Presentaion; Airline Price Discrimination

7/29/2019 Presentaion; Airline Price Discrimination

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PRICE DISCRIMINATION

By Group V

Nikhil Khakhkhar

Sachin Pandey

Sukesh Chandra


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Overview

Price Discrimination is used in industryto maximize profit.

We have Analysed Price Discrimination

across Airline Industry & Movie Theatre For Aviation sector there are many

methods to see price discrimination

Our Research paper involved themethod of cross price elasticity.


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AIRLINE INDUSTRY

Airline Industry use 2nd

& 3rd

order Pricediscrimination.

By 2nd Degree Price discrimination they

charge higher for business class ticket. By 3rd Degree Price Discrimination they

have different price for different

consumer for same seat. We have analyzed Indigo Air fare with

their corresponding demand


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AIRLINE INDUSTRY Price discrimination models assume

that when a consumer chooses topurchase a lower priced fare product

they do so at no additional cost. If the

lower priced fare product requires apurchase of 14 days in advance or any

other restrictions applied to a discount

purchase, which would not have been

encountered by a higher priced fare

product, the assumption states that

there is no cost to the consumer for

accepting more restrictions.


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AIRLINE INDUSTRY Our research shows that while Airline is

using pricing discrimination for earlybirds, say 14 days advance or 7 days

advance or regular booking. The

demands for all the 3 type of tickets arerelated on the price of other three

tickets.


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AIRLINE INDUSTRY We have considered the following

model to Qj = B1.P1 + B2.P2 + B3.P3, where

J=1,2,3

Here, QJ is demand for three differentpriced ticket 14 days advance, 7 days

advance & regular.

B1, B2,B3 are coefficients & P1,P2,P3different price


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AIRLINE INDUSTRY To see whether the Consumer Demand

is just based on one price or alldiscriminated price, we have done

regression considering all 3 price , &

considering the price corresponding toonly specific demand.


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Route AP14 AP7 Regular Q1 Q2 Q3

Chennai-Trivandrum 2900 3240 3740 205185 163287 109490

Chennai-Vizag 2900 3240 3740 405406 347231 200879

Coimbatore-Chennai 2900 3240 3740 35037 30657 20898

Coimbatore-Delhi 5400 6870 7650 112118 98103 50074

Coimbatore-Hyderabad 2900 3240 3740 43796 38322 27373

Delhi-Goa 5400 6870 7650 309055 302923 180659

Delhi-Guwahati 5000 6870 7650 350369 306573 178981

Delhi-Hyderabad 5050 6200 6980 122629 107301 76643

Delhi-Indore 2900 3240 3740 87592 76643 54745

Delhi-Jammu 2900 3240 3740 115111 91972 65694

Delhi-Lucknow 2900 3240 3740 250222 183944 131389

Delhi-Nagpur 3550 4640 5420 280296 245259 175185

Delhi-Patna 3550 4640 5420 245259 214601 153287

Delhi-Pune 5050 6200 6980 75329 65913 47081

Delhi-Raipur 3550 4640 5420 40292 35256 25183

Delhi-Srinagar 2900 3240 3740 140148 122629 56592

Mumbai-Chandigarh 5050 6200 6980 140148 102629 87592

Mumbai-Chennai 5050 6198 6978 140148 102629 77592

Mumbai-Coimbatore 5050 6200 6980 105111 81972 65694

Mumbai-Delhi 5050 6200 6980 420443 327888 262777


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R square considering all three price band

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.371971

R Square 0.148362

Adjusted R Square -0.02319

Standard Error 73695.09

Observations 20

ANOVA

df SS MS F Significance F

Regression 3 1.4E+10 4.65E+09 0.85643 0.483591

Residual 16 8.69E+10 5.43E+09

Total 19 1.01E+11

CoefficientsStandard

Error t Stat P-value Lower 95% Upper 95%

Intercept 175862.1 151134.6 1.163612 0.261641 -144529 496253.2

X Variable 1 -137.538 101.1707 -1.35946 0.192852 -352.01 76.93436

X Variable 2 280.7898 495.8093 0.566326 0.57903 -770.279 1331.858

X Variable 3 -170.643 411.3924 -0.41479 0.683802 -1042.76 701.4702

SUMMARY OUTPUT


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R square considering corresponding price band

SUMMARY OUTPUT


Multiple R 0.137794

R Square

0.01898

7

Adjusted R Square -0.03551

Standard Error 122473

Observations 20

ANOVA


Regression 1 5.23E+09 5.23E+09 0.348385 0.562366

Residual 18 2.7E+11 1.5E+10

Total 19 2.75E+11



Intercept 119325.1 108322.9 1.101569 0.285162 -108253 346903.1

X Variable 1 15.47458 26.21738 0.590241 0.562366 -39.6061 70.55525

SUMMARY OUTPUT



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Inference

We have observed the Value of Rsquare to be low in both case , but it is

very low when regression is done only

for corresponding Price data. Hence,based on relative value of R square we

infer Cross Price elasticity exist and

consumer demand vary as per price for

different bands.

Sinec R square value was low we tride

to include one more variable, income


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Inference

Since the majority of fliers between twocity must be belonging to either city we

used average per capita income of the

two place to include income in model. The Result of regression was :


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SUMMARY OUTPUT


Multiple R 0.421266

R Square 0.277465Adjusted R Square -0.04188

Standard Error 122848.7

Observations 20

ANOVA


Regression 4 4.88E+10 1.22E+10 0.809079 0.538514

Residual 15 2.26E+11 1.51E+10

Total 19 2.75E+11



Intercept 365685.1 256071.4 1.428059 0.173764 -180118 911488.4

X Variable 1 -278.522 206.3133 -1.34999 0.197039 -718.268 161.2248

X Variable 2 415.0591 750.3904 0.553124 0.588328 -1184.36 2014.478

X Variable 3 -187.078 622.6087 -0.30047 0.76794 -1514.14 1139.981

X Variable 4 -0.67486 0.683857 -0.98685 0.33937 -2.13247 0.782745

R square considering all three price band and income

Again we observed the R square value to increase, Hence Demand is

also dependent on Consumer Income


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Inference

Demand is dependent on price across. Consumer Demand Change as per

Income.

Indian Aviation Sector has not utilizedfully the price discrimination, in

comparison to other market.

Many hidden charges are there toanalyze any particular Airlines.

Database are not properly maintained .

Documents

Presentaion; Airline Price Discrimination