SOSYOEKONOMİK YAKLAŞIMLA ZİNCİR PERAKENDE MAĞAZALARININ SEGMENTASYONU

Yıl 2019, Cilt: 41 Sayı: 2, 338 - 363, 06.01.2020

Emrah Bilgiç Özgür Çakır

https://doi.org/10.14780/muiibd.665054

Öz

Günümüzde perakende zincir mağazaların sayısı giderek artmaktadır. Sayıları binlerle ifade edilen bu
zincirler, sayıları milyonlarla ifade edilebilecek müşterilere sosyoekonomik açıdan farklı özelliklerdeki
şehirlerde hatta aynı şehirlerin farklı özelliklerdeki semtlerinde hizmet vermektedirler. Bu bakımdan,
zincir firmalar tarafından özellikle pazarlama süreçleri için kararlar alınırken genel olarak mağazaların
tamamına değil de bölgelere veya belirli mağaza gruplarına yönelik stratejiler geliştirmek gerekmektedir.
Bu çalışmada, zincir mağazalara sahip perakende firmalarının kümeleme analizini kullanarak
sosyoekonomik faktörlere göre mağazalarını nasıl segmentlere ayırabileceği araştırılmaktadır. Bu
amaçla, bir perakendecinin İstanbul’daki 175 mağazasına ait farklı veriler çeşitli kaynaklardan bir
araya getirilmiş ve Ward’ın kümeleme tekniği kullanılarak mağazalar altı segmente ayrılmıştır.
Mağaza segmentasyonu sonucunda elde edilen segmentler incelendiğinde segmentlerin gerçekten
farklı özelliklerde olduğu konum olarak birbirine yakın olan mağazaların bile farklı segmentlerde yer
alabilecekleri tespit edilmiştir.

Anahtar Kelimeler

Mağaza Segmentasyonu, Kümeleme, Ward Algoritması, Zincir Mağazalar

Kaynakça

• AGGARWAL, C. C. (2015). Data mining: The textbook. Switzerland, Springer.
• BADEA, L. M. (2014). “Predicting consumer behavior with artificial neural networks.” Procedia Economics and Finance 15: 238-246.
• BENNETT, P. D. (1995). Dictionary of Marketing Terms, NTC Business Books.
• BERMINGHAM, P., Hernandez, T. ve Clarke, I. (2013). Network Planning and Retail Store Segmentation: A Spatial Clustering Approach. International Journal of Applied Geospatial Research, 4(1): 67-79.
• BIJAK, K. ve Thomas, L. C. (2012). “Does segmentation always improve model performance in credit scoring?” Expert Systems with Applications 39(3): 2433-2442.
• BORNAC, G. (2015). “The Power of Store Clustering.” http://www.manh.com/resources/articles/2015/08/27/power-store-clustering (30.05.2016).
• CEYLAN, H. H. (2013). “Perakende Sektöründe Konjoint ve Kümeleme Analizi ile Fayda Temelli Pazar Bölümlendirme.” Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 20(1): 141-154.
• CLARKE, I., Mackaness, W. ve Ball, B. (2003). “Modelling Intuition in Retail Site Assessment (MIRSA): making sense of retail location using retailers’ intuitive judgements as a support for decisionmaking.” The International Review of Retail, Distribution and Consumer Research 13(2): 175-193.
• COMPANY, W. P. (2013). “A Simple Approach to Retail Clustering.” http://www.wilsonperumal.com/media/publications/PDFs/Vantage_Point_2013_Issue3.pdf (30.05.2016).
• DE OÑA, R. ve De Oña, J. (2015). “Analysis of transit quality of service through segmentation and classification tree techniques.” Transportmetrica A: Transport Science 11(5): 365-387.
• DÍAZ-PÉREZ, F. M. ve Bethencourt-Cejas, M. (2016). “CHAID algorithm as an appropriate analytical method for tourism market segmentation.” Journal of Destination Marketing & Management.
• DJOKIC, N., Salai, S., Kovac-Znidersic, R., Djokic, I. ve Tomic, G. (2013). “The use of conjoint and cluster analysis for preference-based market segmentation.” Engineering Economics 24(4): 343-355.
• DONOFRIO, T. J. (2009). “Advanced Planning and Optimization Part 3: Store Clustering.” Retail Systems and Services http://risnews.edgl.com/retail-news/Advanced-Planning-and-Optimization-Part-3—Store-Clustering38904 (26.05.2016).
• DOYLE, C. (2011). A dictionary of marketing, Oxford University Press.
• EKİNCİ, Y., Ulengin, F. ve Uray, N. (2014). “Using customer lifetime value to plan optimal promotions.” The Service Industries Journal 34(2): 103-122.
• EVERITT, B. (1974). Cluster analysis 122, Heinemann, London.
• EVERITT, B. ve Hothorn, T. (2011). Cluster analysis. An Introduction to Applied Multivariate Analysis with R, Springer: 163-200.
• GOWER, J. C. (1971). “A general coefficient of similarity and some of its properties.” Biometrics: 857-871.
• GREEN, P. E. ve Rao, V. R. (1971). “Conjoint measurement for quantifying judgmental data.” Journal of Marketing research: 355-363.
• GUO, Y., Denizci Guillet, B., Kucukusta, D. ve Law, R. (2015). “Segmenting Spa Customers Based on Rate Fences Using Conjoint and Cluster Analyses.” Asia Pacific Journal of Tourism Research: 1-19.
• HAWKES, G. F. ve McLaughlin, E. W. (1994). STARS: Segment Targeting at Retail Stores, Department of Agricultural, Resource, and Managerial Economics, Cornell University.
• HENNIG, C. ve Liao, T. F. (2013). “How to find an appropriate clustering for mixed‐type variables with application to socio‐economic stratification.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 62(3): 309-369.
• JACCARD, P. (1908). Nouvelles recherches sur la distribution florale.
• JAIN, A. K. ve Dubes, R. C. (1988). Algorithms for clustering data, Prentice-Hall, Inc.
• KARGARI, M. ve Sepehri, M. M. (2012). “Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs.” Expert Systems with Applications 39(5): 4740-4748.
• KAUFMAN, L. ve Roussew, P. (1990). Finding Groups in Data-An Introduction to Cluster Analysis. A Wiley-Science Publication John Wiley & Sons, Inc.
• KOEHN, N. F. (2001). “Howard Schultz and Starbucks Coffee Company.” Harvard Business School Cases.
• LANCE, G. N. ve Williams, W. T. (1967). “A general theory of classificatory sorting strategies II. Clustering systems.” The computer journal 10(3): 271-277.
• LILIEN, G. L. ve Kotler, P. (1983). Marketing decision making: A model-building approach, Harper & Row New York, NY.
• LIPPMAN, B. W. (2003). “Retail revenue management—Competitive strategy for grocery retailers.” Journal of revenue and pricing management 2(3): 229-233.
• MEHROTRA, A. ve Agarwal, R. (2009). “Classifying customers on the basis of their attitudes towards telemarketing.” Journal of Targeting, Measurement and analysis for Marketing 17(3): 171-193.
• MENDES, A. B. ve Cardoso, M. G. M. S. (2006). “Clustering supermarkets: the role of experts.” Journal of Retailing and Consumer Services 13(4): 231-247.
• MYERS, J. H. (1996). Segmentation and positioning for strategic marketing decisions.
• NAKAHARA, T. ve Yada, K. (2012). “Analyzing consumers’ shopping behavior using RFID data and pattern mining.” Advances in Data Analysis and Classification 6(4): 355-365.
• NAKİP, M. (2006). Pazarlama araştırmaları teknikler ve (SPSS destekli) uygulamalar. Ankara, Seçkin Yayıncılık.
• PAVOINE, S., Vallet, J., Dufour, A. B., Gachet, S. ve Daniel, H. (2009). “On the challenge of treating various types of variables: application for improving the measurement of functional diversity.” Oikos 118(3): 391-402.
• SMITH, W. R. (1956). “Product Differentiation And Market Segmentation As Alternative Marketing Strategies.” Journal of Marketing 21(1): 3-8.
• TİMOR, M. ve Şimşek, U. (2008). “Veri Madenciliğinde Sepet Analizi ile Tüketici Davranışı Modellemesi.” Yönetim, 19 (59): 3-10.
• VOHRA, G. (2011). “Store Clustering.” http://analyticstraining.com/2011/store-clustering/ (26.05.2016).
• WEINSTEIN, A. (2004). Handbook of market segmentation: Strategic targeting for business and technology firms, Psychology Press.
• XU, R. ve Wunsch, D. C. (2009). “Clustering.” from http://site.ebrary.com/id/10257659.
• YAMAN, T. T. ve Çakır, Ö. “ÜNİVERSİTE TERCİHLERİNİN SEÇİME DAYALI KONJOİNT ANALİZİ İLE BELİRLENMESİ.” Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 1(1): 65-84.
• ZAKI, M. J. ve Meira Jr, W. (2014). Data mining and analysis: fundamental concepts and algorithms, Cambridge University Press.
• ZHIYU, Z. ve Congdong, L. (2009). Research on Application to Customer Classification in Management Decision-Making Based on Multivariate Statistics. Information Technology and Applications, 2009. IFITA’09. International Forum on, IEEE.