[Becky Budgeter]

Hello — I’m curious whether AI tools can help a non-technical person estimate a market size using only public datasets (government stats, open company data, web reports, etc.). I want something practical and realistic, not a promise of perfect accuracy.

My main questions:

• What types of public datasets are most useful for rough market-size estimates?
• Can AI help combine incomplete sources and suggest plausible ranges or assumptions?
• Which beginner-friendly tools or simple workflows would you recommend?
• What common pitfalls should a non-expert watch out for?

If it helps, I’ve been exploring sites like data.gov and open industry reports. I’d really appreciate short, practical replies: examples of datasets, simple methods, or tools you’ve used (no heavy math required). Thanks — looking forward to hearing about real experiences and easy first steps!

[Jeff Bullas]

Good question — focusing on public data is the right place to start. AI can speed up the work, surface useful numbers and help turn them into a defensible market-size estimate. It won’t magically replace judgment, but it does give you quick, repeatable calculations and clear sensitivity checks.

What you’ll need

• Clear definition of the market (who, what, where, timeframe).
• Public data sources: government stats, industry reports, company filings, trade associations, job listings, and basic web pages.
• A simple tool: an AI chat (like ChatGPT), a spreadsheet (Excel or Google Sheets) and a web browser.
• Curiosity and a habit of validating surprising numbers.

Step-by-step practical method

1. Define the market: product/service, geography, time period (e.g., annual US online tutoring market 2025).
2. Top-down (quick): find overall related industry revenue or population numbers and apply reasonable adoption/penetration rates to get a rough TAM.
3. Bottom-up (credible): identify unit economics — number of customers × average price × purchase frequency. Use company reports or surveys to anchor assumptions.
4. Use AI to gather and summarize data: ask for key stats, conservative and aggressive assumptions, and to create the spreadsheet formulas.
5. Create a range: build conservative, base, optimistic scenarios and run sensitivity (±10–30% on key inputs).
6. Validate: cross-check with at least two independent public sources and flag gaps or big assumptions.

Example (short)

Estimate: US annual revenue for an online hobby course market.

• US adult population: 260M adults. Assume 5% interested = 13M potential.
• Paid conversion 3% → 390k customers. Avg revenue per customer $60/year → $23.4M market (base case).
• Show conservative (half conversion) and optimistic (double) scenarios.

Common mistakes & quick fixes

• Mistake: Mixing monthly and annual figures. Fix: normalize units before calculating.
• Mistake: Single-source bias. Fix: always cross-check 2–3 public sources.
• Mistake: Over-precision. Fix: report ranges and state assumptions.

Copy-paste AI prompts

Simple prompt (non-technical):

“Help me estimate the annual market size for [product/service] in [country/region] for [year]. I want a quick top-down and a bottom-up estimate. For top-down, find public stats I can use (population, industry revenue). For bottom-up, suggest realistic customer numbers and average revenue per customer, and give conservative, base, and optimistic ranges. List the sources and key assumptions.”

Detailed prompt (build spreadsheet-ready output):

“Provide a step-by-step market size estimate for [product/service] in [region] for [year]. Give: 1) key public data points with URLs or citations, 2) bottom-up calculation with explicit formulas (customers = addressable population × interest rate × conversion), 3) dollar calculations (conservative/base/optimistic), 4) a sensitivity table showing impact if key inputs change by ±20%. Output the formulas so I can paste into a spreadsheet.”

Action plan (do-first, 60–90 minutes)

• 15 min: Define market clearly.
• 30 min: Run the simple AI prompt to pull data and assumptions.
• 20 min: Paste formulas into a spreadsheet and create 3 scenarios.
• 15–25 min: Cross-check one or two public sources and adjust assumptions.

Closing reminder — AI speeds the work and helps structure thinking, but treat outputs as hypotheses to test. Start small, get a defensible range, and improve it as you find better data.

[Rick Retirement Planner]

Nice follow-up — you’re on the right track. Focusing on public data and structuring the work around simple, repeatable steps gives you a defensible estimate without needing a data science degree.

Sensitivity analysis — plain English: it’s a quick way to see which assumptions matter most. Change a single input (like conversion rate) up and down and watch how much the final market number moves. If a small change blows your estimate up or down a lot, that’s a signal to find better data for that input.

What you’ll need

• Clear market definition (who, what, where, timeframe).
• Public sources: gov stats, industry reports, company filings, trade groups and credible articles.
• A web browser, an AI chat tool, and a spreadsheet (Excel or Google Sheets).
• Note-taking habit: list assumptions and where each number came from.

Step-by-step practical method

1. Define the market: state product/service, geographic boundary (e.g., US), and period (annual 2025).
2. Quick top-down: find a high-level stat (industry revenue, population) and apply realistic penetration rates to get a ballpark TAM.
3. Credible bottom-up: estimate number of buyers × average spend × purchase frequency. Anchor each input with a public source or a comparable company metric.
4. Ask AI to gather & summarize: request key public data points, conservative/base/optimistic assumptions, and simple spreadsheet formulas (don’t accept a single output without sources).
5. Build scenarios: conservative/base/optimistic and a sensitivity table (change key inputs by ±10–30% to see impact).
6. Validate: cross-check with 2–3 independent sources; flag big assumptions and mark where better data is needed.

How to ask AI — two practical variants

• Quick ask (non-technical): ask for a short top-down and bottom-up estimate, the three main assumptions, and the best public stats to support them.
• Spreadsheet-ready ask: ask the AI to list the public data points and give explicit formulas you can paste into a sheet, plus a small sensitivity table showing the effect of changing 2–3 inputs.

What to expect

• AI will speed up locating numbers and formatting calculations, but treat outputs as hypotheses.
• Most useful result: a defensible range, a sensitivity chart, and a short list of the assumptions you must verify.
• Plan to spend 60–90 minutes for a first pass and another round after verifying 1–2 key inputs.

Keep it iterative: start with a simple range, identify the most sensitive assumption, then go find better public data for that one. Small, structured improvements build real confidence.

[Ian Investor]

Good point — sensitivity analysis is the clearest way to separate signal from noise. I’d add that treating the AI output as a structured hypothesis makes the whole process faster and safer: use it to surface numbers and build a spreadsheet, then verify the one or two inputs that move the needle most.

Below is a compact checklist (do / do-not), a clear step-by-step workflow you can follow in 60–90 minutes, and a short worked example so you can see the math in plain English.

• Do: Define the market precisely (who, what, where, timeframe).
• Do: Use both top-down and bottom-up approaches and report a range (conservative/base/optimistic).
• Do: List sources for every key input and flag the most sensitive ones.
• Do-not: Treat a single AI output as fact — it’s a draft to verify.
• Do-not: Mix units (monthly vs annual) or confuse potential audience with likely buyers.

1. What you’ll need:
   • A clear market definition (example: paid online hobby courses in the US, annual 2025).
   • A web browser to find public stats (government, trade groups, company reports).
   • A spreadsheet (Excel or Google Sheets) and an AI chat to speed summarizing numbers.
   • Notebook or sheet to record assumptions and sources.
2. How to do it (step-by-step):
   1. Run a quick top-down: find a high-level related stat (industry revenue or population) and apply a plausible penetration rate to get a ballpark TAM.
   2. Build a bottom-up: estimate customers = addressable population × interest rate × paid-conversion, then multiply by average revenue per customer (price × frequency).
   3. Ask AI to summarize likely public data points and produce the simple formulas you’ll paste into your sheet. Keep the AI answers as a checklist of numbers to verify.
   4. Create three scenarios (conservative/base/optimistic) and run sensitivity on the two most impactful inputs (±20–30%).
   5. Cross-check 1–2 public sources for the most sensitive input; update the sheet and note the change in your range.
3. What to expect:
   • Time: 60–90 minutes for a first pass; another 30–60 minutes to verify key inputs.
   • Output: a defensible range, a short list of assumptions, and a sensitivity table that tells you where to do deeper research.
   • Common gap: adoption/conversion rates — these often require finding a comparable company or survey.

Worked example (simple, transparent)

• Market: US paid online hobby courses, annual.
• Addressable adults: 260,000,000. Assume interest = 5% → 13,000,000 people.
• Paid conversion (base) = 3% → paying customers = 390,000.
• Avg revenue per customer = $60/year → market size (base) = 390,000 × $60 = $23,400,000.
• Conservative: half conversion (1.5%) → $11.7M. Optimistic: double conversion (6%) → $46.8M.

Tip: After your base pass, identify the single most sensitive input (often conversion or average spend). Spend your next hour finding a public survey or company metric to anchor that number — that one verification typically halves your uncertainty.

[Jeff Bullas]

Here’s the repeatable, 90‑minute “triangulation” to get a defensible market size with public data. AI does the grunt work; you verify the 1–2 inputs that move the needle.

What you’ll bring

• A precise market statement: who buys, what they buy, where, and the period (annual).
• Three public anchors you can quickly find: population or company counts, prices, and at least one comparable company’s revenue.
• Tools: browser, spreadsheet, AI chat, and a short list of assumptions to track.

The 3-anchor method (simple, credible)

1. Demand bottom-up (PPP ladder): Population → Participation → Pay.
   • Customers = Addressable population × Interest/eligibility × Paid conversion.
   • ARPU = Average price × Purchase frequency (per year).
   • Revenue = Customers × ARPU.
2. Top-down sanity check: Start from a related category spend or population and apply realistic penetration and share-of-wallet.
3. Comparable-company triangulation: Use 2–3 public companies: if one player has $X revenue and ~Y% share, implied market ≈ X / Y%.

Spreadsheet layout (copy into a sheet)

• Inputs: B2 Addressable_population, B3 Interest_rate, B4 Paid_conversion, B5 Avg_price, B6 Purchase_frequency.
• Formulas:
  • B7 ARPU = =B5*B6
  • B8 Customers = =B2*B3*B4
  • B9 Revenue = =B8*B7
  • Scenario cells: set C2:E6 with conservative/base/optimistic inputs and repeat the above formulas in each column.
• Quick sensitivity: create three rows that change Paid_conversion by −20%, base, +20% and show the resulting Revenue values.

Insider tricks to find public numbers fast

• Use search patterns: “keyword + market size + report”, “keyword + 10-K”, “keyword + investor presentation”, “keyword + pricing”, “number of locations + keyword”, “trade association + keyword”.
• Anchor participation with surveys or platform stats; anchor price with visible pricing pages or average order values mentioned in filings.
• Cross-check with per-capita spend: Revenue / population. If it implies an unrealistic spend per person, revisit assumptions.
• Supply-side proxy (if two-sided): Providers × Capacity × Utilization × Price → a second triangulation against your demand model.

Robust, copy‑paste prompts (use as-is)

Master prompt — full market size with checks

“Act as a market sizing analyst. Estimate the annual market size for [product/service] in [region] for [year]. Deliver:
1) Two approaches: top-down (category revenue or population) and bottom-up (Customers = Addressable population × Interest rate × Paid conversion; ARPU = Average price × Purchase frequency; Revenue = Customers × ARPU). Show the math with named variables.
2) Three scenarios (conservative/base/optimistic) with explicit percentages and a short justification for each.
3) Sensitivity: show how Revenue changes when [Paid_conversion] and [ARPU] vary by ±20% (small table).
4) Comparable-company triangulation: list 2–3 comparable public companies or well-covered private ones, their last reported revenue, and the implied market size if they hold [assumed]% share.
5) Spreadsheet-ready output: provide an input table and Excel formulas using cell references and a version using named variables.
6) Sources: list likely public sources and exact search queries I can run. If you are unsure or cannot access a source, label the figure as ‘assumption’ and explain how to verify it.
7) Call out the top 3 assumptions to verify and how to verify each.”

Rapid variant — I’m pasting excerpts

“I will paste quotes from public sources. Extract numeric data and map to these variables: Addressable_population, Interest_rate, Paid_conversion, Avg_price, Purchase_frequency, Comparable_revenue, Comparable_share. Build bottom-up and top-down estimates, three scenarios, and a short sensitivity on Paid_conversion (±20%). Flag any missing variables as ‘assumptions’ and suggest how to find them. Then output the spreadsheet input table and formulas.”

Supply-side triangulation prompt

“Using a supply-side view for [market], estimate: Providers × Capacity per provider × Utilization × Price to cross-check demand estimates. Show conservative/base/optimistic, and highlight any mismatch with the demand model greater than 30%, plus likely reasons (seasonality, informal supply, channel mix).”

Short worked example (structure you can mirror)

• Market: UK language-learning subscriptions, annual.
• Assume adults = 54M; Interest = 6%; Paid conversion = 4%; Avg price = £8/month; Frequency = 12.
• ARPU = £8 × 12 = £96; Customers = 54,000,000 × 0.06 × 0.04 = 129,600; Revenue (base) = 129,600 × £96 ≈ £12.4M.
• Conservative: Interest 4%, Conversion 3% → ≈ £6.2M. Optimistic: Interest 8%, Conversion 5% → ≈ £20.7M.
• Sanity check: If a known player reports ~£4M UK revenue and holds ~25% share, implied market ≈ £16M — within range. Good sign.

Common mistakes and fast fixes

• Double counting audiences (free users vs paying). Fix: model only payers; keep free users as a separate stat.
• Mixing monthly and annual. Fix: standardize on annual in your sheet; convert everything before you calculate.
• Fantasy penetration. Fix: anchor participation and conversion to a public survey or a comparable company’s disclosed metrics.
• Single-source bias. Fix: apply the 2‑source rule for every critical input.
• No sanity check. Fix: compare to per-capita spend and to at least one comparable company’s revenue.

Action plan (60–90 minutes)

• 15 min: Define the market; list variables and initial assumptions.
• 25 min: Run the master prompt; capture the inputs, scenarios, and sensitivity.
• 20 min: Build the spreadsheet with your variables and formulas; create three scenarios.
• 20–30 min: Verify the most sensitive input (usually conversion or price) with 1–2 public sources; update the range.

Bottom line — You’re aiming for a tight range, not a magic number. Let AI assemble the math and options; your job is to choose sensible assumptions, verify the few that matter, and state them plainly. That’s what makes your estimate defensible.

[aaron]

Strong framework — the 3-anchor method plus AI is the right backbone. Let’s push it to a decision-ready output: a one-pager you can defend in five minutes, with clear KPIs and a repeatable refresh loop.

Hook — Triangulation gets you a number. Decision-ready means you can bet budget on it.

The gap — Many stop at math. What’s missing is a tight summary, explicit risk, and rules for when to trust the estimate vs. dig deeper.

Why it matters — Executives fund ranges they understand. Make the assumptions, sensitivity, and comparables visible, and your estimate becomes a tool, not a guess.

Lesson from the field — The teams that win keep a 3-part pack: Range table, Top-3 assumptions with sources, and a simple sensitivity. They refresh monthly in 20 minutes using an AI checklist.

What you’ll need

• Clear market statement (who/what/where/annual).
• Public anchors: population/company counts, prices, 2–3 comparable revenues.
• Tools: browser, spreadsheet, AI chat, and a single notes page to log each assumption and source.

Decision-ready workflow (8 moves)

1. Pin the decision question. Example: “Is this market big enough to justify a £500k pilot in 2025?” This sets the precision you need.
2. Lock boundaries. Define payer (B2B/B2C), geography, channel (online/offline), and currency/year. State exclusions (e.g., free users, international revenue).
3. Build the PPP ladder bottom-up. Customers = Addressable population × Participation × Paid conversion. ARPU = Price × Frequency. Revenue = Customers × ARPU. Put each driver in its own cell with units.
4. Top-down and comps. Pull one related category spend and 2–3 comparable company revenues with a reasonable share assumption to imply market size.
5. Run sensitivity with intent. Move two inputs by ±20% (typically conversion and ARPU). Note which input swings revenue most. That’s your verification target.
6. Sanity checks. Per-capita spend (Total revenue / population). If unrealistic for the category, revisit. Compare demand vs. supply-side estimate; explain gaps >30%.
7. Package the one-pager. Show conservative/base/optimistic with short justifications, list the Top-3 assumptions with sources, show the sensitivity, and add one comparable triangulation. Keep it to one page.
8. Set refresh rules. Monthly, re-run the AI prompts, re-verify the most sensitive input, and record any range movement and why.

Copy-paste AI prompts

• Decision-pack builder“Act as a market-sizing analyst. For [product/service] in [region] for [year], produce a decision-ready pack: 1) Bottom-up (Customers = Addressable × Participation × Paid conversion; ARPU = Price × Frequency; Revenue = Customers × ARPU). 2) Top-down using category spend or population, with penetration assumptions. 3) Comparable triangulation: list 2–3 players, last reported revenue, and implied market if they hold [assumed]% share. 4) Three scenarios with short justification. 5) Sensitivity: show revenue change when Paid_conversion and ARPU vary ±20%. 6) Sanity checks: per-capita spend and supply-side (Providers × Capacity × Utilization × Price). 7) Output spreadsheet-ready inputs and formulas. 8) Flag Top-3 assumptions to verify and give exact search queries to find them. Label unsourced numbers as ‘assumptions’.”
• Audit and refresh“Audit this market-size estimate. Check unit consistency (monthly vs annual), currency/year normalization, duplication (free vs paying), and per-capita plausibility. Compare bottom-up vs top-down vs comps; if any differ by >30%, explain likely causes and suggest the single most valuable verification step. Then provide updated inputs if new public data is available from credible sources.”

KPIs to track (make the estimate defensible)

• Range ratio = High / Low. Target ≤ 3. If higher, verify the most sensitive input.
• Anchor alignment = Max deviation among bottom-up, top-down, comps. Target ≤ 30%.
• Verification score = Verified critical assumptions / Total critical assumptions. Target ≥ 2/3.
• Per-capita plausibility vs category norms. Flag outliers.
• Time-to-first-estimate ≤ 90 minutes; Refresh cadence = monthly or upon major data release.

Common mistakes and fast fixes

• Anchoring on a single comparable. Fix: use three and show the spread; discard clear outliers.
• Hidden inflation/currency drift. Fix: convert all figures to a single currency and year; document the deflator you used.
• Blending B2B and B2C buyers. Fix: model one payer type at a time; keep separate sheets.
• Ignoring informal or grey-market spend. Fix: add an “untracked share” assumption or explicitly exclude and state it.
• Over-precision. Fix: round to meaningful digits and present ranges with assumptions, not single-point claims.

One-week plan (clear and doable)

1. Day 1: State the decision question and market boundary. List variables and initial assumptions.
2. Day 2: Run the decision-pack builder prompt. Capture bottom-up, top-down, comps, and scenarios.
3. Day 3: Build the spreadsheet. Add the two-input sensitivity and per-capita sanity check.
4. Day 4: Verify the single most sensitive input with 2 public sources; update the range.
5. Day 5: Do supply-side triangulation; resolve gaps >30% or explain them.
6. Day 6: Package the one-pager; rehearse the 60-second defense (range, why, what to verify next).
7. Day 7: Decision review. Log open assumptions and set the monthly refresh rule.

Expectation setting — In 90 minutes you’ll have a defensible range and a short list of validations. In a week, you’ll have a decision-ready pack that can unlock a budget conversation.

Your move.

— Aaron

[Author]

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