Market Sizing Framework: Step-by-Step Method with 6 Worked Examples (2026)

Market sizing cases require you to estimate a market's total revenue, units, or customers using top-down or bottom-up approaches with explicit assumptions. They appear in roughly 25% of McKinsey, BCG, and Bain first-round interviews and in nearly every market entry case, according to IGotAnOffer. Interviewers score your reasoning structure more heavily than the exact number — your target is to land within one order of magnitude. This guide covers a repeatable 4-step framework, 6 fully worked examples across B2C, B2B, and service markets, a reference data table for 5 countries, and the one step most candidates skip: stating what the number means for the client's strategy.

Why Market Sizing Shows Up in Every Interview Round

Consulting cases ask "how big is the market?" because the answer drives real strategic decisions: should a client enter this market, how should they price, what's a realistic revenue target? Firms like McKinsey, BCG, and Bain use these questions to evaluate how you decompose ambiguity, handle imperfect information, and communicate under pressure. The exact number matters less than showing clear, MECE reasoning.

According to Management Consulted, market sizing appears in roughly 1 in 4 first-round case interviews and nearly every market entry case. PrepLounge notes that interviewers score the logical approach more heavily than the exact number — the three golden rules are to follow an answer-first structure, use a sanity check, and state strategic implications. That frequency and scoring weight alone make market sizing worth mastering.

The Five-Step Framework

Use the same mental sequence every time so you stay organized under pressure. Notice the fifth step: implications. This is where you turn a number into a strategic recommendation, which is what consultants actually do.

This framework aligns with how you'd structure any case interview question. The key difference is that market sizing is self-contained: you generate your own data through assumptions rather than receiving exhibits.

Understanding Market Levels: TAM, SAM, SOM

Before diving into calculations, understand how markets break down from total opportunity to a realistic target. This matters because interviewers often want you to size a specific slice, not the whole pie. Drag the sliders to see how each level changes.

• TAM (Total Addressable Market): Total market demand if you had 100% share and could sell to everyone.
• SAM (Serviceable Available Market): The portion you can realistically reach based on geography, channels, and product fit.
• SOM (Share of Market): The realistic portion you can capture given competition and execution constraints.

When your interviewer says "size this market," ask whether they want TAM or something more targeted. That clarification alone shows commercial awareness.

Reference Data Table: Know These Numbers

You won't have Google in an interview. Memorize these approximate figures so you can anchor your estimates. They don't need to be exact, but being in the right ballpark keeps your calculations credible. According to World Bank open data and US Census Bureau reporting (2024 estimates):

Step-by-Step Breakdown

Step 1: Clarify the Question

Before doing any math, lock down three things:

• What exactly are we sizing? Revenue, units, or customers? These give wildly different numbers.
• Geography and time period. "US market, last 12 months" is specific. "The global market" is not.
• Any exclusions? B2B vs. B2C, certain channels, certain customer segments.

This avoids solving the wrong problem and signals that you listen before you calculate. Skipping this step is the single most common market sizing mistake, as discussed in our how to practice case interviews guide.

Step 2: Choose Your Segmentation

• Top-down: Start from a large number (GDP, total population, total industry spending) and apply filters to narrow down.
• Bottom-up: Start from a small unit (one store, one user, one transaction) and scale up.

Pick whichever approach fits the question and where you can justify assumptions. You can combine both: top-down for the number of users, bottom-up for revenue per user.

For customer segmentation in more complex sizing questions, consider breaking the population into 2-3 distinct groups with different usage patterns rather than applying a single average.

Step 3: Estimate Each Piece

• Use round numbers (340M rounds to 340M, $74K becomes $75K). This makes mental math faster and cleaner.
• State each assumption out loud so the interviewer can correct you or steer you.
• Anchor to reference points ("I'll assume 10% penetration, similar to early adoption curves for comparable products").

For tips on doing the arithmetic quickly and accurately, see our mental math for case interviews guide and the math practice drills.

Step 4: Sanity-Check and Conclude

• Compare to a known benchmark ("That's about 0.1% of GDP, which seems plausible for this category").
• State a range ("Roughly $X to $Y billion") to show you understand uncertainty.
• If something looks off, say so and adjust one assumption. Self-correction is a positive signal.

Step 5: State the Implications

This is the step most candidates skip, and it's the one that separates "competent" from "impressive." After calculating, always tell the interviewer what your number means for the client's strategy.

For example:

• "At $30B, this is a large enough market to justify entry, but given our client's size, they'd be capturing less than 1%, so differentiation will be critical."
• "At only 50K units per year, this is a niche market. The client should consider whether the margins justify the investment, or whether an adjacent segment is more attractive."
• "This market is roughly $2B. If our client targets 5% share in three years, that's $100M in revenue, which would require roughly 200 enterprise sales reps given typical SaaS productivity benchmarks."

This step connects your analysis to the broader case interview synthesis skill of tying numbers back to recommendations.

Worked Example 1: US Coffee Shop Revenue (Bottom-Up)

Clarify: We're sizing US revenue of coffee shops (chains and independents) in the last 12 months. Revenue only, not units of coffee.

Segment (bottom-up): Revenue = Number of coffee shops x Average revenue per shop per year.

Estimate:

• Number of coffee shops: US population is ~330M. Assume one coffee shop per 3,000 people (blending dense urban areas with sparse rural ones). That gives ~110,000 shops. Round to 100,000 for clean math.
• Revenue per shop: A Starbucks does roughly $1.5-2M per year, but most independent shops do far less. Assume an average of $300K per year across all shops (pulling the mean well below chains). So: 100,000 x $300K = $30B.

Sanity-check: US consumer spending on food away from home is roughly $1T per year (USDA Economic Research Service). Coffee shops being 3% of that ($30B) feels right. The National Coffee Association reports that about 66% of Americans drink coffee, which supports a large market.

Implications: "At $30B, this is a mature, competitive market. For a new entrant, the play isn't growing the overall pie but rather capturing share through differentiation, whether that's specialty drinks, convenience, or a subscription model."

Range: $25-35B.

Sensitivity Check: Communicating Uncertainty

After you state your base estimate, show you understand uncertainty by varying one or two key assumptions and stating the impact:

• Base: 100,000 shops x $300K = $30B.
• If shops per capita were 20% higher (one per 2,500 people): ~132K shops, so 132K x $300K = ~$40B.
• If revenue per shop were 20% lower ($240K): 100K x $240K = $24B.

How to say it: "My base is around $30B. If there are 20% more shops than I assumed, we'd be closer to $40B. If average revenue per shop is 20% lower, we'd be around $24B. So I'd give a range of roughly $24-40B."

That shows you can stress-test your logic without redoing the whole calculation. This kind of sensitivity analysis also appears in profitability cases when testing revenue and cost assumptions.

Worked Example 2: US Diaper Market (Bottom-Up, Consumer Goods)

Clarify: We're sizing the number of disposable diapers used in the US per year.

Segment (bottom-up): Total diapers = Number of children in diapers x Diapers per child per day x 365.

Estimate:

• Children in diapers: US births are roughly 3.6M per year. Children wear diapers from birth to about age 2.5 on average. So at any given time, about 3.6M x 2.5 = 9M children are in diapers.
• Diapers per child per day: Newborns use about 10 per day, toddlers closer to 5. Average across the age range: roughly 7 per day.
• Calculation: 9M children x 7 diapers/day x 365 days = 9M x 2,555 = ~23B diapers per year. Round to ~23 billion diapers.

Sanity-check: The US diaper market revenue is reported at roughly $10-12B per year (Statista). At an average price of $0.30-0.40 per diaper, that implies 25-40B diapers. Our estimate of 23B is on the lower end, which makes sense because we might be slightly underestimating diaper changes per day for younger infants. The order of magnitude is right.

Implications: "At 23B diapers per year, this is a massive volume business where even small cost efficiencies per unit create significant margin impact. A client entering this market should think about distribution scale and supply chain, not product differentiation alone."

Range: 20-28B diapers.

Worked Example 3: US Enterprise SaaS Licenses (Top-Down, B2B)

Clarify: We're sizing the number of enterprise SaaS licenses (paid software subscriptions for businesses with 500+ employees) sold in the US per year.

Segment (top-down): Start from the number of enterprises, then estimate licenses per enterprise.

Estimate:

• Number of enterprises (500+ employees) in the US: According to US Census Bureau data, there are roughly 20,000 firms with 500+ employees in the US. These range from 500-person companies to firms with 100,000+ employees.
• Average employees per enterprise: The distribution is heavily skewed. Most of these firms are in the 500-2,000 range. Assume an average of ~5,000 employees across the cohort (pulled up by large firms).
• Total enterprise employees: 20,000 firms x 5,000 employees = 100M enterprise employees.
• SaaS licenses per employee: A typical enterprise employee uses 5-15 SaaS tools (CRM, email, HR, collaboration, analytics, etc.). Assume an average of 8 paid SaaS licenses per employee.
• Total licenses: 100M x 8 = 800M enterprise SaaS licenses per year.

Sanity-check: The US enterprise SaaS market is roughly $150-200B in revenue (Gartner). At an average price of $150-250 per license per year (blending cheap per-seat tools with expensive enterprise platforms), 800M licenses would imply $120-200B in revenue. That aligns well.

Implications: "At 800M licenses, the volume is enormous, but this is a market with significant concentration among large vendors (Microsoft, Salesforce, SAP). A new entrant shouldn't try to be the ninth SaaS tool on every desk. They should target a specific workflow gap where incumbents are weak."

Range: 600M-1B licenses.

Worked Example 4: UK Weddings Per Year (Top-Down, Service Market)

Clarify: We're sizing the number of weddings that happen in the UK per year.

Segment (top-down): Start from the UK adult population, then estimate the marriage rate.

Estimate:

• UK population: ~68M. Adults (18+) make up about 80%, so ~54M adults.
• Marriage rate approach: In any given year, what fraction of adults get married? The UK has a crude marriage rate of about 4 per 1,000 population (Office for National Statistics). So: 68M x (4/1,000) = 272K marriages.
• Alternative check: Each marriage involves two people. So in a given year, about 544K people out of 54M adults get married, which is about 1% of adults. That feels reasonable for a country where average first marriage age is around 32 and people marry roughly once in their lifetime.
• Round to ~270K weddings per year.

Bottom-up cross-check:

• Venues approach: Estimate ~5,000 wedding venues in the UK (hotels, country houses, registry offices, religious venues). Assume each venue hosts an average of 50 weddings per year (some host 100+, small ones host 20). That gives 5,000 x 50 = 250K. Close to our top-down estimate.

Sanity-check: ONS data confirms roughly 250-270K marriages per year in recent years. Our estimate is right on.

Implications: "At 270K weddings per year with an average spend of roughly $30K per wedding, the UK wedding market is about $8B. That's large enough to support specialized entrants, but the market is highly fragmented across venues, caterers, photographers, and planners. A platform play that aggregates demand could be compelling."

Range: 240-300K weddings.

Worked Example 5: Rideshare Trips Per Day in Paris (Top-Down, Service)

Clarify: Daily rideshare trips in Paris (city proper, population ~2.2M), all providers combined.

Segment (top-down): Start from the population and estimate what fraction takes a rideshare trip on any given day.

Estimate:

• Paris population: ~2.2M people (city proper, not greater metro).
• Who uses rideshare? Rideshare skews toward ages 20-50, which is roughly 50% of the population. So the addressable population is about 1.1M.
• Rideshare penetration: Not everyone in that age bracket uses rideshare. Assume 30% have used a rideshare app at least once. That's ~330K registered users.
• Daily usage rate: Of those 330K registered users, what fraction takes a ride on any given day? Most users take a rideshare a few times per month, not daily. Assume 5% of registered users ride on any given day. That's 330K x 5% = ~16,500 users per day.
• Trips per user per day: Most users who ride on a given day take 1-2 trips (there and back, or just one way). Assume 1.5 trips per active user per day.
• Total trips: 16,500 x 1.5 = ~25,000 trips per day.

Bottom-up cross-check:

• Driver approach: Estimate ~3,000 active rideshare drivers in Paris on a typical day (Uber, Bolt, etc.). Each driver completes roughly 8-10 trips per shift. That gives 3,000 x 9 = ~27,000 trips per day. Consistent with our top-down estimate.

Sanity-check: Paris has excellent public transit (Metro carries ~4M trips/day), so rideshare being a small fraction of total mobility is expected. 25K rideshare trips vs. 4M metro trips means rideshare is less than 1% of metro volume. That's plausible.

Implications: "At 25K trips per day, Paris rideshare is a relatively small market by trip volume but high value per trip (average fare ~$15). For a new entrant, competing on price alone against Uber would require massive subsidy. A better angle might be a premium or niche segment, like airport transfers or corporate accounts."

Range: 20-35K trips per day.

Worked Example 6: US Fitness Industry Revenue (Top-Down from GDP)

This example demonstrates a pure top-down approach, starting from GDP rather than counting individual units.

Clarify: We're sizing total US fitness industry revenue (gyms, studios, personal training, fitness apps) in the last 12 months.

Segment (top-down from GDP):

Estimate:

• US GDP: ~$27T per year.
• Consumer spending as % of GDP: About 68% of US GDP is consumer spending = ~$18T.
• Health and wellness spending: Health and wellness (including fitness, supplements, wellness tourism, etc.) is roughly 5% of consumer spending = ~$900B.
• Fitness as a share of health/wellness: Fitness (gyms, studios, apps, equipment, personal training) is a subset, maybe 10-15% of the broader wellness category. Call it 12% = ~$108B.
• Narrow to just fitness services (gyms, studios, training, apps): Exclude home fitness equipment (roughly 30% of that $108B). That leaves ~$75B in fitness services.

Bottom-up cross-check:

• Gym memberships approach: Roughly 65M Americans have gym memberships (IHRSA/Health & Fitness Association). Average annual membership cost: ~$500-600. Revenue from memberships alone: 65M x $550 = ~$36B. Add personal training, boutique studios (which charge $150-200/month), and fitness apps, and $70-80B total is plausible.

Sanity-check: IBISWorld and Statista estimate the US gym/fitness market at roughly $35-40B for gym/studio revenue alone, with the broader fitness services market reaching $80B+. Our estimate of $75B is in the right zone.

Implications: "At $75B, this is a large and growing market, but fragmented. No single chain controls more than 10%. For our client, the opportunity might not be opening more gyms but rather building a platform that connects across the fragmented ecosystem, scheduling, payments, and content."

Range: $65-90B.

Common Mistakes

1. Skipping clarification and sizing the wrong thing or geography. This is the most frequent error and the easiest to avoid.
2. Mumbling assumptions instead of stating them clearly. The interviewer needs to hear each number and its rationale.
3. Overcomplicating with too many segments. Three segments is usually enough. More than five and you'll run out of time.
4. Ignoring sanity-check and not comparing to reality or giving a range.
5. Stopping at the number without interpreting what it means. Always add the "so what."

For more on structuring case answers, see the market entry framework, the growth strategy guide, and the case interview examples for practice with different case types.

Test Yourself

Practice Drills

Work through these sizing questions. Each one has a suggested answer, but your structure matters more than matching the exact number.

Practice with AI Feedback

Try a market sizing question and get feedback on your structure and assumptions. Use "Another question" to get a new prompt.

Connecting Market Sizing to Other Case Types

Market sizing rarely stands alone. It usually feeds into a bigger question:

• Market entry cases: "Is this market big enough to justify entry?" Your sizing estimate becomes the foundation. See the market entry framework.
• Pricing cases: "What price point captures maximum value?" You need the market size to calculate revenue at different price points. See pricing strategy cases.
• Growth cases: "Where should we grow next?" Sizing adjacent markets helps compare opportunities. See growth strategy cases.
• Profitability cases: "Why is revenue declining?" Understanding whether the total market shrank vs. losing share requires sizing. See the profitability framework.

If you're building a consulting interview prep timeline, practice market sizing early. It builds the mental math and structured thinking that transfer to every other case type.

Mental Math and Market Sizing

Market sizing involves quick multiplication and percentages. If you want to sharpen your speed and accuracy, see mental math for case interviews for drills and shortcuts. The most common operations you'll need:

• Multiplying numbers in the millions and billions (e.g., 330M x 0.05 = 16.5M)
• Percentage calculations (e.g., "what's 0.2% of $27T?")
• Quick division for sanity checks (e.g., "$45B / 330M people = ~$136 per person per year")

For structured case interview math practice, focus on doing these operations without a calculator in under 10 seconds each.

Sources and Further Reading (checked March 10, 2026)

• McKinsey case interview preparation: https://www.mckinsey.com/careers/interviewing
• BCG case interview preparation: https://careers.bcg.com/global/en/case-interview-preparation
• Bain case interview prep: https://www.bain.com/careers/interview-prep/
• IGotAnOffer, market sizing guide (frequency data): https://igotanoffer.com/blogs/mckinsey-case-interview-blog/market-sizing
• PrepLounge, market sizing case approach: https://www.preplounge.com/en/case-interview-basics/market-sizing
• Management Consulted market sizing guide: https://managementconsulted.com/market-sizing/
• CaseInterview.com market sizing resources: https://www.caseinterview.com/market-sizing
• CaseInterview.com framework overview: https://www.caseinterview.com/case-interview-frameworks
• Crafting Cases free practice resources: https://www.craftingcases.com/
• World Bank Open Data: https://data.worldbank.org/
• US Census Bureau, 2024 population estimates (340.1 million): https://www.census.gov/library/stories/2024/12/population-estimates.html
• US Census Bureau QuickFacts: https://www.census.gov/quickfacts/fact/table/US/PST040224
• US Census Bureau, Statistics of US Businesses (enterprise counts): https://www.census.gov/programs-surveys/susb.html
• USDA Economic Research Service, Food Service Industry: https://www.ers.usda.gov/topics/food-markets-prices/food-service-industry/market-trends/
• National Coffee Association: https://www.ncausa.org/
• Health & Fitness Association (formerly IHRSA): https://www.healthandfitness.org/
• Office for National Statistics, UK marriages: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/marriagecohabitationandcivilpartnerships
• Statista, US fitness market: https://www.statista.com/outlook/mmo/fitness/united-states
• Statista, US diaper market: https://www.statista.com/outlook/cmo/tissue-hygiene-paper/disposable-diapers/united-states
• Gartner SaaS market research: https://www.gartner.com/en/newsroom/press-releases/
• Hacking the Case Interview, market sizing: https://www.hackingthecaseinterview.com/pages/case-interview-mental-math

Related Guides

• Mental Math for Case Interviews
• Case Interview Math Practice
• Profitability Framework
• How to Practice Case Interviews
• Case Interview Examples