Market Sizing Drills Template

Market Sizing Drills: [Your name, drill set, date]

A workbook of 12 original market sizing drills for consulting and strategy interviews, with top-down and bottom-up methods, computed answers, sanity checks and Excel formulas you can rebuild in a practice sheet. It is for MBA students preparing for case interviews and for anyone who has to estimate a market quickly and defend the logic. Source basis: the logic of Fermi estimation, Little's law (Little 1961) for capacity drills, and research on deliberate and retrieval practice, written in our own words. Every number in the drills is an assumption chosen for practice, set in a fictional "Country A"; none is a real market statistic. Replace every [bracketed] value and every row marked Example. This is a preparation aid, not market research.

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Contents

1. What market sizing tests
2. Top-down and bottom-up methods
3. A five-step routine
4. Sanity checks
5. The 12 drills
6. Sensitivity and ranges
7. Your drill log
8. Common mistakes
9. Self-test

1. What market sizing tests

A market sizing question asks you to estimate a quantity you cannot look up in the room: annual sales of a product, the number of machines a site needs, the revenue of a single store. Physicist Enrico Fermi was known for posing such questions to students, and they are often called Fermi problems. Interviewers use them because they reveal, in a few minutes, whether you can break an unfamiliar quantity into parts you can reason about, state assumptions clearly, calculate accurately aloud and judge whether the result is plausible.

Market sizing appears in three forms: as a stand-alone short question, as one step inside a larger case (for example "how big is the opportunity?" in a market entry case), or as a written exercise in an online assessment. The same routine works for all three.

2. Top-down and bottom-up methods

Top-down starts from a large population and narrows it with shares and rates until you reach the target quantity: people, then the share who use the product, then purchases per year, then price. It is fast and works well for consumer products.

$\text{Market value} = N \times s_1 \times s_2 \times \dots \times f \times p$

Bottom-up starts from a unit you can picture (one store, one machine, one sales rep) and scales up: customers per hour, conversion, basket size, opening hours. It is better for capacity questions, B2B markets and the revenue of a single business, because each factor is observable.

$\text{Revenue of one unit} = \text{traffic} \times \text{conversion} \times \text{average ticket} \times \text{periods}$

Stock versus flow. Be explicit about whether you are counting a stock (the number of tents people own) or a flow (tents sold per year). Converting one into the other needs a replacement cycle or a lifetime: annual sales are approximately the stock divided by the product's life in years, plus growth.

3. A five-step routine

1. Clarify. "Do you want annual sales in units or value? New purchases only? For Country A?"
2. Structure. Draw the tree before any number. Show it to the interviewer.
3. Assume. Give round numbers with one-phrase justifications ("about one in five adults hikes a few times a year"). Segment where it matters (urban and rural, frequent and occasional users).
4. Calculate. Multiply in an order that keeps numbers simple; write units on every line.
5. Check and conclude. Compare with a benchmark, name the most uncertain assumption, give a range, and say what the number means for the case.

4. Sanity checks

5. The 12 drills

Fictional setting for all drills: Country A has 60m people, 48m adults and 24m households; about 2.0m new cars are sold there a year. These are drill assumptions, not facts about any real country. Company settings (Northfield Outdoor Co., Corvane Robotics, Tallow Creek Medical Center) are fictional.

D01. Hiking boots sold in Country A per year (top-down, units and value)

Excel: =60000000*20%/3 returns 4,000,000 pairs; =60000000*20%/3*120 returns 480,000,000. Sanity check: USD 8 per person a year (=480000000/60000000). The most uncertain input is the share of regular hikers; casual buyers of light hiking shoes are excluded by definition and would be a second segment.

D02. Annual revenue of one Northfield store (bottom-up)

Excel: =40*12*360*25%*70 returns 3,024,000. Sanity check: USD 8,400 a day (=40*12*25%*70), about 10 transactions an hour at USD 70: plausible for a mid-size specialist store. A stronger answer splits weekdays and weekends, since traffic differs.

D03. Addressable market for warehouse robots in Country A (top-down, B2B)

Excel: =4000*30%*50*40000 returns 2,400,000,000; per year over 5 years =4000*30%*50*40000/5 returns 480,000,000. Sanity check: about USD 2m of robots per adopting site (=50*40000), which should be compared with the labour cost the robots replace. For Corvane Robotics, this is the addressable market, not its expected revenue: share and timing come next.

D04. Robots needed in one Corvane customer warehouse (bottom-up, capacity)

Excel: =ROUNDUP(40000*3/(120*20*85%),0) returns 59. Sanity check: capacity questions must round up, and peaks matter more than averages. If the peak day is 1.5 times the average, the answer changes materially: ask about seasonality.

D05. MRI scans per year at Tallow Creek Medical Center (bottom-up, capacity)

Excel: =2*16*60/45*360*85% returns 13,056. Sanity check: about 18 scans per scanner per day (=16*60/45*85% returns 18.13). Utilisation below 100% reflects no-shows, emergencies and maintenance.

D06. Coffee cups bought per day on a university campus (top-down)

Excel: =25000*70%*50%*1.4*60% returns 7,350. Sanity check: 0.42 cups per person present (=7350/17500). With perhaps 10 to 15 outlets, that is about 490 to 735 cups each a day, a busy but feasible level.

D07. EV home chargers installed in Country A per year (top-down from new car sales)

Excel: =2000000*20%*60%*80% returns 192,000; =2000000*20%*60%*80%*1200 returns 230,400,000. Sanity check: the flow is driven by new EV sales; replacements of existing chargers are excluded and would add a second, smaller segment. Section 6 runs a sensitivity on this drill.

D08. Revenue of a ski rental shop in one resort (bottom-up)

Excel: =120*3000*30%*15%*45 returns 729,000. Sanity check: 135 rentals a day on average (=16200/120), which one shop with a few fitting stations can serve.

D09. Hospital beds needed for a region (Little's law)

Little's law (Little 1961) states that, in a stable system, the average number of items in the system equals the arrival rate times the average time each item spends in it: $L = \lambda W$. For beds, $L$ is the average number of occupied beds, $\lambda$ the admissions per day and $W$ the average length of stay.

Excel: =800000*120/1000*4.5/365 returns 1,183.56; =ROUNDUP(800000*120/1000*4.5/365/85%,0) returns 1,393. Sanity check: about 1.7 beds per 1,000 people (=1393/800). Target occupancy below 100% leaves room for variability; queueing theory shows that waiting time rises steeply as utilisation approaches 100%.

D10. Spending on running shoes in Country A (top-down)

Excel: =48000000*15%*1.5*110 returns 1,188,000,000. Sanity check: about USD 25 per adult a year (=1188000000/48000000 returns 24.75). Shoes bought for fashion rather than running are excluded by definition; say so.

D11. Delivery vans needed in a city of 2m people (bottom-up from parcel demand)

Excel: =2000000*60/300 returns 400,000; =ROUNDUP(2000000*60/300/(150*1.25),0) returns 2,134. Sanity check: about 1.15 parcels per person a week (=60/52). Peak season would need more vans; the answer is an average-day fleet.

D12. Tents sold in Country A per year (top-down and bottom-up, triangulated)

Top-down

Bottom-up

Excel: top-down =24000000*12%/6 returns 480,000; bottom-up =1600*4*52/(1-35%) returns 512,000; gap =512000/480000-1 returns 6.7%. Sanity check: the two methods agree within 7%, which supports a market of about USD 120m to USD 130m. If they had differed by a factor of two, the right response is to find the assumption that drives the gap, not to average.

Summary of drill answers

6. Sensitivity and ranges

A point estimate built from four multiplied assumptions carries the uncertainty of all four. Change one input at a time between a low and a high value to see which matters most; the result is often drawn as a tornado chart. For D07, with the base case at 192,000 chargers a year:

The EV share drives the answer most, so that is the assumption to research first. Setting every input to its low or high value at once gives 94,500 to 346,500, a range so wide that it overstates the uncertainty, because all inputs are unlikely to be extreme together. Excel: put the inputs in B2:B5 and use =PRODUCT(B2:B5); a one-variable data table (What-If Analysis) runs the low and high cases.

7. Your drill log

Write your own drills. Pick a product or service you see this week, write the question, solve it, then ask a partner to solve it independently and compare trees. Good sources of ideas: Northfield's product lines, Corvane's customers, Tallow Creek's departments.

8. Common mistakes

1. Starting to multiply before defining units, geography, time period and whether the answer is a stock or a flow.
2. Using the whole population when only a segment buys (counting children for running shoes, for example).
3. Forgetting purchase frequency or replacement cycle, which turns a stock into a wrong flow.
4. Using precise-looking assumptions (17.3%) that suggest false accuracy and slow the arithmetic.
5. Losing zeros between thousands, millions and billions.
6. Rounding capacity answers down; robots, beds and vans must be rounded up.
7. Planning capacity for the average day and ignoring peaks.
8. Skipping the sanity check, or not responding when it fails.
9. Averaging two very different estimates instead of finding the assumption that causes the gap.
10. Presenting a single number without naming the most uncertain assumption or a range.
11. Confusing the addressable market with the company's expected revenue.

9. Self-test

1. What is the difference between a top-down and a bottom-up estimate, and when is bottom-up the better choice?
2. A product has an installed base of 9m units and lasts 6 years. Roughly how many are sold a year, ignoring growth?
3. State Little's law and name each term.
4. A clinic sees 72 patients a day and each spends 50 minutes on site on average. How many patients are on site on average during a 12-hour day?
5. Why should capacity answers be rounded up?
6. In D07, which input would you research first and why?
7. Two estimates of a market differ by a factor of two. What do you do?
8. A café sells 300 cups a day at USD 4, 350 days a year. What is its annual coffee revenue?

Sources and further reading

• Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383-387.
• Ericsson, K. A., Krampe, R. T., and Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363-406.
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., and Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4-58.
• Further reading (by name only): Lawrence Weinstein and John A. Adam, Guesstimation; Victor Cheng, Case Interview Secrets; Marc Cosentino, Case in Point.
• Related templates: mba/mba-case-interview-prep for the full case routine, mba/pre-mba-quant-refresher for arithmetic.

Answer key

1. Top-down narrows a large population with shares, frequency and price; bottom-up scales up from one observable unit (store, machine, rep). Bottom-up is better for capacity questions, B2B markets and a single business's revenue, where each factor can be observed.
2. About 1.5m a year: =9000000/6.
3. $L = \lambda W$: average number in the system equals arrival rate times average time in the system (Little 1961).
4. About 5 patients: arrivals of 6 an hour (=72/12) times 50/60 hours gives 5 (=72/12*50/60).
5. Because a fraction of a robot, bed or van cannot be bought, and rounding down leaves demand unserved; peaks add further need.
6. The EV share of new cars: it produces the largest swing (96,000 chargers between the low and high cases).
7. Find the assumption that drives the gap (often frequency, segment definition or price), test it, and correct the weaker method rather than averaging.
8. USD 420,000: =300*4*350.

Revision history

Standard and licence note

Based on the general logic of Fermi estimation and on Little's law (Little 1961). The drills, assumptions and answers are original and set in a fictional country with fictional companies; they are not market data. This template is not affiliated with or endorsed by any firm or school. Template licence: MIT.