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Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama

Yıl 2021, Cilt: 10 Sayı: 1, 1 - 8, 15.01.2021
https://doi.org/10.28948/ngumuh.766503

Öz

Son yıllarda kampanya temelli satışlar çok büyük bir hızla artmaya devam etmektedir. Günümüzde birçok sektörde satışlar kampanyaya dayalı olarak gerçekleşmektedir. Bu nedenle kampanya yönetimi oldukça önemli bir konu haline gelmiştir. Literatürde kampanya planlaması ve yönetimi konusunda detaylı bir çalışma bulunmamaktadır. Bu makalede, bankacılık sektöründe kampanya yönetimi ve müşterilerin kampanyaya yönelik ileriye dönük davranışlarının tahmini için bir model geliştirilmiştir. Bankacılık sektörüne özgü geliştirilen birliktelik analizi yapısı kullanılarak bir Portekiz bankasına ait kampanya verilerinden yaygın öğe kümeleri ve birliktelik kuralları oluşturulmuştur. Elde edilen kurallarla kampanyaya katılan müşterilerin ileriye dönük davranışları tahmin edilmiştir. Ayrıca, müşterilerin davranışlarını etkileyen öznitelikler belirlenmiştir. Deneysel sonuçlar, en çok medeni hal ve kredi durumunun müşteri davranışını etkilediğini göstermiştir. Müşterilerin kampanyaya katılıp katılmayacakları geliştirilen model kullanılarak tahmin edilmiştir. Müşterilerin kampanyaya katılım tahmininde %87 oranında başarı sağlanmıştır.

Kaynakça

  • H. Hippner, W. Leusser and K. D. Wilde, Campaign management of the fourth generation. Proceedings of 42nd Hawaii International Conference on System Sciences, pp. 1-9, Waikoloa, Hawaii, 5-8 January 2009.
  • S. T. U. Huq and V. Ravi, Evolutionary multi-objective optimization framework for mining association rules. arXiv preprint arXiv:2003.09158, 2020.
  • R. Agrawal, T. Imieliński, and A. Swami, Mining association rules between sets of items in large databases. ACM SIGMOD international conference on management of data, pp. 207-216, Washington D.C., USA, June 1993.
  • N. C. Hsieh, An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications, 27 (4), 623-633, 2004. https://doi.org/10.1016/j.eswa.2004.06.007.
  • K. W. Wong, S. Zhou, Q. Yang, and J. M. S. Yeung, Mining customer value: From association rules to direct marketing. Data Mining and Knowledge Discovery, 11 (1), 57-79, 2005. https://doi.org/10.1007 /s10618-005-1355-x.
  • M. C. Chen, A. L. Chiu, and H. H. Chang, Mining changes in customer behavior in retail marketing. Expert Systems with Applications, 28 (4), 773-781, 2005. https://doi.org/10.1016/j.eswa.2004.12.033.
  • S. Moro, R. Laureano, and P. Cortez, Using data mining for bank direct marketing: An application of the crisp-dm methodology. Proceedings of European Simulation and Modelling Conference-ESM ’2011, pp. 117-121, Guimaraes, Portugal, 24-26 October 2011.
  • J. Dongre, G. L. Prajapati, and S. V. Tokekar, The role of Apriori algorithm for finding the association rules in data mining. Proceedings of 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 657-660, Ghaziabad, India, 7-8 February 2014.
  • Y. Li, P. Murali, N. Shao, and A. Sheopuri, Applying data mining techniques to direct marketing: challenges and solutions. Proceedings of 2015 IEEE International Conference on Data Mining Workshop (ICDMW) ’11, 2015, pp. 319-327, Atlantic City, NJ, USA, 14-17 November 2015.
  • M. Amini, J. Rezaeenour, and E. Hadavandi, A cluster-based data balancing ensemble classifier for response modeling in bank direct marketing. International Journal of Computational Intelligence and Applications, 14 (4), 1550022, 2015. https://doi.org/ 10.1142/S1469026815500224.
  • V. L. Miguéis, A. S. Camanho, and J. Borges, Predicting direct marketing response in banking: comparison of class imbalance methods. Service Business, 11 (4), 831-849, 2017. https://doi.org/ 10.1007/s11628-016-0332-3.
  • T. Parlar, Using data mining techniques for detecting the important features of the bank direct marketing data. International Journal of Economics and Financial Issues, 7 (2), 692, 2017.
  • P. Ładyżyński, K. Żbikowski, and P. Gawrysiak, Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Systems with Applications, 134, 28-35, 2019. https://doi.org/10.1016 /j.eswa.2019.05.020.
  • M. A. Valle, G. A. Ruz, and R. Morrás, Market basket analysis: Complementing association rules with minimum spanning trees. Expert Systems with Applications, 97, 146-162, 2018. https://doi.org/ 10.1016/j.eswa.2017.12.028.
  • G. Agapito, P. H. Guzzi, and M Cannataro, Parallel extraction of association rules from genomics data. Applied Mathematics and Computation, 350, 434-446, 2019. https://doi.org/10.1016/j.amc.2017.09.026.
  • A. Verma, A. Taneja, and A. Arora, Fraud detection and frequent pattern matching in insurance claims using data mining techniques. Tenth International Conference on Contemporary Computing (IC3), pp. 1-7, Noida, India, 10-12 August 2017.
  • K. C. Lekha, and S. Prakasam, Data mining techniques in detecting and predicting cyber crimes in banking sector. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1639-1643, Chennai, India, 1-2 August 2017.
  • C. C. Shen, L. Y. Hu, and Y. H. Hu, Comorbidity study of borderline personality disorder: applying association rule mining to the Taiwan national health insurance research database. BMC Medical Informatics and Decision Making, 17 (1), 8, 2017. https://doi.org/ 10.1186/s12911-016-0405-1.
  • H. Changhai, and H. Shenping, Factors correlation mining on maritime accidents database using association rule learning algorithm. Cluster Computing, 22 (2), 4551-4559, 2019. https://doi.org/ 10.1007/s10586-018-2089-z.
  • R. Agrawal, and R. Srikant, Fast algorithms for mining association rules. Proceedings of 20th int. conf. very large data bases, VLDB, pp. 487-499, Santiago de Chile, Chile, 12-15 September 1994.
  • S. Rathee, and A. Kashyap, Adaptive-Miner: An efficient distributed association rule mining algorithm on Spark. Journal of Big Data, 5 (1), 6, 2018. https://doi.org/10.1186/s40537-018-0112-0.
  • S. Biswas, N. Biswas, and K. C. Mondal, Parallel Apriori based distributed association rule mining: A comprehensive survey. Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 202-207, Kolkata, India, 22-23 November 2018.
  • E. Stamoulakatou, A. Gulino, and P. Pinoli, DLA: A distributed, location-based and Apriori-based algorithm for biological sequence pattern mining. IEEE International Conference on Big Data (Big Data), pp. 1121-1126, Seattle, WA, USA, 10-13 December 2018.
  • B. Liu, Web data mining: exploring hyperlinks, contents, and usage data. Leipzig: Springer Science & Business Media, 2007. https://doi.org/10.1007/978-3-540-37882-2
  • H. Yang, C. Rudin, and M. Seltzer, Scalable Bayesian rule lists. Proceedings of International Conference on Machine Learning, pp. 3921-3930, Sydney, Australia, 6-11 August 2017.
  • T. Wang, Multi-value rule sets for interpretable classification with feature-efficient representations. Proceedings of Advances in Neural Information Processing Systems, pp. 10835-10845, Montreal, Canada, 2-8 December 2018.
  • C. Song, and T. Ge, Discovering and managing quantitative association rules. Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp. 2429-2434, San Francisco, California, USA, October 2013.
  • F. Mendoza Palechor, A. Carrascal Oviedo, and E. De la Hoz, Association rules implementation for affinity analysis between elements composing multimedia objects. Journal of Theoretical and Applied Information Technology, 97 (6), 1764-1774, 2019. Available: http://hdl.handle.net/11323/5262.

A model for prediction of customer behavior: A case study for banking sector

Yıl 2021, Cilt: 10 Sayı: 1, 1 - 8, 15.01.2021
https://doi.org/10.28948/ngumuh.766503

Öz

Campaign based sales continue to increase at a very rapid rate in recent years. Today, sales in many sectors are based on the campaign. Therefore, campaign management has become a very important issue. There is no detailed study on campaign planning and management in the literature. In this article, a model is developed for campaign management in the banking sector and prediction of prospective behaviors of customers towards the campaign. Using the association analysis structure developed specifically for the banking sector, frequent itemsets and association rules were created from the campaign data of a Portuguese bank. The prospective behavior of the customers participating in the campaign was estimated with the rules obtained. In addition, attributes that affect the behavior of customers have been identified. Experimental results have shown that marital status and credit status affect customer behavior the most. Using the developed model, a prediction is made on whether the customers will participate in the campaign or not. 87% success was achieved in the prediction of customers' participation in the campaign.

Kaynakça

  • H. Hippner, W. Leusser and K. D. Wilde, Campaign management of the fourth generation. Proceedings of 42nd Hawaii International Conference on System Sciences, pp. 1-9, Waikoloa, Hawaii, 5-8 January 2009.
  • S. T. U. Huq and V. Ravi, Evolutionary multi-objective optimization framework for mining association rules. arXiv preprint arXiv:2003.09158, 2020.
  • R. Agrawal, T. Imieliński, and A. Swami, Mining association rules between sets of items in large databases. ACM SIGMOD international conference on management of data, pp. 207-216, Washington D.C., USA, June 1993.
  • N. C. Hsieh, An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications, 27 (4), 623-633, 2004. https://doi.org/10.1016/j.eswa.2004.06.007.
  • K. W. Wong, S. Zhou, Q. Yang, and J. M. S. Yeung, Mining customer value: From association rules to direct marketing. Data Mining and Knowledge Discovery, 11 (1), 57-79, 2005. https://doi.org/10.1007 /s10618-005-1355-x.
  • M. C. Chen, A. L. Chiu, and H. H. Chang, Mining changes in customer behavior in retail marketing. Expert Systems with Applications, 28 (4), 773-781, 2005. https://doi.org/10.1016/j.eswa.2004.12.033.
  • S. Moro, R. Laureano, and P. Cortez, Using data mining for bank direct marketing: An application of the crisp-dm methodology. Proceedings of European Simulation and Modelling Conference-ESM ’2011, pp. 117-121, Guimaraes, Portugal, 24-26 October 2011.
  • J. Dongre, G. L. Prajapati, and S. V. Tokekar, The role of Apriori algorithm for finding the association rules in data mining. Proceedings of 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 657-660, Ghaziabad, India, 7-8 February 2014.
  • Y. Li, P. Murali, N. Shao, and A. Sheopuri, Applying data mining techniques to direct marketing: challenges and solutions. Proceedings of 2015 IEEE International Conference on Data Mining Workshop (ICDMW) ’11, 2015, pp. 319-327, Atlantic City, NJ, USA, 14-17 November 2015.
  • M. Amini, J. Rezaeenour, and E. Hadavandi, A cluster-based data balancing ensemble classifier for response modeling in bank direct marketing. International Journal of Computational Intelligence and Applications, 14 (4), 1550022, 2015. https://doi.org/ 10.1142/S1469026815500224.
  • V. L. Miguéis, A. S. Camanho, and J. Borges, Predicting direct marketing response in banking: comparison of class imbalance methods. Service Business, 11 (4), 831-849, 2017. https://doi.org/ 10.1007/s11628-016-0332-3.
  • T. Parlar, Using data mining techniques for detecting the important features of the bank direct marketing data. International Journal of Economics and Financial Issues, 7 (2), 692, 2017.
  • P. Ładyżyński, K. Żbikowski, and P. Gawrysiak, Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Systems with Applications, 134, 28-35, 2019. https://doi.org/10.1016 /j.eswa.2019.05.020.
  • M. A. Valle, G. A. Ruz, and R. Morrás, Market basket analysis: Complementing association rules with minimum spanning trees. Expert Systems with Applications, 97, 146-162, 2018. https://doi.org/ 10.1016/j.eswa.2017.12.028.
  • G. Agapito, P. H. Guzzi, and M Cannataro, Parallel extraction of association rules from genomics data. Applied Mathematics and Computation, 350, 434-446, 2019. https://doi.org/10.1016/j.amc.2017.09.026.
  • A. Verma, A. Taneja, and A. Arora, Fraud detection and frequent pattern matching in insurance claims using data mining techniques. Tenth International Conference on Contemporary Computing (IC3), pp. 1-7, Noida, India, 10-12 August 2017.
  • K. C. Lekha, and S. Prakasam, Data mining techniques in detecting and predicting cyber crimes in banking sector. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1639-1643, Chennai, India, 1-2 August 2017.
  • C. C. Shen, L. Y. Hu, and Y. H. Hu, Comorbidity study of borderline personality disorder: applying association rule mining to the Taiwan national health insurance research database. BMC Medical Informatics and Decision Making, 17 (1), 8, 2017. https://doi.org/ 10.1186/s12911-016-0405-1.
  • H. Changhai, and H. Shenping, Factors correlation mining on maritime accidents database using association rule learning algorithm. Cluster Computing, 22 (2), 4551-4559, 2019. https://doi.org/ 10.1007/s10586-018-2089-z.
  • R. Agrawal, and R. Srikant, Fast algorithms for mining association rules. Proceedings of 20th int. conf. very large data bases, VLDB, pp. 487-499, Santiago de Chile, Chile, 12-15 September 1994.
  • S. Rathee, and A. Kashyap, Adaptive-Miner: An efficient distributed association rule mining algorithm on Spark. Journal of Big Data, 5 (1), 6, 2018. https://doi.org/10.1186/s40537-018-0112-0.
  • S. Biswas, N. Biswas, and K. C. Mondal, Parallel Apriori based distributed association rule mining: A comprehensive survey. Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 202-207, Kolkata, India, 22-23 November 2018.
  • E. Stamoulakatou, A. Gulino, and P. Pinoli, DLA: A distributed, location-based and Apriori-based algorithm for biological sequence pattern mining. IEEE International Conference on Big Data (Big Data), pp. 1121-1126, Seattle, WA, USA, 10-13 December 2018.
  • B. Liu, Web data mining: exploring hyperlinks, contents, and usage data. Leipzig: Springer Science & Business Media, 2007. https://doi.org/10.1007/978-3-540-37882-2
  • H. Yang, C. Rudin, and M. Seltzer, Scalable Bayesian rule lists. Proceedings of International Conference on Machine Learning, pp. 3921-3930, Sydney, Australia, 6-11 August 2017.
  • T. Wang, Multi-value rule sets for interpretable classification with feature-efficient representations. Proceedings of Advances in Neural Information Processing Systems, pp. 10835-10845, Montreal, Canada, 2-8 December 2018.
  • C. Song, and T. Ge, Discovering and managing quantitative association rules. Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp. 2429-2434, San Francisco, California, USA, October 2013.
  • F. Mendoza Palechor, A. Carrascal Oviedo, and E. De la Hoz, Association rules implementation for affinity analysis between elements composing multimedia objects. Journal of Theoretical and Applied Information Technology, 97 (6), 1764-1774, 2019. Available: http://hdl.handle.net/11323/5262.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Bilgisayar Mühendisliği
Yazarlar

Kevser Özdem 0000-0002-6695-200X

M. Ali Akcayol 0000-0002-6615-1237

Yayımlanma Tarihi 15 Ocak 2021
Gönderilme Tarihi 8 Temmuz 2020
Kabul Tarihi 11 Kasım 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 10 Sayı: 1

Kaynak Göster

APA Özdem, K., & Akcayol, M. A. (2021). Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 1-8. https://doi.org/10.28948/ngumuh.766503
AMA Özdem K, Akcayol MA. Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama. NÖHÜ Müh. Bilim. Derg. Ocak 2021;10(1):1-8. doi:10.28948/ngumuh.766503
Chicago Özdem, Kevser, ve M. Ali Akcayol. “Müşteri davranış Tahmini için Bir Model: Bankacılık sektörü için Uygulama”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10, sy. 1 (Ocak 2021): 1-8. https://doi.org/10.28948/ngumuh.766503.
EndNote Özdem K, Akcayol MA (01 Ocak 2021) Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10 1 1–8.
IEEE K. Özdem ve M. A. Akcayol, “Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama”, NÖHÜ Müh. Bilim. Derg., c. 10, sy. 1, ss. 1–8, 2021, doi: 10.28948/ngumuh.766503.
ISNAD Özdem, Kevser - Akcayol, M. Ali. “Müşteri davranış Tahmini için Bir Model: Bankacılık sektörü için Uygulama”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10/1 (Ocak 2021), 1-8. https://doi.org/10.28948/ngumuh.766503.
JAMA Özdem K, Akcayol MA. Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama. NÖHÜ Müh. Bilim. Derg. 2021;10:1–8.
MLA Özdem, Kevser ve M. Ali Akcayol. “Müşteri davranış Tahmini için Bir Model: Bankacılık sektörü için Uygulama”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 10, sy. 1, 2021, ss. 1-8, doi:10.28948/ngumuh.766503.
Vancouver Özdem K, Akcayol MA. Müşteri davranış tahmini için bir model: Bankacılık sektörü için uygulama. NÖHÜ Müh. Bilim. Derg. 2021;10(1):1-8.

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