What market sizing is (and isn’t)
Market sizing questions are inspired by a class of estimation problems popularized by Enrico Fermi, an Italian Nobel Prize–winning physicist known for challenging his students to estimate complex, unfamiliar quantities using only logic, decomposition, and reasonable assumptions. The objective was never precision, but whether the reasoning could be defended step by step.
Why consulting interviews use market sizing
Consulting interviews use market sizing for the same underlying reason. Interviewers are not testing how many facts you know, nor how fast you can calculate. They are testing whether you can bring structure to ambiguity: clearly define the problem, choose a sensible way to think about it, and reason coherently under uncertainty.
How answers are actually evaluated
In this context, a good answer is not one that is numerically accurate, but one that is logically defensible. Each step should follow clearly from the previous one, and each assumption should be explainable and appropriate for the approach you chose.
Why this is harder than it looks
This is why market sizing is often challenging even for strong candidates. The difficulty does not come from math, but from deciding how to think about the problem before any numbers are used.
Most candidates don’t struggle with market sizing because they lack intelligence or preparation. They struggle because sizing questions introduce ambiguity under pressure. Common mistakes include:
Once either of these mistakes occurs, the rest of the solution rarely recovers — regardless of how polished the calculations appear.
What separates strong candidates from average ones is recognition: identifying which kind of sizing logic the question requires before youstart solving. At the highest level, two types of sizing problems exist:
Within Type 1 (market-related) sizing cases, the market equilibrium can be driven by different constraints. In practice, these cases fall into three subtypes, depending on whether demand, supply, or bothsides of the market are binding.
Description: This approach focuses on estimating the market size by analyzing the potential demand for a product/service.
How to recognize it: Most market sizing cases are solved with a demand perspective, as it is usually easy to identify and size demand (i.e., people/businesses interested in buying the product/service).
Common steps involved: Segmentation of the population by age (or alternatively, estimation of the number of households); considering the useful life of products that are durable (i.e., last more than 1 year) to adjust the frequency of purchase.
Example:
Estimating the number of bikes sold in Italy
1. Italians between 0–60 years are potential bike buyers → target of 45mn Italians, out of a total of 60mn, assuming 80 years of life and the population being uniformly distributed
2. Only 33% of Italians are interested in biking → 15mn potential buyers (assuming 1 bike on avg. per user)
3. The average life of a bike is 7–8 years → 2mn bikes sold every year
4. The average price of a bike is EUR 500 → EUR 1bn total market
Some non-market sizing questions (e.g., “how many soccer players does Italy have”) behave like demand-driven market sizing even thoughno actual market exists. In practice, the logic is the same: define the relevant population, apply aprevalence or participation rate, and aggregate.
Description: This approach focuses on estimating the potential market size based on the supply of a product/service.
How to recognize it: The business is heavily influenced by capacity limits or other supply-side factors (e.g., number of waiters working at a restaurant, room number of a hotel), and demand is hard to size.
Common steps involved: Estimation of the utilization rate given the capacity limit; utilization can be based on an average between peak and non-peak moments.
Example:
Estimating the daily revenue of a coffee shop
1. The coffee shop has 50 seats (avg. client stays 1h) and is open 10h per day → 500 total potential clients served daily (50/h)
2. Assume average utilization rate during the day of 75% → 10h × 75% × 50 = 375 clients served daily
3. Average spending per client is EUR 10 → EUR3.75k total daily revenues
Description: This approach is a hybrid between the demand and supply perspective. In businesses with peak and non-peak moments, the relevant constraint technically shifts frombeing demand-side to supply-side (and back). A more precise sizing exercise thus requires considering both perspectives.
How to recognize it: The business has a relevant supply constraint, experiences peak and non-peak moments, and has a potentially sizable demand (e.g., restaurant in an airport).
Common steps involved: Analysis of demand vs. supply flow in peak and non-peak moments.
Example:
Estimating the daily revenue of a coffee shop in an airport
1. The airport has 6k passengers per day (2/3 in 4 peak hours, 1/3 in 20 non-peak hours) → 1,000 passengers/hour during peak hours and 100 passengers/hour during non-peak hours
2. The coffee shop has 150 seats (avg. client stays 1h) and is always open → during peak hours the shop is constrained by supply; during non-peak hours the shop is constrained by demand
3. 4 peak hours at 150 clients/hour and 20 non-peak hours at 100 clients/hour → total of 2,600 clients per day
No product or service is sold; you’re estimating a quantity with no explicit market equilibrium. For this reason, Type 2 cases are split into two subtypes: geometry-based sizing, where the estimate is driven by spatial or physical constraints,and process-based sizing, where the estimate depends on time, flow, orthroughput and is driven by rates, capacity, and operational constraints.
Description: This approach estimates the size of an object or system by approximating it using continuous physical relationships such as distance, area, volume, or mass.
How to recognize it: The question asks “how big”, “how long”, “how far”, or “how heavy”. The answer represents a static quantity and can be approximated using geometry, physics,or simple formulas (e.g., speed × time).
Common steps involved: Approximation of the object using simple shapes or physical rules; use of basic relationships (e.g., distance = speed × time, mass = volume × density); sanity check against known reference magnitudes.
Example:
Estimating the size of the Earth
1. Long-haul flights cruise at 900 km/h
2. A full trip around the Earth would take 45 hours
3. 45 hours × 900 km/h → 40k km Earth circumference
Description: This approach estimates a total quantity by breaking a system into countable,repeatable components and aggregating them.
How to recognize it: The question asks “how many X are there?”. The system can be decomposed into discrete units, each with a reasonably estimable average size or count.
Common steps involved: Decomposition of the system into major components; estimation of the number of units per component; multiplication by an average size per unit; aggregation across components.
Example:
Number of streetlights in a city
1. Area of city 200 km²; roads are 10% of land → road area 20 km²
2. Road width 10 m (0.01 km) → road length 20 / 0.01 = 2,000 km
3. One light every 25 m (40 lights per km) → 80k total lights in the city
Description: This approach estimates a quantity that depends on time, flow, or throughput, where outcomes are driven by rates, capacity, and operational constraints.
How to recognize it: The question involves frequency, waiting time, throughput, or capacity (e.g., “per hour”, “per day”, “how often”). The system operates over time and may have peak vs. non-peak periods or bottlenecks.
Common steps involved: Estimation of demand rate and effective capacity; calculation of throughput as rate × time × utilization Identification of bottlenecks or service constraints; translation into frequency, capacity, or total output.
Example:
Estimating required number of trains to operate metroline
1. 6 peak hours with 1 train every 3 min and 10 non-peak hours with 1 train every 10 min → 20 trains per hour in peak hours, 6 trains per hour in non-peak hours
2. Assume 1 hour to full round-trip → 20 trains needed to operate in peak hours (disregard non-peak hours, as the trains used will be a subset)
Some non-market, process-based sizing questions are essentially non-market versions of supply-driven sizing.
More complex cases can require first translating demand into a flow and then mapping that flow through a production or turnover process to determine thesteady-state stock (e.g., estimating the number of chickens in Italy by first translating food consumption into an annual demand flow).
Market sizing answers are not evaluated on numerical accuracy. Interviewers know that precise data is unavailable and that reasonable assumptions can vary. What they are assessing instead is how you think under uncertainty. In practice, strong sizing answers share three characteristics:
Most candidates understand these principles intellectually. Very few can apply them consistently across unfamiliar cases, under time pressure. These skills require repeated, disciplined practice under constraints that mirror real interviews. This is exactly the gap InterviewLab is designed to fill.
InterviewLab forces you to:
What you get from practice
With repeated use, candidates develop the habits that matter most in interviews: faster and more confident framework recognition and cleaner, more defensible structures. This is the difference between knowing how market sizing works and being able to perform consistently in interviews.
How to use InterviewLab
You can practice whenever you want, for as long as you want, and at the difficulty level you choose. Each session follows a structured interview-style flow, with feedback designed to reinforce the skills interviewers actually care about.
