Journals / Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi / 2019 / Cilt: 7 - Sayı: 3

Market Basket Analysis with R programming Language: An Application on consumer purchasing behavior of a Supermarket in Muş

R Programlama Dili İle Pazar Sepet Analizi: Muş İl Merkezindeki Bir Süpermarkette Tüketicilerin Satın Alma Davranışlarının Tespiti Üzerine Bir Uygulama

Pages
89–97
Publication Date
2022-10-11
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)

The results of analyzes indicate that the most powerful purchasing behavior is the rule of: the customers purchased eggs also purchased some fruit or vegetable from the greengrocery.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study applies one of the tools of Data Mining, Association Rules Analysis by using R programming language for a grocery located in the city Mus/Turkey.
📊
Key Findings & Data:Understanding consumers’ preferences and behaviors is very crucial for the companies in terms of constructing optimal production and marketing strategies.
💡
Conclusion & Impact:The results of analyzes indicate that the most powerful purchasing behavior is the rule of: the customers purchased eggs also purchased some fruit or vegetable from the greengrocery.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This study applies one of the tools of Data Mining, Association Rules Analysis by using R programming language for a grocery located in the city Mus/Turkey. Understanding consumers’ preferences and behaviors is very crucial for the companies in terms of constructing optimal production and marketing strategies. In addition to providing useful information to the company, this study will also encourage researcher and retailers to use R language for analyzing data with advanced algorithms. Furthermore, while finding the relationships between the products, detailed product groups are used rather than general product categories. The results of analyzes indicate that the most powerful purchasing behavior is the rule of: the customers purchased eggs also purchased some fruit or vegetable from the greengrocery.

Özet

Bu araştırma, Veri Madenciliği tekniklerinden biri olan Birliktelik Kuralları Analizi’ni, Muş ilindefaaliyet gösteren bir süpermarkete ait verilere R programlama dilini kullanarak uygulamaktadır.Perakende sektöründe tüketicilerin tercihlerini, satın alma davranışlarını anlamak, en uygun üretimve pazarlama stratejileri geliştirebilmek açısından çok önem arz etmektedir. Çalışma, araştırmayakonu olan firmaya faydalı sonuçlar sağlamakla kalmayıp, hem araştırmacıların hem deperakendecilerin sahip oldukları verileri gelişmiş algoritmalarla analiz edebilmeleri için çalışmaboyunca detaylı bir şekilde kodları paylaşılan R programlama dilini kullanmaya teşvik ediciniteliktedir. Ayrıca birçok çalışmadan farklı olarak ürünler arasındaki ilişkiler bulunurken çokgenel ürün gruplarıyla çalışmak yerine daha ayrıntılı ürün gruplarıyla çalışılmıştır. Analizlersonucunda en güçlü satın alma davranışının; yumurta alan müşterilerin manav reyonundan daalışveriş yaptığı kuralı olduğu tespit edilmiştir.

🔖

Cite This Article

BİLGİÇ, E. (2022). Market Basket Analysis with R programming Language: An Application on consumer purchasing behavior of a Supermarket in Muş. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, Cilt: 7 - Sayı: 3, 89–97. https://ajindex.com/articles/439976

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer. Agrawal, R., & Srikant, R. (1994). Fast Algorithms For Mining Association Rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, 487-499).
  2. Aguinis, H., Forcum, L. E., & Joo, H. (2013). Using market basket analysis in management research. Journal of Management, 39(7), 1799-1824.
  3. Akgün, A., & Çizel, B. (2016). Günlük Tur Programları Oluşturmada Veri Madenciliği: A Grubu Seyahat Acentası Örneği. Turar Turizm & Araştırma Dergisi, 6(1), 73-87.
  4. Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P., & Marchetti, M. (2011). CAS-Mine: providing personalized services in context-aware applications by means of generalized rules. Knowledge and information systems, 28(2), 283-310.
  5. Bayardo Jr, R. J., & Agrawal, R. (1999, August). Mining the most interesting rules. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 145-154).
  6. ACM. Berry, M. J. & G. Linoff. (1997). Data Mining Techniques: For Marketing, Sales, And Customer Support: John Wiley & Sons, Inc. Bilgic, E.,& Çakır, Ö. (2018). Comparing Clusterings: A Store Segmentation Application. Erusosbilder, 32(44), 41-57
  7. Budak, V. Ö., Kartal, E., & Gülseçen, S. (2018). Site-içi Aramalar ve Apriori Algoritması Kullanılarak Web Sitesi Ziyaretçilerinin İhtiyaç Tespitine Yönelik Bir Örnek Olay İncelemesi. Bilişim Teknolojileri Dergisi, 11(2), 211-222.
  8. Chiang, W. Y. (2018). Applying Data Mining For Online Crm Marketing Strategy: An Empirical Case Of Coffee Shop İndustry in Taiwan. British Food Journal, 120(3), 665-675.
  9. Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, L. A. (2007). Data Mining: A Knowledge Discovery Approach. Springer Science & Business Media. Ertugrul, I., Oztas, T., Oztas, G. Z., & Ozcil, A. (2016). Shelf Layout With Integrating Data Mining And MultiDimensional Scaling. European Scientific Journal, ESJ, 12(10). Guo, Y., Wang, M., & Li, X. (2017). Application Of An Improved Apriori Algorithm in a Mobile e-Commerce Recommendation System. Industrial Management & Data Systems, 117(2), 287-303.
  10. Gulluoglu, S. S. (2015). Segmenting customers with data mining techniques. Digital Information, Networking, and Wireless Communications (DINWC), 2015, 154-159.
  11. Giudici, P. (2005). Applied Data Mining: Statistical Methods For Business And Industry. John Wiley & Sons. Hahsler, M., & Chelluboina, S. (2011). Visualizing Association Rules: Introduction to the R-extension Package arulesViz. R pr