Dunmore Cargo Bikes: Market Sizing Case Study with Answer

I would size it top-down as a funnel and cross-check bottom-up by segment. Annual market = couriers who would adopt the bike, divided by how many years a bike lasts, times the price per bike.

Before the numbers I would confirm scope: US only, couriers working in cities, individuals and small businesses buying for daily work, no consumer family cargo bikes.

• Start: all urban last-mile couriers in the US.
• Filter 1: couriers who work where a bike is practical (dense zones, short trips).
• Filter 2: those who would adopt a cargo e-bike as their main work vehicle.
• Convert: adopters become bikes sold per year through the replacement cycle, then dollars through price per bike.
• Cross-check: build the same number bottom-up from segments such as meal and grocery, parcel and local business.

What the interviewer is looking for

• Good: a clear funnel from population to adopters to annual bikes to dollars.
• Strong: segments couriers by type, because adoption differs between a meal courier and a parcel courier.
• Excellent: separates users in place from bikes sold per year, and proposes a bottom-up cross-check before starting.

Push-back: Why start from couriers instead of from the number of e-bikes sold in the US each year?

Assumptions: 2.4 million urban couriers, 25% work in bike-friendly zones, 5% of those adopt a cargo e-bike. That gives 600,000 couriers in bike-friendly zones and 30,000 adopters.

A bike lasts 3 years, so the market replaces about one third of the installed base each year: 10,000 bikes a year. At $4,500 per bike, the annual market is $45 million.

Sanity checks: 30,000 adopters is 1.25% of all couriers, which feels small but plausible for a new vehicle type. And $45 million over 30,000 adopters is $1,500 per adopter per year, which is the $4,500 price spread over 3 years.

What the interviewer is looking for

• Good: states each assumption out loud and reaches about 10,000 bikes and $45 million.
• Strong: converts adopters to annual sales with the replacement cycle instead of counting the whole installed base as one year of sales.
• Excellent: runs a sanity check on the result and notes that this is a steady-state view that ignores growth in adoption.

Push-back: You said 30,000 adopters. Why did you not count all 30,000 as bikes sold this year?

Bottom-up gives 30,400 adopters against 30,000 top-down, a gap of about 1.3%. The two methods agree, which gives me confidence in the order of magnitude. At a 3-year life that is about 10,100 bikes a year, or about $45.6 million.

The mix is useful: parcel couriers are only 29% of couriers but 37% of adopters, because they ride more predictable routes and are more likely to buy. Meal and grocery couriers are the biggest group but adopt the least, so winning them depends on price and on being able to earn back the cost fast.

What the interviewer is looking for

• Good: calculates each segment correctly, totals 30,400 and says the two methods agree.
• Strong: points out that agreement does not prove either answer, because both rely on similar guesses about adoption.
• Excellent: reads the segment mix and says which segment to target first and why.

Push-back: Both methods landed close together. Does that prove the answer is right?

I would flex each assumption on its own, holding the rest at base. Adoption swings the market the most: 3% to 8% moves it from $27 million to $72 million, a range of $45 million. Bike-friendly zones come second, then the replacement cycle, and price matters least.

Adoption is also the assumption I know least about, since no one has sold these bikes at scale to couriers. So I would firm it up first: interview couriers in two or three cities about what they pay today for a car or scooter and what it would take to switch, and look at pilots run by delivery companies.

What the interviewer is looking for

• Good: names adoption as the key assumption and gives a reason.
• Strong: quantifies the swing from each assumption and notes which are knowable from data and which are guesses.
• Excellent: proposes a specific, fast way to test adoption and says what result would change the decision.

Push-back: You say price matters least. But Dunmore sets the price. Why not just raise it?

The US market for cargo e-bikes sold to urban couriers is about 10,000 bikes and $45 million a year. Two methods agree within about 1%, but the range runs from about $27 million to $72 million because adoption is unproven.

A $10 million goal would need about 22% of this market. A new entrant is more likely to reach about 8%, which is $3.6 million. So the courier market alone does not support the goal.

I would not approve the full design budget on this goal. Either lower the target to about $3 to 4 million, or widen the market to small businesses and consumer cargo buyers. Before spending more, run a courier pilot in two cities to test adoption, since that single assumption decides whether we are near $27 million or $72 million.

What the interviewer is looking for

• Good: gives the market size first, then a clear yes or no on the goal.
• Strong: converts the goal into the market share it needs and compares it with a realistic share.
• Excellent: offers a path forward, such as a lower target or a wider market, and names the pilot that would settle the main uncertainty.

Do not commit to the $10 million goal on couriers alone: the US market for cargo e-bikes sold to urban couriers is about 10,000 bikes and $45 million a year.

Top-down and bottom-up methods agree within about 1%, but the range is wide, from about $27 million to $72 million, because adoption is the least proven assumption.

A $10 million goal needs about 22% of the market, while a new entrant could realistically win about 8%, or $3.6 million. Even the high case needs about 14%.

Next steps: run a courier pilot in two cities to test adoption, start with parcel couriers who adopt most, and either lower the target to about $3 to 4 million or add small businesses and consumer cargo buyers before approving the full design budget.