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 effort will
be increased compared to the conventional method