COMPARISON OF DATA MINING TECHNIQUES FOR DIRECT MARKETING CAMPAINGS

Yıl 2014, Cilt: 32 Sayı: 2, 142 - 152, 01.06.2014

Esra Akdeniz Duran Ayça Pamukcu Hazal Bozkurt

Öz

The intensive increase in the competition of marketing campaigns over time reduced the impact of them on customer base. Economic pressures, intense competition in the industry, changing lifestyles of people and developing technology have caused marketing managers to adopt the concept of direct marketing by entering into new pursuits. The campaigns prepared in accordance with this understanding might be improved using a variety of data mining techniques. This study compares the performances of artifical neural networks, logistic regression and decision tree data mining techniques on a direct marketing campaign. The purpose of the study is to determine the best target group involved in the campaign by comparing estimation powers of the methods used for determining target groups. Based on the results of this study, it is revealed that artificial neural networks method is more reliable than decision tree and logistic regression analysis about estimating the likely responders in the campaign. This model can improve the efficiency of campaigns by determining of the main features that affect the success of the campaign, identifying the best target group and managing of resources.

Anahtar Kelimeler

Direct marketing, data mining, decision trees, artificial neural networks.

Kaynakça

• [1] Moro, S., Laureano, R.M.S., Cortez, P., “Using Data Mining for Bank Direct Marketing: An Application of the CRISP – DM Methodology”, Proceedings of the European Simulation and Modeling Conference, ESM’2011, 117-121, Guimarães, Portugal, October, 2011.
• [2] Budak, H.,Erpolat, S., (2012), Kredi risk tahmininde yapay sinir ağları ve lojistik regresyon analizi karşılaştırılması, AJIT‐e: Online Academic Journal of Information Technology, 3 (9), 23-30.
• [3] Das, R., (2010), A comparison of multiple classification methods for diagnosis of Parkinson disease, Expert Systems with Applications, 37 (2010), 1568-1572.
• [4] Khemphila, A., Boonjing, V., “Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients”, International Conference on Computer Information Systems and Industrial Management Applications (CISIM), AGH University of Science and Technology, 193-198, Cracow, Poland, October, 2010.
• [5] SAS, (2007), “Applied Analytics Using SAS® Enterprise Miner™ 5 Course Notes, NC, USA, SAS Institute Inc..
• [6] Berry, M.J.A., Linoff, G.S., (1999), “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management”, John Wiley&Sons, Inc., 3rd Edition, p. 888.
• [7] Nash, E., (2000), “Direct Marketing: Strategy, Planning, Execution”, McGraw Hill Professional, 4th Edition, p. 600.
• [8] Emel, G.G., Taşkın, Ç., (2005), Veri madenciliğinde karar ağaçları ve bir satış analizi uygulaması, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 6 (2), 221-239.
• [9] Tan, P.N., Steinbach, M., Kumar, V., (2013), “Introduction to Data Mining: Pearson New International Edition”, Pearson Higher& Professional EMA, First Edition, p. 736.
• [10] Yalçın, Ö., (2008), “Veri Madenciliği Yöntemleri”, Papatya Yayıncılık Eğitim A.Ş., İstanbul, 2. Edition, p. 216.
• [11] Gujarati, D.N., Porter, D.C., (2012), “Temel Ekonometri” (G.G. Şenesen, Ü. Şenesen, Çev.), Literatür Yayıncılık, İstanbul, 5th Edition, p. 972.
• [12] Aktaş, C., (2009), Lojistik Regresyon Analizi: Öğrencilerin sigara içme alışkanlıkları üzerine bir uygulama, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 26, 107-121.
• [13] Ling, C.X., Li, C., (1998), Data Mining for Direct Marketing: Problems and Solutions, In The Fourth International Conference on Knowledge Discovery and Data Mining (KDD’98), New York, USA, 73-79.
• [14] Giudici, P., (2003), “Applied Data Mining: Statistical Methods for Business and Industry”, John Wiley&Sons, Inc., First Edition, p. 376.
• [15] Chitra, K., Subashini, B., (2013) , Data Mining Techniques and its Applications in Banking Sector, International Journal of Emerging Technology and Advanced Engineering, 3 (8), 219-226.