Example of Application Strategy – Canadian Programs
If you aspire to join an MBA program in near future, you would’ve most likely come across at least one popular ranking for MBA programs. There are several parameters that go into those rankings, and each of those (average GMAT, post-MBA compensation, recruiter perception, faculty, and so on) is a reflection of the popularity of a program.
But, those parameters rarely include acceptance rate and yield – two of the most customer-driven (applicant-driven) statistics.
You’ll only obscurantly come across the two, almost in conjunction with each other, in few MBA admission-outreach events, articles comparing MBA programs, and MBA program websites.
Nevertheless, these are two of the most keenly monitored and, often, proactively-managed indices by MBA programs.
Why?
Because they are strong, market-determined reflection of popularity of their programs.
Let’s understand them in detail.
Acceptance rate
Source: Poets & Quants
Acceptance rate of an incoming class of an MBA program is the percentage of all applicants who are offered admission. (Here, B-schools are in the driving seat: they select.) In other words:
Acceptance rate = (number of accepted applicants/ number of applicants)*100Lower the acceptance rate, higher the difficulty in getting into an MBA program. For top schools, acceptance rate is typically in proximity of 20%. Historically, Stanford with approximately 7% (i.e. only 7 in every 100 applicants get admission offer) has had the lowest acceptance rate, followed by HBS (approximately 11%).
However, a lower acceptance rate may not always mean greater popularity of the program, and vice versa. For example, Wharton (20.7%) has a higher acceptance rate than that of Haas (13.2%), but Wharton is clearly a more popular program. Therefore, in order to get a complete picture of the quality of an MBA program, it’s important to see acceptance rate (and, also, yield) in a wider context.
Larger programs have more seats to fill, and, therefore, usually have higher acceptance rates unless it’s a popular program (an example being HBS which has the second-lowest acceptance rate despite the largest class of all MBA programs) which attracts plentiful applicants.
Can MBA programs, though, influence acceptance rate?
Yes, but very difficult.
They can increase their outreach efforts to create awareness about their programs to new regions which may result in increase in the number of applicants, or they can increase their yields (see the example at the end of this post to understand how yield can impact acceptance rate), which isn’t easy.
These efforts, at best, will have minor, gradual impact on the acceptance rate, which depends on external factors (such as state of the economy) as well.
Yield
Source: Poets & Quants
Not all accepted applicants, though, join (or enroll into) an MBA program for various reasons, main being opting for other schools.
Yield of an incoming class of an MBA program is the percentage of accepted applicants who join the program. (Here, applicants are in the driving seat: they select.) In other words:
Yield = (number of enrolled students/ number of accepted applicants)*100Note: For calculating yield, most schools calculate number of accepted applicants net of deferrals.
For example, 60% yield for a program implies that only 60% of the accepted applicants eventually join the program. Majority of the remaining 40% join rival schools and few decide not to enroll anywhere.
Higher the yield, more popular the program is. However, there can be exceptions, which you need to be aware of.
Historically, HBS has had the highest yield (89%) followed by Stanford (80%); most other top schools fall in 60-70% range. It may surprise you, but even after getting accepted in schools such as Wharton and MIT, nearly 30% of the accepted applicants do not enroll there, as most of them accept offers from rival schools.
Because accepted applicants (those who get accepted to multiple schools) are free to join any program, yield, in a way, is market-determined and one of the most meritocratic factors reflecting the popularity of a program.
Example: calculating acceptance rate and yield for HBS
As a quick example, let’s take these numbers for HBS:
Source: HBS website
Here,
Number of applicants = 9,543
Acceptance rate = 12%
Plugging in these values in the formula for acceptance rate,
Number of accepted applicants = 12% of 9,543 = 1,145
That means, HBS extended admission offer to 1,145 out of 9,543 applicants in the class of 2016.
Out of these 935 enrolled.
Further, using the formula for yield,
Yield = (935/ 1,145)*100 = 82% which is not the same as 89% mentioned by the school. This is because not all accepted applicants declined the offer. Some deferred their admission by a year, and hence they have been excluded from the number of accepted applicants.
HBS, in fact, explains this in the footnote on class profile page for the class of 2016:
Yield is calculated by removing students who have postponed to a future year for joint degree, military, or medical reasons.
If we plug in the value of yield as 89% and calculate the number of accepted applicants (after excluding deferred applicants), we get:
Number of accepted applicants (after deferral) = 935/0.89 = 1,050
That is, 1,145 minus 1,050 or 95 accepted applicants deferred their admission.
Test example (tougher than the previous)
Question:
For the class of 2017, Exceptional School, a reputed school based in New York City, had following admission statistics:
Yield = 60%
Acceptance rate = 15%
Class size = 600
% of students who deferred their admission = 10%
The school is about to kick start the admission cycle for the next class (2018), and estimates that its outreach efforts will help increase the number of applicants by 10% over the current year. It also plans to better target applicants using Talisma software, and, as a result, expects to increase the yield to 65%. However, they don’t expect the proportion of admitted applicants seeking deferral to change.
Assuming that the school wants to maintain its class size, what % of applicants should the school accept in the next admission cycle?
Pause, and try this on your own. If you get this one, then you understand this topic well.
And if you can’t, here is the solution:
Solution:
For the class of 2017:
Number of accepted applicants (after deferral) = (600/60)*100 = 1,000
Number of accepted applicants (before deferral) = 1,000/0.9 = 1,111
Number of applicants = 1,111/0.15 = 7,406
For the class of 2018:
Number of applicants = 1.1*7,406 = 8,147 (They increase by 10% as a result of the school’s outreach efforts.)
Now,
Yield/100 = (number of enrolled applicants/ number of accepted applicants (after deferral))
65/100 = 600/ number of accepted applicants (after deferral)
⇒ Number of accepted applicants (after deferral) = 923
Number of accepted applicants (before deferral) = 923/0.9 = 1,026
Therefore, acceptance rate for the class of 2018 = (1,026/8,147)*100 = 13%
Do you notice the drop in acceptance rate from 15% to 13%, with an increase in yield? This can be understood intuitively as well: as yield increases, the school needs to accept fewer applicants to fill its class.
I hope it wasn’t too complicated.
Question: Do you now understand how to calculate acceptance rate and yield? I would love to know who got solution to the test example right? Please comment in the space below.
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