Journals / GSI Journals Serie B: Advancements in Business and Economics / 2022 / Cilt: 5 Sayı: 2

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

PAZARLAMA AKTİVİTELERİNİN COĞRAFİ KARAR DESTEK SİSTEMİ ÜZERİNDEN ANALİZİ: GEOMARKET UYGULAMASI

Pages
1–16
Publication Date
2022-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Businesses have had to develop their marketing strategies to compate other companys with the effect of globalization in the world.
📊
Key Findings & Data: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.
💡
Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Geomarket · coğrafi pazarlama · POI noktaları

Abstract

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.

Özet

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.

🔖

Cite This Article

TÜKEL, E., BEYHAN, H. C., KÜÇÜKPEHLİVAN, M. (2022). ANALYSIS OF MARKETING ACTIVITIES THROUGH GEOGRAPHICAL DECISION SUPPORT SYSTEM: GEOMARKET APPLICATION. GSI Journals Serie B: Advancements in Business and Economics, Cilt: 5 Sayı: 2, 1–16. https://ajindex.com/articles/970176

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  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),
  3. Competition in the Food Chain, Business Geographics, 3 (3), 32-34.
  4. 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. Beaujeu-Garnier, J., & Delobez, A. (1983).
  5. Geographie du commerce. Belyaeva, Z., Krivorotov, V., Kalina, A., and Yerypalov, S. (2016). Competitiveness of Russian regional oil complexes, Competitiveness Review, 26(2), 147 – 165. Benoit, D., & Clarke, G. P. (1997). Assessing GIS for retail location planning. Journal of Retailing and Consumer Services, 4(4), 239–258.
  6. 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.
  7. Bozdo, R., Thanasi, M., & Hysi, V. (2013).
  8. Shopping Centers, Retail location, and Trade Area: The Case of Shopping Centers in Albania. Journal of Marketing & Management, 4(1). Chasko yrigoyen, C. (2003). Econometría espacial aplicada a la predicciónextrapolación de datos microterritoriales, Ed. Consejería de Economía e Innovación Tecnológica, Comunidad de Madrid. Cliquet, G., & Baray, J. (2020). Location-based Marketing: Geomarketing and Geolocation. John Wiley & Sons. 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. 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).
  9. Springer, Berlin, Heidelberg. 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.
  10. 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.