IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/114737.html

بررسی کاربردهای داده‌کاوی در مدیریت مشتریان شرکت‌های هواپیمایی
[Data mining for managing customers of airline companies]

Author

Listed:
  • Mahdiani, Pegah
  • Ranjbarfard, Mina

Abstract

Data mining is one of the useful techniques for customer relationship management which detect customer behavior pattern from a huge volumes of data. This patterns can be helpful for decision making in areas such as aircraft industry. Applying data mining techniques on data from an airline company, existing patterns of customers can be detected and finally purposive actions for improving airline services can be taken. In this case customer churn will be reduced and customer satisfaction and loyalty will be increased along with customer retention which all lead to profit raise in long term. The main objective of this paper is to introduce data mining techniques for managing customers of airline companies which emphasize on DRSA approach in service and cost management. The result of this research can help airline companies to identify worthy customers and forecasting their future behavior which lead to better decision making.

Suggested Citation

  • Mahdiani, Pegah & Ranjbarfard, Mina, 2018. "بررسی کاربردهای داده‌کاوی در مدیریت مشتریان شرکت‌های هواپیمایی [Data mining for managing customers of airline companies]," MPRA Paper 114737, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:114737
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/114737/1/MPRA_paper_114737.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nassiri, Habibollah & Rezaei, Ali, 2012. "Air itinerary choice in a low-frequency market: A decision rule approach," Journal of Air Transport Management, Elsevier, vol. 18(1), pages 34-37.
    2. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    2. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
    4. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    5. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    6. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
    7. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    8. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    9. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    10. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    11. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    12. Thao Tran & Ngoc Hong Nguyen & Binh Thien Le & Nam Thanh Vu & Duc Hong Vo, 2023. "Examining financial distress of the Vietnamese listed firms using accounting-based models," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-17, May.
    13. David Alaminos & Manuel Ángel Fernández, 2019. "Why do football clubs fail financially? A financial distress prediction model for European professional football industry," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    14. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    15. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
    16. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    17. Nandita Mishra & Shruti Ashok & Deepak Tandon, 2024. "Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models," Global Business Review, International Management Institute, vol. 25(6), pages 1540-1558, December.
    18. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    19. Jiaming Liu & Chong Wu, 2017. "Dynamic forecasting of financial distress: the hybrid use of incremental bagging and genetic algorithm—empirical study of Chinese listed corporations," Risk Management, Palgrave Macmillan, vol. 19(1), pages 32-52, February.
    20. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • N7 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:114737. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.