Telekomünikasyon Sektöründe PSO ile Müşteri Bölümlemesi

Yıl 2018, , 163 - 173, 30.04.2018

Mihrimah Özmen , Yılmaz Delice Emel Kızılkaya Aydoğan

https://doi.org/10.17671/gazibtd.368460

Cited By: 3

Öz

Rekabetçi
piyasa ekonomisi koşullarında, işletmelerin gelişiminde etkili olan en önemli
kaynak müşterilerdir. Farklı müşteri gruplarının tercihlerini, alışveriş
tutumlarını ve fiyat duyarlılıklarını anlamak pazarlama faaliyetlerinin
yönelimi açısından çok önemlidir. Bu durumda müşteri segmentasyonu hedef
pazardaki uygun müşteri gruplarını seçmek için kullanılmaktadır. Bu çalışmada
Türkiye'nin ilk 100 telekomünikasyon şirketlerinden birine müşteri
segmentasyonu uygulanmıştır. Çalışmada yer alan firma, veri ambarında müşteri
davranışlarıyla ilgili çağrı detayları, fatura bilgisi, müşteri demografik
özellikleri gibi çok miktarda veri toplamıştır. Bu verilerin boyutu, manuel
analizin mümkün olmadığı kadar büyüktür. Bununla birlikte; bu veriler
operasyonel ve stratejik amaçlar için uygulanabilecek değerli bilgileri
barındırmaktadır. Bu verilerden anlamlı bilgi çıkarmak için gelişmiş veri
madenciliği teknikleri gereklidir. Bu çalışmada, PSO tabanlı kümeleme tekniği
ve DB uygunluk fonksiyonu ile müşteri segmentleri belirlenmiştir. 

Anahtar Kelimeler

Müşteri Segmentasyonu, Kümeleme, Meta Sezgisel Optimizasyon, PSO

Customer Segmentation with PSO in Telecommunication Sector

Öz

Under the
competitive market economy conditions, the most important source of the
development of businesses is the customers. Understanding the preferences,
shopping attitudes and price sensitivities of different customer groups is very
important in terms of the direction of marketing activities. In this case,
customer segmentation is used to select the appropriate customer groups in the
target market. In this study, customer segmentation was applied to one of the
Turkey's top 100 telecommunication companies. The company involved in the study
collects a lot of data on customer behaviors such as call details, billing
information, customer demographics, etc. in the data warehouse. The size of
this data is so large that manual analysis is not possible. However, these data
contain valuable information that can be applied for operational and strategic
purposes. Advanced data mining techniques are required to obtain meaningful
information from these data. In this study, customer segments were identified
with PSO-based clustering technique and DB fitness function.

Anahtar Kelimeler

Customer Segmentation, Clustering, Meta Heuristic Optimization, PSO

Kaynakça

• 1. Kim, J., Suh, E., & Hwang, H. (2003). A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing, 17(2), 5–19.
• 2. Kim, S. Y., Jung, T. S., Suh, E. H., & Hwang, H. S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert systems with applications, 31(1), 101-107.
• 3. Jansen, S. M. H. (2007). Customer segmentation and customer profiling for a mobile telecommunications company based on usage behavior. A Vodafone Case Study.
• 4. Han, S. H., Lu, S. X., & Leung, S. C. (2012). Segmentation of telecom customers based on customer value by decision tree model. Expert Systems with Applications, 39(4), 3964-3973.
• 5. Cowgill, M. C., Harvey, R. J., & Watson, L. T. (1999). A genetic algorithm approach to cluster analysis. Computers & Mathematics with Applications, 37(7), 99-108.
• 6. Murthy, C. A., & Chowdhury, N. (1996). In search of optimal clusters using genetic algorithms. Pattern Recognition Letters, 17(8), 825-832.
• 7. Krishna, K., & Murty, M. N. (1999). Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(3), 433-439.
• 8. Kwedlo, W. (2011). A clustering method combining differential evolution with the K-means algorithm. Pattern Recognition Letters, 32(12), 1613-1621.
• 9. Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied soft computing, 11(1), 652-657.
• 10. Zhang, C., Ouyang, D., & Ning, J. (2010). An artificial bee colony approach for clustering. Expert Systems with Applications, 37(7), 4761-4767.
• 11. Afshar, A., Haddad, O. B., Mariño, M. A., & Adams, B. J. (2007). Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Journal of the Franklin Institute, 344(5), 452-462.
• 12. Fathian, M., Amiri, B., & Maroosi, A. (2007). Application of honey-bee mating optimization algorithm on clustering. Applied Mathematics and Computation, 190(2), 1502-1513.
• 13. Cheng, Y., Jiang, M., & Yuan, D. (2009, August). Novel clustering algorithms based on improved artificial fish swarm algorithm. In Fuzzy Systems and Knowledge Discovery, 2009. FSKD'09. Sixth International Conference on (Vol. 3, pp. 141-145). IEEE.
• 14. Zhu, W., Jiang, J., Song, C., & Bao, L. (2012). Clustering algorithm based on fuzzy C-means and artificial fish swarm. Procedia Engineering, 29, 3307-3311.
• 15. Wan, M., Li, L., Xiao, J., Wang, C., & Yang, Y. (2012). Data clustering using bacterial foraging optimization. Journal of Intelligent Information Systems, 38(2), 321-341.
• 16. Saida, I. B., Nadjet, K., & Omar, B. (2014). A new algorithm for data clustering based on cuckoo search optimization. In Genetic and Evolutionary Computing (pp. 55-64). Springer International Publishing.
• 17. Chowdhury, A., Bose, S., & Das, S. (2011, December). Automatic clustering based on invasive weed optimization algorithm. In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 105-112). Springer, Berlin, Heidelberg.
• 18. Hatamlou, A., Abdullah, S., & Nezamabadi-Pour, H. (2012). A combined approach for clustering based on K-means and gravitational search algorithms. Swarm and Evolutionary Computation, 6, 47-52.
• 19. Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222, 175-184.
• 20. Olamaei, J., Mazinan, A. H., Arefi, A., & Niknam, T. (2010, February). A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration. In Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on (Vol. 4, pp. 265-269). IEEE.
• 21. Diskaya, F., Emir, S., & Orhan, N. (2011). Measuring the technical efficiency of telecommunication sector within global crisis: comparison of G8 countries and Turkey. Procedia-Social and Behavioral Sciences, 24, 206-218.
• 22. Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecom churn management. Expert Systems with Applications, 31(3), 515-524.
• 23. Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26(2), 181-188.
• 24. Hamka, F., Bouwman, H., De Reuver, M., & Kroesen, M. (2014). Mobile customer segmentation based on smartphone measurement. Telematics and Informatics, 31(2), 220-227.
• 25. Ye, L., Qiuru, C., Haixu, X., Yijun, L., & Guangping, Z. (2013). Customer segmentation for telecom with the k-means clustering method. Information Technology Journal, 12(3), 409-413.
• 26. Zhao, J., Zhang, W., & Liu, Y. (2010, December). Improved K-means cluster algorithm in telecommunications enterprises customer segmentation. In Information Theory and Information Security (ICITIS), 2010 IEEE International Conference on (pp. 167-169). IEEE.
• 27. Ahn, J. H., Han, S. P., & Lee, Y. S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications policy, 30(10), 552-568.
• 28. Cheng, L. C., & Sun, L. M. (2012). Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study.Electronic Commerce Research and Applications, 11(2), 89-100.
• 29. Lim, J., Nam, C., Kim, S., Lee, E., & Lee, H. (2015). A new regional clustering approach for mobile telecommunications policy in China. Telecommunications Policy, 39(3), 296-304.
• 30. Chen, C. H., Chiang, R. D., Wu, T. F., & Chu, H. C. (2013). A combined mining-based framework for predicting telecommunications customer payment behaviors. Expert Systems with Applications, 40(16), 6561-6569.
• 31. Vidya, N. A., Fanany, M. I., & Budi, I. (2015). Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers. Procedia Computer Science, 72, 519-526.
• 32. Weiss, G., & Hirsh, H. (1998), Learning to predict rare events in event sequences. In R. Agrawal & P. Stolorz (Eds.),Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (pp.359-363). Menlo Park, CA: AAAI Press.
• 33. Farvaresh, H., & Sepehri, M. M. (2011). A data mining framework for detecting subscription fraud in telecommunication. Engineering Applications of Artificial Intelligence, 24(1), 182-194.
• 34. Olszewski, D. (2012). A probabilistic approach to fraud detection in telecommunications. Knowledge-Based Systems, 26, 246-258.
• 35. Joseph, M. V. (2013). Data mining and business intelligence applications in telecommunication industry. International Journal of Engineering and Advanced Technology (IJEAT) ISSN, 2249-8958.
• 36. Chao, D. O. N. G., LEI, Z. M., & Feng, L. I. U. (2011). Internet quality abnormal analysis with k-means clustering. The Journal of China Universities of Posts and Telecommunications, 18, 94-100.
• 37. Ren, D. Q., Zheng, D., Huang, G., Zhang, S., & Wei, Z. (2013, November). Parallel Set Determination and K-Means Clustering for Data Mining on Telecommunication Networks. In High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), 2013 IEEE 10th International Conference on (pp. 1553-1557). IEEE.
• 38. Velmurugan, T. (2014). Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data. Applied Soft Computing, 19, 134-146.
• 39. Pakrashi, A., & Chaudhuri, B. B. (2016). A Kalman filtering induced heuristic optimization based partitional data clustering. Information Sciences, 369, 704-717.
• 40. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
• 41. Nanda, S. J., & Panda, G. (2014). A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm and Evolutionary computation, 16, 1-18.
• 42. Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
• 43. Kao, Y. T., Zahara, E., & Kao, I. W. (2008). A hybridized approach to data clustering. Expert Systems with Applications, 34(3), 1754-1762.
• 44. Niknam, T., Firouzi, B. B., & Nayeripour, M. (2008, June). An efficient hybrid evolutionary algorithm for cluster analysis. In World Applied Sciences Journal.
• 45. Rana, S., Jasola, S., & Kumar, R. (2013). A boundary restricted adaptive particle swarm optimization for data clustering. International journal of machine learning and cybernetics, 4(4), 391-400.
• 46. Ahmadyfard, A., & Modares, H. (2008, August). Combining PSO and k-means to enhance data clustering. In Telecommunications, 2008. IST 2008. International Symposium on (pp. 688-691). IEEE.
• 47. X. Cui, T.E. Potok, Document Clustering Analysis Based on Hybrid PSO+k-means Algorithm, Special Issue (2005) 27–33.
• 48. Van der Merwe, D. W., & Engelbrecht, A. P. (2003, December). Data clustering using particle swarm optimization. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 1, pp. 215-220). IEEE.
• 49. Niknam, T., & Amiri, B. (2010). An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing, 10(1), 183-197.
• 50. Ye, F., & Chen, C. Y. (2005). Alternative KPSO-clustering algorithm.Tamkang J. Sci. Eng.,, 8(2), 165-174.
• 51. Kuo, R. J., & Lin, L. M. (2010). Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decision Support Systems, 49(4), 451-462.
• 52. Kuo, R. J., Syu, Y. J., Chen, Z. Y., & Tien, F. C. (2012). Integration of particle swarm optimization and genetic algorithm for dynamic clustering. Information Sciences, 195, 124-140.
• 53. Mattison, R. (2006). The telco churn management handbook. Lulu. com.
• 54. Özmen, M., 2017. Telekomünikasyon Sektöründe Müşteri Kaybı Yönetimi İçin Meta Sezgisel Tabanlı Karar Destek Sistemi. Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, Kayseri, 121 sy
• 55. Acuna, E., & Rodriguez, C. (2004). The treatment of missing values and its effect on classifier accuracy. Classification, clustering, and data mining applications, 639-647.
• 56. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, (2), 224-227.