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Most teams don’t fail because they lack ideas. They fail because they don’t really know their market. AI changes that equation by turning messy, fragmented signals into clear, usable insight.
Instead of running a few surveys and guessing the rest, AI systems read everything at scale: CRM records, support tickets, LinkedIn conversations, email replies, website behavior, even public reviews. Through continuous enrichment, customer profiles stop being static fields and become living, evolving pictures of segments, needs, and buying triggers.
Patterns that used to take weeks to spot suddenly appear in minutes: why certain deals stall, which messages resonate with specific industries, which channels actually move pipeline. AI doesn’t replace classic market research. It supercharges it, so B2B teams can:
- Identify micro-segments that competitors ignore
- Test positioning and messaging with real data, not opinions
- Prioritize markets, accounts, and personas with the highest upside
With the right AI stack, market research stops being a one-shot project and becomes an always-on competitive advantage.
What Is A Market Research in B2B?
Market research in B2B means understanding who your buyers are, what they need, and how they decide to buy. It focuses on companies, decision-makers, influencers, and buying committees rather than individual consumers.
Instead of studying broad lifestyle habits, B2B research zooms in on things like industry, company size, tech stack, budget, timelines, and business pain points. The goal is simple: reduce guesswork and make smarter calls about which markets to target, what to offer, and how to position it.
A solid B2B market research process usually covers:
- Market sizing: How big is the opportunity and is it growing?
- Segmentation: Which industries, regions, or company profiles offer the best fit?
- Buyer personas: Who is involved in the deal and what each stakeholder cares about.
- Competitive landscape: Who else sells similar solutions and how they position themselves.
- Customer voice: How prospects describe their problems, goals, and success criteria.
In a nutshell, market research guides product decisions, pricing, messaging, and sales strategy so teams invest where impact is highest.
How Can AI Help A Company Do Deeper Market Research?
AI helps B2B teams go beyond static reports and surface-level insights. Instead of relying on a few interviews and manual spreadsheets, AI engines scan huge volumes of data, connect signals, and update insights continuously as the market moves.
1. Enrich and Centralize Customer Data
Most market research problems start with incomplete or scattered data. AI-based enrichment fixes that by pulling signals from multiple sources and stitching them into a single, reliable view of each account and contact.
Instead of manual research on LinkedIn, company websites, and tools like Crunchbase, AI can automatically add firmographic, technographic, and behavioral data to CRM records. Job titles, industries, headcount ranges, tech stack, funding events, and intent signals stay up to date without extra manual work.
This turns raw contact lists into market intelligence. Teams can:
- See which segments are most active or fastest to convert
- Compare performance by industry, size, or region
- Filter accounts by real potential, not just guesswork
A clean, enriched database becomes the foundation for deeper analysis later: segmentation, messaging tests, win–loss analysis, and forecasting all become more accurate because the underlying data finally reflects reality.
2. Analyze Customer Conversations to Spot Real Pain Points
Market research often relies on what buyers say in a survey, not what they reveal in real conversations. AI changes that reality: it scans sales calls, email threads, support tickets, and LinkedIn messages to detect recurring topics, objections, and outcomes. Instead of scattered notes, teams see clear themes: Which problems appear first, which outcomes matter most, and which competitors enter the discussion.
When AI turns unstructured conversations into structured insight, patterns emerge around how different segments talk about value, risk, and urgency. Product, marketing, and sales finally align on the same reality: what the market actually cares about, how it describes success, and where friction slows deals down.
💡 folk tip: With folk CRM, you centralize email, calendar, and LinkedIn interactions in a single relationship timeline so AI can analyze patterns across every touchpoint, not just a few hand-picked calls.
3. Scoring & Find the Best Segments
AI scoring takes the market you already reach and tells you exactly which segment deserves priority first. The model looks at hard data in your CRM: industry, company size, country, tech stack, deal size, sales cycle length, reply rate, demo attendance, and close rate for the last 6–12 months. Each account, and each segment, receives a score from 0 to 100 based on real performance.
A practical workflow looks like this:
- Select all closed-won and closed-lost deals from the last year in your CRM.
- Let an AI model compare attributes (industry, size, region, channel, product line) against outcomes.
- Get a ranked list such as: “IT services in DACH with 50–200 employees and active outbound = score 92”, “US agencies under 20 employees = score 48”.
The result is concrete: sales and marketing know that “IT services in DACH, 50–200 employees” currently converts best, brings the highest ACV, and closes faster. Campaigns, budget, and SDR time move toward that segment first instead of spreading effort across the entire market.
4. Use AI to Read LinkedIn and Spot Real Buying Signals
LinkedIn shows what your market cares about each day: posts, comments, job changes, and new connections. AI tools read this activity at scale and turn it into simple answers 👉 Who talks about what, and what starts real sales conversations.
A concrete workflow looks like this: sales and marketing save target profiles and conversations into the CRM, then AI groups posts and comments into clear themes such as “outbound automation”, “data enrichment”, or “pipeline health”. For each theme, the team sees how many people react, click, or book a demo afterward.
A few examples of what this makes visible:
- Topics that bring the most replies and meetings from your ideal job titles.
- Posts and angles that attract decision-makers, not just general followers.
- Signals that someone moves closer to a buying decision, such as content about tools, pricing, or process change.
With this, LinkedIn stops being a noisy feed and becomes a live radar of what your market wants to talk about right now.
5. Fast Lead and Company Research
Before a call or an email, reps often open ten tabs to understand who they talk to. AI removes that step and shows a clear snapshot for each lead and company in one place: what the company does, its size, recent changes, and what the contact owns in the org. The tool reads public data and past interactions, then turns it into a short brief that even a new rep understands.
Concrete example: a sales team prepares outreach to 50 new leads. Inside folk, they open the list and use lead and company research to see for each record:
- A one-line summary of what the company sells and to whom.
- Simple facts such as industry, headcount range, and region.
- A short suggestion for an angle that fits the role and context (for instance, “focus on pipeline visibility for this Head of Sales”).
With this, market research happens at deal level: Every email and call starts from real context instead of generic messaging, and teams see which types of companies react best to each angle.
6. Automated Email Questionnaires to Ask Market Questions
Some information never appears in a call: budget range, tools already in place, reasons to buy now or later. Short email questionnaires solve that fast. A two or three question message goes to a clear segment (for example “closed-won customers” or “lost deals”), collects answers, and feeds your research with fresh, structured data.
Inside folk CRM, a team can create a list, attach a simple questionnaire to an email sequence, and let AI read the replies. Closed questions update fields such as budget or tool stack, open answers group into themes like “price”, “onboarding effort”, or “reporting”. Over time, those automated emails show very clearly who plans to invest, what they want to fix first, and why they choose one solution over another.
5 Best AI Tools for Market Research in 2026
Conclusion
Market research stays shallow when it runs once a year and lives in a slide deck. With AI, it becomes a continuous loop: new data from CRM, LinkedIn, surveys, and product usage feeds simple answers to basic questions — who to target, what to say, and where the next euro of budget should go.
The difference comes from execution, not buzzwords. Teams that centralize their relationships, enrich context, and read patterns across conversations always understand their market better than those that rely on guesswork. A tool like folk CRM helps at this level: lead and company research, smart lists, and shared timelines give sales and marketing the context they need before every campaign or call.
Used consistently, AI-supported research guides the whole go-to-market motion: sharper targeting, clearer positioning, and faster decisions aligned with what the market expresses right now.
Frequently Asked Questions
How is AI used in market research?
AI scans large volumes of data — CRM records, surveys, calls, reviews, and social content — and turns them into patterns and trends. It helps find the best segments, common pain points, winning messages, and early buying signals much faster than manual analysis.
What AI tool is best for market research?
There is no single “best” tool; it depends on the job. For relationship-driven B2B work, a CRM like folk with AI lead and company research works well. For survey-heavy projects, platforms like Qualtrics or SurveyMonkey Genius help analyze feedback at scale.
Can ChatGPT conduct market research?
ChatGPT can help design surveys, explore ideas, summarize data you provide, and suggest hypotheses or angles. It does not replace real customer data: teams still need CRM insight, interviews, surveys, and usage metrics to run serious B2B research.
Will market research be taken over by AI?
AI will automate data collection and analysis, but not the human part of research. Teams still need to ask the right questions, judge context, and decide what to do with the insights. The future looks more like “AI-assisted researchers” than “AI instead of researchers.”
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