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Scientific Journal of E-Business January 2014, Volume 3, Issue 1, PP.1-5

Comprehensive Evaluation Analysis of Airlines Frequent Flyer Value Guohe Feng 1#, Xiaoxiao Zhang 1, Xia Feng 2, Yun Xue 3 1. College of Economic &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 3. The School of Physics & telecommunications Engineering, South China Normal University, Guangzhou, 510006, China #Email:

Abstract Inductive three categories of indicators that aviation effects customer value: flight value indicators, partnership value indicator, loyalty value indicator, and based on the expert investigation method give different weights to different types of index, in addition, its contribution to customer value is described. As to class containing many indicator variables, principal component analysis method is applied to analyze indexes which influence the customer value, according to the calculation formula to calculate comprehensive score of customer value, and sort out after normalizing the total scores into percentile scores, and sorting. According to different member group value, different marketing strategies are designed, in order to achieve maximum profits. Keywords: Airlines; Weight Setting; Principal Component Analysis; Value Evaluation

1 INTRODUCTION In the fierce market competition, for airlines, how to meet customer needs and to identify valuable customers are the important parts of implementation of marketing strategy. Having fully grasped customer conditions and behaviors by customer segmentation, airlines can implement diversity efficient marketing strategies and customer services for different kinds of customers [1]. Frequent flyer (hereinafter referred to as the “members”) is the most precious resources of aviation enterprise. Using high quality rating score prediction model, each member is given a score, to assess the value of customers and potential customers, according to the output of the model, and operating department intuitively judges the values of members based on the scores, which provides the corresponding service standards[2]. The main work of analysis based on the customer value is to determine the key factors of customer value assessment. Customer value assessment model established is applied to the frequent flyer program business process. Based on each customer’s comprehensive quantitative assessment of value and loyalty, targeted value mining strategy is developed. By improving the high value of customers’ loyalty, mining the potential value of low customer, makes the contribution of customer value maximization.

2 THE ANALYSIS MODEL OF CUSTOMER VALUE Based on SAS analysis platform, several categories of value index are concluded that impact customer variable, by using expert investigation method to give different weights to different types of index, then its contribution to customer value is described. As to class containing many index variables, principal component analysis method (PCA) is used to analyze indexes which influence customers’ value scores, with the purpose to reduce the number of variables, and turn them into a few mutual independent variables in the form of a linear combination, thus making the original P-dimension data show the largest individual differences in the ingredients [3]. Finally according to the comprehensive formula, values of members are obtained.

2.1 Analyzing Variables Generally, three indicators are adopted, namely, recent time interval, frequency of consumption, consumption amount (i.e., usually recognized R, F, M) as evaluation variables, in the process of customer value evaluation, to calculate customers’ value sorted based on value scores. Stone [4]and Miglautsch[5] improved the RFM model, and divided R into different duration attached with different weights, at the same time they modified F and M then calculated customer value by sum of F and M. Sung[6] using SOM classified the value of the customer's RFM at first, then compared all kinds of RFM’s average value and the average of all the customers’ RFM, in order to observe the change rule of all kinds of customers; further, through its rise or fall, it was determined whether the various types of customer are high Platinum customer with high loyalty or customer about to lose. There’s a lot of research constructing customer value evaluation based on RFM model, and using RFM model obtaining customer value assessment has become a mainstream technology. But note that different industries have different specific business meaning, the use of substitution variables are not necessarily the same, even the same variable, whose representative meaning may vary greatly. In the traditional indicators, the consumption amount factor does not apply to the airline customer evaluation. Through analyzing the airline business, a more reasonable approach uses the customer's upgrade mileage and the average discount coefficient of flight instead, and customer relationship length is added which impact evaluation of customer loyalty [7], additionally, taking the complexity of aviation business into account, the foregoing analysis method uses only a few indicators portraying customer value which seems too simple. Through deep and careful analysis of aviation business, the index variables are ultimately determined which impact aviation customer value evaluation including the following three parts: an indication of value opportunity. 1) Flight Value Indicators In the embarkation value indicator, the following variable should be considered: The interval from the last time by air (the number of month since the last time customer taking our aircraft), The times of the flight(the number of customer’ accumulated taking our aircraft up to now), two cabins’ proportion (the number of customer’ accumulated taking our FC aircraft up to now/the times of the flight(FC cabins are first class and economy class)), the average discount (accumulated mileage up to now after discount/actual accumulated flight mileage), upgrade mileage credit(only customer’s accumulated basic mileage up to now), Promotional Credits (so far customer’s accumulated promotion points taking our aircraft ). 2) Partnership Value Indicator Mainly include partner redemption, that is, the customer’s accumulated redemption point from partners of the company. 3) Loyalty Value Indicators Loyalty value indicators mainly include: Membership duration (the number of month after member’s enrollment), integral rate (the total exchange integral/all the accumulative history integral), condition of registering to participate in promotional activities (a value of 0, 1, indicating whether or not registered to participate in the activities of the company's sales promotion). Since the interval from the last time by air variable is inverter, the reciprocal value is actual used. As to other variables, if it is empty, zero is used instead.

2.2 Analysis Model 1) General Analysis Model Through careful study of indicator variables affecting the evaluation of customer value, the following value models are proposed: TABLE 1 TOTAL VALUE ANALYSIS MODEL

Step1:Using expert investigation to determine flight value, partners’ value, the proportion of the loyalty value, and thus acquiring their final value weights (w1, w2, w3); Step2:Based on principal component analysis method to calculate the flight value and loyalty value of customers; Step3: Take advantage of weights given by Step1, and compute the customer total value score, i.e. Score = w1 * Flight value + w2 * Partner Value + w3 * Loyalty value Step4: Normalize the total score into percentile scores, and sorting.

2) Expert Investigation Weight Analysis Three civil aviation senior experts are extracted, and then paired comparison method in AHP analysis method is adopted which is proposed by Satie, to gather judgment matrix produced by experts and then by comparing three types of index, the respective feature vectors are adopted, finally average to obtain ratio of various types of indicators (0.7312,0.1251,0.1437). 3) The Principal Component Analysis Model Through exploration-type data analysis, data are prepared that the analysis needs, choose the appropriate data mining tools and data mining technology to construct customer value model. Principal component analysis aims to use the idea of dimensionality reduction, and further to turn the multi-index into a few composite indicators. In the study of empirical question, in order to comprehensively and systematically analyze the problem, we must consider a number of factors. Because each variable to a different extent reflects some information on such issues, and the indexes have a certain correlation with each other, thus resulting in the information statistics reflected to some extent of overlapping. When studying multivariate problems by a statistical method, many variables may increase the computational and complexity of the analyzed problem, fewer variables are expected to be involved and a higher amount of information is obtained in the process of a quantitative analysis. Principal component analysis is exactly implemented to meet this requirement, and the ideal tool is to solve such problems. Airline customer value model mainly uses principal component analysis. The SAS analysis tools are used to analyze airlines customer value, and the steps are as follows. TABLE 2 MEMBER VALUE ANALYSIS PROCESS USING SAS

Step1: Integrate data according to business needs, extract the key variables for involving and modeling from the original table. Step2: Data preprocessing, if the value of a key extracted variable mentioned above is empty, the average discount rate is set greater than 1.03 to 1.03. Then summarize the results after these processing, further to examine data, until each index value is in line with business requirements Step3: Eliminate dimension impact on the results, and normalize the data got by step2 Step4: Do correlation analysis of data in step 3, according to the Pearson correlation coefficient test rules, inspect whether the data is suitable for principal component analysis or not. Step5: Do principal component analysis of the data after normalization, and keep the main component satisfied the rule. Step6: Calculate member value scores.

3 ANALYSIS OF THE MODEL RESULTS 3.1 Data Preparation Extract history record of frequent flyer accumulated to April 2009 and July 2009 from the data marts, then choose the former as a modeling training samples; further select the later as the test samples.

3.2 Training Sample Results Through correlation analysis of flight value indicators, correlation coefficient is observed between flight times and upgrade mileage credit is 0.87. Excluding upgrade mileage credit variables, and based on the analysis of remaining five variables principal component, the eigenvalues calculated are: 1.60, 1.14, 0.984, 0.75, 0.54; of which the first four principal components of variance account for 89.2% of total variance, so taking the first four principal component as scores calculated variables; moreover, correlation coefficient between these four principal components and the original variable are as follows in Table 3. TABLE 3 COEFFICIENT RELATIONSHIP BETWEEN PRINCIPAL COMPONENTS WITH THE ORIGINAL VARIABLE

Principal component Prin1 Prin2 Prin3 Prin4

Times of flights 0.465 0.481 -0.0432 -0.669

Two cabins proportion 0.520 -0.498 0.149 0.288

Average discount 0.610 -0.323 -0.001 -0.147

Customer Relationship Length 0.323 0.431 -0.629 0.559

Promotional Credits 0.192 0.480 0.762 0.368

According to score formula: Z1  1 * Pr in1  2 * Pr in2  3 * Pr in3  4 * Pr in4 , each member’s flight value score is calculated. Do principal component analysis on loyalty variables, and the eigenvalues are: 1.36, 0.95, 0.69, of which the first two principal components accumulated variance account for 78% of total variance taking as the score calculation variables, and the coefficients relationship with the original variables are shown in Table 3. TABLE 4 COEFFICIENT RELATIONSHIP BETWEEN PRINCIPAL COMPONENTS WITH THE ORIGINAL VARIABLE

Principal component Prin1 Prin2

Membership duration 0.591 -0.542

Integral exchange rate 0.680 -0.065

Participate in the promotion 0.435 0.838

According to score formula: Z2  1 * Pr in1  2 * Pr in2 , each member’s loyalty value score is calculated. For Intuitive comparison on business applications, generally it need to be standardized composite score, i.e. standardize membership scores to [0,100] range, with the customary centesimal system representation compare members’ value, then centesimal scores are divided into 12 intervals, the top 1% of the high value of the member, the top 1% to 5% before the members, and so on, and the final form is shown in Table 4, an interval score distribution table. TABLE 4 MEMBER STANDARD SCORES AND INTERVAL (partial)

Member ID 680006655657 380012643581 480012748977 380012643597 680006171128 580006668823 380014844682 380006637465

centesimal system score 100 82 81 79 77 15 15 15

Interval 100 100 100 100 100 99 99 99

Member ID 480006158119 380096015720 580006373166 030004020667 580006134425 080015047811 380006912921 680006310200

centesimal system score 15 15 14 9 9 9 9 9

Interval 99 99 99 95 95 95 95 95

3.3 Test Sample Results The same method is adopted for training samples to score membership value for test samples, then the flight variable characteristic values obtained are: 1.702, 1.017, 0.922, 0.773, 0.226, of which the first four principal components of variance of the total variance of 87.0%, so taken as the score calculation variables, and correlation coefficient between these four principal components and the original variable are as follows in Table 5. TABLE 5 COEFFICIENT RELATIONSHIP BETWEEN PRINCIPAL COMPONENTS WITH THE ORIGINAL VARIABLE

Principal Components Prin1 Prin2 Prin3 Prin4

Flight Frequency 0.598 -0.180 -0.256 -0.654

Average Discount rate 0.306 0.347 0.806 0.283

The length of the customer relationship 0.443 0.088 0.196 0.560

Upgrade mileage 0.589 -0.189 -0.241 -0.156

The recent flight time interval 0.084 0.897 -0.433 0.346


Membership ID 680006655657 680006171128 480012748977 380012643581 380012643597 580006668823 380014844682 380006637465

Percentile scores 100 85 82 80 79 18 9 13

Interval 100 100 100 100 100 100 99 99

Member ID 480006158119 380096015720 580006373166 030004020667 580006134425 080015047811 380006912921 680006310200

Percentile scores 16 16 14 9 11 9 7 12

Interval 100 100 99 99 99 99 95 99

Similarly component principal of loyalty is calculated through principal component computation, as well as the score, finally each of the three categories of the main indicators is weighted and overall scores are obtained. The same process for training samples is adopted to compute membership value score and its interval, then the same members are selected as training sample; thus getting the following table 6.

3.4 Analysis Conclusion The data above in Table 2 and Table 5 show that coefficient relationship between principal components obtained from the test sample and the training sample with the original variable is basically similar; while Table 4 and Table 6 data show that member values calculated change slightly. It is illustrated that slight change of data does not affect the membership value score, simultaneously it is shown that the principal component score model calculating the value of member is stable.

4 CONCLUSION Different weights of different types of indicators have been calculated, and class containing many indicator variables has been evaluated, by using principal component analysis method, to obtain the membership value score. Finally, each scores was synthesized to obtain total membership value score, meantime, the total scores were normalized into centesimal system and according to its score value intervals were divided, which allows marketers to determine the customer's value orientation quickly. According to different member group value, different marketing strategies have been designed, so as to achieve maximum profits. Next step is customer segmentation based on customer value analysis, and according to the value, members are divided into different groups, in order to analyze characteristics of each group members, and therefore, targeted, personalized service and marketing initiatives are taken.

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


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AUTHORS Guohe Feng (1971- ), male, Professor, Ph.D. Master Instructor. His research field is






library, system,

and in

addition, he has published more than 60

Xiaoxiao Zhang (1989- ), female, postgraduate. Xia Feng (1970- ), female, Professor, Ph.D. Master Instructor. Yun Xue (1975- ), male Associate Professor, Ph.D. Master Instructor.

papers. Email:

Comprehensive evaluation analysis of airlines frequent flyer value