Market Basket Analysis of Basket Data with Demographics: A Case Study in E-Retailing
Ural Gökay Çiçekli, Ph.D.
İnanç Kabasakal, Ph.D.
Businesses overcome with a high degree of competition that necessitates customer-focused strategies in most industries. In a digitalized business environment, the implementation of such strategies often requires the analysis of customer data. Market basket analysis is a well-known method in marketing that examines basket data to discover useful information about customers’ purchase intentions. The analysis has been a playground for data mining researchers that aim to overcome with its practical challenges. Our study extends the conventional basket analysis by incorporating demographic variables along with purchase transactions. With such modification, we provide an example for the extraction of segment-specific rules that relate product-level purchase decisions with gender, location, and age group. For this purpose, we present a case study on monthly basket data obtained from an e-retailer in Turkey. Our findings demonstrate association rules that might guide marketing practitioners who need to discover segment-specific purchase patterns to designate personalized promotions.
Keywords: Association Rule Mining, Data Mining, Demographic Association Rules, Market Basket Analysis
Jel Classification: C01
Kabasakal, İ. (2021). Market Basket Analysis of Basket Data with Demographics: A Case Study in E-Retailing. Alphanumeric Journal, 9(1), 1-12. http://dx.doi.org/10.17093/alphanumeric.752505
- Anderson, J. L., Jolly, L. D., & Fairhurst, A. E. (2007). “Customer relationship management in retailing: A content analysis of retail trade journals”, Journal of Retailing and Consumer Services, 14(6), 394-399.
- Aggarwal, C. C. (2015). Data mining: The Textbook. Springer.
- Agrawal, R., Imieliński, T., Swami A. (1993). “Mining association rules between sets of items in large databases”, In Proceedings of the 1993 ACM SIGMOD international conference on Management of data, Washington, DC, USA, 207-216.
- Aksoy, R. (2008). İnternet Ortamında Pazarlama, Seçkin Yayıncılık, Ankara.
- Bala, P. K. (2008). “Exploring Various Forms of Purchase Dependency in Retail Sale”, In Proceedings of the World Congress on Engineering and Computer Science 2008, San Francisco, USA, 1101-1104.
- Bayardo Jr, R. J., Agrawal, R. (1999). “Mining the most interesting rules”, In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, USA, 145-154.
- Bodapati, A. (2008). “Recommendation Systems with Purchase Data”, Journal of Marketing Research, 45(1), 77-93.
- Bramer, M. (2016). Principles of Data Mining, Third Edition, Springer.
- Chen, Y. L., Chen, J. M., Tung, C. W. (2006). “A Data Mining Approach for Retail Knowledge Discovery with Consideration of the Effect of Shelf-Space Adjacency on Sales”, Decisions Support Systems, 3(42),1503-1520.
- Ching, W. K., Pong, M. K. (2002). Advances in Data Mining and Modeling, World Scientific, 1st Edition, Hong Kong, China.
- Dalal, M. K. (2012). “Automatic Text Classification of Sports Blog Data”, Computing, Communications and Applications Conference, Hong Kong, China, 219-222.
- Dippold, K., & Hruschka, H. (2013a). “Variable selection for market basket analysis”, Computational Statistics, 28(2), 519-539.
- Dippold, K., Hruschka, H. (2013b). “A model of heterogeneous multicategory choice for market basket analysis”, Review of Marketing Science, 11(1), 1-31.
- Emel, G. G., Taşkın, Ç. (2005). “Veri Madenciliğinde Karar Ağaçları ve Bir Satış Analizi Uygulaması”, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 6, 221-239.
- Griva, A., Bardaki, C., Pramatari, K., & Papakiriakopoulos, D. (2018). “Retail business analytics: Customer visit segmentation using market basket data”, Expert Systems with Applications, 100, 1-16.
- Halim, S., Octavia, T., & Alianto, C. (2019). “Designing Facility Layout of an Amusement Arcade using Market Basket Analysis”, Procedia Computer Science, 161, 623-629.
- Han, J., Kamber, M., Pei, J. P. (2012). Data Mining Concepts and Techniques, Morgen Kaufmann Publishing, Third Edition, USA.
- Häubl, G., Trifts, V. (2000). “Consumer Decision Making in Online Shopping Environments: The effects of interactive decision aids”, Marketing Science, 19(1), 4-21.
- Hormozi, A. M., Giles, S. (2004). Data mining: A competitive weapon for banking and retail industries. Information Systems Management, 21(2), 62-71.
- Hudairy, H. (2004). Data mining and decision making support in the governmental sector, Master Thesis, Faculty of Graduate School of the University of Louisville, Kentucky, USA.
- Kabasakal, İ. (2020). Understanding shopping behaviors with category and brand-level market basket analysis. In Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage, Editor: Yurdagül Meral, IGI Global, 242-267.
- Kooti, F., Lerman, K., Aiello, L. M., Grbovic, M., Djuric, N., & Radosavljevic, V. (2016). Portrait of an online shopper: Understanding and predicting consumer behavior. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, USA, 205-214.
- Kotu, V., & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann, USA.
- Kronberger, G., Affenzeller, M. (2011). Market Basket Analysis of Retail Data: Supervised Learning Approach. In Proceedings of the 13th International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, 464-471.
- Linoff, G. S., Berry, M. J. A. (2011). Data Mining Techniques for Marketing, Sales and Customer Relationship Management, Wiley, Third Edition, Canada.
- Marakas, G. M. (2003). Decision Support Systems in the 21st Century, Prentice Hall, Second Edition, USA.
- Osadchiy, T., Poliakov, I., Olivier, P., Rowland, M., & Foster, E. (2019). “Recommender system based on pairwise association rules”, Expert Systems with Applications, 115, 535-542.
- Özçakır, F. C., Çamurcu, A. Y. (2007). “Birliktelik Kuralı Yöntemi İçin Veri Madenciliği Yazılımı Tasarımı ve Uygulaması”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 12, 21-37.
- Savaş, S., Topaloğlu, N., Yılmaz, M. (2012). “Veri Madenciliği ve Türkiye’deki Uygulama Örnekleri”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 21, 1-23.
- Sota, S., Chaudhry, H., Chamaria, A., Chauhan, A. (2018). “Customer relationship management research from 2007 to 2016: An academic literature review”, Journal of Relationship Marketing, 17(4), 277-291.
- Şimşek-Gürsoy, U. T., Kasapoğlu, Ö. A., Atalay, K. (2019). “R Programlama ile Birliktelik Kuralları Analizi: Tüketicilerin İnternet Üzerinden Yaptıkları Alışveriş Verisinin Apriori ve Eclat Algoritmalarıyla İncelenmesi”, Alphanumeric Journal, 7(2), 357-368.
- Solnet, D., Boztug, Y., Dolnicar, S. (2016). “An untapped gold mine? Exploring the potential of market basket analysis to grow hotel revenue”, International Journal of Hospitality Management, 56, 119-125.
- Strauss, J., Frost, R. (2009). E-Marketing, Pearson Education, New Jersey.
- Tsiptsis, K., Chorianopoulos, A. (2009). Data Mining Techniques in CRM: Inside Customer Segmentation, John Wiley & Sons Ltd., Chichester, United Kingdom.
- Vahaplar, A., İnceoğlu, M. M. (2001). “Veri Madenciliği ve Elektronik Ticaret”, VII. Türkiye’de İnternet Konferansı, İstanbul.
- Winer, R. S. (2001). “A framework for customer relationship management”, California management review, 43(4), 89-105.
- Zhang, Y., Pennacchiotti, M. (2013). Predicting purchase behaviors from social media. In Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro Brazil, 1521-1532.
Volume 9, Issue 1, 2021
Received: June 13, 2020
Accepted: Jan. 26, 2021
Published: June 30, 2021
Full Text [552.3 KB]
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
scan QR code to access this article from your mobile device