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

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.

Keywords

• precision marketing
• sme
• machine learning
• customer preference
• supervised learning
• decision tree