterprise base and seamless integration into research ecosystems.

Comparison Table: Top Generative AI Tools for Market Research

FAQs

How does generative AI help with survey design?

Generative AI helps with survey design by drafting, refining, and testing questions tailored to specific audiences. It reduces bias, improves clarity, and optimizes phrasing to increase response rates. Researchers utilize it to accelerate survey creation and test hypotheses quickly.

What are the benefits of using Gen AI for qualitative research?

The benefits of using Gen AI for qualitative research include faster coding of open-ended responses, real-time summarization, and theme detection. It reduces manual effort by up to 70%, allowing teams to focus on insights rather than data processing.

How is generative AI used for competitor analysis in market research?

Generative AI supports competitor analysis by scanning public data to summarize features, pricing, and user sentiment. It benchmarks brands instantly, enabling faster SWOT analysis and market positioning without the need for manual comparison.

Can Gen AI simulate market scenarios?

Gen AI simulates market scenarios by using historical data to model potential outcomes. It quickly tests “what-if” situations, helping teams anticipate risks, forecast demand, and validate strategies before investing resources.