CUSTOMER'S RESPONSES TOWARDS  IN-VEHICLE COUPON RECOMMENDATION  AN IMPLEMENTATION OF BUSINESS ANALYTICS CONCEPT

Genesis Sembiring Depari; Efin Shu; Cut Alya Fachriza; Joceline Chow; Jerica Wijaya; Ryanto Winata

Authors

Genesis Sembiring Depari Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan Medan
Efin Shu Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan Medan
Cut Alya Fachriza Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan Medan
Joceline Chow Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan Medan
Jerica Wijaya Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan Medan
Ryanto Winata Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan Medan

Keywords:

Customer's response, In-vehicle coupon recommendation, Business analytics

Abstract

Marketers are constantly searching for innovative tactics to increase sales performance. One of the most popular methods is through offering coupons to potential customers. However, selecting the most potential customers is not an easy task. Customer selection and segmentation become urgently important for business. To tackle these problems, an application of business analytics method is introduced. Besides, 3 machine learning algorithms such as random forest, naive bayes, and decision tree were utilized in predicting the likelihood of coupon to be accepted by users. Eventually, Random forest was found as the most accurate algorithm with the highest prediction accuracy

Downloads

Download data is not yet available.

References

. Arben Asllani. (2014). Business Analytics with Management Science Models and Methods. Pearson.

. Bagus, I., Peling, A., Arnawan, N., Putu, I., Arthawan, A., & Janardana, I. (n.d.). Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm. In International Journal of Engineering and Emerging Technology (Vol. 2, Issue 1).

. Dou, X. (2020). Online Purchase Behavior Prediction and Analysis Using Ensemble Learning. 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 532–536. https://doi.org/10.1109/ICCCBDA49378.2020.9095554

. Gentile, C., Spiller, N., & Noci, G. (2007). How to Sustain the Customer Experience:: An Overview of Experience Components that Co-create Value With the Customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/J.EMJ.2007.08.005

. Hair, J. F. (2007). Knowledge creation in marketing: The role of predictive analytics. European Business Review, 19(4), 303–315. https://doi.org/10.1108/09555340710760134

. Kantardzic, Mehmed. (n.d.). Data mining : concepts, models, methods, and algorithms.

. Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70. https://doi.org/10.1016/J.IJINFOMGT.2019.04.003

. Leskovec, J., Rajaraman, A., & Ullman, J. D. (n.d.). Mining of Massive Datasets.

. Madyatmadja, E. D., Jordan, S. I., & Andry, J. F. (2021). Big data analysis using rapidminer studio to predict suicide rate in several countries. ICIC Express Letters, Part B: Applications, 12(8), 757–764. https://doi.org/10.24507/icicelb.12.08.757

. Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1). https://doi.org/10.1186/s40537-014-0007-7

. Nilsson, N. J. (1998). INTRODUCTION TO MACHINE LEARNING AN EARLY DRAFT OF A PROPOSED TEXTBOOK.

. Padmavathi Mahila Viswa Vidyalayam, S. (2009). Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils. In IJCSNS International Journal of Computer Science and Network Security (Vol. 9, Issue 8).

. Rajni, J., & Malaya, D. B. (2015). Predictive Analytics in a Higher Education Context. IT Professional, 17(4), 24–33. https://doi.org/10.1109/MITP.2015.68

. Steven Finlay. (2014). Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods (Business in the Digital Economy).

. UCI Machine Learning Repository: in-vehicle coupon recommendation Data Set. (n.d.). UCI Machine Learning Repository. Retrieved April 7, 2022, from https://archive.ics.uci.edu/ml/datasets/in-vehicle+coupon+recommendation

. Thompson, C. B. (2009). Descriptive Data Analysis. Air Medical Journal, 28(2), 56–59. https://doi.org/10.1016/j.amj.2008.12.001

. Wang, T., Rudin, C., Liu, Y., Klampfl, E., & Macneille, P. (2017). A Bayesian Framework for Learning Rule Sets for Interpretable Classification. In Journal of Machine Learning Research (Vol. 18). http://jmlr.org/papers/v18/16-003.html.

. Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests. Journal of Manufacturing Science and Engineering, 139(7). https://doi.org/10.1115/1.4036350