ANALYSIS OF MARKETING ACTIVITIES THROUGH GEOGRAPHICAL DECISION SUPPORT SYSTEM: GEOMARKET APPLICATION

Öz

Businesses have had to develop their marketing strategies to compate other companys with the effect of globalization in the world. A geographical marketing approach has emerged where businesses can conduct market research more easly with the use of developing software technologies and geographic information systems. One of the most effective methods of spatial problem solving is using geographic information systems. Thanks to this method, associating and analyzing geographical features with social and economic data has greatly facilitated the market analysis of retail. In this study, the Geomarket application is mentioned, which was developed to provide geographical decision support for marketing activities to sectors serving in various fields. Çankaya, Ankara district was selected as a study area and implemented respectively catchment analysis, demographic analysis, points of interest (POI) analysis, street-based trade volume analysis, and density analysis in case businesses. Retail trade areas are one of the sectors most affected by the Covid-19 pandemic and it is extremely important to develop geographic marketing tools for this sector and visualize them with thematic maps. With the change in shopping habits, it has become necessary for people to see their expenditures on the basis of sector and location, therefore, combining and examining marketing activities with geographical locations has made it even more important. Results show that density of competitors, the average income per household and people’s spending in the selected area with using both demographic data, POI points and traffic density. These results can be the reference of individuals, institutions and organizations in making the decision on the location of the branches.

Anahtar Kelimeler

• Geomarket
• geomarketing
• POI analysis
• demographic analysis

PAZARLAMA AKTİVİTELERİNİN COĞRAFİ KARAR DESTEK SİSTEMİ ÜZERİNDEN ANALİZİ: GEOMARKET UYGULAMASI

Öz

Dünyada etkisini gösteren küreselleşme ile birlikte işletmeler rekabet edebilmek için pazarlama stratejilerini geliştirmek zorunda kalmıştır. Gelişen yazılımlar ve coğrafi bilgi sistemlerinin de kullanılmaya başlanması ile işletmelerin daha kolay pazar araştırması yapabilecekleri coğrafi pazarlama yaklaşımı ortaya çıkmıştır. Mekansal problemlerin çözümünde coğrafi bilgi sistemleri kullanımı en etkili yöntemlerden biridir. Coğrafi bilgi sistemleri kullanımı sayesinde coğrafi özellikleri sosyal ve ekonomik verilerle ilişkilendirip analiz etmek perakende ticaretin pazar analizinde büyük ölçüde kolaylık sağlamıştır. Bu çalışmada çeşitli alanlarda hizmet veren sektörlere pazarlama aktiviteleri için coğrafi karar destek sağlaması amacıyla geliştirilen Geomarket uygulamasından bahsedilecektir. Ankara ilinin Çankaya ilçesindeki örnek işletmeler üzerinde sırasıyla etki alanı analizi, demografik analizler, kendi işletmelerinin yanında rakip işletmelerin de görülebileceği ilgi çekici noktalar (POI) analizi, işletmenin sokak bazlı ticaret hacmi analizi ve yoğunluk analizi yapılmıştır. Covid-19 pandemisinden en çok etkilenen sektörlerden biri olan perakende ticaret alanları için coğrafi pazarlama araçlarının geliştirilebilmesi, bunların tematik haritalarla gösterilmesi son derece önemli bir konudur. Alışveriş alışkanlıklarının değişmesi ile kişilerin yaptıkları harcamaların sektör ve konum bazlı görebilmesini gerektirmiş bu yüzden de pazarlama aktivitelerinin coğrafi konumlar ile birleştirilip incelenmesini daha da önemli hale getirmiştir. Yapılan analizlerde ise hem demografik veriler hem de POI noktaları ve trafik yoğunluğu da kullanılarak; seçili alandaki rakip firma yoğunluğu, kişi sayısı, hane başına düşen ortalama gelir, o alandaki kişilerin hangi alanlarda ne kadarlık harcama yaptığı gibi sonuçlar verilebilecektir. Bu sonuçlar şubelerin yer seçimindeki kararı almasında kişilerin, kurum ve kuruluşların referans noktası olabilecektir.

Anahtar Kelimeler

• Geomarket
• coğrafi pazarlama
• POI noktaları
• demografik analiz

Kaynakça

1. Applebaum, W. (1966). “Methods for Determining Store Trade Areas, Market Penetration, and Potential Sales.” Journal of Marketing Research 3, no. 2, 127–41.
2. Bailey, Trevor C. ve Gatrell, Anthony C. (1995). “Interactive Spatial Data Analysis”, Essex: Addison Wesley Longman, Harlow. Battista C. (1995), Competition in the Food Chain, Business Geographics, 3 (3), 32-34.
3. Baviera-Puig, A., Buitrago-Vera, J. M., Escriba, C., & Clemente, J. S. (2009). Geomarketing: Aplicacio ´nde los sistemas de informacio´n geogra´fica al marketing. Paper presented at the Octava Conferencia Iberoamericana en Sistemas, Ciberne´tica e Informa´tica, Orlando, FL.
4. Beaujeu-Garnier, J., & Delobez, A. (1983). Geographie du commerce.
5. Belyaeva, Z., Krivorotov, V., Kalina, A., and Yerypalov, S. (2016). Competitiveness of Russian regional oil complexes, Competitiveness Review, 26(2), 147 – 165.
6. Benoit, D., & Clarke, G. P. (1997). Assessing GIS for retail location planning. Journal of Retailing and Consumer Services, 4(4), 239–258.
7. Birkin, M., Clarke, G., & Clarke, M. (2010). Refining and Operationalizing Entropy‐Maximizing Models for Business Applications. 商业应用模式下熵最大化模型的应用与改进. Geographical Analysis, 42(4), 422-445.
8. Bozdo, R., Thanasi, M., & Hysi, V. (2013). Shopping Centers, Retail location, and Trade Area: The Case of Shopping Centers in Albania. Journal of Marketing & Management, 4(1).

9. Chasko yrigoyen, C. (2003). Econometría espacial aplicada a la predicciónextrapolación de datos microterritoriales, Ed. Consejería de Economía e Innovación Tecnológica, Comunidad de Madrid.
10. Cliquet, G., & Baray, J. (2020). Location-based Marketing: Geomarketing and Geolocation. John Wiley & Sons.
11. Cui, C., & Han, Z. (2015). Spatial patterns of retail stores using POIs data in Zhengzhou, China. ICSDM 2015 - Proceedings 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 88–92.
12. De Tré, G., Van Britsom, D., Matthé, T., & Bronselaer, A. (2013). Automated cleansing of POI databases. In Quality issues in the management of web information (pp. 55-91). Springer, Berlin, Heidelberg.
13. Dolega, L., Pavlis, M., & Singleton, A. (2016). Estimating attractiveness, hierarchy and catchment area extents for a national set of retail centre agglomerations. Journal of Retailing and Consumer Services, 28, 78-90.
14. Erçoskun, O. Y., & Özüduru, B. (2013). Social Sustainability of Shopping Streets in Ankara. MEGARON / Yıldız Technical University, Faculty of Architecture E-Journal, 8(1), 29–44.
15. Ergun, M., Uyguçgil, H., & Atalik, Ö. (2020). Creating a geodemographic classification model within geo-marketing: The case of Eskişehir province. Bulletin of Geography. Socio-Economic Series, 47(47), 45–61.
16. Fidan, H. (2009). Pazarlama Bilgi Sistemi (Pbs) ve Coğrafi Bilgi Sistemi (Cbs)nin Pazarlamada Kullanımı. Journal of Yasar University, 4(14), 2151–2171.
17. Giovanardi, M., & Lucarelli, A. (2018). Sailing through marketing: A critical assessment of spatiality in marketing literature. Journal of Business Research, 82(September 2017), 149–159.
18. Gündoğdu, G. (2010). Coğrafi Bilgi Teknolojileri Kullanılarak Trafik Kaza Analizi: Adana Örneği, Yayımlanmamış Yüksek Lisans Tezi, Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Anabilim Dalı, Adana: Çukurova Üniversitesi Fen Bilimleri Enstitüsü.
19. Kim, Y., Kim, G., Lee, Y., & Jang, K. (2020). Bandwidth Selection of Kernel Density Estimation for GIS-based Crime Occurrence Map Visualization. International Conference on ICT Convergence, 2020-October, 1705–1708.
20. Krek, A. (2000). Efficient Pricing of Geo-Marketing Internet Services: European vs. American Approach. In Proceedings of The Spatial Information Society: Shaping the Future, 6th EC-GIS Workshop, in Lyon, France.
21. Lin, G., Chen, X., & Liang, Y. (2018). The location of retail stores and street centrality in Guangzhou, China. Applied geography, 100, 12-20.
22. Maguire, D. J., Batty, M., & Goodchild, M. F. (2005). GIS, spatial analysis, and modeling. Esri Press.
23. Melnyk L., & Nyzhnyk L. (2018). Geomarketing Is an Innovative Technology Business. International Scientific Journal “Industry 4.0,” 143, 141–143.
24. Murad, A. A. (2008). Creating a GIS application for defining retail catchment area at Jeddah City. International Journal of Services, Technology and Management, 9(1), 79–92.
25. Nistor, M. M., Nicula, A. S., Dezsi, Ş., Petrea, D., Kamarajugedda, S. A., & Carebia, I. A. (2020). Gis-based kernel analysis for tourism flow mapping. Journal of Settlements and Spatial Planning, 11(2), 137–145.
26. Ramadani, V., Zendeli, D., Gerguri-Rashiti, S., & Dana, L. P. (2018). Impact of geomarketing and location determinants on business development and decision making. Competitiveness Review, 28(1), 98–120.
27. Schüssler, F. (2006), Geomarketing – Anwendungen Geographischer Informationssysteme im Einzelhandel, Tectum Verlag, Marburg.
28. Suhaibah, A. Uznir, U. Rahman, A.A. Anton, F. nad Mioc, D. (2016). 3D Geo-marketing Segmentation: A Higher Spatial Dimension Planning Perspective. International Archives of the Photogrammetry, Remote Sensing ve Spatial Information Sciences, 42.
29. Tkhorikov, B., Kazybayeva, A., Gerasimenko, O., & Zhakypbek, L. (2020). Theoretical and Methodological Approaches and Stages of Formation Concept Geomarketing. 157(Defcs), 261–266.
30. Van Den Bossche, B., Meersman, R., Vanhaverbeke, J., & Schoutteet, A. (2010, June). Maximizing the return on investment for FTTX-rollout through the use of GIS street maps and geomarketing data. In 2010 9th Conference of Telecommunication, Media and Internet (pp. 1-6). IEEE.
31. Widaningrum, D. L. (2015). A GIS-Based Approach for Catchment Area Analysis of Convenience Store. Procedia Computer Science, 72, 511–518.