Brand loyalty analysis system using K-Means algorithm

Yıl 2016, Cilt: 1 Sayı: 3, 107 - 126, 17.12.2016

Ayla Saylı Isil Ozturk Merve Ustunel

https://doi.org/10.30931/jetas.287786

Cited By: 7

Öz

The aim of this paper is to implement a brand loyalty analysis system to find out the brand loyalty using data mining techniques. Data are increasing day by day and companies require a need for new techniques and analysis to be able to support their system automatically and intelligently by analyzing large data repositories to obtain useful information. As a specific approach, the study aims to develop a brand loyalty analysis system for the cases of general brand loyalty, item brand loyalty and categorical brand loyalty. We use the data clustering algorithm of K-means for data analysis. Our system is based on the data preparation algorithm and then it constructs the sales tables which contains sale quantity for each product. The case study is done in the stores of Migros Ticaret A.S. Our approach is based on the clustering analysis is used to provide a better knowledge about the role played by each case and emphasizes the role of attributes for the brand loyalty.

Anahtar Kelimeler

Data mining, brand loyalty, clustering, K-means, data preparation algorithm

Kaynakça

• [15] Shuihua Han, Yongjie Ye, Xin Fu and Zhilong Chen, “Category role aided market segmentation approach to convenience store chain category management” Decision Support Systems 57 (2014): 296–308.
• [14] Manish Verma, Mauly Srivastava, Neha Chack, Atul Kumar Diswar, Nidhi Gupta, A Comparative, “Study of Various Clustering Algorithms in Data Mining” International Journal of Engineering Research and Applications (IJERA) 2 (2013): 1379-1384
• [13] Mohammed J. Zaki, Wagner Meira, “Data Mining and Analysis: Fundamental Concepts and Algorithms”, (2014).
• [12] J. Turow, L. McGuigan and E. R. Maris, “Making Data Mining a Natural Part of Life: Physical Retailing, Customer Surveillance and the 21st Century Social Imaginary” European Journal of Cultural Studie 18 (2015) 464-478.
• [11] Gordon S. Linoff and Michael J. A. Berry, “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management”, Third Edition, (2011).
• [10] Ian H. Witten and Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques” Second Edition, (2005).
• [9] C.L. Philip Chen and Chun-Yang Zhang, “Data-intensive applications, challenges, techniques and technologies. ” A survey on Big Data, Information Sciences 275 (2014) 314–347.
• [8] Sofie De Cnudde and David Martens. “A Data Mining Analysis of a Public Service Loyalty Program” Decision Support Systems 73 (2015): 74–84
• [7] K. Mercier, B. Richards and R. Shockley, Analytics: “The Real-World Use of Big Data in Retail” IBM Global Business Services, Business Analytics and Optimization, Executive Report, (2012).
• [6] S.M.S.H. Hosseini, A. Maleki and M.R. Gholamian, “Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty, Expert Systems with Applications 37 (2010) 5259-5264.
• [5] A.K. Jain, “Data clustering: 50 years beyond K-means, Pattern Recognition Letters 31 (2010) 651–666.
• [4] J. Han and M. Kamber, “Data Mining Concepts and Techniques, (2006).
• [3] R. Chinomona and D. Dubihlela, “Does Customer Satisfaction Lead to Customer Trust, Loyalty and Repurchase Intention of Local Store Brands, Mediterranean Journal of Social Sciences, MCSER Publishing, Rome-Italy, 9 (2014).
• [2] Shital H. Bhojani, Dr. Nirav Bhatt, “Data Mining Techniques and Trends – A Review. ” Global Journal for Research Analysis (2016) 252-254.
• [1] R. Chinomona, and M. Sandada, Customer Satisfaction, “Trust and Loyalty as Predictors of Customer Intention to Re-Purchase South African Retailing Industry, Mediterranean.” Journal of Social Sciences, MCSER Publishing, Rome-Italy 14 (2013).