Copy-paste prompts for TAM analysis, customer persona building, survey design, competitive landscapes, pricing research, trend analysis, and customer interview synthesis. Each prompt includes the exact text, expected output, and research methodology tips.
Last updated: March 2026 · Reading time: 18 min
A ChatGPT market research prompt is a structured instruction that generates a specific research deliverable: market sizing estimates, customer persona profiles, survey questionnaires, trend analyses, or competitive landscape maps based on data and context you provide.An important caveat upfront: ChatGPT doesn’t have access to current proprietary market data. It can’t pull live Statista reports or industry databases. These prompts work best when you feed ChatGPT data you’ve already gathered (from reports, surveys, interviews, or public sources) and ask it to structure, analyze, and find patterns in that data. That’s where the 10x speed advantage lives. According to Inc. (2025), a single well-structured ChatGPT prompt can condense hours of market research into minutes. With 92% of Fortune 500 companies using ChatGPT or the API (DemandSage, 2026) and 57% of content marketers using AI for drafting (HubSpot, 2025), AI-powered research is no longer experimental. It’s standard workflow.
Calculate the Total Addressable Market (TAM), Serviceable Addressable Market (SAM), and Serviceable Obtainable Market (SOM) for [product/service] in [geography/market]. Available data points: - Total industry revenue: [if known, cite source] - Number of potential customers: [estimate or source] - Average contract value / price point: [your pricing] - Geographic scope: [where you sell or plan to sell] - Target segment filters: [industry, company size, role] For each tier: 1. TAM: Total market if everyone who could buy, did buy 2. SAM: Portion of TAM you can realistically serve (filtered by geography, segment, capability) 3. SOM: Realistic share you can capture in [timeframe], given your current resources and competition Show the math for each calculation. State all assumptions explicitly. Flag any assumptions that require validation with primary research.What it produces: A structured market sizing document with explicit assumptions. The assumptions list is as valuable as the numbers because it tells you what to validate next. Pro tip: Run both a top-down (industry total / filters) and bottom-up (number of target accounts x average deal size) calculation. If they’re within 30% of each other, your sizing is defensible. If they diverge by more than 2x, investigate the gap.
Calculate the market size for [product/service] using a bottom-up approach: Inputs: - Number of target companies in [geography]: [estimate or source] - Average number of potential users per company: [estimate] - Percentage likely to need this type of solution: [estimate with rationale] - Average annual spend per customer: [your pricing or industry average] - Expected adoption rate in year 1/2/3: [estimates] Build a 3-year model showing: Year 1: [addressable accounts] x [conversion rate] x [ARPU] = [revenue potential] Year 2: [adjusted for growth/churn] Year 3: [adjusted for market expansion] Show all assumptions in a separate table. Label each assumption as "verified" (from data) or "estimated" (needs validation).What it produces: A bottoms-up revenue model with year-by-year projections. This is the format most VC pitch decks use because it shows the thinking behind the numbers, not just the numbers themselves. Pro tip: Use conservative adoption rates. Investors and executives trust a model that shows 2-5% market penetration in year 1 over one that claims 15%. If 2% makes the business viable, the model is more convincing.
I'm entering the [market] with [product/service]. Help me prioritize which market segments to target first. Potential segments: 1. [Segment A: description, estimated size, key characteristics] 2. [Segment B: same format] 3. [Segment C: same format] 4. [Segment D: same format] 5. [Segment E: same format] Score each segment on: - Market size (revenue potential) - Growth rate (expanding, stable, or contracting) - Competition intensity (how many players, how established) - Our fit (how well our product matches their needs) - Ease of access (sales cycle length, decision-maker accessibility) - Willingness to pay (price sensitivity) Score each dimension 1-5 and calculate a weighted total (provide recommended weights). Output as a prioritization matrix with recommended sequence: which segment to enter first, second, third.What it produces: A scored segment prioritization matrix. This prevents the common mistake of entering the largest segment first when a smaller, easier-to-win segment would be a better beachhead. Geoffrey Moore’s “Crossing the Chasm” framework supports this staged approach. Pro tip: Add “reference customer availability” as a scoring dimension. If a segment has no referenceable early adopters, it’s hard to build momentum regardless of size.
Analyze the growth trajectory of the [industry/market segment] using these data points: Historical market size data: - [Year 1]: $[X] (source: [report name]) - [Year 2]: $[X] - [Year 3]: $[X] - [current year estimate]: $[X] Calculate: (1) CAGR over the available period, (2) whether growth is accelerating, linear, or decelerating, (3) the key growth drivers (what's causing the growth), (4) the key growth inhibitors (what could slow it), (5) a projection for the next 3 years with best-case, base-case, and worst-case scenarios. For the projection, state the assumptions behind each scenario. What needs to be true for the best case to happen? What risk triggers the worst case?What it produces: A growth analysis with scenario modeling. The three-scenario framework (best/base/worst) is standard for strategy documents because it shows range rather than false precision. Pro tip: Use at least 3 years of historical data for meaningful CAGR. Single-year growth rates can be misleading (one-time events, COVID distortion, etc.).
I've estimated my TAM at $[X] for [product] in [market]. Challenge this estimate: My assumptions: [List all assumptions used in the sizing] My data sources: [List sources with dates] Play devil's advocate: (1) which assumptions are weakest and why, (2) what data would I need to validate each assumption, (3) what could make the real TAM significantly smaller than my estimate (list 5 deflation risks), (4) what could make it significantly larger (list 3 expansion opportunities), (5) provide an adjusted range (low/mid/high) based on your challenge. Show the adjusted math.What it produces: A stress-tested market sizing. Running your own analysis through a critical lens is what separates a pitch deck number from a strategy-grade number. This prompt forces the validation exercise. Pro tip: Share the challenged estimate with 2-3 people who know the market. If they immediately spot an assumption you and ChatGPT both missed, add it to the model. Fresh eyes catch blind spots.
Build a detailed customer persona based on this data: Customer interview transcripts (key quotes): [paste 10-15 relevant quotes] Survey data: [paste summary of survey results] CRM demographics: [average company size, industry, role, geography] Purchase behavior: [average deal size, sales cycle length, common objections] Product usage: [most-used features, usage frequency, support tickets] Create a persona with: 1. Name and photo description (for internal use) 2. Demographics: title, company size, industry, reporting structure 3. Goals: top 3 professional goals (with quotes from interviews) 4. Pain points: top 3 frustrations (with quotes) 5. Decision-making process: who else is involved, what triggers evaluation 6. Information sources: where they learn about products like ours 7. Objections: top 3 reasons they hesitate to buy 8. Messaging angle: the ONE message that would resonate most strongly 9. Keywords they'd search: 10 terms they'd Google when looking for a solutionWhat it produces: A persona profile anchored in real data. The “keywords they’d search” section is especially useful for SEO and paid search teams. Personas without keyword implications miss half their value. Pro tip: Build 3-4 personas maximum. More than that creates analysis paralysis. Focus on the personas that represent 80% of your revenue or target revenue.
Map the buyer journey for [persona name] purchasing [product/service]. Include 5 stages: 1. UNAWARE: What is happening in their world that creates the need? What event or frustration triggers awareness? 2. PROBLEM-AWARE: What do they Google? Who do they ask? What content do they consume? 3. SOLUTION-AWARE: How do they evaluate options? What criteria matter? Who else gets involved? 4. PRODUCT-AWARE: What do they need to see from us specifically? Demo, trial, case study, pricing? 5. PURCHASE: What's the final approval process? Who signs off? What could block the deal? For each stage: - Duration (how long they typically spend in this stage) - Key question on their mind - Content format that serves them best - Our touchpoint opportunity - Risk of losing them at this stage Persona details: [paste persona profile or summarize] Product: [describe] Typical sales cycle: [length]What it produces: A stage-by-stage buyer journey with content and touchpoint recommendations. This maps directly to marketing automation, content strategy, and sales enablement plans. Pro tip: Validate the journey map against actual CRM data. Look at the sequence of content touches for your last 20 closed deals. Does the real journey match the modeled one? Adjust where they diverge.
Apply the Jobs-to-Be-Done (JTBD) framework to understand why customers hire [product/service]. Context: - Product: [describe] - Target customer: [describe] - What they were doing BEFORE our product: [previous solution or workaround] - Why they switched: [what triggered the change] Identify: 1. The functional job (what task they need completed) 2. The emotional job (how they want to feel) 3. The social job (how they want to be perceived) 4. 5 "hiring criteria" — the specific requirements the product must meet 5. 5 "firing criteria" — the things that would make them stop using the product 6. The "struggling moment" — the exact situation where the need becomes urgent Use this data to recommend: (1) our core marketing message, (2) the competitive alternative we're really replacing (not always a direct competitor), (3) the features we should emphasize vs. de-emphasize.What it produces: A JTBD analysis that reveals the real reasons customers buy. Clayton Christensen’s JTBD framework consistently produces insights that traditional demographic-based personas miss. The “struggling moment” is the most actionable output because it tells you exactly when to reach the customer. Pro tip: The “competitive alternative” is often not a direct competitor. A project management tool might be replacing email threads, not another PM tool. Understanding the REAL alternative you’re displacing changes your messaging.
Analyze my customer data and help me refine my Ideal Customer Profile (ICP). Current customer data (paste or summarize): - Top 20 customers by revenue: [company names, sizes, industries] - Top 20 by retention: [company names, sizes, industries] - Top 20 by NPS score: [company names, sizes, industries] - Bottom 20 by churn: [company names, sizes, industries] - Average deal size by segment: [data] Find patterns: 1. What do the BEST customers have in common? (size, industry, tech stack, team structure) 2. What do the CHURNED customers have in common? 3. Where's the overlap between high-revenue AND high-retention? 4. What's the anti-ICP? (customers who look good on paper but churn or underperform) 5. Write a refined ICP statement: "Our ideal customer is a [size] company in [industry] with [characteristics] that is currently [situation/trigger]." Include negative filters: "We should NOT sell to companies that [list disqualifying traits]."What it produces: A data-driven ICP with positive AND negative filters. The negative filters (anti-ICP) save more money than the positive ones because they prevent your sales team from pursuing bad-fit accounts that churn. Pro tip: Run this analysis quarterly as your customer base grows. Your ICP shifts as you move from early adopters to mainstream customers.
Segment my customer base using this data: Customer list with attributes: [paste or describe data fields available — industry, size, geography, product usage, NPS, revenue, tenure] Create 4-6 distinct segments using behavioral and firmographic clustering. For each segment: 1. Segment name (descriptive, memorable) 2. Size (% of customer base and % of revenue) 3. Defining characteristics (what makes this segment distinct) 4. Average metrics (LTV, NPS, usage frequency, support ticket volume) 5. Growth potential (expanding, stable, at-risk) 6. Recommended strategy (grow, maintain, reduce investment, exit) 7. Content and messaging angle Also identify: which segment is underserved, which is over-served, and which represents the best growth opportunity with current resources.What it produces: A behavioral segmentation with strategy recommendations per segment. This is the analysis that informs resource allocation, content strategy, and product prioritization. Pro tip: Segments should be actionable, not just descriptive. If you can’t change your marketing, sales, or product approach for a segment, the segmentation isn’t useful. Merge segments you’d treat identically.
Design a market research survey to understand [research objective] among [target audience]. Requirements: - 15-20 questions (completing in under 8 minutes — surveys longer than 10 minutes have 50%+ drop-off rates) - Mix of question types: 3-4 multiple choice, 3-4 Likert scale, 2-3 ranking, 2-3 open-ended, 1 NPS-style - Include 1 screening question at the start (to filter out non-target respondents) - Questions flow from general to specific (funnel structure) - No leading questions, double-barreled questions, or loaded language Research objective: [what you need to learn] Target respondent: [job title, industry, experience level] Key decisions this data will inform: [what you'll DO with the results] For each question, note: (1) why this question is included, (2) what decision it informs, (3) the analysis method (cross-tab, frequency, sentiment coding).What it produces: A complete survey with methodology notes. The “what decision does this inform” annotation prevents the common problem of collecting data nobody uses. If a question doesn’t drive a specific decision, cut it. Pro tip: Pilot the survey with 5-10 respondents before the full launch. Track completion time and look for questions where everyone gives the same answer (those aren’t differentiating and can be cut).
Create a semi-structured interview guide for [number] 30-minute customer interviews. Research objective: [what we need to learn]. Structure: 1. WARM-UP (3 minutes): 2 easy, rapport-building questions about their role and daily work 2. CURRENT STATE (7 minutes): How they currently handle [the problem your product addresses]. Get specific: tools, workflow, time spent, frustrations. 3. DECISION PROCESS (7 minutes): How they evaluate and buy products in this category. Who's involved. What criteria matter. What sources they trust. 4. PRODUCT EXPERIENCE (7 minutes): Their experience with [our product OR competitor product]. What works, what doesn't, what they wish existed. 5. FORWARD-LOOKING (3 minutes): What they expect to change in their work in the next 12 months. What would make their job significantly easier. 6. WRAP-UP (3 minutes): "Anything I didn't ask that I should have?" For each question: - The main question - 2-3 follow-up probes (for when answers are vague) - What to listen for (specific signals that indicate opportunity or risk)What it produces: An interview guide that produces consistent, comparable data across interviews. The “what to listen for” notes train the interviewer to catch important signals that might otherwise be missed. Pro tip: Record and transcribe every interview (with permission). You’ll miss nuances during the live conversation. The transcripts also become input for the persona builder prompt (#6).
Design a 60-minute focus group discussion guide for [number] participants. Topic: [describe research topic]. Structure: 1. INTRODUCTIONS (5 minutes): Name, role, one sentence about their relationship with [topic] 2. GENERAL PERCEPTIONS (10 minutes): Open-ended discussion about [broad topic]. Capture unaided awareness, attitudes, and language. 3. CONCEPT EXPLORATION (15 minutes): Present [concept/product/message] and gather reactions. Specific questions about clarity, appeal, and concerns. 4. DEEP DIVE (15 minutes): Focused discussion on [specific aspect]. Use projective techniques if applicable. 5. COMPARATIVE EXERCISE (10 minutes): Compare 2-3 options and discuss preferences. Use ranking or dot-voting. 6. WRAP-UP (5 minutes): "If you could change one thing about [topic], what would it be?" Include: - Moderator instructions (when to probe, when to redirect) - Activity descriptions (card sorting, dot voting, whiteboard exercises) - Participant screener criteria Participant profile: [describe] Number of groups: [recommended based on research objectives]What it produces: A moderated discussion guide with activities. Focus groups generate different insights than surveys or 1:1 interviews because group dynamics reveal social proof patterns, competitive perceptions, and language that individual responses miss. Pro tip: Run at least 2 focus groups before drawing conclusions. One group can be dominated by a single strong personality. Two groups provide comparison data.
Design a post-purchase survey for [product/service] customers who bought within the last 30 days. Objectives: 1. Understand what triggered the purchase decision 2. Identify which marketing touchpoints influenced them 3. Measure initial satisfaction 4. Collect testimonial-quality feedback Survey structure (10 questions, under 5 minutes): - 1 question: How did you first hear about us? (attribution) - 1 question: What was the primary reason you chose us? (decision driver) - 1 question: What alternatives did you consider? (competitive intelligence) - 1 question: What almost stopped you from buying? (objection data) - 2 questions: Satisfaction with purchase experience and product (Likert scale) - 1 question: NPS (0-10 scale) - 1 question: Would you recommend? If yes, "What would you tell them?" (testimonial mining) - 1 question: What's one thing we could improve? (product feedback) - 1 question: Open comments Product: [describe] Average purchase value: [amount] Purchase channel: [online, in-store, sales-assisted]What it produces: A post-purchase survey that doubles as an attribution tool, testimonial mining tool, and competitive intelligence tool. The “what almost stopped you” question is the most valuable because it reveals objections your marketing needs to address. Pro tip: Send the survey 7-14 days after purchase. Too early and they haven’t used the product. Too late and purchase memory fades. For SaaS, send after the first successful use, not after signup.
Synthesize these [X] customer interview transcripts into actionable findings: [Paste transcripts or detailed notes from 5-15 interviews] Produce: 1. KEY THEMES: Top 5 recurring themes across interviews, with the number of interviews where each appeared 2. VERBATIM QUOTES: 2-3 strongest quotes per theme (exact customer language) 3. PAIN POINTS RANKED: Prioritized by frequency and severity 4. DECISION FACTORS: What drives purchase decisions (ranked by mention frequency) 5. COMPETITIVE MENTIONS: Every competitor mentioned, context, and sentiment 6. SURPRISE FINDINGS: 2-3 insights that contradict our assumptions or weren't expected 7. RECOMMENDATIONS: 5 specific actions based on these findings Format as an executive summary (one page) + detailed findings (3-5 pages).What it produces: A structured research report from raw interview data. This is the most time-saving prompt in the set. Synthesizing 10 interview transcripts manually takes a full day. This prompt produces a first-draft synthesis in 5 minutes that you then refine in 30-60 minutes. Pro tip: Feed transcripts in batches if they exceed the context window. After processing each batch, ask ChatGPT to integrate the batch findings into a cumulative synthesis.
Generate a trend analysis report for the [industry] market based on these data points and observations: Recent data: - [Paste relevant statistics, report excerpts, news headlines from the last 6-12 months] - [Include Google Trends data if available] - [Include any investment/funding data] Create a report with: 1. EXECUTIVE SUMMARY: 3 sentences on the state of the market 2. TOP 5 TRENDS: For each trend: - What's happening (one paragraph) - Evidence (specific data points) - Who benefits and who's at risk - Timeline: emerging, accelerating, or maturing - Impact rating: high/medium/low for [my business type] 3. COUNTER-TRENDS: 2-3 trends that push in the opposite direction 4. IMPLICATIONS FOR [MY BUSINESS]: 5 specific strategic recommendations 5. WHAT TO WATCH: 3 early signals that would indicate a trend is accelerating or stallingWhat it produces: A structured trend report with strategic implications. The counter-trends section is what makes this output valuable beyond a simple list. Every trend has a counter-force, and identifying those prevents overcommitting to a trend that may not sustain. Pro tip: Run this quarterly with fresh data. Trends that appeared “emerging” 6 months ago may be “accelerating” or “stalling” now. The value is in tracking trajectory, not just identifying trends.
Assess the impact of [emerging technology: AI agents, AR/VR, blockchain, voice search, etc.] on the [industry] market. What I know so far: [paste any data, articles, or observations] Analyze: 1. CURRENT STATE: Where is this technology in the adoption curve? (Innovators, Early Adopters, Early Majority, Late Majority) 2. USE CASES: 5 specific applications relevant to [industry], ranked by feasibility and impact 3. TIMELINE: When will each use case hit mainstream adoption? (this year, 1-2 years, 3-5 years, 5+ years) 4. WINNERS AND LOSERS: Which types of businesses benefit? Which are threatened? 5. BARRIERS: What's slowing adoption? (cost, regulation, skills gap, infrastructure) 6. RECOMMENDED POSITION: Should we invest now, watch closely, or wait? Include confidence levels (high/medium/low) for each prediction.What it produces: A technology impact assessment with adoption timeline estimates. The confidence levels force intellectual honesty about what’s known vs. speculated. Pro tip: Focus on “use cases” rather than “the technology.” Executives care about what it does for their business, not how the technology works.
Analyze how consumer behavior in [market/industry] is shifting based on these data points: Data inputs: - Survey data: [paste relevant findings] - Sales data trends: [describe changes in purchasing patterns] - Social media sentiment: [describe observed shifts] - Search trend data: [Google Trends observations] - Competitor moves: [how competitors are responding] Identify: 1. 5 BEHAVIORAL SHIFTS: What customers are doing differently compared to 2 years ago 2. ROOT CAUSES: Why each shift is happening (economic, technological, generational, cultural) 3. PERMANENCE ASSESSMENT: Is each shift temporary or permanent? Why? 4. MARKETING IMPLICATIONS: How should our messaging, channels, and offers adapt? 5. PRODUCT IMPLICATIONS: What product changes would serve these new behaviors? For each shift, provide a specific example of a brand that's responding well and one that's responding poorly.What it produces: A behavioral analysis with strategic implications. Understanding WHY behavior is changing (root causes) matters more than cataloging the changes. Temporary shifts require different responses than permanent ones. Pro tip: Compare your data against published consumer behavior reports from McKinsey, Deloitte, or Pew Research. If your local data aligns with macro trends, the shifts are real. If they diverge, you may have a niche opportunity.
Assess the disruption risk for [our business/industry] from these potential disruptors: Disruptor 1: [describe — could be a technology, business model, competitor, regulation] Disruptor 2: [describe] Disruptor 3: [describe] For each disruptor: 1. Probability of significant impact (next 3 years): high/medium/low with rationale 2. Affected areas: which parts of our business are most vulnerable 3. Speed: sudden (cliff) or gradual (erosion) 4. Defensive options: 3 moves to protect against this disruption 5. Offensive options: 3 ways to turn this disruption into an advantage Create a 2x2 risk matrix: probability (low/high) vs. impact (low/high). Place each disruptor on the matrix and recommend response priority.What it produces: A disruption risk matrix with response strategies. This is the format strategy consultants use, and it’s effective because it forces prioritization. Not every disruption threat deserves the same response. Pro tip: Include at least one “unlikely but devastating” scenario. Black swan events are, by definition, unpredictable, but having considered them makes your response faster if they happen.
Analyze the business impact of [specific regulation or policy change] on [industry/business]. Regulation details: [describe the regulation, effective date, scope] What I know: [paste relevant details or articles] Analyze: 1. WHAT CHANGES: Specific requirements or restrictions (bullet list) 2. WHO'S AFFECTED: Which businesses, segments, or functions 3. COMPLIANCE COST: Estimated cost to comply (time, money, resources) 4. COMPETITIVE IMPACT: Who benefits and who's disadvantaged by this regulation? 5. TIMELINE: Key compliance dates and milestones 6. RECOMMENDED ACTIONS: 5 specific steps to prepare, ordered by priority 7. OPPORTUNITIES: Ways to turn compliance into a competitive advantage Note: flag any interpretation that requires legal review. I need directional business analysis, not legal advice.What it produces: A business-oriented regulatory impact assessment. The “opportunities” section is often the most valuable because regulations that create compliance costs for everyone also create differentiation opportunities for those who comply fastest or most visibly. Pro tip: Always pair this analysis with actual legal review. ChatGPT can structure the business impact assessment, but a lawyer needs to validate the compliance interpretation.
Map the competitive landscape for [market/category]. Include all significant players. Known competitors: [list the ones you know] Also identify: competitors I may be missing (check adjacent categories, international players, startups, and indirect competitors) For each competitor: 1. Company name and founding year 2. Market position: leader, challenger, niche, or emerging 3. Target segment: who they primarily serve 4. Pricing tier: enterprise, mid-market, SMB, freemium 5. Key differentiator: the ONE thing they're known for 6. Recent moves: funding, acquisitions, product launches in the last 12 months 7. Estimated market share: if known or estimatable Group competitors into: Direct competitors (same product, same market), Indirect competitors (different product, same need), and Potential future competitors (adjacent market, could enter). Output as a table and a narrative summary.What it produces: A comprehensive competitive landscape with categories. The “potential future competitors” category is often the most strategically important because those entries catch you off guard if you haven’t considered them. Pro tip: Ask ChatGPT to check Crunchbase, Product Hunt, and G2 categories for players you might have missed. Cross-referencing multiple directories prevents blind spots.
Create a competitive positioning analysis for the [market] using a quadrant framework. Players to map: [list 8-12 competitors including yourself] Suggest the most useful two axes for this specific market (the dimensions that best differentiate players). Default options: - Feature depth vs. ease of use - Price vs. capability - Niche focus vs. platform breadth - SMB focus vs. enterprise focus For each quadrant: 1. Name (e.g., "Affordable & Simple," "Premium & Complex") 2. Companies in this quadrant 3. The typical customer in this quadrant 4. Whether this quadrant is crowded or underserved Recommend where WE should position and why. If we need to reposition, describe the move required.What it produces: A positioning quadrant analysis. This is the format used by Gartner Magic Quadrant, Forrester Wave, and strategy consulting firms because it makes market structure visually clear. Pro tip: Choose axes that reveal genuine differentiation in your market. If every competitor clusters in one quadrant, your axes aren’t differentiating enough. Experiment with less obvious dimensions.
Assess the feasibility of entering the [new market/segment/geography] with [product/service]. Current business: [describe what you do today] Target market: [describe the new market] Entry strategy: [organic growth, acquisition, partnership, or build new product] Evaluate: 1. MARKET ATTRACTIVENESS: size, growth, profitability, competitive intensity (score 1-10) 2. OUR FIT: How much of our current capability transfers? What's the gap? (score 1-10) 3. BARRIERS TO ENTRY: Regulatory, capital, brand awareness, channel access, technology (list and rate each) 4. COMPETITIVE RESPONSE: How will incumbents react? (ignore, compete, acquire, partner) 5. TIME TO BREAKEVEN: Estimated months/years based on entry strategy 6. RISK FACTORS: Top 5 things that could go wrong 7. GO/NO-GO RECOMMENDATION: Based on the analysis, should we enter? Under what conditions? Include a decision framework: "Enter if [conditions are true]. Delay if [conditions exist]. Don't enter if [deal-breakers]."What it produces: A market entry assessment with a clear go/no-go recommendation. The conditional framework (“enter if, delay if, don’t enter if”) is more useful than a binary recommendation because markets evolve. Pro tip: Weight “our fit” heavily. The most attractive market in the world is a bad entry if you don’t have the capabilities to compete. McKinsey’s GE-McKinsey Matrix uses market attractiveness x competitive strength for exactly this reason.
Map the value chain for the [industry] and identify where value is created and captured. Value chain stages (customize for your industry): 1. [Raw materials / R&D] 2. [Manufacturing / Product development] 3. [Distribution / Platform] 4. [Marketing / Sales] 5. [Customer service / Support] 6. [End customer] For each stage: - Key players (name specific companies) - Margin profile (high/medium/low margin) - Competitive dynamics (consolidated or fragmented) - Where technology is disrupting this stage - Where we participate and our margin position Identify: (1) the highest-margin stages, (2) stages ripe for disruption, (3) vertical integration opportunities, (4) where our business should expand or contract along the chain.What it produces: A value chain analysis with strategic positioning recommendations. Understanding where margin concentrates in your industry tells you where to invest and where to avoid competing. Pro tip: Look for stages where one player captures disproportionate margin. That’s either a defensible moat worth respecting or a monopoly ripe for disruption, depending on the source of their advantage.
Perform a strategic group analysis for the [industry] market. List of competitors: [list 10-15 companies with brief descriptions] Group these competitors into 3-5 strategic groups based on similar strategies, resources, and competitive positions. For each group: 1. Group name and defining characteristics 2. Members (list companies) 3. Shared strategy elements (pricing, distribution, target market, product scope) 4. Mobility barriers: what prevents companies in one group from moving to another? 5. Rivalry intensity: is competition fiercest within groups or between groups? 6. Profitability: which group is most profitable and why? Identify: (1) which group we belong to, (2) whether we should stay or move to a different group, (3) gaps between groups where a new strategic position could exist.What it produces: A strategic group map showing competitive clusters. This framework (from Michael Porter) reveals that companies compete most intensely with others in the same strategic group, not with all industry participants. Pro tip: The most profitable positions are often in small groups or unique positions between groups. If you can occupy a position that no one else holds, competitive pressure decreases.
Design a Van Westendorp Price Sensitivity survey for [product/service] and analyze the results. Product: [describe, including what's included at each potential tier] Target respondent: [describe buyer profile] Create the survey with these 4 standard questions: 1. At what price would you consider [product] to be so inexpensive that you'd question its quality? 2. At what price would you consider [product] to be a bargain? 3. At what price would [product] start to seem expensive, but you'd still consider buying it? 4. At what price would [product] be too expensive, and you'd definitely not buy it? Then, if I provide the response data: [paste responses or describe distribution] Analyze: (1) the acceptable price range, (2) the optimal price point (intersection of "too cheap" and "too expensive"), (3) the indifference price point, (4) recommended pricing strategy based on the data.What it produces: A pricing survey instrument plus analysis framework. Van Westendorp is the most widely used price sensitivity methodology because it’s simple for respondents and produces clear, actionable price ranges. Pro tip: Run the survey with at least 100 respondents from your target market for statistically meaningful results. Under 50 respondents, the intersection points shift significantly with each additional response.
Compare pricing models for [product/service]. Analyze each model's fit for my specific situation. Pricing models to evaluate: 1. Per-user/seat pricing 2. Usage-based pricing (pay-per-use) 3. Flat-rate subscription 4. Tiered pricing (good/better/best) 5. Freemium + paid tiers 6. Value-based pricing For each model: - Revenue predictability (1-5) - Customer perception (fair/complex/opaque) - Expansion revenue potential - Churn risk - Implementation complexity - Best suited for [product type and customer type] - Example companies using this model successfully My product: [describe] Average customer: [size, usage patterns, budget] Current pricing: [if applicable] Growth goal: [optimize for revenue, adoption, or retention] Recommend the best model and explain the transition strategy if I'm changing from my current model.What it produces: A pricing model evaluation matrix. The “growth goal” input is critical because the optimal pricing model differs depending on whether you’re optimizing for adoption (freemium wins), revenue (value-based wins), or retention (flat-rate wins). Pro tip: If you’re unsure between two models, test both. Run a 90-day A/B test showing different pricing pages to different traffic segments. Let customer behavior decide.
Design a willingness-to-pay research study for [product/service]. Product: [describe, including key features and current/proposed pricing] Target buyer: [describe] Competitive alternatives: [list with pricing if known] Create: 1. Research methodology: Which approach to use and why (conjoint analysis, MaxDiff, Van Westendorp, direct questioning, or Gabor-Granger) 2. Survey questions: 8-12 questions that measure willingness to pay without leading responses 3. Feature-value assessment: Which features justify premium pricing? (include feature list) 4. Analysis plan: How to segment results by buyer type, company size, and current solution 5. Sample size recommendation: How many responses for statistical significance 6. Expected deliverable: What the output report should containWhat it produces: A complete WTP research design. The methodology recommendation matters because different approaches suit different products. Conjoint analysis works best for complex products with many features. Van Westendorp works best for simpler pricing decisions. Pro tip: Never ask “how much would you pay for this?” directly. Respondents consistently understate willingness to pay in direct questions. Use indirect methods (conjoint, MaxDiff) for more accurate results.
Estimate the price elasticity for [product/service] based on this data: Historical pricing and volume data: - Price Point 1: $[X], units sold: [X], time period: [dates] - Price Point 2: $[X], units sold: [X], time period: [dates] - [additional data points if available] Competitive context: - Competitor A price: $[X] - Competitor B price: $[X] - Number of alternatives available: [X] Calculate: 1. Price elasticity coefficient (if enough data points) 2. Whether demand is elastic or inelastic 3. The revenue-maximizing price point 4. The volume-maximizing price point 5. The profit-maximizing price point (given margin of [X]%) 6. Recommended pricing action based on elasticity State confidence level and note any limitations in the data.What it produces: A price elasticity analysis with pricing recommendations. Even a rough elasticity estimate (elastic vs. inelastic) dramatically improves pricing decisions. If demand is inelastic, you’re leaving money on the table by not raising prices. If elastic, small price increases cause disproportionate volume loss. Pro tip: Need at least 3-4 price points for meaningful elasticity estimation. If you only have one historical price, design a controlled test (geographic split or time-based split) to generate data points.
Design a tiered pricing structure for [product/service]. Available features: [list all features the product can offer] Current pricing: [if any] Target customer segments: - Segment 1: [name, needs, budget range, size] - Segment 2: [name, needs, budget range, size] - Segment 3: [name, needs, budget range, size] Design 3 tiers: 1. STARTER: The tier that gets people in the door. Which features? What price? What limits? 2. PROFESSIONAL: The tier where most revenue comes from. Which features upgrade? What's the price anchor? 3. ENTERPRISE: The tier for large accounts. What's included? How is it priced (flat vs. custom)? For each tier: (1) name, (2) monthly price, (3) included features, (4) usage limits, (5) target segment, (6) upgrade trigger (what makes someone move up). Apply the decoy effect: design the middle tier to be the obvious "best value" choice.What it produces: A three-tier pricing structure with behavioral economics applied. The decoy effect (where the middle tier is designed to look like the best deal) is used by 90%+ of SaaS companies because it consistently drives buyers to the most profitable tier. Pro tip: Price the starter tier at the point where it’s profitable on its own (not a loss leader). Then price the professional tier at 2-3x the starter. The gap should feel like a deal because of the features included, not because the starter is artificially cheap.
Synthesize these research findings into an executive summary for [audience: board, CEO, marketing team]: Research inputs: - Market sizing: [paste key findings] - Customer personas: [paste key findings] - Survey results: [paste key findings] - Competitive analysis: [paste key findings] - Trend analysis: [paste key findings] Create: 1. ONE-PAGE EXECUTIVE SUMMARY: The 5 most important findings, each in 2 sentences 2. SO-WHAT: What these findings mean for our strategy (3 implications) 3. RECOMMENDED ACTIONS: 5 specific actions ranked by impact and urgency 4. RISKS IF WE DON'T ACT: What happens if we ignore these findings 5. NEXT STEPS: What additional research is needed (if any) Write for [audience type]. Use numbers. No abstract observations. Every sentence should be actionable or informative.What it produces: A decision-ready executive summary. Research that sits in a deck and never reaches decision-makers is wasted research. This prompt forces the output into an action-oriented format. Pro tip: Lead with the most surprising or uncomfortable finding. Executives engage with information that challenges their assumptions. If everything in the summary confirms what they already believe, they’ll question whether the research was worth doing.
Create a slide-by-slide outline for a market research presentation. Audience: [describe]. Research to present: [paste or summarize all findings] Structure (15-20 slides): 1. Title slide: [Research title, date, presenter] 2. Objectives: What we set out to learn 3. Methodology: How we gathered the data (1 slide, brief) 4-6. Key findings (1 slide per major finding, each with: finding, data visualization suggestion, implication) 7. Market sizing summary (TAM/SAM/SOM visual) 8. Customer persona summary (1-2 persona cards) 9. Competitive landscape (positioning map) 10. Trend analysis (timeline or matrix visual) 11-12. Detailed findings (supporting data) 13. Opportunities (ranked) 14. Risks and threats (ranked) 15. Recommendations (prioritized action list) 16. Next steps and timeline For each slide: title, 2-3 bullet points, and a recommended chart type or visual.What it produces: A presentation outline with visualization recommendations. Research presentations fail when they present data without interpretation. This structure ensures every data slide has an implication slide following it. Pro tip: Build the recommendations slide first, then work backward. The presentation should be structured to build the case for your recommendations. Every preceding slide should contribute evidence toward those conclusions.
Translate these market research findings into a strategic plan: Research findings: [paste summary of all research] Current strategy: [describe what we're doing now] Resources available: [team size, budget, timeline] Constraints: [what we CAN'T do — geographic limits, regulatory, capacity] Create a strategic plan with: 1. STRATEGIC PRIORITIES: Top 3 priorities based on research (with evidence linking each to research findings) 2. INITIATIVES: 2-3 specific initiatives per priority (with owner, timeline, budget estimate, success metric) 3. TRADE-OFFS: What we're choosing NOT to do and why (equally important as what we choose to do) 4. MILESTONES: Quarterly milestones for the next 12 months 5. MEASUREMENT: KPIs to track whether the strategy is working (leading and lagging indicators) 6. ASSUMPTIONS: What must remain true for this strategy to work 7. CONTINGENCIES: If [assumption breaks], then [alternative action]What it produces: A strategy document that explicitly connects research findings to strategic actions. The “trade-offs” section is essential because strategy is as much about what you say no to as what you pursue. Pro tip: Share the draft strategy with 3-5 stakeholders before finalizing. Ask them: “What’s missing? What’s unrealistic? What would you change?” Stakeholder buy-in at the draft stage prevents resistance at the execution stage.
Audit the quality of this market research and identify weaknesses: Research summary: [paste complete findings] Methodology: [describe how data was collected] Sample: [describe who was surveyed/interviewed and how many] Timeline: [when the research was conducted] Budget: [what was spent] Evaluate: 1. SAMPLE BIAS: Is the sample representative? What perspectives are missing? 2. METHODOLOGY GAPS: What questions should have been asked but weren't? 3. DATA FRESHNESS: Are any data points outdated? 4. CONFIDENCE LEVEL: For each major finding, rate confidence as high/medium/low 5. CONFLICTING EVIDENCE: Do any findings contradict each other? How should we reconcile them? 6. WHAT'S MISSING: 5 things we still don't know that would improve decision-making 7. RECOMMENDED FOLLOW-UP: Priority research to fill the gaps (in order of importance)What it produces: A research quality assessment. This is the most valuable prompt for teams that want to act on research but need to know how much to trust it. Overconfident research leads to bad decisions as readily as no research at all. Pro tip: Run this audit before presenting findings to leadership. Proactively addressing limitations (“here’s what we’re confident about and here’s what we’d need to validate”) builds more trust than presenting everything as definitive.
Five principles from our research practice:“Market research with ChatGPT is 80% data preparation and 20% prompting. The teams that get the best output are the ones that collect real customer data first, through surveys, interviews, and CRM exports, and then use ChatGPT to find patterns they’d miss manually. The teams that get generic output are the ones that ask ChatGPT to research from scratch. It’s an analyst, not a data collector.”
Hardik Shah, Founder of ScaleGrowth.Digital
32 prompts for deep competitor analysis: SWOT, content gaps, backlink profiling, pricing comparison, and market positioning. View Prompts →
A structured spreadsheet for tracking competitive data over time. Pairs with the competitive landscape prompts on this page. Get Template →
Turn your market research into a structured marketing plan with our template. Includes sections for market analysis, positioning, and KPIs. Get Template →
No. ChatGPT accelerates analysis and synthesis but cannot replace primary data collection. It can’t run surveys, conduct interviews, or access proprietary databases. Use ChatGPT to structure research designs, analyze data you’ve collected, and synthesize findings into reports. The data gathering still requires traditional methods: surveys, interviews, CRM exports, and third-party data platforms.
ChatGPT’s market sizing is only as accurate as the data you provide. When you feed it verified industry reports and public data, the calculations are mathematically sound. When you ask it to estimate from its training data alone, accuracy drops significantly. Always provide actual data points and ask ChatGPT to show the math and list assumptions for validation.
At minimum: your CRM data (customer demographics, deal sizes, win/loss data), competitor pricing pages, 2-3 recent industry reports with market sizing, 5-10 customer interview transcripts or survey responses, and Google Trends data for your category. The more real data you feed ChatGPT, the more specific and useful the output. Prompts fed with actual data produce output that’s 5-10x more actionable than prompts using only ChatGPT’s training knowledge.
ChatGPT’s Deep Research feature (upgraded to GPT-5.2 in early 2026) directs an AI agent to compile comprehensive, cited reports. Give it a specific research question like “What is the current market size and growth trajectory for AI-powered content creation tools in North America?” It will search multiple sources and produce a cited report. Use this for initial market scoping, then validate key findings with primary research.
Market sizing: annually. Customer personas: every 6 months or when your customer mix shifts significantly. Competitive landscape: quarterly monitoring, annual deep dive. Pricing research: annually, or immediately when a competitor changes pricing. Trend analysis: quarterly. The cost of acting on outdated research is higher than the cost of refreshing it.
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