Precision Marketing Model using Decision Tree on e-Commerce Case Study Orebae.com

Fadil Indra Sanjaya; Anna Dina Kalifia

doi:10.29207/resti.v7i5.4531

Fadil Indra Sanjaya Universitas Teknologi Yogyakarta
Anna Dina Kalifia Universitas Teknologi Yogyakarta

DOI: https://doi.org/10.29207/resti.v7i5.4531

Keywords: precision marketing, SME, machine learning, customer preference, supervised learning, decision tree

Abstract

The development of the industrial world towards industry 4.0 has resulted in changes in the lifestyle of the wider community in carrying out their activities through digital media, one of which is shopping. This has an impact on the emergence of many business actors in the e-Commerce field, which brings its own challenges to stay alive and face the competition. The demands for innovation in competitive competition are also increasingly diverse with various approaches ranging from technology, social science, management science, and even artificial intelligence. One form of innovation that is widely carried out by e-Commerce today is looking for an ideal and effective form of marketing, where the form of marketing itself is considered less able to accommodate e-Commerce needs. One form of real innovation in finding the ideal and effective marketing is precision marketing. Precision marketing itself is marketing that is carried out by utilizing data where consumers are the center of preference for data collection. In fact, many of the e-commerce companies that were launched were unable to keep up with the competition because they were unable to develop marketing strategies and eventually went bankrupt. Therefore, we need a special way to bridge these problems so that e-Commerce can stay alive, especially for e-Commerce classified as Small and Medium Enterprises (SMEs). This research will focus on developing a precision marketing model in e-Commerce for small businesses, namely orebae.com which can be used as a tool in the development of marketing strategies. This research was carried out using a machine learning approach by adopting a decision tree algorithm. The results of this study showed that the precision marketing model for orebae.com based on customer preferences can be used to increase the number of sales of orebae.com and to reduce marketing costs.

Downloads

Download data is not yet available.

References

Ö. Aydın, A. Ozen, F. N. Gürel, and D. Mhlanga, “THE IMPACTS OF DIGITAL TRANSFORMATION Business environments in Bulgaria and Brazil View project Competitiveness View project,” 2020. [Online]. Available: https://www.researchgate.net/publication/344071634

D. Tanna, D. Raval, and Z. Raval, “Internet Marketing Over Traditional Marketing,” 2014. [Online]. Available: https://www.researchgate.net/publication/267395144

J. Cheng, “An evaluation strategy for commercial precision marketing based on artificial neural network,” Revue d’Intelligence Artificielle, vol. 34, no. 5, pp. 637–644, 2020, doi: 10.18280/ria.340515.

I. Maryani and D. Riana, “Clustering and profiling of customers using RFM for customer relationship management recommendations,” 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017, pp. 2–7, 2017, doi: 10.1109/CITSM.2017.8089258.

Z. Zhu, Y. Zhou, X. Deng, and X. Wang, “A graph-oriented model for hierarchical user interest in precision social marketing,” Electron Commer Res Appl, vol. 35, May 2019, doi: 10.1016/j.elerap.2019.100845.

M. jie Liao, J. Zhang, R. mei Wang, and L. Qi, “Simulation research on online marketing strategies of branded agricultural products based on the difference in opinion leader attitudes,” Information Processing in Agriculture, vol. 8, no. 4, pp. 528–536, Dec. 2021, doi: 10.1016/j.inpa.2020.12.001.

F. A. Shaw, X. Wang, P. L. Mokhtarian, and K. E. Watkins, “Supplementing transportation data sources with targeted marketing data: Applications, integration, and internal validation,” Transp Res Part A Policy Pract, vol. 149, pp. 150–169, Jul. 2021, doi: 10.1016/j.tra.2021.04.021.

J. R. Saura, “Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics,” Journal of Innovation and Knowledge, vol. 6, no. 2, pp. 92–102, Apr. 2021, doi: 10.1016/j.jik.2020.08.001.

L. I. U. Xiao-Yuan, “Agricultural products intelligent marketing technology innovation in big data era,” in Procedia Computer Science, 2021, vol. 183, pp. 648–654. doi: 10.1016/j.procs.2021.02.110.

W. Jun et al., “Evaluation of precision marketing effectiveness of community e-commerce–An AISAS based model,” Sustainable Operations and Computers, vol. 2, no. April, pp. 200–205, 2021, doi: 10.1016/j.susoc.2021.07.007.

Z. You, Y. W. Si, D. Zhang, X. Zeng, S. C. H. Leung, and T. Li, “A decision-making framework for precision marketing,” Expert Syst Appl, vol. 42, no. 7, pp. 3357–3367, 2015, doi: 10.1016/j.eswa.2014.12.022.

G. Zhu, “Precision Retail Marketing Strategy Based on Digital Marketing Model,” Science Journal of Business and Management, vol. 7, no. 1, p. 33, 2019, doi: 10.11648/j.sjbm.20190701.15.

Y. Zheng, “Decision tree algorithm for precision marketing via network channel,” Computer Systems Science and Engineering, vol. 35, no. 4, pp. 293–298, 2020, doi: 10.32604/csse.2020.35.293.

H. Wang, J. Wang, and Z. Zhong, “Research on precision marketing strategy based on cluster analysis algorithm,” Proceedings - 2020 International Conference on E-Commerce and Internet Technology, ECIT 2020, pp. 208–211, 2020, doi: 10.1109/ECIT50008.2020.00054.

S. Budilaksono et al., “Customer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree,” SinkrOn, vol. 6, no. 1, pp. 191–200, 2021, doi: 10.33395/sinkron.v6i1.11225.

Z. Zhu, L. Kong, X. Deng, and B. Tan, “A 2020 perspective on ‘A graph-oriented model for hierarchical user interest in precision social marketing,’” Electron Commer Res Appl, vol. 41, no. February, p. 100962, 2020, doi: 10.1016/j.elerap.2020.100962.

Y. Xiao and F. Ling, “On E-Commerce Precision Marketing Strategy Based on Big Data,” Big Data and Cloud Innovation, vol. 3, no. 1, pp. 36–39, 2019, doi: 10.18063/bdci.v3i1.1149.

B. Zhang and B. Zhang, “Precise marketing of precision marketing value chain process on the H group line based on big data,” Journal of Intelligent and Fuzzy Systems, vol. 35, no. 3, pp. 2837–2845, 2018, doi: 10.3233/JIFS-169637.

H.-D. Wehle, “Machine Learning, Deep Learning, and AI: What’s the Difference?” 2017. [Online]. Available: https://www.researchgate.net/publication/318900216

N. Kappelhof et al., “Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke,” Comput Biol Med, vol. 133, Jun. 2021, doi: 10.1016/j.compbiomed.2021.104414.

D. H. Lee, S. H. Kim, and K. J. Kim, “Multistage MR-CART: Multiresponse optimization in a multistage process using a classification and regression tree method,” Comput Ind Eng, vol. 159, Sep. 2021, doi: 10.1016/j.cie.2021.107513.