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Estimate the number of uber rides?

Assuming:

  • Talking about uber car-based rides and not scooters, public transit, or helicopter.

  • This is # of rides per day.

  • US only.

Approach:

Estimate the # of uber riders x their average number of rides per day. --- (a)

Splitting (a) by power users, normal users dormant users.

Estimate the volume of each of the above user types and count their rides/day separately.

US population = 300M

Target Uber audience in the range 15-65, with average life expectance of 80 years.

Thus, the target user count 50/80x300M = 190M

Assuming 70% of these users actually use Uber → 130M users

Based on my experience seeing users, 20% of users, in general, tend to be power ridesharing user, they use it atleast 1 time a week.

60% are normal users - Use it atleast 1 time a month

20% are dormant users use it once every quarter,

  1. 26M power → Daily rides → 26M/7 rides per day → 3.7M rides/day

  2. 26M casual users → 26M/90 rides per day → 0.2M rides/day

  3. 78M normal users → 78M/30 rides per day → 2.6 M rides/day

Thus total rides per day → 3.7M + 0.2M + 2.6M ~6.5M rides per day

Any feedback on my approach and assumptions?

Assuming:

  • Talking about uber car-based rides and not scooters, public transit, or helicopter.

  • This is # of rides per day.

  • US only.

Approach:

Estimate the # of uber riders x their average number of rides per day. --- (a)

Splitting (a) by power users, normal users dormant users.

Estimate the volume of each of the above user types and count their rides/day separately.

US population = 300M

Target Uber audience in the range 15-65, with average life expectance of 80 years.

Thus, the target user count 50/80x300M = 190M

Assuming 70% of these users actually use Uber → 130M users

Based on my experience seeing users, 20% of users, in general, tend to be power ridesharing user, they use it atleast 1 time a week.

60% are normal users - Use it atleast 1 time a month

20% are dormant users use it once every quarter,

  1. 26M power → Daily rides → 26M/7 rides per day → 3.7M rides/day

  2. 26M casual users → 26M/90 rides per day → 0.2M rides/day

  3. 78M normal users → 78M/30 rides per day → 2.6 M rides/day

Thus total rides per day → 3.7M + 0.2M + 2.6M ~6.5M rides per day

Any feedback on my approach and assumptions?

(edited)

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Hi PK,

Overall a good and logical approach!

Just some small points on assumptions.

(1) 70% penetration of ride-hailing seems high. While it could be possible in the big metropolitan cities like NY or SF, outside the handful of big cities the penetration rate would be rather low.

(2) Don't forget there is Lyft also. So besides the 70% penetration rate, you probably need to think about market share as well. At the moment Lyft probably is comparable or just a bit smaller than Uber?

Cheers,

Emily

Hi PK,

Overall a good and logical approach!

Just some small points on assumptions.

(1) 70% penetration of ride-hailing seems high. While it could be possible in the big metropolitan cities like NY or SF, outside the handful of big cities the penetration rate would be rather low.

(2) Don't forget there is Lyft also. So besides the 70% penetration rate, you probably need to think about market share as well. At the moment Lyft probably is comparable or just a bit smaller than Uber?

Cheers,

Emily

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Hi PK,

Generally a very solid approach. What would give you plus points is to sanity check the numbers throughout. Complementary to what others have said the numbers are high.

1) The general consensus is that this 70% is way too high and this might a bias that you want to be aware of (given your tech background). What would have scored you extra points are to think about alternative modes of transportation and that would have prompted you that 70% is likely to be high. Having said that, your segmentation somewhat corrects for this.

2) Second, clear reasoning on the 70% is missing as well. For these high impact numbers, it is smart to mention why you made that assumption (even if it is based on your own experience).

3) Important with market sizing questions is to do a quick sensitivity check at the end. E.g. acknowledging that your analysis is based on a few sensitive decisions/data points.

Hi PK,

Generally a very solid approach. What would give you plus points is to sanity check the numbers throughout. Complementary to what others have said the numbers are high.

1) The general consensus is that this 70% is way too high and this might a bias that you want to be aware of (given your tech background). What would have scored you extra points are to think about alternative modes of transportation and that would have prompted you that 70% is likely to be high. Having said that, your segmentation somewhat corrects for this.

2) Second, clear reasoning on the 70% is missing as well. For these high impact numbers, it is smart to mention why you made that assumption (even if it is based on your own experience).

3) Important with market sizing questions is to do a quick sensitivity check at the end. E.g. acknowledging that your analysis is based on a few sensitive decisions/data points.

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Hi PK,

Overall I think your approach makes sense. I have a few ideas which I think could improve the market sizing even further:

1. Remove U.S. Population that does not live in urban areas or outside the 300 or so cities in which Uber has a presence. Around 50% of people in developed countries live in urban areas so that could be an easy assumption to use.

2. Reduce overall % of people that are Uber as 70% seems a little bit high to me.

3. Account for Uber's market share in the market. You have other competitors such as Lyft to account for.

Another way to think of this market sizing problem would be to start with the US population and try to estimate how many trips of various kinds that people would need to undertake per day, then what share Uber rides what take from the total.

Hi PK,

Overall I think your approach makes sense. I have a few ideas which I think could improve the market sizing even further:

1. Remove U.S. Population that does not live in urban areas or outside the 300 or so cities in which Uber has a presence. Around 50% of people in developed countries live in urban areas so that could be an easy assumption to use.

2. Reduce overall % of people that are Uber as 70% seems a little bit high to me.

3. Account for Uber's market share in the market. You have other competitors such as Lyft to account for.

Another way to think of this market sizing problem would be to start with the US population and try to estimate how many trips of various kinds that people would need to undertake per day, then what share Uber rides what take from the total.

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Hi PK,

A reasonably good and detailed approach....I like your segmentation. Few points that could make it stronger:

1. Market share - The addressable market for ride sharing - 70% assumption seems high considering so may alternatives (own car, public transport in arge cities, etc). In addition, you have to add a "market share" factor for Uber which would reduce the potential Uber rider population

2. Reality check - Once you come up with market estimation, always do a gut check. Your 6.5M rides per day seems v. large (2% of US population or 1 in 50 people will take Uber). A lot of people forget to do this - a good way to differentiate yourself (also a desirable characteristics for a Consultant).

Good luck!

Sanjeev

Hi PK,

A reasonably good and detailed approach....I like your segmentation. Few points that could make it stronger:

1. Market share - The addressable market for ride sharing - 70% assumption seems high considering so may alternatives (own car, public transport in arge cities, etc). In addition, you have to add a "market share" factor for Uber which would reduce the potential Uber rider population

2. Reality check - Once you come up with market estimation, always do a gut check. Your 6.5M rides per day seems v. large (2% of US population or 1 in 50 people will take Uber). A lot of people forget to do this - a good way to differentiate yourself (also a desirable characteristics for a Consultant).

Good luck!

Sanjeev

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Hello PK,

I can see at least two different approachs for this estimation.

You can either think about (i) the # of users, like you did, or (ii) the # of drivers. To choose one, think about which one has a lower level of uncertainty on your assumptions.For instance, why do you believe 70% of target users actually use Uber? How can you anchor and justify this number?

Personally, I would choose the second options because I believe it is easier to justify the assumptions needed:

* # drivers in the US

* Drivers segmentation: full-time vs part-time

* Average working hours for both full-time and part-time

* Average rides per hour: average ride (min) + average waiting time (min)

Please note that we can add as many layers of complexity as we wish. For example, we could have split working hours into peak and non-peak.

Try this approach to do the estimation and, then, we can discuss your assumptions as well.

Also, use it to double check your first answer!

Cheers,

Hello PK,

I can see at least two different approachs for this estimation.

You can either think about (i) the # of users, like you did, or (ii) the # of drivers. To choose one, think about which one has a lower level of uncertainty on your assumptions.For instance, why do you believe 70% of target users actually use Uber? How can you anchor and justify this number?

Personally, I would choose the second options because I believe it is easier to justify the assumptions needed:

* # drivers in the US

* Drivers segmentation: full-time vs part-time

* Average working hours for both full-time and part-time

* Average rides per hour: average ride (min) + average waiting time (min)

Please note that we can add as many layers of complexity as we wish. For example, we could have split working hours into peak and non-peak.

Try this approach to do the estimation and, then, we can discuss your assumptions as well.

Also, use it to double check your first answer!

Cheers,

Hi Raphael, how would you make a solid assumption for the # of drivers in the US? I know is a common practice for some consultants but sometimes it seems like getting one's finger in the air. I assume we do not do another market sizing to get the number, we just make an assumption. Ruben — RUBEN on Apr 20, 2020

Ruben, you are right. Usually, a candidate will not have time to do this "extra" estimation. So, you will have to make an assumption and here is important to show your business sense. Nobody has to know the exact number of Uber drivers in the US, but you are supposed to know the order of magnitude of this number. Are we talking about thousands or millions of drivers? Here, we cannot commit this kind of mistake. — Raphael on Apr 21, 2020

If a candidates says "Let's assume there are 1 MM Uber drivers in the US", the interviewer could say "Humm, I think this number is a little bit low. Let's use 2 MM instead" and then you continue the case. — Raphael on Apr 21, 2020

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Again, good logic but too high estimations

Again, good logic but too high estimations

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Hi,

Your approach is indeed a good one.

If you want to master your market sizing skills I suggest you focus on segmentations patterns. You can use the following segmentation for market sizes:

B2C:
-Demographics (Age, education, income, family size, race, gender, occupation, nationality)
-Behavioral (Purchasing behavior, customer journey stage, occasion & timing,
customer loyalty & interest, risk tolerance, user status)
-Psychographic (Lifestyle, personality traits, values, opinions, interests of consumers)
-Geographic (Geographical boundaries)

B2B:
-Company characteristics (Industry, company size, number of employees)
-Geography (Geographical boundaries)
-Purchasing Approach (Occasion & timing, customer capabilities, nature of existing relationship)
-Personal Characteristics (Loyalty, risk attitude, user status)

B2G:
-Demographics (Type of agency, size of budget, the amount of autonomy)
-Geographic (Geographical boundaries)
-Government Tier (Federal , State, Local, Quasi-governmental, International)
-Bid type (Closed, Open)

But sometimes you don’t need to segmentation. Here is an example of case that could be solved with high level top down approach - estimate the size of credit card market in the US:
https://www.preplounge.com/en/consulting-forum/how-should-i-approach-the-following-question-estimate-the-market-size-of-credit-cards-in-the-us-6695

Hi,

Your approach is indeed a good one.

If you want to master your market sizing skills I suggest you focus on segmentations patterns. You can use the following segmentation for market sizes:

B2C:
-Demographics (Age, education, income, family size, race, gender, occupation, nationality)
-Behavioral (Purchasing behavior, customer journey stage, occasion & timing,
customer loyalty & interest, risk tolerance, user status)
-Psychographic (Lifestyle, personality traits, values, opinions, interests of consumers)
-Geographic (Geographical boundaries)

B2B:
-Company characteristics (Industry, company size, number of employees)
-Geography (Geographical boundaries)
-Purchasing Approach (Occasion & timing, customer capabilities, nature of existing relationship)
-Personal Characteristics (Loyalty, risk attitude, user status)

B2G:
-Demographics (Type of agency, size of budget, the amount of autonomy)
-Geographic (Geographical boundaries)
-Government Tier (Federal , State, Local, Quasi-governmental, International)
-Bid type (Closed, Open)

But sometimes you don’t need to segmentation. Here is an example of case that could be solved with high level top down approach - estimate the size of credit card market in the US:
https://www.preplounge.com/en/consulting-forum/how-should-i-approach-the-following-question-estimate-the-market-size-of-credit-cards-in-the-us-6695

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Hi, I think it is a good approach. Always try to stay in 20 minutes and - if needed - accelerate some estimations by asking to the interviewer

Best,
Antonello

Hi, I think it is a good approach. Always try to stay in 20 minutes and - if needed - accelerate some estimations by asking to the interviewer

Best,
Antonello

(edited)

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