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Scientific Journal of E-Business April 2013, Volume 2, Issue 2, PP.23-31

Analysis of Airlines Marketing Based on Double Clustering Feng Guohe1,2# Liang Xiaoting1 1. School of Economics & Management, South China Normal University, Guangzhou, 510006, China 2. Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, 300300, China #

Email:ghfeng@163.com.

Abstract The results from the analysis of air line’s marketing based on characteristics cluster both of customer behavior and flight routes was obtained in this paper to investigate the cluster change in different periods. , By means of the analyzed information in this paper, several management and marketing policy for different kinds of customers were put forward to maximize profits of airlines. Keywords: Airlines; Cluster Analysis; Marketing Plan

1 INTRODUCTION In the fierce market competition, for airlines, how to meet customer needs and how to identify valuable customers are the important parts of implementation of marketing strategy. Having fully grasped customer conditions and behavior by customer segmentation, airlines can implement efficient marketing strategies and customer services for different kinds of customers [1, 2]. Customer segmentation has a close relationship with formulation of marketing strategy, which helps to make a marketing promotion plan or a new product design, companies focus first on: ①According to customer behavior, customers can be divided into several groups. ②Which kind of customer will create maximum benefit for airline? ③These customers have in common characteristic. ④High-spending customers have in common characteristic. The tendency to spend money. ⑤Business life cycle and value of each group. [3] This paper analyzes characteristics of behavior and flight routes based on cluster technology, and finds out some customer groups having the same behavior. These useful conclusions help CRM become a booster which makes business successful, expands product sales and promotes optimization of marketing strategy. [4]

2 SEGMENTATION MODEL CONSTRUCTION First of all, segmentation model based on characteristics of behavior and routes should be prepared before the construction of cross-analysis; available measures should be put forward for the establishment of airline marketing.

2.1 Segmentation Variables of Customer Behavior According to these consumers behaviours such as booking, payment, check-in and flying, customer segmentation model was constructed. Modeling variables used in the model are as follows, booking behavior variable including: ratio of B2C booking, booking, agents booking, and direct point booking; payment behavior variable including ratio of bankcard payment, cash payment, other ways to pay; check-in behavior variable including: self check-in rate, SMS check-in rate, online check-in rate, counter check-in rate; flight behavior variable including: flight frequency, interval from the last time by air, ration between first class and economy class, average discount rate of economy class, average discount rate between first class and economy class, pice per kilometer, ratio of outing in the weekday, ratio of outing in the weekend, ratio of outing during holidays, ratio of outing by team.

2.2 Segmentation Variables of Flight Routes After statistical data analysis, according to the size of customer traffic, these flight routes are sorted in descending - 23 -


way. It is found that the top 20 routes selected as variables to be investigated in this model (two-way) cover more than 90% of customer traffic and the routes selected are shown in table1. Table1. MAJOR ROUTES INVOLVED IN THE MODEL HongqiaoGuangzhou

Hongqiao-Shenzhen

Pudong-Dalian

Pudong-Wuhan

Hongqiao-Zhengzhou

Hongqiao-Changsha

Hongqiao-Beijing

Pudong-Shenzhen

Hongqiao-Urumqi

Pudong-Zhuhai

Hongqiao-Shantou

Hongqiao-Guiyang

PudongZhengzhou PudongGuangzhou Pudong-Haikou

Pudong-Harbin

Hongqiao-Nanning

Pudong-Sanya

Pudong-Taibei

Pudong-Shenyang

2.3 Data Analysis Frequent flying records from April 2008 to June 2009 are extracted, and the data from April 2008 to March 2009 are used for modeling as training samples, while the data from July 2008 to June 2009 are used as cross-check samples, then these data are pre-processed and standardized. .

2.4 Modelling Methods Selecting the SAS, a large integrated software packages that has a strong advantage in data management and mining, query analysis, capable of being applied to the build of enterprise information analysis application systems [5], has been selected as an analysis platform. Cluster analysis is non-supervised machine learning, suitable for tasks hacing larger samples and more variables. K-means the most common clustering algorithm, is often used in the customer behavior [6][7]. Analysis steps of k-means algorithm based on SAS are shown in Table2. Table2. STEPS OF CLUSTER ANALYSIS Step1: Integrating data according to business needs to generate a set of variables involved in the clustering; Step2: Using factor analysis to retain these factors whose cumulative information is greater than 80%; Step3: Analyzing the correlation between variables by using proc corr, for variables pairs whose Pearson correlation coefficient is greater than 70%, and retaining a variable according to the importance of business meaning; Step4: Doing a factor analysis again to determine the number of retention factor, and outputting a collection of statistical parameters from the results of the factor analysis; Step5: Clustering on these factors by using fast clustering method, in principle, the number of each sample is not less than 5% of that of overall samples; Step6: Observing clustering results, in principle, removing factors whose R-Square is less than 0.5,and repeating Step5, 6 until all the factors whose R-Square is greater than 0.5. Then observing Over-all R-Square, if the value is greater than 0.7, indicating that cluster effects can be accepted; Step7: Outputting clustering results to calculate the mean and the variance of each type of the original variables. According to the difference of the mean, the results of the model from a business perspective can be explained.

3 MODEL RESULTS 3.1 Behaviour Segmentation Model 1) Analysis of Characteristic Values of Behavior Cluster Group Customers were divided into eight categories by behaviour characteristics cluster, characteristics of each of which is shown in Table3, in which, in every row, the red cell indicates the maximum value, and yellow cell follows, while the green cell indicates the minimum value, in addition the final column of the table indicates average value of 8 groups to facilitate analysis and comparison of each group. 2) Analysis of Advantageous Characteristics and Weak Characteristics of Group According to characteristic values of behavior, advantageous characteristics and weak characteristics of each category have been extracted to deduce the characteristic description of strengths and weaknesses of the group. - 24 -


TABLE 3 CHARACTERISTIC VALUES OF BEHAVIOR CLUSTER Class No 1

2

3

4

5

6

7

8

Overall Mean

Age of customer

39.55

39.2

45.67

38.6

38.73

36.18

37.11

38.06

38.999

Age of card

4.1

4.19

4.47

3.77

3.34

2.59

3.61

2.81

3.687

The interval from the last time by air

100.56

70.83

76.21

86.11

148.22

137.45

88.55

168.55

109.980

The times of the flight

9.14

11.83

11.17

7.13

3.87

1.88

5.7

1.48

7.059

Ratio of outing during the holidays Ratio of outing in the weekend Ratio of outing in the weekday

0.05

0.31

0.23

0.4

0.01

0.95

0.2

0

0.214

0.38

0.1

0.2

0.35

0.02

0.01

0.19

0.98

0.211

0.56

0.59

0.57

0.25

0.97

0.04

0.6

0.01

0.575

Ratio of outing by team

0.37

0.32

0.33

0.35

0.4

0.42

0.38

0.41

0.371

Ration between first class and economy class Average discount rate between first class and economy class Average discount rate of economy class

0.01

0.01

0.66

0.01

0

0

0

0

0.041

0.1

0.13

1.44

0.05

0.02

0.01

0.04

0.01

0.138

1.24

1.33

4.7

1.25

1.2

1.16

1.25

1.06

1.422

Price per kilometer

1.23

1.36

2.54

1.26

1.24

1.31

1.21

1.15

1.334

Ratio of agents booking

0.916

0.92

0.944

0.89

0.916

0.835

0.915

0.877

0.907

Ratio of B2C booking

0.031

0.028

0.004

0.056

0.039

0.1

0.039

0.072

0.042

Ratio of telephone booking Ratio of direct point booking Ratio of bankcard payment

0.03

0.033

0.029

0.034

0.027

0.046

0.024

0.031

0.031

0.022

0.02

0.023

0.02

0.018

0.018

0.022

0.02

0.020

0.243

0.231

0.21

0.247

0.248

0.3

0.272

0.262

0.248

Ratio of other ways to pay

0.06

0.058

0.089

0.068

0.061

0.08

0.02

0.097

0.063

Ratio of cash payment

0.696

0.711

0.701

0.685

0.69

0.62

0.708

0.641

0.689

Counter check-in rate

0.919

0.9

0.957

0.901

0.957

0.93

0.332

0.963

0.888

Self check-in rate

0.042

0.053

0.023

0.047

0.014

0.014

0.629

0.009

0.073

SMS check-in rate

0.013

0.018

0.005

0.021

0.009

0.024

0.014

0.01

0.014

Online check-in rate

0.022

0.026

0.012

0.026

0.017

0.029

0.019

0.015

0.021

Behavior Variables

①The 1st group: Though The group has no maximum and minimum characteristic values, the age of the customers, ratio of outing in the weekend, ratio between first class and economy class, ratio of direct point booking of the group are the second largest These characteristics indicate that strength and weakness of the group are not obvious. ②The 2nd group: The ratio of cash payment and the times of flight of the group are the maximum value, and ratio of outing by team and the interval from the last time by air are the minimum value, while the ratio between first class and economy class, average discount rate between first class and economy class, average discount rate of economy, price per kilometre, ratio of agents booking, self check-in rate, online check-in rate of the group are the second largest. These characteristics indicate that the ratio of cash payment and the times of the fight are advantageous characteristics, while ratio of outing by team and the interval from the last time by air are weak characteristics, and there is an obvious difference among all characteristics. ③The 3rd group: The group whose age of customer, age of card, ratio between first class and economy class, average discount rate between first class and economy class, price per kilometer, ratio of agents booking, ratio of - 25 -


direct point booking are maximum, whose times of the flight, ratio of other ways to pay, counter check-in rate are the second largest, while whose ratio of B2C booking, ratio of bankcard payment, SMS check-in rate, online check-in rate of the group are minimum. These characteristics which have maximum values are advantageous characteristics of the group, otherwise are weak characteristics of the group, so there is an obvious difference among all characteristics. ④The 4th group: The group has no maximum, minimum characteristic, so the characteristics of the group are not obvious. ⑤The 5th group: Ratio of outing in the weekday of the group is maximum, and the interval from the last time by air and counter check-in rate of the group are the second largest. Therefore, there is no minimum characteristic in the group, which leads to the conclusion that no obvious difference among all characteristics of the group has been observed. ⑥The 6th group: Ratio of outing during the holidays, ratio of outing by team, ratio of B2C booking, ratio of telephone booking, ratio of bankcard payment, SMS check-in rate, online check-in rate of the group are maximum, while the age of the customer, the age of the card, ratio of outing in the weekend, ratio of agents booking, ratio of cash payment of the group are minimum, so there is a obvious difference among all characteristics of the group. ⑦The 7th group: Self check-in rate, ratio of telephone booking, ratio of other ways to pay, counter check-in rate of the group are minimum, so there is a more obvious difference among all characteristics of the group. ⑧The 8th group: The interval from the last time by air, ratio of outing in the weekend, ratio of other ways to pay, counter check-in rate of the group are maximum, while ratio of outing by team, ratio of B2C booking, the times of the flight, ratio of outing during the holidays, ratio of outing in the weekday, average discount rate of the economy class, price per kilometre, self check-in rate of the group are minimum, so there is a obvious difference between advantageous characteristics and weak characteristics. Above-mentioned eight groups, group1, group4, group7 have no obvious characteristics, while other groups have distinctive characteristics.

3.2 Flight Routes Segmentation Model 1) Analysis of Characteristics Values of Flight Routes Cluster Group Customers were divided into thirteen groups by flight routes characteristics cluster, characteristics of each of which are shown in Table 4, in every row, the red cell indicates the maximum value, and yellow cell follows, while green cell indicates the minimum value, and blue cell follows it, the final column of the table indicates average value of 13 groups. TABLE 4 CHARACTERISTICS VALUES OF FLIGHT ROUTES Class no

1

2

3

4

5

6

7

8

9

10

11

12

13

Overall mean

0.506

0.024

0.098

0.064

0.064

0.137

0.172

0.036

9.29

0.054

0.186

0.074

0.051

0.636

0.131

0.029

0.081

0.056

0.056

0.114

3.796

0.033

0.244

0.053

0.118

0.061

0.047

0.300

0.148

0.021

0.046

0.032

0.062

0.06

0.043

0.046

0.067

0.1

7.601

0.118

0.034

0.213

PudongShenyang

0.076

0.016

0.055

0.027

0.077

0.066

0.028

0.043

0.051

3.765

0.163

0.079

0.022

0.182

PudongWuhan

0.007

0.016

0.089

1.945

0.045

0.113

0.051

0.021

0.105

0.035

0.06

0.033

0.028

0.144

HongqiaoZhengzhou

0.045

0.027

3.871

0.035

0.03

0.08

0.031

0.019

0.048

0.031

0.039

0.037

0.036

0.108

Variables HongqiaoGuangzhou HongqiaoShenzhen PudongDalian

- 26 -


TABLE 4 CHARACTERISTICS VALUES OF FLIGHT ROUTES (CONT.) Class no 1 Variables HongqiaoChangsha PudongChangchun HongqiaoBeijing PudongShenzhen HongqiaoUrumqi PudongGuangzhou PudongShanghai HongqiaoShantou HongqiaoGuiyang PudongHaikou PudongHarbin HongqiaoNanning PudongSanya PudongTaibei

2

3

4

5

6

7

8

9

10

11

12

13

Overall mean

0.044

0.018

0.061

0.041

0.035

3.727

0.041

0.02

0.08

0.025

0.038

0.035

0.035

0.104

0.04

0.01

0.024

0.018

3.605

0.034

0.013

0.021

0.024

0.04

0.05

0.033

0.013

0.105

0.091

0.032

0.067

0.041

0.063

0.064

0.078

0.039

0.156

0.048

0.084

0.031

0.033

0.081

0.081

0.009

0.022

0.022

0.021

0.032

0.451

0.024

0.041

0.019

0.038

0.024

0.016

0.083

0.002

1.646

0.022

0.007

0.006

0.021

0.009

0.01

0.012

0.011

0.01

0.017

0.012

0.071

0.075

0.005

0.013

0.015

0.014

0.019

0.02

0.013

0.476

0.011

0.025

0.019

0.007

0.068

0.064

0.01

0.021

0.021

0.031

0.032

0.046

0.027

0.042

0.02

0.029

0.049

0.023

0.052

0.066

0.01

0.021

0.01

0.004

0.017

0.032

0.009

0.045

0.009

0.011

0.014

0.022

0.049

0.001

0.005

0.015

0.006

0.007

0.03

0.007

0.006

0.013

0.005

0.007

0.008

1.56

0.031

0.037

0.01

0.022

0.013

0.028

0.029

0.013

0.031

0.02

0.012

0.024

0.053

0.016

0.031

0.001

0.005

0.008

0.005

0.018

0.016

0.006

1.441

0.013

0.02

0.031

0.062

0.004

0.037

0.032

0.009

0.022

0.011

0.007

0.026

0.011

0.009

0.02

0.006

0.01

0.02

0.023

0.026

0.001

0.005

0.009

0.006

0.018

0.014

0.007

0.013

0.011

0.007

0.025

1.214

0.005

0.023

0.009

0.001

0.001

0.002

0.001

0.001

0.004

0.001

0.003

0.002

0.004

0.002

0.001

0.007

2) Analysis of Advantageous Characteristics and Weak Characteristics of Group According to characteristic values of flight routes, advantageous characteristics and weak characteristics of each category have been extracted to gain the characteristic description of strengths and weaknesses of the group. Such as the 1st group, there are Pudong-Zhuhai, Hongqiao-Shantou, Hongqiao-Nanning, Pudong-Taiwan routes whose characteristic values are maximum, there are Pudong-Wuhan, Hongqiao-Urumqi, Hongqiao-Guiyang, PudongHaikou, Pudong-Harbin, Pudong-Sanya routes whose characteristic values are minimum, there are other routes whose characteristic values are the second largest, there is a distinctive difference between advantageous characteristics and weak characteristics. Taking into account the length of the literature, the characteristics of other groups are exclusive from the description.

3.3 Cross Analysis and Cluster Change Analysis 1) Cross Analysis 448,280 customers extracted from April 2008 to March 2009 who left or arrive in a place are used for modelling training samples, and the 383,344 data records from July 2008 to June 2009 are used as a cross-check sample. Customers were clustered with characteristics of behaviour and flight routes respectively; two cluster results were combined to get Table5 and Table6. Cross-analysis was made in order to know how many members have characteristics of behaviour groups and flight routes groups at the same time, for example, in Table5 red data indicates that there are 21,064 members with a characteristic of the 8th behaviour group and the 1st flight route group. While the 8th group has following characteristics: the interval from the last time by air, the ratio of outing in the weekend, the ratio of others ways to pay, counter check-in rate are maximum, the ratio of outing by team, while the ratio of B2C booking are the second largest, the times of flight, the ratio of outing during holidays, average discount ratio of economy class, price by kilometre, self check-in rate are minimum. The 1st flight route group has - 27 -


following characteristics: the characteristic values of Pudong-Zhuhai, Hongqiao-Shantou, Hongqiao-Nanning, Pudong-Taiwan route are maximum, the characteristic values of Hongqiao-Guangzhou, Pudong-Dalian, PudongShenzhen, Hongqiao-Beijing, Pudong-Haikou, Pudong-Guangzhou route are the second largest, while the characteristic values of Pudong-Wuhan, Hongqiao-Urumqi, Hongqiao-Guiyang, Pudong-Haikou, Pudong-Sanya route are minimum, namely, members of the cross-group have characteristics of these two groups at the same time. TABLE 5 CROSS-GROUP FROM APRIL 2008 TO MARCH 2009 Behavior Cluster

Flight Routes Cluster

1

1

2

3

4

5

6

7

8

N

2

52018

53961

15448

20605

80652

28303

22873

21064

294924

3

4507

3684

930

1706

4209

1500

547

1056

18139

4

2218

2290

598

821

1313

272

117

102

7731

5

5526

6178

1353

2121

7461

2294

2173

1603

28709

6

2512

2781

387

978

1624

421

37

155

8895

7

1885

2474

597

657

1439

261

142

78

7533

8

5527

6372

1771

2173

3842

809

1807

285

22586

9

2230

2104

424

879

2639

913

254

680

10123

10

2858

6086

745

992

1566

64

929

10

13250

11

3603

3859

831

1208

2336

559

1493

178

14067

12

1280

2510

378

554

840

71

114

28

5775

13

1523

1077

450

561

1989

924

77

549

7150

N

1774

1570

417

712

2008

625

348

483

7937

87461

94946

24329

33967

1E+05

37016

30911

26271

TABLE 6 CROSS-GROUP FROM JULY 2008 TO JUNE 2009 Behavior Cluster

Flight Routes Cluster

1

2

3

4

5

6

7

8

N

1

52323

55942

15475

21689

68551

23202

27312

16614

281108

2

4544

3964

916

1695

3632

1291

950

892

17884

3

2103

2268

631

811

1198

225

285

108

7629

4

5483

6510

1434

2194

6407

1950

2581

1265

27824

5

2554

2847

396

997

1588

408

64

154

9008

6

1923

2515

590

711

1175

238

361

74

7587

7

5118

6422

1765

2267

3403

696

2678

281

22630

8

2081

1970

434

854

2201

772

330

517

9159

9

2977

5801

764

970

1444

63

1390

14

13423

10

3439

3869

820

1255

2093

533

1613

165

13787

11

1318

2493

351

555

822

73

138

28

5778

12

1477

1077

426

596

1802

814

102

444

6738

13

1750

1647

427

738

1803

502

526

391

7784

N

87090

97325

24429

35332

96119

30767

38330

20947

- 28 -


2) Analysis of Customer Change

The change data, from April 2008-March 2009 customer clustering results and June 2008-July 2009 customer clustering results, were analysed on behavior and flight routes in two dimensions, as shown in Table7 and Table8. The table is full of percentage in matrix form, A(i, j) represents the percentage of customer change, that is to say, there is proportion of customers who move to j group after 3 months, data in the main diagonal represent the proportion of customers who stay in the original group, data in the “out” column represent the proportion of customers who no longer meet the requirements of customer segmentation after three months. The following steps can be done based on the change of customer: ①Observing the customer change among different groups, such as to know why high-value customers change into low-value customers after a period of time. The description of customer change gives an enlightment to the market on what causes the change of high-value customers, and what measures can retain high-value, etc. ②Verifying the stability of the model, according to data in the main diagonal in Table7 and Table8 showing the percentage of customers who stay in the original group after 3 months, in which the greater the data are, the more stable the group is. ③The tips to hold models: if the data in the main diagonal are generally small, indicating that the model needs to be retrained to adapt to needs of new data. TABLE 7 THE PROPORTION OF CUSTOMER BEHAVIOR CHANGE July 2008-June 2009

April 2008–March 2009

Class no

1

2

3

4

5

6

7

8

Out

1

60.995%

8.800%

0.614%

4.541%

6.423%

0.099%

2.497%

2.103%

13.926%

2

7.945%

60.684%

0.847%

4.711%

8.285%

2.212%

3.031%

0.082%

12.204%

3

2.047%

3.247%

78.540%

0.904%

1.398%

0.452%

0.530%

0.251%

12.631%

4

11.529%

13.613%

0.612%

51.055%

0.689%

3.624%

2.641%

2.452%

13.784%

5

5.756%

8.249%

0.330%

0.340%

65.859%

0.457%

1.861%

0.168%

16.981%

6

0.316%

8.345%

0.286%

5.217%

1.043%

66.417%

1.648%

0.178%

16.550%

7

3.911%

5.124%

0.320%

1.621%

2.478%

0.605%

74.724%

0.340%

10.876%

8

9.276%

0.400%

0.305%

4.918%

0.636%

0.305%

1.523%

64.044%

18.595%

TABLE 8 THE PROPORTION OF FLIGHT ROUTES CHANGE

Class no 1

April 2008 –March 2009

2 3 4 5 6 7 8 9

July 2008 –June 2009 6 7 8

1

2

3

4

5

78.1 7% 2.66 % 11.1 4% 4.51 % 9.49 % 12.7 4% 12.8 7% 3.88 % 18.9 8%

0.16 % 81.8 6% 0.41 % 0.17 % 0.10 % 0.41 % 0.09 % 0.24 % 0.05 %

0.32 % 0.11 % 77.6 0% 0.24 % 0.12 % 0.37 % 0.10 % 0.07 % 0.05 %

0.46 % 0.28 % 0.91 % 80.16 % 0.53 % 1.14 % 0.56 % 0.46 % 0.31 %

0.34 % 0.05 % 0.13 % 0.21 % 79.80 % 0.21 % 0.08 % 0.18 % 0.08 %

0.37 % 0.15 % 0.34 % 0.37 % 0.15 % 74.90 % 0.18 % 0.17 % 0.13 %

1.17 % 0.17 % 0.41 % 0.51 % 0.18 % 0.66 % 77.92 % 0.22 % 0.63 %

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0.10 % 0.17 % 0.13 % 0.07 % 0.18 % 0.16 % 0.07 % 76.28 % 0.02 %

9

10

11

12

13

0.92 % 0.04 % 0.18 % 0.12 % 0.07 % 0.05 % 0.32 % 0.05 % 79.08 %

0.50 % 0.12 % 0.22 % 0.17 % 0.31 % 0.29 % 0.10 % 0.33 % 0.05 %

0.36 % 0.02 % 0.13 % 0.05 % 0.09 % 0.03 % 0.07 % 0.10 % 0.02 %

0.10 % 0.15 % 0.14 % 0.14 % 0.13 % 0.23 % 0.11 % 0.28 % 0.02 %

0.15 % 0.17 % 0.26 % 0.17 % 0.10 % 0.41 % 0.12 % 0.09 % 0.05 %

OUT 16.87 % 14.06 % 8.01% 13.13 % 8.75% 8.39% 7.42% 17.66 % 0.52%


TABLE 8 THE PROPORTION OF FLIGHT ROUTES CHANGE(CONT.) April 2008 –March 2009

Class no 10 11 12 13

July 2008 –June 2009 6 7 8

1

2

3

4

5

10.0 2% 17.8 9% 4.14 % 4.51 %

0.14 % 0.10 % 0.21 % 0.24 %

0.11 % 0.10 % 0.18 % 0.24 %

0.36 % 0.28 % 0.71 % 0.49 %

0.25 % 0.14 % 0.42 % 0.16 %

0.16 % 0.12 % 0.28 % 0.29 %

0.27 % 0.42 % 0.43 % 0.30 %

0.21 % 0.17 % 0.21 % 0.11 %

9

10

11

12

13

0.02 % 0.17 % 0.07 % 0.05 %

78.9 9% 0.17 % 0.25 % 0.18 %

0.11 % 78.63 % 0.21 % 0.04 %

0.11 % 0.40 % 84.20 % 0.13 %

0.09 % 0.03 % 0.20 % 77.0 7%

OUT 9.17% 1.37% 8.49% 16.19 %

4 APPLICATION OF MARKETING STRATEGY According to strengths and weakness of behavior cluster and flight routes cluster, cross-probe and the actual needs of the business department, the following marketing strategies can be developed: ①The 1st group: The group whose ratio between first class and economy class and ratio of outing in the weekend are the second lagest, belonging to a middle-value customer base with the most customers. The customers from the group often choose the first route, frequently the seventh route, Hongqiao-Urumqi route, Pudong-Wuhan route. Strategies can be taken as : phone calls or notification. ②The 2nd group: The group whose online check-in rate is higher, belonging to a quite high-value customer base. The customers from the group often choose the first route, frequently the seventh route, Pudong-Wuhan route, Hongqiao-Guangzhou route, Hongqiao-Beijing route, Pudong-Guangzhou route. Strategies can be taken: such as the offers of more convenient online service. ③The 3rd group: The group belongs to a high-value customer base. The customers from the group often choose the first route, frequently the seventh route, Pudong-Wuhan route. Strategies can be taken such as a better understanding on the needs of the customer further, or the open of VIP service hotline, the provision of preferential access to tickets, transportation and boarding services, extension services, implementation of VIP club marketing programs and the increase in special promotions in the relevant routes. ④The 4th group: The group belongs to a low-value customer base. The customers from the group often choose the first route, while some customers of the group choose the seventh route frequently, Pudong-Wuhan route. Usually, customers choose going outing during the holidays and like the way of SMS check-in and online check-in. Strategies can be taken such as the introduction of different services during the holidays. ⑤The 5th group: The group belonging to a middle-value and business-type group has the most customers. The customers from the group often choose the first route, frequently the seventh route, Hongqiao-Urumqi route, Pudong-Wuhan route. Usually, customers choose going outing in the weekday and are used to counter check-in. Strategies can be taken on the provision of efficient and convenient counter-in service. ⑥The 6th group: The group whose average age is the smallest, ratio of B2C booking is the highest, and ratio of outing by team is relatively high, ratio of bankcard to pay, SMS check-in rate, online check-in rate are the highest, belongs to a low-value customer base and prefers to travel on holiday. So most of the customers from the group are young people, who often choose the first route and the seventh route, Hongqiao-Urumqi route and Pudong-Wuhan route. Strategies can be taken on personalized online service and concessions of team travel. ⑦The 7th group: The group belongs to a low-value and business-type customer base. The customers from the group often choose the first route, frequently the seventh route, Pudong-Wuhan route, Pudong-Shenyang route. Most of them regularly like going outing in the weekday, and prefer the way of self check-in. From above characteristics, it can be inferred that the customers from the group are more receptive to new things. Strategies can be taken on the recommendation of company’s new business. ⑧The 8th group: The group belonging to a low-value type, customers from which often choose going outing in the - 30 -


weekend. They often choose the first route, Hongqiao-Urumqi route, Pudong-Wuhan route. They are inclined to B2C booking, like the way of counter check-in and going outing by team. Strategies can be taken: such as concessions to customers who go outing in the weekend, to make personalized online services, and so on. (Notes: The first route includes following routes: Pudong-Zhuhai, Hongqiao-Shantou, Hongqiao-Nanning, PudongTaiwan, Hongqiao-Guangzhou, Pudong-Dalian,Pudong-Shenzhen, Hongqiao-Beijing, Pudong-Haikou, PudongGuangzhou; The seventh route includes Hongqiao-Shenzhen, Pudong-Shenzhen and Pudong-Taiwan routes.)

5 CONCLUSIONS Customers with characteristics of behaviour and flight routes were clustered, two cluster results from which is combined to analyse the cluster change in different periods. After all, according to final results, a precision marketing has been made by using the result of clustering segmentation. The next work will focus on theory of social network analysis and combine the behavioural characteristic of the customers, and an analysis of customer value and customer churn will be developed to optimize precise formulation and implement marketing strategies of airlines.

ACKNOWLEDGMENT The work is supported by Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (NO. CAAC-ITRB-201206).

REFERENCES [1]

Guohe Feng. Analysis of Aviation CRM System Based on SAS Data Mining Technology [J]. Journal of Information, 2006, 25(5): 56-59.

[2]

Guohe Feng, Weiye Li. Designing and Applying Data Warehouse for Airline Company Customer Relationship Management System [J].Journal of Information, 2006, 25(7):32-35.

[3]

Customer segment.http://wiki.mbalib.com/wiki/%E5%AE%A2%E6%88%B7%E7%BB%86%E5%88%86.[2013/1/29]

[4]

Luo Liangsheng, et al. Research Airline Customer Segmentation Method Based on Frequent Flyer Database [J]. Modern Business, 2008(23): 54-55.

[5]

SAS.http://www.sas.com/.[2013/1/29].

[6]

Liang Wang, Jiancang Xie, Jungang Luo. Emergency Supplies Scheduling Based on K-Mean Cluster and LK Algorithm. Computer Engineering and Applications, 2012, 48(21):35-40.

[7]

Xingming Zheng, Ningzhong Liu. Color Recognition of Clothes Based on K-Means and Mean Shift. Proceedings of 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment (ICADE 2012),2012,7:124-131

AUTHORS Guohe Feng (1971- ), male, Professor,

Xiaoting Liang (1986- ), female, postgraduate.

Ph.D. Master Instructor, research field is data mining, digital library, management information system, more than 60 published papers. Email: ghfeng@163.com.

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Analysis of airlines marketing based on double clustering