SERPİL ÜSTEBAY, Ilkay YELMEN, Metin ZONTUL

Customer Segmentation Based on Self-Organizing Maps: A Case Study on Airline Passengers

Customer segmentation is a customer grouping model based on common features and it directly relates with customer satisfaction of the companies. It provides access to the right customer with the right methods by knowing the customer better. Dealing with changes in a competitive market means airlines have to redefine customer segmentations, which translates from social-demography to a more complex behavioral approach that covers the entire travel experience and the way airlines deliver at every touch point. In this paper, a customer segmentation was performed using an airline ticket sales data and focused on two concepts such as customer loyalty and customer return. Customers with similar sales tendencies were clustered by using self- organizing map method and totally 15 clusters were obtained. In purchasing trends, the highest return was obtained in cluster 2 and the minimum return in cluster 6. Loyalty rate was calculated as 38% and it was seen that the most loyal customer profile was in the cluster 12.

Kendi Kendini Düzenleyen Haritalara Dayalı Müşteri Segmentasyonu: Havayolu Yolcuları Üzerine Bir Vaka Çalışması

Müşteri segmentasyonu, ortak özelliklere dayanan bir müşteri gruplama modelidir ve şirketlerin müşteri memnuniyeti ile doğrudan ilişkilidir. Müşteriyi daha iyi tanıyarak doğru müşteriye doğru yöntemlerle erişim sağlar. Rekabetçi bir pazardaki değişikliklerle başa çıkmak, havayolu şirketlerinin müşteri segmentasyonlarını yeniden tanımlamaları gerektiği anlamına gelir; bu da sosyal demografiden tüm seyahat deneyimini ve havayollarının her temas noktasında teslim şeklini kapsayan daha karmaşık bir davranışçı yaklaşıma dönüşür. Bu makalede, bir uçak bileti satış verileri kullanılarak bir müşteri segmentasyonu gerçekleştirilmiş ve müşteri sadakati ve müşteri iadesi gibi iki konsepte odaklanmıştır. Benzer satış eğilimlerine sahip müşteriler kendi kendini düzenleyen haritalar yöntemi kullanılarak kümelenmiş ve toplam 15 küme elde edilmiştir. Satın alma eğilimlerinde en yüksek getiri küme 2’de, minimum getiri ise küme 6’da elde edilmiştir. Sadakat oranı ise %38 olarak hesaplanmış olup, en sadık müşteri profilinin küme 12’de olduğu görülmüştür.

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[1] F. Alamdari and K. Mason, “The future of airline distribution,” Journal of Air Transport Management, vol. 12, pp. 122-134, 2006.

[2] M. C. Chen, A. L. Chiu, and H. H. Chang, “Mining changes in customer behavior in retail marketing,” Expert Systems with Applications, vol. 28, pp. 773-781, 2005.

[3] R. Gupta, and C. Pathak, “A machine learning framework for predicting purchase by online customers based on dynamic pricing,” Procedia Computer Science, vol. 36, pp. 599-605, 2014.

[4] I. Maryani, D. Riana, R. D. Astuti, A. Ishaq and E. A. Pratama, “Customer segmentation based on RFM model and clustering techniques with K-means algorithm,” Proceedings of the 3rd International Conference on Informatics and Computing, ICIC 2018, 2018. pp. 1–6.

[5] J. Wu, and Z. Lin, “Research on customer segmentation model by clustering,” In Proceedings of the 7th international conference on Electronic commerce, 2005. pp. 316-318.

[6] C. Y. Tsai, and C. C. Chiu, “A purchase-based market segmentation methodology,” Expert Systems with Applications, vol. 27, pp. 265-276, 2004.

[7] S. S. Haykin, Neural networks and learning machines, New Jersey: Pearson Education, Inc., 2009.

[8] M. Patak, H. Lostakova, M. Curdova, and V. Vlckova, “The e-pharmacy customer segmentation based on the perceived importance of the retention support tools,” Procedia-Social and Behavioral Sciences, vol. 150, pp. 552-562, 2014.

[9] M. Carnein, and H. Trautmann, “Customer segmentation based on transactional data using stream clustering,” In Pacific-Asia Conference on Knowledge Discovery and Data Mining,Springer, Cham, 2019. pp. 280-292.

[10] K. Khalili-Damghani, K., F. Abdi, and S. Abolmakarem, “Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customercentric industries,” Applied Soft Computing, vol. 73, pp. 816-828, 2018.

[11] H. Zhang, A. Zhou, S. Song, Q. Zhang, X. Z. Gao, and J. Zhang, “A self-organizing multiobjective evolutionary algorithm,” IEEE Transactions on Evolutionary Computation, vol. 20, pp. 792-806, 2016.

[12] B. Lariviere, and D. Vandenpoel, “Predicting customer retention and profitability by using random forests and regression forests techniques,” Expert systems with applications, vol. 29, pp. 472-484, 2005.

[13] T. Jiang, and A. Tuzhilin, “Segmenting customers from population to individuals: Does 1-to-1 keep your customers forever?,” IEEE Transactions on knowledge and data engineering, vol. 18, pp. 1297-1311, 2006.

[14] P. Zerbino, D. Aloini, R. Dulmin, and V. Mininno, “Big Data-enabled customer relationship management: A holistic approach,” Information Processing & Management, vol. 54, pp. 818-846, 2018.

[15] T. F. Bahari, and M. S. Elayidom, “An efficient CRM-data mining framework for the prediction of customer behaviour,” Procedia computer science, vol. 46, pp. 725-731, 2015.

[16] J. Sheng, J. Amankwah-Amoah, and X. Wang, “A multidisciplinary perspective of big data in management research,” International Journal of Production Economics, vol. 191, pp. 97-112, 2017.

[17] K. Mason, K., “Observations of fundamental changes in the demand for aviation services,” Journal of Air Transport Management, vol. 11, pp. 19-25, 2005.

[18] H. Lindstadt, B. Fauser, “Separation or integration? Can network carriers create distinct business streams on one integrated production platform?,” Journal of Air Transport Management, vol. 10, pp. 23-31, 2004.

[19] E. W. Ngai, L. Xiu, and D. C. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert systems with applications, vol. 36, pp. 2592- 2602, 2009.

[20] J. Han, M. Kamber, Data mining: Concepts and techniques, Morgan Kaufmann Publisher, 2001.

[21] C. H. Cheng, and Y. S. Chen, “Classifying the segmentation of customer value via RFM model and RS theory,” Expert systems with applications, vol. 36, pp. 4176-4184, 2009.

[22] B. Avram, “Airlines Customer Segmentation in the Hyper-Competition Era,” Expert Journal of Marketing, vol. 7, 2019.