Müşteri şikâyet yönetiminde firmaların performanslarının değerlendirilmesi: Kümeleme analizi incelemesi

Year 2022, , 447 - 456, 30.09.2022

Gamze Ödev Serhat Peker

https://doi.org/10.24012/dumf.1126199

Abstract

Müşteri memnuniyetinde, hizmet ve ürünün kalitesi kadar müşteri şikayetlerinin dikkate alınması ve etkili bir şekilde yönetilmesi de oldukça önemli rol oynar. Günümüzde online ortamlarda şikayet daha fazla tercih edilmektedir. Bu çalışmanın amacı, kümeleme analizini kullanarak internet ortamında firmaların aldığı müşteri şikayetlerini ve bunları yönetim performanslarını değerlendirmektir. Bu amaca yönelik Sikayetvar.com internet sitesinden elde edilen veriler, CRISP-DM (Cross Industry Standard Process for Data Mining; Çapraz Endüstri Veri Madenciliği Standart Süreci) adımları baz alınarak iki aşamalı kümele analizi yöntemiyle analiz edilmiş ve elde edilen firma kümeleri profillenmiştir. Ayrıca elde edilen sonuçlar sektör bazlı olarak değerlendirilmiştir. Bu çalışmada önerilen yaklaşım ile firmalar şikayet yönetim performanslarını tespit edebilecek, diğer firmalar içindeki yerini görebilecek ve bu bağlamda başarılı firma profillerini baz alarak kendilerini geliştirebileceklerdir.

Keywords

Veri madenciliği, kümeleme analizi, veri yönetimi, iş analitiği, karar destek sistemleri

References

• [1] T.-L. B. Tseng and C. C. Huang, “Rough set-based approach to feature selection in customer relationship management,” Omega, vol. 35, no. 4, pp. 365–383, 2007.
• [2] M. Zairi, “Managing customer dissatisfaction through effective complaints management systems,” TQM Mag., vol. 12, no. 5, 2000.
• [3] J. L. Ferguson and W. J. Johnston, “Customer response to dissatisfaction: A synthesis of literature and conceptual framework,” Ind. Mark. Manag., vol. 40, no. 1, pp. 118–127, 2011.
• [4] C. H. Lee, Y. H. Wang, and A. J. C. Trappey, “Ontology-based reasoning for the intelligent handling of customer complaints,” Comput. Ind. Eng., vol. 84, pp. 144–155, 2015.
• [5] Y. Yang, D. L. Xu, J. B. Yang, and Y. W. Chen, “An evidential reasoning-based decision support system for handling customer complaints in mobile telecommunications,” Knowledge-Based Syst., vol. 162, pp. 202–210, 2018.
• [6] X. Luo, “Consumer negative voice and firm-idiosyncratic stock returns,” J. Mark., vol. 71, no. 3, pp. 75–88, 2007.
• [7] P. F. Wu, “In search of negativity bias: An empirical study of perceived helpfulness of online reviews,” Psychol. Mark., vol. 30, no. 11, pp. 971–984, 2013.
• [8] A. J. Kimmel and P. J. Kitchen, “WOM and social media: Presaging future directions for research and practice,” J. Mark. Commun., vol. 20, no. Kimmel, A. J., Kitchen, P. J. (2014). WOM and social media: Presaging future directions for research and practice. Journal of Marketing Communications, 20(1-2, pp. 5–20, 2014.
• [9] S. Senecal and J. Nantel, “The influence of online product recommendations on consumers’ online choices,” J. Retail., vol. 80, no. 2, pp. 159–169, 2004.
• [10] E. W. T. Ngai, L. Xiu, and D. C. K. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Syst. Appl., vol. 36, no. 2, pp. 2592–2602, 2009.
• [11] A. Dursun and M. Caber, “Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis,” Tour. Manag. Perspect., vol. 18, pp. 153–160, 2016.
• [12] S. Peker, A. Kocyigit, and P. E. Eren, “LRFMP model for customer segmentation in the grocery retail industry: a case study,” Mark. Intell. Plan., vol. 35, no. 4, pp. 1–16, 2017.
• [13] A. Sheikh, T. Ghanbarpour, and D. Gholamiangonabadi, “A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting,” J. Business-to-bus. Mark., 2019.
• [14] S. Guney, S. Peker, and C. Turhan, “A combined approach for customer profiling in video on demand services using clustering and association rule mining,” IEEE Access, vol. 8, pp. 84326–84335, 2020.
• [15] M. Khajvand, K. Zolfaghar, S. Ashoori, and S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study,” Procedia Comput. Sci., vol. 3, pp. 57–63, 2011.
• [16] E. Nikumanesh and A. Albadvi, “Customer’s life-time value using the RFM model in the banking industry: A case study,” Int. J. Electron. Cust. Relatsh. Manag., vol. 8, no. 1–3, pp. 15–30, 2014.
• [17] F. Safari, N. Safari, G. A. Montazer, T. Brashear Alejandro, and T. Brashear Alejandro, “Customer lifetime value determination based on RFM model,” Mark. Intell. Plan., vol. 34, no. 4, 2016.
• [18] TÜİK, “Hanehalkı Bilişim Teknolojileri (BT) Kullanım Araştırması, 2021,” https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2021-37437, Aug-2021. .
• [19] T. M. Tripp and Y. Grégoire, “When unhappy customers strike back on the internet,” MIT Sloan Manag. Rev., 2011.
• [20] W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, “Knowledge discovery in databases: An overview,” AI Mag., vol. 13, no. 3, p. 57, 1992.
• [21] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI Mag., vol. 17, no. 3, p. 37, 1996.
• [22] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
• [23] A. . S. S. . & C. R. H. Abbasi, “Big data research in information systems: Toward an inclusive research agenda,” J. Assoc. Inf. Syst., vol. 17, no. 3, 2016.
• [24] F. Martinez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Trans. Knowl. Data Eng., vol. 33, no. 8, pp. 3048–3061, 2019.
• [25] C. Pete et al., “Crisp-Dm 1.0,” Cris. Consort., 199AD.
• [26] H. Jiawei and M. Kamber, “Data mining: concepts and techniques,” San Fr. CA, itd Morgan Kaufmann, vol. 5, 2001.
• [27] I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
• [28] G. Punj and D. W. Stewart, “Cluster analysis in marketing research: Review and suggestions for application,” J. Mark. Res., pp. 134–148, 1983.
• [29] A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 651–666, 2010.
• [30] Y. H. Hsiao, L. F. Chen, Y. L. Choy, and C. T. Su, “A novel framework for customer complaint management,” Serv. Ind. J., vol. 36, no. 13–14, pp. 675–698, 2016.
• [31] S. Chugani, K. Govinda, and S. Ramasubbareddy, “Data Analysis of Consumer Complaints in Banking Industry using Hybrid Clustering,” in Proceedings of the 2nd International Conference on Computing Methodologies and Communication, ICCMC 2018, 2018, pp. 74–78.
• [32] A. Ghazzawi and B. Alharbi, “Analysis of Customer Complaints Data using Data Mining Techniques,” in Procedia Computer Science, 2019, pp. 62–69.
• [33] R. Sann, P.-C. Lai, S.-Y. Liaw, and C.-T. Chen, “Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews,” Sustainability, vol. 14, no. 3, p. 1800, 2022.
• [34] L. S. Caliskan and S. Kiran, “İş Süreclerinin Otomasyonunda RPA Faydaları,” https://dergipark.org.tr/tr/download/article-file/1104503, 2020.
• [35] J. Y. C. . D. M. Ho, “Viral marketing: Motivations to forward online content,” J. Bus. Res., vol. 63, pp. 1000–1006, 2010.