Systematic Literature Review on Solving Personalization Problem in Digital Marketing using Machine Learning and Its Impact

Nowadays, the online shopping cycle has experienced a rapid increase. Customers are spending more time on social media, which leads to them leaving digital footprints on the internet and generating massive amounts of data. With the help of machine learning, the process of gathering and analysing data becomes faster and easier. However, a recent survey shows that most marketing firms lack an effective personalization strategy for reaching their target market. Therefore, the purpose of this study is to find out how machine learning can be used to solve the personalization problem in digital marketing and its impact on future businesses. The authors would like to conduct a Systematic Literature Review (SLR) on machine learning and big data in digital marketing based on previous studies related to this topic. Several previous studies have tried to provide effective ways to improve personalization strategies in digital marketing. These studies show that machine learning can speed up the marketing process with the right target. This is because machine learning can automate, optimize, then collect data, analyse it, and store data from each user. This allows a promotion system that is right on target according to the users' needs. In general, the authors conclude that by using big data, machine learning can help marketing companies to create more effective personalized marketing strategies so that they can be directed to the right consumers. The authors also believe that this topic of personalization should be further researched for future businesses.