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4

Number of Applications McKinsey Receives Per Year

Just wanted to know if this is how you would solve this estimation question.

# Applicants = # Vacancies x (100 / Acceptance Rate)

# Vacancies = # Consultants / Typical Length of Employment

*****Fast Method*****

Assumptions

  1. # Consultants = 8000
  2. Typical Length of Stay = 4 years
  3. Acceptance Rate = 3%

# Vacancies = / 4 = 2000

# Applicants = 2000 x (100/3) = 67000 Applicants per Year

*****More Granular Method*****

3 Staff Levels (Junior, Mid and Senior)

Additional Assumptions

  1. % Junior Consultants = 60%
    1. # Junior Consultants = 4800
    2. Typical Length of Employment = 3 years
    3. Acceptance Rate = 3%
  1. % Mid-Level Consultants = 30%
    1. # Mid-Level Consultants = 2400
    2. Typical Length of Employment = 6 years
    3. Acceptance Rate = 10% (since fewer people apply for these roles)
  1. % Senior Consultants = 10%
    1. # Senior-Level Consultants = 800
    2. Typical Length of Employment = 10 years
    3. Acceptance Rate = 60% (since way fewer people apply for these roles)

# Junior Applicants = (4800/3) x (100/3) = 52,800 Applicants per Year

# Mid-Level Applicants = (2400/6) x (100/10) = 4000 Applicants per Year

# Senior Applicants = (800/10) x (100/60) = 132 Applicants per Year

Total = Around 57k applicants

Just wanted to know if this is how you would solve this estimation question.

# Applicants = # Vacancies x (100 / Acceptance Rate)

# Vacancies = # Consultants / Typical Length of Employment

*****Fast Method*****

Assumptions

  1. # Consultants = 8000
  2. Typical Length of Stay = 4 years
  3. Acceptance Rate = 3%

# Vacancies = / 4 = 2000

# Applicants = 2000 x (100/3) = 67000 Applicants per Year

*****More Granular Method*****

3 Staff Levels (Junior, Mid and Senior)

Additional Assumptions

  1. % Junior Consultants = 60%
    1. # Junior Consultants = 4800
    2. Typical Length of Employment = 3 years
    3. Acceptance Rate = 3%
  1. % Mid-Level Consultants = 30%
    1. # Mid-Level Consultants = 2400
    2. Typical Length of Employment = 6 years
    3. Acceptance Rate = 10% (since fewer people apply for these roles)
  1. % Senior Consultants = 10%
    1. # Senior-Level Consultants = 800
    2. Typical Length of Employment = 10 years
    3. Acceptance Rate = 60% (since way fewer people apply for these roles)

# Junior Applicants = (4800/3) x (100/3) = 52,800 Applicants per Year

# Mid-Level Applicants = (2400/6) x (100/10) = 4000 Applicants per Year

# Senior Applicants = (800/10) x (100/60) = 132 Applicants per Year

Total = Around 57k applicants

4 answers

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Best Answer

No expert by any means but in an estimation question is always good to end with a sanity check.

In this case, the sanity check could be that according to Dominic Barton (McKinsey's global managing partner) McK receives around 200,000 applications per year (this data is well known).

That being said, the obtained result is rather low which means one or more assumptions are wrong.

Like Andrés said, average time consultant is usually around 2 years. Also, acceptance rate according to Dominic Barton is closer to 1% (usually 1.1 or 1.2%). Taking into consideration these two revisions your final value gets much closer to the real number.

No expert by any means but in an estimation question is always good to end with a sanity check.

In this case, the sanity check could be that according to Dominic Barton (McKinsey's global managing partner) McK receives around 200,000 applications per year (this data is well known).

That being said, the obtained result is rather low which means one or more assumptions are wrong.

Like Andrés said, average time consultant is usually around 2 years. Also, acceptance rate according to Dominic Barton is closer to 1% (usually 1.1 or 1.2%). Taking into consideration these two revisions your final value gets much closer to the real number.

Okay, makes more sense. Thank you. — sithan on Apr 04, 2017 (edited)

Nice approach! however, I think I would revise my assumption of the average time a consultant remains at the firm. I have heard from MCK consultants (Switzerland) that the average is more around 2 years, which should be similar for the MBB firms.

Nice approach! however, I think I would revise my assumption of the average time a consultant remains at the firm. I have heard from MCK consultants (Switzerland) that the average is more around 2 years, which should be similar for the MBB firms.

The approaches so far are demand-driven. Basically, you are trying to estimate how many people apply in a given year. However, there is another approach, which is supply-driven. Here one can use the capacity of McKinsey to handle applications given time constraints. I would start as follows:

# Applicants = (# McK offices globally) x (# of staff screening applications/office) x (# of working hours/week/person) x (Share of time dedicated to application screening/week) x (# weeks per year) x (# applications checked/hour).

Let's plug in the numbers and see what we get:

# Applicants = ~ 120 offices x 2 persons x 40h x 1/5 of a week x 25 weeks x 5 applications/hour

# Applicants = ~ 240,000 applications.

Assumptions:

  1. There are around 2 recruiters and HR in every office that deal with new applications and they spend around 20% of their working week in reviewing applications
  2. Since not all offices are equally popular and the flow of applications is not constant over the months within a year, the recruiting staff only has to dedicate this amount of time half of the year (25 weeks).
  3. When does handling of applications happen? Often it is on a Friday or Thursday of a given week. Hence, I have assumed that only 1/5 of the working week of a recruiting staff gets allocated going through applications.
  4. But what really matters is how much a recruiter spends on average per application. I have assumed that per hour a HR staff can go through 5 CVs ~ 12 min. per application. Even though some may get rejected quickly, others may require some more time to be evaluated.

Greetings!

The approaches so far are demand-driven. Basically, you are trying to estimate how many people apply in a given year. However, there is another approach, which is supply-driven. Here one can use the capacity of McKinsey to handle applications given time constraints. I would start as follows:

# Applicants = (# McK offices globally) x (# of staff screening applications/office) x (# of working hours/week/person) x (Share of time dedicated to application screening/week) x (# weeks per year) x (# applications checked/hour).

Let's plug in the numbers and see what we get:

# Applicants = ~ 120 offices x 2 persons x 40h x 1/5 of a week x 25 weeks x 5 applications/hour

# Applicants = ~ 240,000 applications.

Assumptions:

  1. There are around 2 recruiters and HR in every office that deal with new applications and they spend around 20% of their working week in reviewing applications
  2. Since not all offices are equally popular and the flow of applications is not constant over the months within a year, the recruiting staff only has to dedicate this amount of time half of the year (25 weeks).
  3. When does handling of applications happen? Often it is on a Friday or Thursday of a given week. Hence, I have assumed that only 1/5 of the working week of a recruiting staff gets allocated going through applications.
  4. But what really matters is how much a recruiter spends on average per application. I have assumed that per hour a HR staff can go through 5 CVs ~ 12 min. per application. Even though some may get rejected quickly, others may require some more time to be evaluated.

Greetings!

(edited)

I think your answer relies too heavily on what you know already. A better approach would be to assume nothing and work your way towards it.

e.g. add 1 + 2

1. #target MBA schools x avg. # students per target school x % apply x 2 (to account for those who are from non-target schools, but still apply)

2. #target undergrad schools x avg. # students per target school x % apply x 2 (to account for those who are from non-target schools, but still apply)

I think your answer relies too heavily on what you know already. A better approach would be to assume nothing and work your way towards it.

e.g. add 1 + 2

1. #target MBA schools x avg. # students per target school x % apply x 2 (to account for those who are from non-target schools, but still apply)

2. #target undergrad schools x avg. # students per target school x % apply x 2 (to account for those who are from non-target schools, but still apply)

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