price discrimination-final (purple)

8/3/2019 Price Discrimination-Final (Purple)

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Saints of Market

MBA I “B”

Group 4

Price Discrimination:

Case of Airfare Tickets

Shishir Chaudhry (34161)

Divya Kapadia (34175)

Sinlo Kemp (34178)

Debadatta Mishra (34182)Anupam Mukherjee (34187)

Hari Om (34188)

Aniket Shah (34198)

Rahul Sharma (34200)

Rishi Sinha (34206)



Price Discrimination

• Charging of different prices for different quantities of a

product at different times to different customers or in

different markets

• Examples : Telecom tariffs, Public utility such as gas,

electricity, airlines, hotels etc.



Conditions to be met

• Imperfect competition

• Price elasticity of demand must differ

• Quantities, times, customers groups must

be separable



Types of Price Discrimination• Perfect Price Discrimination or First

degree price discrimination

• Second Degree Price Discrimination

• Third Degree Price Discrimination



Price Discrimination in the

context of Airline tickets • Customers booking early normally pay

lower prices

• Closer to the date and time of the

scheduled service, the price rises, on the

simple justification that consumer’s

demand for a flight becomes more inelastic

the nearer to the time of the service• This has led us to model a simple

scenario based on Second Degree

Price Discrimination



How does an airline

determine the optimum

number ?• Booking limit

• Maximum no. of seats that can be booked at discount price.

Once the booking limit is reached, all future customers will

be offered the full price.

• Protection Limit • The protection level is the no. of seats that will be booked

by those customers who come in late (mainly business

people) at full price.



Assumptions • Seats - 120 (all economy class).

• Only 2 fare rates considered (discount rate i.e. Rs. 2500

& full price i.e. Rs. 3500).

• Booking limit = 120 – the protection level.

Demands at full fare (Q) No. of days of demand Cumulative probability F(Q) =

Prob{D<= Q} 0-60 6 0.0667 61 7 0.1444 62 13 0.2889 63

12

0.4222

64 21 0.6556 65 19 0.8667

Above 65 12 1.0000 Total 90 1.0000

Historical data of airline over a 90 day period



Calculation and

Findings• We can calculate the value of lowering the protection level from Q+1

to Q. Lowering the protection level results in selling the (Q+1)th seatat a discount which guaranteed return of 2500.

• Protecting the Q+1 seats has an expected (chance) value equal to: (1-F(Q))(3500)

• Then, we can lower the booking limit to Q as long as:

• (1-F (Q)) * 3500) <= 2500

• F (Q) >= (3500-2500)/3500

• = 0.285714

• On referring from the conditional probability table the

value of Q comes out to be 62

• Hence, Discounted no. of tickets = 120 - 62 = 58



Other Scenarios:• Versioning -

• One can buy an expensive, flexible ticket or one can buy a cheap

ticket, with many restrictions. Since a passenger can choose

between different versions of an air ticket, it is natural to consider

the theory of versioning when analysing the price discrimination

•

Discounts to large costumers-• Another common characteristic in many national markets is that

large firms that demand airline tickets write a contract with an

airline, where the firms’ employees receives a certain discount on

each airline ticket

• Frequent flyer programs –

• Frequent flyers programs are important in the airline industry. It

implies that those who are loyal members of such a program can

earn member points for each flight and later redeem the points to

claim a free bonus flight. It can be seen as a kind of discount Also

discounts are given for round trips travel too



Other Untouched

Scenario: Overbooking • As there is a chance of customer not showing up

for the departure, the airline routinely overbook their flights, as in this case the airline stands achance of losing revenue. The airlines determinethe optimum level of overbooking. If allcustomers turn up then the airline is forced to‘bump’ to other flights. This calculation is similar to that of above scenario.

• The optimal overbooking level balances• Lost revenue due to empty seats

• Compensation to bumped customers

• Loss of customer goodwill.



THANK YOU

price discrimination-final (purple)

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