+

COLLEGE-STUDENT SURVEY – PHASE IIStephanie Levonne

Fei YanYukihiro TsuyaZhiyong Yang

My (Roxy) Dinh

+

AGENDA1. Important Attributes & Survey2. Dummy variables & Regression model 3. Part-worths of the attributes4. Relative importance of an attribute5. Expected utility of each college choice6. Expected market shares7. Conclusion + Q&A

+IMPORTANT ATTRIBUTES:PRICE

10K25K50K

RANKTop 25Others

SIZELargeMediumSmall

LOCATIONCitySuburban

+SURVEY

+REGRESSION MODELUtility = 8.107 – 1.172*Tuition 25K – 2.508*Tuition 50K – 2.065*Rank(others) - .463*Size(medium) - .379*Size(small) -.649*Location(Suburban)

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Part-Worth Utilities of the Attributes Top 25 Others

-2.5

-2

-1.5

-1

-0.5

0

Part-worths for Rank

Larg

e

Smal

l

-0.5

-0.4

-0.3

-0.2

-0.1

0

Part-worths for Size

Series1

+Part-Worths for Rank

Top 25 Others

-2.5

-2

-1.5

-1

-0.5

0

Part-worths for Rank

+ Part-Worths for Price

10,000 25,000 50,000

-3

-2.5

-2

-1.5

-1

-0.5

0

Part-worths for Price

+Part-Worths for Size

Large Medium Small

-0.5

-0.45

-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

Part-worths for Size

Series1

+ Part-Worths for Location

City Suburb

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

Part-worths for Location

+

Relative Importance of Each Attribute

RI for Tuition RI for Rank RI for Size RI for Location

44.11% 36.33% 8.14% 11.42%

Lowest

Highest

+ Expected utility of each college choice:

Ten different combinations

+Expected utility of each college choice:

LowestHighest

+Market shares of 10 combinations

Formula:

+Market Shares of 10 Combinations

MS(U1) = Exp(U1)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 23.453/4892.438 = 0.479%

MS(U2) = Exp(U2)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 141.175/4892.438 = 2.885%

MS(U3) = Exp(U3)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 365.602/4892.438 = 7.473%

MS(U4) = Exp(U4)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 646.776/4892.438 = 13.220%

MS(U5) = Exp(U5)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 130.321/4892.438 = 2.664%

MS(U6) = Exp(U6)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 319.862/4892.438 = 6.538%

MS(U7) = Exp(U7)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 68.102/4892.438 = 1.392%

MS(U8) = Exp(U8)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 17.904/4892.438 = 0.366%

MS(U9) = Exp(U9)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 1091.164/4892.438 = 22.303%

MS(U10) = Exp(U10)/[Exp(U1)+Exp(U2)+Exp(U3)+Exp(U4)+Exp(U5)+Exp(U6)+Exp(U7)+Exp(U8)+Exp(U9)+Exp(U10)] = 2088.079/4892.438 = 42.680%

Lowest: Combination 8 (50k, others, large, suburban)

Highest: Combination 10 (10k, Top 25, medium, city)

+Conclusion (from our sample)

Students wants to attend a large-sized college located in the urban area which is ranked in the Top 25 and only costs 10k for tuition => it is difficult to reach this ideal combination, so universities should pay attention to more important attributes.

Tuition is the most important attribute (44.11%), while size is the least important factor that affects a college student’s decision (8.14%)

Hence, universities should give financial incentives e.g. scholarship, financial aids, on-campus jobs to attract the target audience: potential college students and parents

Ranking is the second most important factor, so the admission office should focus a lot on PR and students’ feedbacks in order to improve ranking

+THANKS FOR YOUR ATTENTION

Questions?