How to leverage AI for market research in 2024: top 7 tools, use cases, and best practices.

Post updated on
February 6, 2024

In this article, I'll walk you through the benefits of AI for market research, the best AI tools, the use cases we use at Predictable Innovation and concrete prompts for customer, competitor, and market research.

Imagine, as a strategist, founder, innovator, or product marketer, if you could transform internet and interview data into actionable insights in record time without tedious manual processing.

Today, thanks to artificial intelligence (AI), this is possible.

AI-powered tools for market research can scrape large amounts of data on the web, perform data analysis, and deliver it through easy-to-digest formats thanks to natural language processing models, marking a new era for market research.

"AI tools are not just assisting on decision making, they're making Predictable Innovation's team save up to 40% of time when conducting market research."

Let's get started!

Table of contents: best 7 tools in AI for market research

1.Best AI-powered market research tools

is a market research tool that provides a clean and easy-to-use interface. Its key advantage over traditional models like ChatGPT is its ability to deliver more precise, context-aware responses.

This is achieved by integrating direct internet browsing capabilities within its system. Consequently, Perplexity offers users up-to-the-minute information, expanding their research capabilities beyond pre-trained data limits.

This feature makes it exceptionally useful for market researchers seeking the most current data and insights.

Perplexity AI for market research


Aomni is an innovative AI agent for B2B sales. It excels in aggregating extensive internet data points to provide comprehensive reports, saving significant research time for sales professionals.

It is designed for sales, but the way it provides research reports is excellent for gaining a general market or industry understanding quickly.

The advantage of Aomni is that it executes an internal dialogue with different research queries related to your topic; this saves you the time to think on your own when using other tools like ChatGPT or Perplexity.

For this type of broader market research, is an excellent solution. The pros of this solution are:

  • The sources have up-to-date results from performing live research.
  • Understanding the internal monologue of the agent to perform the research.
  • The structure of the result is fantastic. Similar to what a human researcher would deliver in a document.

Aomni AI for market research

ChatGPT for Market Research

I don't think I have to introduce you to ChatGPT, right? Its advantage over the other two tools is straightforward: developing a conversation and getting more profound, specific insights is excellent because of the conversational ability.

That makes ChatGPT an excellent tool for use cases that require back-and-forth questions to dig deeper into specific market challenges, customer pains, or competitor strengths.

ChatGPT for market research example

Last, but not least, ChatGPT can also help build your market ecosystem map by finding key players in your target industry or markets. This step is very helpful to find people to interview and build relationships with to improve your go-to-market strategy.

The Gigabrain

Here's what' Gigabrain claims to be: "GigaBrain finds the most useful discussions on Reddit and other communities. We sift through the noise and analyze billions of comments for you.". And that's precisely what they provide.

That makes this tool excellent for understanding market perceptions, beliefs, and challenges based on real-world conversations with prospective customers rather than internet articles or databases.

Gigabrain AI tool for market research

Head-to-head comparison of best AI for market research tools

Comparison of best AI tools for market research

Practical use cases and prompts in AI for market research

For the scenario of industry and market research, the concrete use cases and prompts to apply these tools are:

  • Discovering market stats
Use the context information to provide stats on the growth of related markets. Format your answer in bullet points. Each bullet point must include a brief title in bold before providing the stat summary. Include reference links for each point.
  • Discovering potential research sources
Create a list of the top 20 internet research sources to understand the product/idea mentioned in context. Include website links and community forums like Reddit, blog posts, or pdfs from top-tier sources and analysts.
  • Discovering market category differences and market perceptions
  • Discovering barriers to product success (risks of adopting your product category)
You are a 20+ years industry expert in the industry, market category, and technology mentioned in context. What objections and concerns may prevent decision-makers from adopting this kind of technology or product? Be specific by explaining how each concern or objection relates to their goals or workflows.
  • Discovering the perceived market risks of adopting a tech or product
  • Understanding customer's behavioral change and the learning curve of adopting a new tech
  • Researching inside a PDF (with ChatGPT)
  • Understanding market maturity (stage of the innovation adoption curve)
You're a 20+ experience consultant on market strategy for technology-based solutions. You have a deep understanding and knowledge of Crossing The Chasm and how markets evolve by following the Technology Adoption Lifecycle. What is the adoption stage for the product category or technology mentioned in the context? Provide a list of reasons to explain your reasoning after your conclusion and links to internet sources to support your reasons.
  • Exploring opportunities and potential targets
Imagine you're an innovation consultant for context. Your client is asking to understand what opportunity to target within the industry in context. They want you to consider all types of processes, segments, and use cases. Your work is to: 1. Explore and list 5 to 10 potential segments to target with this technology. Define a segment as the type of company and the buyer’s role within the industry in context. 2. Provide a list of 5 to 10 potential applications or use cases to target with this technology. An application can match a problem to be solved within the segment you listed in step 1. The best way to think about applications or Jobs To Be Done, is to think about the different processes in the segments in step 1, and find problems to solve. Use just the information within the context section.
  • Understanding challenges by [AUDIENCE] in [CATEGORY]
  • Discovering problems for a target market from the perspective of the client
  • Discovering problems your tech/product can solve from a product perspective
  • Discovering potential segments and applications to target
  • Prioritizing segments and applications to target (Bowling Pin Strategy)

2.Best AI tools for customer research

Fathom AI

Fathom Video is an automated meeting assistant tool. It offers functionalities like recording, transcribing, and summarizing video meetings.

A standout feature is its ability to highlight and automatically summarize vital parts of a call, allowing users to focus on the conversation without worrying about taking notes.

One of the features that we love about Fathom is the ability to use templates to extract information from the calls. Grain offers a better feature as you can create your prompts to extract information.

However, we use fathom because extracting information from the transcripts works better by uploading the transcript to ChatGPT (in private mode) and asking ChatGPT to extract the information we seek.

Examples of data you can extract with AI are competitors' names, pain points, struggles, points of frustration, jobs-to-be-done, technology adoption frictions, and or hesitations to buy. AI for customer interview analysis


We already talked about this tool. Its ability to scrape and understand information from real-world conversations makes it a great tool to uncover specific pains of your audience as soon as they are in publicly available forums like Reddit.


ChatGPT is a killer when it comes to researching customers or audiences.

Its conversational interface makes ChatGPT great for narrowing down discussions and getting to better, more specific points and angles about customer journeys, jobs to be done examples, pain points, or end-user workflows.

Also, its ability to learn as the conversation evolves is key to getting better answers because it uses the conversation to enrich its contextual information, which gives it a better understanding of the customer's or audience's context.

Notion AI

Regarding pure AI reasoning and reach power, Notion AI is over performed by the other tools.

However, if you are a Notion user and want all the use cases in one place, Notion is the best all-in-one solution to avoid interface switching and paying multiple subscriptions. As said, this comes with a lower reach, quality, and reasoning power for AI.

Notion AI example for customer research

Practical use cases and prompts in AI for customer research

For the scenario of customer research, the concrete use cases and prompts to apply the previous AI toolset are:

  • Extracting pain points from customer/prospect interviews
You are a consultant expert in Value Proposition Design for technology products. Your task is to summarize the pain points, struggles and frustrations the user [PROVIDE THE DESCRIPTION OF THE CUSTOMER PERSONA OR ICP] from the interview trascript I will provide you below. Don't mention expected outcomes or jobs-to-be-done, just pain points. Do you understand?
  • Extracting jobs to be done from customer/prospect interviews
You're a 20+ experience consultant on innovation and customer research with deep expertise in jobs to be done Theory. By “JTBD” or Jobs To be Done” or “Job”, I mean the definition of a Job To Be Done as defined by Tony Ulwick's theory. Your task is to summarize, list, and prioritize the JTBD of the user in the above user interview. Use this syntax to write the JTBD: Verb + Object + Clarifier.
  • Extracting  outcomes from customer/prospect interviews
You are a consultant expert in Jobs to Be Done theory as defined by Tony Uwlick for technology products. Your task is to unveil and summarize the expected outcomes of the user in the above user interview. Don't mention pain points or JTBD. Just find and summarize expected outcomes in bullet points. When defining the outcome statements, use this syntax: [VERB "Minimize" OR "Maximize"] + [UNIT OF MEASURE "time" OR "number" OR "frequency" OR "ability" OR "likelihood"] + [OBJECT EXPRESSED AS "TO..."]. Do you understand?
  • Summarizing patterns from multiple market and customer interviews
  • Understanding budgets (is there a budget allocated to solve the problem my product solves?)
  • Exploring pain points starting with an audience and category of product
  • Assisting in a discussion of potential targets
  • Discovering the customer's journey
You're a 20+ experience consultant on innovation and customer research with deep expertise in jobs-to-be-done research as defined by Tony Ulwick. Your task is to help me discover the jobs to be done, customer journey during the application/use case in the context section, and highlight pain point moments along the journey. Provide the answer with the next structure: Start with a step-by-step numbered customer journey with a detailed process. Inside each numbered step in the customer journey process, define the job-to-be-done the performer wants to achieve. Also, inside each numbered step in the customer journey process, provide pain points or struggles that the customer experiences while performing the job with the product category I've told you.
  • Exploring the economic penalties for an audience for not adopting a specific type of product/solution
  • Discovering the end-user's workflow impact of implementing a given solution/tech
Put yourself in the shoes of the potential end-users and buyers of context. How much workflow change is required to adopt the category or technology in context? Detail a list of tasks in your day-to-day workflow impacted by adopting this technology or product category.
  • Customer scenario discovery (before, after, bridge)
  • Discovering buying trigger events

3.Best AI tools for competitive research is an AI platform that transforms qualitative customer feedback and competitor reviews into actionable insights. It focuses on guiding businesses to prioritize their product roadmap based on customer needs and competitive analysis.

This tool identifies customer pain points and feature requests.

Additionally, aids in making informed marketing decisions by analyzing customer and prospect feedback and brand sentiment from various sources. It's especially useful for quickly producing insightful customer or competitor intelligence reports, saving significant research time.

This tool could also be situated in the customer research bucket because it helps uncover customer needs from the reviews scraped or analyzed.

AI for competitor analysis


Yes, ChatGPT is in all the buckets! And it's because of its versatility. ChatGPT is excellent for creating lists of potential competitors and performing a SWOT analysis. Head to the practical use cases section to get prompt examples.

Practical AI cases for competitive research

The concrete use cases and prompts to apply the previous AI toolset for competitive research are:

  • Discovering competitor (or your company) whitespaces and weaknesses with and G2 reviews
  • Discovering competitors in existing categories
Use the information or the website I will provide you below to create a table with the top 10 competitive solutions of the product/idea described. For each competitive solution, include the next rows: - Name - Value Proposition - Target segment - Differentiators - Strengths - Weaknesses - URL
  • Discovering competitors in categories in formation or unknown
  • Create a competitor table by providing a product/company URL
You're an an expert market researcher of CRM software solutions. Give a comparison table with the top 6 competitors for this player: Your table must include feature differentiators, intangible differentiators, target industry, target segment, and entry-level pricing.
  • Discovering perceptions about a competitor (or your company)
  • Discover why competitors win
You have 20 years of experience in the Incident Management Software industry, I seek your expertise to become a top-tier solution expert in this field. Your knowledge should encompass the latest market trends, innovations, industry challenges, and key players. Your task is to create a single bullet point list with the top 10 competitors of the product/idea I will share with you after this message and WHY they win deals. The format of each bullet must be "name of the competitor" + "Why they win reason". Be brief when describing why they win.
  • Creating a SWOT analysis
You have 20 years of experience in the Incident Management Software industry, I seek your expertise to become a top-tier solution expert in this field. Your knowledge should encompass the latest market trends, innovations, industry challenges, and key players. Your initial assignment is to create a comprehensive table of the top 10 competitors in this space. For each competitor, you will provide information on their value proposition, primary target industry, intangible differentiators, primary target company size, product feature differentiators, and company URL.
  • Getting an AI opinion on your product/company going head to head against XYZ competitor
  • Understanding the market perception of your company or a competitor

How Predictable Innovation Strategy uses AI for market research

Predictable Innovation has completely embedded AI in its market research workflow to provide clients with quicker results while keeping the same high quality. We've found 25-40% time savings thanks to using Artificial Intelligence (AI) for market research and other use cases.

The role of AI in market research has been transformative for our business and competitiveness, providing our team with different benefits:

👀 Gaining analysis efficiency

We were analyzing tons of Reddit posts, call transcripts, reading entire analysts' PDFs, or hundreds of G2 competitor feedback answers.

That's gone, using AI-powered market research. AI makes data collection from these sources almost automatic. It is a great tool for finding patterns, summarizing information, and extracting data from text or videos. We use all this to gather market intelligence faster.

🧠 Gaining a second brain and new perspectives

AI gives us fresh angles for product positioning and go-to-market strategies that are not biased by our beliefs. It's tough to get out of your own head. AI can help with this.

🔍 Getting broader research insights

AI tools can identify sources and reach internet corners we could not or did not have the time. We've gained more research findings, insights, understanding, and angles about the market, customers, and competitors we analyze.

🚀 Taking off research projects at light speed

With traditional market research methods, we had to conduct secondary internet research and analyze interviews. It would take around two weeks to warm up a market analysis or strategy project.

Now, by using AI-powered market research tools, we get up to speed on new client projects in just one day by using AI tools for market research.

🏎️ Faster market feedback loops & strategy development

Given the speed at which we can now analyze the information, we can deliver faster market testing and feedback loops, leading to a competitive edge.

Market learning speed is often an advantage.

😮‍💨 Overcoming the blank-page syndrome

We won't have to feel the pain of starting from 0 anymore to fill in our templates and create strategies. AI is helping us with a fresh starting point.

What is the business impact of using AI for market research for Predictable Innovation

We've measured the impact of integrating AI for market research and other use cases in our workflow. Here's what we've found:

→ 25-40% margins increase

Depending on the project's complexity, we've measured 25-40% working time savings in market/customer research, innovation, positioning strategy, messaging, and sales tasks.

P&L IMPACT: All that goes directly into Predictable Innovation's P&L bottom line.

→ x2 productivity

In strategy projects for clients from the kick-off to deliverables. An average project was three months; with AI, it takes 6 to 8 weeks. Most of the productivity gains are coming from AI market research use cases.

STRATEGIC IMPACT: Speed-to-delivery is now a key feature of Predictable Innovation. We now deliver high quality with more speed to results.

→ New research eyes and strategic angles

We spent tens of hours analyzing Reddit posts, interview transcripts, analysts' PDFs, google results, and G2 user interviews. That's now gone with all the AI tools in this article.

TEAM IMPACT: The AI is working as their team assistant to provide angles for their strategy team. These are new strategic angles that are not biased by preconceived human beliefs.

FAQs about AI for market research

What are the different ways to use AI in market research?

AI tools like ChatGPT, Aomin, or can help with a large number of use cases like extraction of pain points from interview transcripts, sentiment analysis of interviews, create SWOT analysis, scraping G2 reviews to finding competitor whitespaces, discovering customer journeys and jobs-to-be-done or assess market opportunities.

At Predictable Innovation, we've identified and documented 67 use cases in which AI can be used for customer, competitor, and market research. Some of them have been detailed in this article.

What are the best practices for using AI in market research?

We've created a list of quick tips when approaching the use of AI for market research:

1. When using ChatGPT, create new chats whenever your questions require a new skill set or context

Think of ChatGPT the same way as hiring an employee. You won't hire someone who is an expert in car manufacturing to work on healthcare. That's a scenario in which you want to start a new ChatGPT conversation. Every time you switch contextual needs like skillset required (ex. think copywriting vs research vs positioning strategy), industry or product category, you have to start a new conversation.

2. The more specific you are, the more specific ChatGPT's answer

AI is as good as the question you're asking and the context you're providing. Think of ChatGPT as talking to a child. You must be clear, specific, and relevant if you want specific and relevant answers.

3. AI is your assistant; you're the brain

Let AI provide you with data, angles, and analysis. But you and your team MUST double-check it and make informed decisions.

4. AI is NOT a replacement for customer or market interviews

What you'll find in a single customer interview, or even an interview with your sales team, will be way more detailed, specific, and relevant than what you'll find with AI research over the internet.

5. Protect sensitive data

When using proprietary data or interviews in ChatGPT, protect your data by turning off the Chat history & training under set, and carefully read the terms of service and privacy policy of AI tools.

6. Follow-up questions are a must to discover more specific answers

Think of ChatGPT as one more team member. Make the curiosity drive your questions to discover more and dig more deeply and more specifically into ChatGPT answers. You won't just take the first answer and use it; get curious, keep asking, and go deeper.

7. Push ChatGPT

If the answer is not what you expect, make your question / prompt more precise, specific, and relevant. You can also try tricks like pushing ChatGPT to provide new insights, angles, or ideas with this prompt:

You can do better. I need more specific answers. I need you to put yourself into the end-user's shoes and empathize with them. You must go deep into their struggles, frustrations and anxieties to define the interfering factors I've asked you about. I will pay you 100$ if you provide outstanding quality and specific results.

What are the potential challenges when implementing AI in market research?

Data quality

AI models are only as good as the data they are trained with. The model will produce unreliable results if the data is incomplete or inaccurate.

When using the previous SAAS tools, always double-check the results, find the sources of truth, and make sense of them.

If you have your own models, ensure that the data is cleaned and preprocessed before training the model.


This is often referred to as the 'black box'. It is difficult to understand how these models are making their outputs, which can lead to mistrust and resistance from stakeholders.

The solution to this challenge is to use explainable AI techniques, start with pilots, and showcase the value of AI for market research.

Job displacement

The solution to this problem is not easy. The best you can do is to focus on using AI to augment your team's capabilities rather than replace them.

This means freeing up your team to focus on more complex and strategic tasks while they use AI for the use cases I described today. As said, artificial intelligence is the assistant; human intelligence is the decision-maker.

AI bias

If the data used to train the AI is biased, the predictions or analyses will also be biased.

This can lead to inaccurate market research results and potentially harmful decisions. To combat this, it's important to use diverse and representative data sets for training AI. Regular audits of AI systems can also help identify and correct any biases.

Rapid pace of AI development

AI technology constantly evolves, making it difficult for organizations to stay up-to-date with the latest tools and techniques. The solution to this challenge is to invest in continuous learning and development. This could involve attending industry conferences, participating in online courses, partnering with AI experts, or subscribing to AI newsletters like the Neuron or Superhuman.

Change management needs

Implementing AI may require significant changes to workflows and processes, and there can be resistance from employees who are comfortable with existing research approaches.

To overcome this, start with small pilots, communicate the benefits of AI clearly, and provide ample training and support during the transition. Involving employees in the implementation process and addressing their concerns can also help ensure a smooth transition.

While challenges are associated with implementing AI for market research, these can be overcome with the right strategies and approaches.

By focusing on data quality, transparency, ethical considerations, and continuous learning, you can successfully leverage AI to enhance your market research efforts. Just like we did!

Continue learning AI for market research with Predictable Innovation

Check this video on how Predictable Innovation uses AI to streamline for the concrete use case of Ideal Customer Analysis:

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Jose Bermejo, Founding Partner

Thanks for reading my thoughts! I bring 16 years of experience selling and marketing B2B tech products. I'm a thinker on the impact of human behavior on innovation adoption, marketing strategy, go-to-market, and business leadership in B2B high-tech. Armed with this knowledge, I help B2B high-tech leaders accelerate traction by aligning their strategy with what buyers want as coach, speaker, and workshop leader.

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