Road to Offer
Road to Offer

Market size calculator

Free tool

Pick top-down or bottom-up, edit the assumptions, and sanity-check the answer like you would in a consulting interview.

1Pick your approach
2Define geography
3Fill in your assumptions
Broadest universe
Filter by demographics
%
Of eligible, who buy
%
Purchases per year
× / yr
Revenue per unit
$
When to use top-down

Best for consumer markets with a known population or category benchmark.

  • Anchor assumptions
  • Use round numbers
  • Check GDP / capita
  • Run both if time allows
4Result
Estimated annual market size
$99B
United States · Top-down (demand-side)
Decomposition
Population
220M
Total universe
% Eligible
60%
132M eligible
% Buyers
100%
132M buyers / yr
Frequency
150× / yr
19.8B units / yr
Avg price
$5.00
per unit
Start broad, filter to buyers, then multiply by frequency and price.
Sanity check
% of United States GDP
0.37%
$ per capita
$296
Plausible: 0.37% of United States GDP, $296 per capita.
Assumptions summary — Top-down
Population
220M

Broadest universe — anchor to a public stat

Census, World Bank, or industry report

% Eligible
60%

Demographic / behavioural filter

Working-age, internet access, income tier, etc.

% Buyers
100%

Of eligibles, share who actually buy in a year

Anchor to an analogue product's penetration

Frequency
150× / yr

Annual purchases per buyer

Use round numbers; speed > precision

Avg price
$5

Revenue per unit

Anchor to public retail price or industry benchmark

Geography
United States

Pop 335M, GDP $27T

Drives the sanity-check ratios

Market sizing drill

Build Your Answer

What is the annual US pet food market size in dollars? Walk through your structure.

Get scored on structure, assumptions, and sanity check. One rep, then a clear fix list.

Fast answer

What you need to know

  • Pick top-down for demand-side cases; pick bottom-up when supply is the constraint.
  • Say the formula before the math: Population × Eligible × Buyers × Frequency × Price.
  • Use round assumptions and explain why each one is reasonable.
  • Close with a sanity check: GDP share, spend per capita, or a known benchmark.

The quick version: Market sizing estimates annual revenue or volume from first principles. In interviews, the score comes from structure, assumptions, and sanity checks, not decimal-place precision.

How do you size a market in a case interview?

Market sizing shows up often because it tests 3 interview skills at once: structure, math, and judgment. IGotAnOffer's market sizing guide, PrepLounge, and MConsultingPrep all teach the same core idea: state the approach before doing arithmetic.

The strong version sounds like this: "I will go top-down because the customer population is measurable. I will start with population, filter to likely buyers, estimate purchase frequency, then multiply by price."

That sentence matters more than the final number. It tells the interviewer you know how to decompose an ambiguous problem, explain assumptions, and check whether the answer is plausible.

For more reps, use the market sizing questions library or read the full market sizing framework.

What is the canonical MBB market sizing formula?

Use this 4-step flow:

  1. Clarify the ask. Revenue or volume? Which geography? Which time period?
  2. Choose the model. Use top-down for most consumer markets. Use bottom-up when supply is the constraint.
  3. Run the formula. Top-down = Population × Eligible × Buyers × Frequency × Price. Bottom-up = Outlets × Units/day × Days × Price.
  4. Sanity-check. Compare against GDP share, spend per capita, or a known market benchmark.

The calculator mirrors that flow. It is not trying to give you the "right" market report number. It is helping you practice the interview behavior: name the approach, anchor each assumption, then defend the result.

For adjacent practice, use the case interview math practice tool, issue tree examples, and the McKinsey case interview guide.

View worked examples →

What are real-world MBB market sizing examples?

Each example shows a real market, the approach used, the canonical formula applied, and the sanity check. Use these as reference points for calibrating your own assumptions.

  • Example 1

    US coffee shop market: both approaches converge

    Approach

    Top-down: 220M US adults × 60% drink coffee out × 100% buy in a year × 150 cups/yr × $5/cup = $99B. Bottom-up: 70k US coffee shops × 350 cups/day × 350 days × $5/cup = $43B.

    Answer

    Both methods land in the $45–100B range; real US coffee shop market is ~$45–60B (NCA / IBIS World). Sanity check: ~0.2% of US GDP, ~$130–300 per adult per year. Plausible.

  • Example 2

    EV charging stations, US market (bottom-up)

    Approach

    US has ~290M registered vehicles. 15% are EVs or hybrids in 2026, equals 43.5M EVs. 60% use public charging at least monthly, equals 26M charging customers. Average 4 sessions/month at $12/session.

    Answer

    26M × 4 × 12 × $12 = ~$15B annual market. Sanity check: ~0.06% of US GDP, $45 per capita. Triangulates well against public charging industry reports.

  • Example 3

    B2B SaaS HR tools, mid-market (top-down)

    Approach

    Global mid-market companies (100–2,500 employees): ~1M. % buying HR software in a year: 15% (replacement cycle). Average ACV: $20K. = 1M × 15% × $20K.

    Answer

    $3B annual purchase volume in the mid-market segment. Sanity check: ~0.003% of global GDP, plausible for a niche B2B category. Per Gartner, mid-market HCM spend is in the $2–4B range.

  • Example 4

    Online tutoring for K-12 students in the US (top-down)

    Approach

    US K-12 students: 50M. % with internet + household income compatible: 60%, equals 30M. % using paid online tutoring regularly: 8%, equals 2.4M. Average spend $120/month × 12.

    Answer

    2.4M × $1,440 = ~$3.5B annual market. Sanity check: ~0.013% of US GDP, ~$70 per K-12 student. Cross-check: US private tutoring is ~$7B total, online ~50%, consistent.

  • Example 5

    Coffee in Germany (bottom-up)

    Approach

    Germany has ~25k cafés and bakeries serving coffee. ~250 cups per day per outlet × 320 operating days × €3 average price.

    Answer

    25k × 250 × 320 × €3 = €6B annual market. Sanity check: ~0.15% of German GDP, ~€72 per capita. Aligns with reported German out-of-home coffee market of €5–7B.

  • Example 6

    Pet insurance in the US (top-down)

    Approach

    US households: 130M. % owning a dog or cat: 60%, equals 78M households. % buying pet insurance: 5%, equals 3.9M. Average premium: $700/yr.

    Answer

    3.9M × $700 = ~$2.7B annual market. Sanity check: ~0.01% of US GDP, $8 per capita. Industry reports peg the market at $3–4B; growing 20% YoY.

View common mistakes →

What are common mistakes in market sizing?

Most market sizing errors are not arithmetic errors. They are structural errors: wrong approach, wrong universe, missing sanity check. These are the ones we see most often in MBB-style drills.

  • Skipping the approach declaration
    MBB graders are listening for the words top-down or bottom-up before any numbers. Always say which approach you are picking and why in one sentence: 'I will go top-down because the US adult population is well measured.' This signals canonical structure and earns the structure points before the math runs.
  • Using TAM/SAM/SOM instead of the canonical formula
    TAM/SAM/SOM is a startup pitch-deck framing, not an MBB pedagogy. PrepLounge, MConsultingPrep, IGotAnOffer, and Career in Consulting all teach decomposition via the canonical demand-side equation (Population × Penetration × Frequency × Price) or the supply-side equation. Save TAM/SAM/SOM for investor decks.
  • Wrong universe definition
    The population input in a top-down model should be the broadest group that could ever be a customer, then filter sequentially. Starting with too narrow a universe understates the market; starting too broad and skipping filters overstates it. Define 'universe' before you apply % Eligible.
  • Double-counting filters
    If you multiply 'all US adults' by a penetration rate and then again by a segment filter that overlaps with that penetration, you may be counting the same people twice. Each filter should narrow a distinct dimension (geography, demographics, behaviour, intent) without overlap.
  • No sanity check at the end
    PrepLounge says strong answers stay within ~20% of a known benchmark. IGotAnOffer asks for one order of magnitude. Always close with a comparison: a public company's revenue in the space, an industry report range, % of geography GDP, or spend per capita. The sanity-check card on the calculator runs the GDP and per-capita ratios automatically.
  • Not running both methods when time allows
    Strong analysts run top-down and bottom-up and use the gap between them to find weak assumptions. If top-down says $99B and bottom-up says $43B, the gap tells you something about which assumption is overstated. Per Hacking the Case Interview, this is the move that separates a strong candidate from an exceptional one.
  • Using non-round numbers
    MConsultingPrep is explicit: use round numbers and lean on the order of magnitude. A confident '~150 cups per year' beats a brittle '147.3 cups per year' because it speeds the math, lets you focus on the structure, and signals comfort with approximation.
  • Treating the final answer as the score
    Per Career in Consulting, MBB interviewers grade three dimensions: structure (did you name the approach and use the canonical formula?), judgment (are the assumptions anchored?), and communication (did you narrate the math out loud?). The exact final number is the third priority, not the first.

Frequently Asked Questions

  • How do you size a market in a case interview?

    Use the MBB-canonical 4-step structure: (1) clarify the question (units, geography, time horizon), (2) pick top-down or bottom-up, (3) decompose with the canonical formula (Population × Penetration × Frequency × Price for top-down, or # Outlets × Throughput × Days × Price for bottom-up), and (4) sanity-check the result against an external benchmark such as % of GDP or spend per capita. State your approach out loud before you compute anything.

  • When should you use top-down vs bottom-up?

    Per IGotAnOffer's rule of thumb, top-down (also called demand-side) wins in roughly 80% of cases. Use it when a credible total population or industry total exists, which covers most consumer markets. Use bottom-up (also called supply-side) when supply is the binding constraint, which covers B2B markets, physical-location businesses, and niche or new categories where no industry total exists. Strong analysts run both and reconcile the gap.

  • What is the population × penetration × price formula?

    It is the canonical demand-side market sizing equation taught by PrepLounge, MConsultingPrep, IGotAnOffer, and Career in Consulting. The full version is Population × % Eligible × % Buyers × Frequency × Price. Population is the broadest universe, % Eligible filters by demographics or geography, % Buyers narrows to those who actually purchase, Frequency is annual purchases per buyer, and Price is the unit revenue. Multiplied together, they give annual market size in dollars.

  • How do you sanity-check a market size answer?

    Apply two universal ratios. First, what % of geography GDP does this market represent? A consumer category at 5% of US GDP is almost certainly an arithmetic leak; a niche B2B category at 0.001% is plausible. Second, what is the spend per capita? Compare against a known analogue (US coffee = ~$200 per adult per year, US streaming = ~$50 per adult per year). PrepLounge calibrates strong answers within ~20% of a real benchmark; IGotAnOffer accepts one order of magnitude.

  • What are common mistakes in market sizing?

    The biggest one is skipping the approach declaration and going straight to numbers, which loses the structure points before the math even runs. Other common mistakes: using TAM/SAM/SOM (a startup framing) instead of the MBB-canonical formulas, defining the universe too narrowly or too broadly, double-counting filters, skipping the sanity check, and not running both top-down and bottom-up when time allows. Per Career in Consulting, none of these are arithmetic mistakes; they are structural mistakes.

  • Why do MBB consultants prefer the canonical formula over TAM/SAM/SOM?

    TAM/SAM/SOM is a containment hierarchy designed for investor pitch decks: it shows a startup's addressable, serviceable, and obtainable opportunity. The MBB-canonical formulas are decomposition tools designed for case interviews: they show how a market is built from first principles via either demand-side filters (Population × Penetration × Frequency × Price) or supply-side scaling (Outlets × Throughput × Days × Price). PrepLounge, MConsultingPrep, IGotAnOffer, and Career in Consulting all teach the canonical formulas; TAM/SAM/SOM rarely appears in MBB training material.

  • How precise does a market size estimate need to be?

    Order-of-magnitude accuracy, not precision. PrepLounge calibrates a strong answer at within ~20% of a real benchmark; IGotAnOffer says one order of magnitude is acceptable. A confident '~$50B' with anchored logic beats a brittle '$47.3B' with unexplained assumptions. Focus on the assumption quality, not the decimal place. Use round numbers throughout to speed the math.

  • Can I use this calculator for a real investor deck?

    Yes for the structure, but edit the assumptions to reflect your actual sourcing (industry reports, analogues, primary research). The calculator outputs annual revenue. For an investor deck, you may also want to derive a TAM/SAM/SOM hierarchy from the same calculation by applying realistic geography and capture filters on top of the canonical result. Do not present the default placeholder numbers in a live deck.

Market sizing drill

Run one timed sizing rep

Get a score on structure, assumptions, math, and sanity check, then fix the weak part.

Start Timed Sizing RepFirst graded rep is free.

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