BibTex RIS Kaynak Göster
Yıl 2008, Cilt: 10 Sayı: 3, 74 - 88, 01.09.2008

Öz

Son zamanlarda elde edilen bulgular, internetten yapılan alışveriş hacminin tüm dünyada arttığını göstermektedir. İnternetten alışveriş yapan tüketicilerin bölümlendirilmesi de doğal olarak söz konusu işletmelerin etkin bir şekilde çözmesi gereken önemli bir sorundur. Bu çalışmada, internetten alışveriş yapan tüketicilerin internet ile ilişkili yaşam biçimlerine göre bölümlendirilmesi problemi bütünleşik bir veri madenciliği yaklaşımı ile incelenmektedir. Araştırmada kullanılan bütünleşik veri madenciliği yaklaşımı, Kohonen sinir ağı ve birliktelik kuralı madenciliği yöntemlerini içermektedir. Bu iki yöntemin bütünleşik halde kullanımı ile internetten alışveriş yapan tüketicilerin çeşitli pazar bölümlerine ayrılması amaçlanmıştır. Uluslararası eğilimlere benzer olarak, Türkiye?de de alışveriş internetin en önemli kullanım alanlarından biri olmuştur ve araştırma sanayileşmenin oldukça yüksek olduğu bir bölgede yürütülmüştür. Araştırmada kullanılan çok boyutlu analiz için, Clementine 8.1 adlı bir veri madenciliği yazılımı kullanılmıştır.

Kaynakça

  • Allred, Chad R., Smith, Scott M. and Swinyard, William R. (2006), E-shopping Lovers and Fearful Conservatives: A Market Segmentation Analysis, International Journal of Retail & Distribution Management, 34: 4-5, pp. 308- 333.
  • Angiulli, Fabrizio, Ianni, Giovambattista and Palopoli, Luigi (2004), On the Complexity of Inducing Categorical and Quantitative Association Rules, Theoretical Computer Science, 314:1, pp. 217-249.
  • Bounsaythip, Catherine and Rinta-Runsala, Esa (2001), Overview of Data Mining for Customer Behavior Modeling, Report no. 1. VTT Information Technology, pp. 1-53. Brengman, Malaika, Geuens, Maggie, Weijters, Bert, Smith, Scott M. and
  • Swinyard William R. (2005), Segmenting Internet Shoppers Based on Their Web-usage-related Lifestyle: A Cross-cultural Validation, Journal of Business Research, 58: 1, pp. 79-88.
  • Bloom, Jonathan Z. (2004), Tourist Market Segmentation with Linear and Nonlinear Techniques, Tourism Management, 25: 6, pp. 723-733.
  • Chang, Horng-Jinh, Hung, Lun-Ping, and Ho, Chia-Ling (2007), An Anticipation Model of Potential Customers’ Purchasing Behavior Based on Clustering Analysis and Association Rules Analysis, Expert Systems with Applications, 32: 3, pp. 753-764.
  • Flach P. And Lavrac, N. (2003), Rule Induction, In: Berthold, Michael and Hand, David J. (eds) Intelligent Data Analysis, Springer-Verlag, Berlin Heidelberg, pp. 229-267.
  • Ghosh, Ashish and Nath, Bhabesh (2004), Multi-objective Rule Mining Using Genetic Algorithms, Information Sciences, 163: 1-3, pp. 123 –133.
  • Hand, David. J. (2003), Introduction, In: Berthold, Michael and Hand, David J. (eds) Intelligent Data Analysis, Springer-Verlag, Berlin Heidelberg, pp. 1-15.
  • Hsieh, Nan-Chen (2004), An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers, Expert Systems with Applications, 27: 4, pp. 623-633.
  • Hui, Tak-Kee and Wan, David (2006), Factors Affecting Internet Shopping Behaviour in Singapore: Gender and Educational Issues, International Journal of Consumer Studies, Article in Press. Hung, Chihli and Tsai, Chih-Fong (2006), Market Segmentation Based on Hierarchical Self-organizing Map for Markets of Multimedia on Demand, Expert Systems with Applications, Article in Press.
  • Iglesia, B. De La, Richards, G., Philpott, M.S. and Rayward-Smith, V.J. (2006), The Application and Effectiveness of a Multi-objective Metaheuristic Algorithm for Partial Classification, European Journal of Operational Research, 169: 3, pp. 898-917.
  • Kiang, Melody Y., Hu, Michael.Y. and Fisher, Dorothy M. (2006), An Extended Self-organizing Map Network for Market Segmentation – A Telecommunication Example, Decision Support Systems, 42: 1, pp. 36-47.
  • Kuo, R.J., Ho, L.M. and Hu, C.M. (2002), Integration of Self-organizing Feature Map and K-means Algorithm for Market Segmentation, Computers&OperationsResearch, 29: 11, pp. 1475-1493.
  • Kuo, R.J., An, Y.L., Wang, H.S. and Chung, W.J. (2006), Integration of Selforganizing Feature Maps Neural Network and Genetic K-means Algorithm for Market Segmentation, Expert Systems with Applications, 30: 2, pp. 313-324.
  • Kwan, Irene S.Y., Fong, Joseph and Wong, H.K. (2005), An E-customer Behavior Model with Online Analytical Mining for Internet Marketing Planning, Decision Support Systems, 41: 1, pp. 189-204.
  • Lai, Hsiangchu and Yang, Tzyy-Ching (2000), A Group-based Inference Approach to Customized Marketing on the Web-integrating Clustering and Association Rules Techniques, Proceedings of the 33rd Hawaii International Conference on System Sciences, 6, pp. 1-10.
  • Lee, Sang Chul, Suh, Yung Ho, Kim, Jae Kyeong and Lee, Kyoung Jun (2004), A Cross-national Market Segmentation of Online Game Industry Using SOM, Expert Systems with Applications, 27: 4, pp. 559-570.

Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study

Yıl 2008, Cilt: 10 Sayı: 3, 74 - 88, 01.09.2008

Öz

Recent findings indicate that online shopping is increasing significantly all over the world. Segmentation of online shoppers is an important issue of online firms. This paper handles this issue related to internet-related lifestyle descriptors which are effective in determining segments by an integrated data mining approach. The integrated data mining approach which is used in this study consists of self-organizing map (Kohonen) neural network and association rule mining method which are integrated to identify segments of online shoppers. Similar to the international trends, online shopping has become one of the most noticeable yields of internet in Turkey and the research is conducted in a highly industrialized region. For this multi-dimensional analysis, a visual and a robust data mining software Clementine 8.1 is used for the integrated segmentation task in data mining.

Kaynakça

  • Allred, Chad R., Smith, Scott M. and Swinyard, William R. (2006), E-shopping Lovers and Fearful Conservatives: A Market Segmentation Analysis, International Journal of Retail & Distribution Management, 34: 4-5, pp. 308- 333.
  • Angiulli, Fabrizio, Ianni, Giovambattista and Palopoli, Luigi (2004), On the Complexity of Inducing Categorical and Quantitative Association Rules, Theoretical Computer Science, 314:1, pp. 217-249.
  • Bounsaythip, Catherine and Rinta-Runsala, Esa (2001), Overview of Data Mining for Customer Behavior Modeling, Report no. 1. VTT Information Technology, pp. 1-53. Brengman, Malaika, Geuens, Maggie, Weijters, Bert, Smith, Scott M. and
  • Swinyard William R. (2005), Segmenting Internet Shoppers Based on Their Web-usage-related Lifestyle: A Cross-cultural Validation, Journal of Business Research, 58: 1, pp. 79-88.
  • Bloom, Jonathan Z. (2004), Tourist Market Segmentation with Linear and Nonlinear Techniques, Tourism Management, 25: 6, pp. 723-733.
  • Chang, Horng-Jinh, Hung, Lun-Ping, and Ho, Chia-Ling (2007), An Anticipation Model of Potential Customers’ Purchasing Behavior Based on Clustering Analysis and Association Rules Analysis, Expert Systems with Applications, 32: 3, pp. 753-764.
  • Flach P. And Lavrac, N. (2003), Rule Induction, In: Berthold, Michael and Hand, David J. (eds) Intelligent Data Analysis, Springer-Verlag, Berlin Heidelberg, pp. 229-267.
  • Ghosh, Ashish and Nath, Bhabesh (2004), Multi-objective Rule Mining Using Genetic Algorithms, Information Sciences, 163: 1-3, pp. 123 –133.
  • Hand, David. J. (2003), Introduction, In: Berthold, Michael and Hand, David J. (eds) Intelligent Data Analysis, Springer-Verlag, Berlin Heidelberg, pp. 1-15.
  • Hsieh, Nan-Chen (2004), An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers, Expert Systems with Applications, 27: 4, pp. 623-633.
  • Hui, Tak-Kee and Wan, David (2006), Factors Affecting Internet Shopping Behaviour in Singapore: Gender and Educational Issues, International Journal of Consumer Studies, Article in Press. Hung, Chihli and Tsai, Chih-Fong (2006), Market Segmentation Based on Hierarchical Self-organizing Map for Markets of Multimedia on Demand, Expert Systems with Applications, Article in Press.
  • Iglesia, B. De La, Richards, G., Philpott, M.S. and Rayward-Smith, V.J. (2006), The Application and Effectiveness of a Multi-objective Metaheuristic Algorithm for Partial Classification, European Journal of Operational Research, 169: 3, pp. 898-917.
  • Kiang, Melody Y., Hu, Michael.Y. and Fisher, Dorothy M. (2006), An Extended Self-organizing Map Network for Market Segmentation – A Telecommunication Example, Decision Support Systems, 42: 1, pp. 36-47.
  • Kuo, R.J., Ho, L.M. and Hu, C.M. (2002), Integration of Self-organizing Feature Map and K-means Algorithm for Market Segmentation, Computers&OperationsResearch, 29: 11, pp. 1475-1493.
  • Kuo, R.J., An, Y.L., Wang, H.S. and Chung, W.J. (2006), Integration of Selforganizing Feature Maps Neural Network and Genetic K-means Algorithm for Market Segmentation, Expert Systems with Applications, 30: 2, pp. 313-324.
  • Kwan, Irene S.Y., Fong, Joseph and Wong, H.K. (2005), An E-customer Behavior Model with Online Analytical Mining for Internet Marketing Planning, Decision Support Systems, 41: 1, pp. 189-204.
  • Lai, Hsiangchu and Yang, Tzyy-Ching (2000), A Group-based Inference Approach to Customized Marketing on the Web-integrating Clustering and Association Rules Techniques, Proceedings of the 33rd Hawaii International Conference on System Sciences, 6, pp. 1-10.
  • Lee, Sang Chul, Suh, Yung Ho, Kim, Jae Kyeong and Lee, Kyoung Jun (2004), A Cross-national Market Segmentation of Online Game Industry Using SOM, Expert Systems with Applications, 27: 4, pp. 559-570.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA24GE32HC
Bölüm Makaleler
Yazarlar

Çağatan Taşkın Bu kişi benim

Gül Gökay Emel Bu kişi benim

Yayımlanma Tarihi 1 Eylül 2008
Yayımlandığı Sayı Yıl 2008 Cilt: 10 Sayı: 3

Kaynak Göster

APA Taşkın, Ç., & Emel, G. G. (2008). Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study. ISGUC The Journal of Industrial Relations and Human Resources, 10(3), 74-88.
AMA Taşkın Ç, Emel GG. Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study. isguc. Eylül 2008;10(3):74-88.
Chicago Taşkın, Çağatan, ve Gül Gökay Emel. “Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study”. ISGUC The Journal of Industrial Relations and Human Resources 10, sy. 3 (Eylül 2008): 74-88.
EndNote Taşkın Ç, Emel GG (01 Eylül 2008) Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study. ISGUC The Journal of Industrial Relations and Human Resources 10 3 74–88.
IEEE Ç. Taşkın ve G. G. Emel, “Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study”, isguc, c. 10, sy. 3, ss. 74–88, 2008.
ISNAD Taşkın, Çağatan - Emel, Gül Gökay. “Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study”. ISGUC The Journal of Industrial Relations and Human Resources 10/3 (Eylül 2008), 74-88.
JAMA Taşkın Ç, Emel GG. Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study. isguc. 2008;10:74–88.
MLA Taşkın, Çağatan ve Gül Gökay Emel. “Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study”. ISGUC The Journal of Industrial Relations and Human Resources, c. 10, sy. 3, 2008, ss. 74-88.
Vancouver Taşkın Ç, Emel GG. Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study. isguc. 2008;10(3):74-88.