Research Article
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Year 2020, Volume: 9 Issue: 3, 1188 - 1209, 26.09.2020

Abstract

References

  • Caniato, F., Kalchschmidt, M., Ronchi, S., Verganti, R., and G. Zotteri. 2005. Clustering Customers to Forecast Demand. Production Planning & Control 16(1), 32-43.
  • Chang, H.J., Hung, L.P., and C.L. Ho. 2007. An Anticipation Model of Potential Customers’ Purchasing Behavior based on Clustering Analysis and Association Rules Analysis. Expert Systems with Applications 32, 753–764.
  • Sohn, S.Y., and Y. Kim. 2008. Searching Customer Patterns of Mobile Service using Clustering and Quantitative Association Rule. Expert Systems with Applications 34, 1070–1077.
  • Tsai, C.F., and M.Y. Chen. 2010. Variable Selection by Association Rules for Customer Churn Prediction of Multimedia on Demand. Expert Systems with Applications 37, 2006–2015.
  • Chiang, W.Y. 2011. To Mine Association Rules of Customer Values via a Data Mining Procedure with Improved Model: An Empirical Case Study. Expert Systems with Applications 38, 1716–1722.
  • Soysal, Ö.M. 2015. Association Rule Mining with Mostly Associated Sequential Patterns. Expert Systems with Applications 42, 2582–2592.
  • Sahoo, J., Das, A.K., and A. Goswami. 2015. An Efficient Approach for Mining Association Rules from High Utility Item Sets. Expert Systems with Applications 42, 5754-5778.
  • Jooa, J.H., Bangb, S.W., and G.D. Parka. 2016. Implementation of a Recommendation System using Association Rules and Collaborative Filtering. Procedia Computer Science 91, 944-952.
  • Kaur, M., and S. Kang. 2016. Market Basket Analysis: Identify the Changing Trends of Market Data using Association Rule Mining. Procedia Computer Science 85, 78-85.
  • Lee, D., Quadrifoglio, L., Teulada, B.S., and I. Meloni. 2016. Discovering Relationships between Factors of Round-Trip Car Sharing by using Association Rules Approach. Procedia Engineering 161, 1282-1288.
  • Liao, S.H., and H.K. Chang. 2016. A Rough Set-based Association Rule Approach for a Recommendation System for Online Consumers. Information Processing and Management 52, 1142–1160.
  • Najafabadi, M.K., Mahrin, M.N., Chuprat, S., and H.M. Sarkan. 2017. Improving the Accuracy of Collaborative Filtering Recommendations using Clustering and Association Rules Mining on Implicit Data. Computers in Human Behavior 67, 113-128.
  • Pala, M., and B. Saygı. 2004. Gıda Sanayinde Büyük Mağazaların Perakendeci Markalı Ürün Uygulamaları. İTO Istanbul Chamber of Commerce publications 73, 15-47.
  • Zhao, Q., Jin, J., Deng, X., and D. Wang. 2017. Considering Environmental Implications of Distribution Channel Choices: A Comparative Study based on Game Theory. Journal of Cleaner Production 167, 1155-1164.
  • Biçkes, M.D., and M. Kaplan. 2002. Yeni Tüketici Eğilimleri ve Perakendecilik Sektöründeki Gelişmeler. Pazarlama Dünyası 16(6), 124-147.
  • Erkip, F., and B.H. Ozuduru. 2015. Retail Development in Turkey: An Account after two decades of Shopping Malls in the Urban Scene. Progress in Planning 102, 1–33.
  • Okumuş, A. 2005. İndirimli Mağaza ve Süpermarket Müşterileri Arasındaki Farklılıkların Beklenti ve Memnuniyetlerine göre İncelenmesi, İstanbul University Business Administration Journal 34(1), 105-133.
  • Feng, F., Cho, J., Pedrycz, W., Fujita, H., and T. Herawan. 2016. Soft Set based Association Rule Mining. Knowledge-Based Systems 111, 268–282.
  • Telikani, A., and A. Shahbhrami. 2017. Data Sanitization in Association Rule Mining: An Analytical Review. Expert Systems with Applications 000, 1–21.
  • Ozyirmidokuz, E.K., Uyar, K., and M.H. Ozyirmidokuz. 2015. A Data Mining based Approach to a Firm’s Marketing Channel. Procedia Economics and Finance 27, 77-84.
  • Morais, A., Peixoto, H., Coimbra, C., Abelha, A., and J. Machado. 2017. Predicting the Need of Neonatal Resuscitation using Data Mining. Procedia Computer Science 113, 571–576.
  • Sharma, S., Osei-Bryson, K.M., and G.M. Kasper. 2012. Evaluation of an Integrated Knowledge Discovery and Data Mining Process Model. Expert Systems with Applications 39, 11335-11348.
  • Khader, N., Lashier, A., and S.W. Yoon. 2016. Pharmacy Robotic Dispensing and Planogram Analysis using Association Rule Mining with Prescription Data. Expert Systems with Applications 57, 296–310.
  • Kantardzic, M. 2011. Data Mining: Concepts, Models, Methods, and Algorithms. Second Edition, Institute of Electrical and Electronics Engineers, John Wiley & Sons, Inc.
  • Brandao, A., Pereira, E., Portela, F., Santos, M.F., Abelha, A., and J. Machado. 2014. Managing Voluntary Interruption of Pregnancy using Data Mining. Procedia Technology 16, 1297–1306.
  • Szalkai, B., Grolmusz, V.K., and V.I. Grolmusz. 2017. Identifying Combinatorial Biomarkers by Association Rule Mining in the CAMD Alzheimer’s Database. Archives of Gerontology and Geriatrics 73, 300–307.
  • Doostan, M., and B.H. Chowdhury. 2017. Power Distribution System Fault Cause Analysis by using Association Rule Mining. Electric Power Systems Research 152, 140–147.
  • Marban, O., Segovia, J., Menasalvas, E., and C. Fernandez-Baizan. 2009. Toward Data Mining Engineering: A Software Engineering Approach. Information Systems 34(1), 87-107.
  • Wang, J., Li, H., Huang, J., and C. Su. 2016. Association Rules Mining based Analysis of Consequential Alarm Sequences in Chemical Processes. Journal of Loss Prevention in the Process Industries 41, 178-185.

Customer Behavior Analysis by Association Rules Analysis

Year 2020, Volume: 9 Issue: 3, 1188 - 1209, 26.09.2020

Abstract

Emerging technology and the accompanying globalization further strengthen the conditions of competition. At this point, the concept of “difference making-innovation” has become one of the important concepts in today's world. Diversifying businesses can quickly adapt to this development process and take up its place in the development / growth cycle. To making a difference in terms of businesses means to be able to adapt quickly to developing management understanding and to reflect this difference in their business processes. In this paper, we are looking for an answer to two important questions for a retail firm that serves in textile sector in İstanbul/Turkey. Our retail firm has 56 different sales points throughout Istanbul. 18 of them are in the Anatolia and 38 of them are in the European side of İstanbul. The firm produces and sells textile products with different price ranges and different specifications. According to the sales habits of the customers, what products should be sold in the product range of the company and which products should be sold in two branches that we decided? In our study, we have proposed a model that explains customer behavior and makes forward-looking forecasts with the Data Mining application used in Customer Relationship Management to find out the answers to these questions. The model was evaluated by Association Rules Analysis (ARA) based on past sales slips, the results were obtained and future estimates were made.

References

  • Caniato, F., Kalchschmidt, M., Ronchi, S., Verganti, R., and G. Zotteri. 2005. Clustering Customers to Forecast Demand. Production Planning & Control 16(1), 32-43.
  • Chang, H.J., Hung, L.P., and C.L. Ho. 2007. An Anticipation Model of Potential Customers’ Purchasing Behavior based on Clustering Analysis and Association Rules Analysis. Expert Systems with Applications 32, 753–764.
  • Sohn, S.Y., and Y. Kim. 2008. Searching Customer Patterns of Mobile Service using Clustering and Quantitative Association Rule. Expert Systems with Applications 34, 1070–1077.
  • Tsai, C.F., and M.Y. Chen. 2010. Variable Selection by Association Rules for Customer Churn Prediction of Multimedia on Demand. Expert Systems with Applications 37, 2006–2015.
  • Chiang, W.Y. 2011. To Mine Association Rules of Customer Values via a Data Mining Procedure with Improved Model: An Empirical Case Study. Expert Systems with Applications 38, 1716–1722.
  • Soysal, Ö.M. 2015. Association Rule Mining with Mostly Associated Sequential Patterns. Expert Systems with Applications 42, 2582–2592.
  • Sahoo, J., Das, A.K., and A. Goswami. 2015. An Efficient Approach for Mining Association Rules from High Utility Item Sets. Expert Systems with Applications 42, 5754-5778.
  • Jooa, J.H., Bangb, S.W., and G.D. Parka. 2016. Implementation of a Recommendation System using Association Rules and Collaborative Filtering. Procedia Computer Science 91, 944-952.
  • Kaur, M., and S. Kang. 2016. Market Basket Analysis: Identify the Changing Trends of Market Data using Association Rule Mining. Procedia Computer Science 85, 78-85.
  • Lee, D., Quadrifoglio, L., Teulada, B.S., and I. Meloni. 2016. Discovering Relationships between Factors of Round-Trip Car Sharing by using Association Rules Approach. Procedia Engineering 161, 1282-1288.
  • Liao, S.H., and H.K. Chang. 2016. A Rough Set-based Association Rule Approach for a Recommendation System for Online Consumers. Information Processing and Management 52, 1142–1160.
  • Najafabadi, M.K., Mahrin, M.N., Chuprat, S., and H.M. Sarkan. 2017. Improving the Accuracy of Collaborative Filtering Recommendations using Clustering and Association Rules Mining on Implicit Data. Computers in Human Behavior 67, 113-128.
  • Pala, M., and B. Saygı. 2004. Gıda Sanayinde Büyük Mağazaların Perakendeci Markalı Ürün Uygulamaları. İTO Istanbul Chamber of Commerce publications 73, 15-47.
  • Zhao, Q., Jin, J., Deng, X., and D. Wang. 2017. Considering Environmental Implications of Distribution Channel Choices: A Comparative Study based on Game Theory. Journal of Cleaner Production 167, 1155-1164.
  • Biçkes, M.D., and M. Kaplan. 2002. Yeni Tüketici Eğilimleri ve Perakendecilik Sektöründeki Gelişmeler. Pazarlama Dünyası 16(6), 124-147.
  • Erkip, F., and B.H. Ozuduru. 2015. Retail Development in Turkey: An Account after two decades of Shopping Malls in the Urban Scene. Progress in Planning 102, 1–33.
  • Okumuş, A. 2005. İndirimli Mağaza ve Süpermarket Müşterileri Arasındaki Farklılıkların Beklenti ve Memnuniyetlerine göre İncelenmesi, İstanbul University Business Administration Journal 34(1), 105-133.
  • Feng, F., Cho, J., Pedrycz, W., Fujita, H., and T. Herawan. 2016. Soft Set based Association Rule Mining. Knowledge-Based Systems 111, 268–282.
  • Telikani, A., and A. Shahbhrami. 2017. Data Sanitization in Association Rule Mining: An Analytical Review. Expert Systems with Applications 000, 1–21.
  • Ozyirmidokuz, E.K., Uyar, K., and M.H. Ozyirmidokuz. 2015. A Data Mining based Approach to a Firm’s Marketing Channel. Procedia Economics and Finance 27, 77-84.
  • Morais, A., Peixoto, H., Coimbra, C., Abelha, A., and J. Machado. 2017. Predicting the Need of Neonatal Resuscitation using Data Mining. Procedia Computer Science 113, 571–576.
  • Sharma, S., Osei-Bryson, K.M., and G.M. Kasper. 2012. Evaluation of an Integrated Knowledge Discovery and Data Mining Process Model. Expert Systems with Applications 39, 11335-11348.
  • Khader, N., Lashier, A., and S.W. Yoon. 2016. Pharmacy Robotic Dispensing and Planogram Analysis using Association Rule Mining with Prescription Data. Expert Systems with Applications 57, 296–310.
  • Kantardzic, M. 2011. Data Mining: Concepts, Models, Methods, and Algorithms. Second Edition, Institute of Electrical and Electronics Engineers, John Wiley & Sons, Inc.
  • Brandao, A., Pereira, E., Portela, F., Santos, M.F., Abelha, A., and J. Machado. 2014. Managing Voluntary Interruption of Pregnancy using Data Mining. Procedia Technology 16, 1297–1306.
  • Szalkai, B., Grolmusz, V.K., and V.I. Grolmusz. 2017. Identifying Combinatorial Biomarkers by Association Rule Mining in the CAMD Alzheimer’s Database. Archives of Gerontology and Geriatrics 73, 300–307.
  • Doostan, M., and B.H. Chowdhury. 2017. Power Distribution System Fault Cause Analysis by using Association Rule Mining. Electric Power Systems Research 152, 140–147.
  • Marban, O., Segovia, J., Menasalvas, E., and C. Fernandez-Baizan. 2009. Toward Data Mining Engineering: A Software Engineering Approach. Information Systems 34(1), 87-107.
  • Wang, J., Li, H., Huang, J., and C. Su. 2016. Association Rules Mining based Analysis of Consequential Alarm Sequences in Chemical Processes. Journal of Loss Prevention in the Process Industries 41, 178-185.
There are 29 citations in total.

Details

Primary Language English
Journal Section Araştırma Makalesi
Authors

G.nilay Yücenur 0000-0002-2670-6277

Yeşim Yaygan This is me 0000-0002-5082-8119

Hilal Tevge This is me 0000-0001-9605-1723

Gökçe Demir This is me 0000-0002-0264-4980

Publication Date September 26, 2020
Submission Date April 25, 2019
Acceptance Date June 4, 2020
Published in Issue Year 2020 Volume: 9 Issue: 3

Cite

IEEE G. Yücenur, Y. Yaygan, H. Tevge, and G. Demir, “Customer Behavior Analysis by Association Rules Analysis”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 3, pp. 1188–1209, 2020.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS