Mobile Customer Behaviour Predictive Analysis for Targeting Netflix Potential Customer

The development of Indonesia's ICT environment has made the mobile video-on-demand (VOD) platform one of the emerging lifestyles. With advanced smartphone technology, mobile phone subscribers able to enjoy high-resolution mobile VOD service with a greater user experience. The purpose of this study is to profile and predict potential customers of one of the VOD platforms, Netflix, for personalizing marketing targets. Using machine learning predictive analytic methodology, customer profile and behavior data are divided into 3 clusters using the K-Means model before tested with several supervised models for getting the best model for each cluster. Feature importance analysis is conducted to support marketing insight for product offering follows up to each targeted potential customer. Significant variables affecting Netflix buyers and non-buyers within 3 different clusters are defined clearly with the number of potential customers that can be targeted as Netflix's future subscribers. The result shows the method can be used by the mobile operator to target potential customers with effective promotional or product offering by personalized marketing approach based on the behavioral pattern and customer needs. It is expected by implementing this methodology, effectivity and accuracy of marketing efforts will be increased compared to the conventional method.