  In subscription-based businesses, the churn rate refers to the percentage of
customers who discontinue their subscriptions within a given time period.
Particularly, in the mobile games industry, the churn rate is often pronounced
due to the high competition and cost in customer acquisition; therefore, the
process of minimizing the churn rate is crucial. This needs churn prediction,
predicting users who will be churning within a given time period. Accurate
churn prediction can enable the businesses to devise and engage strategic
remediations to maintain a low churn rate. The paper presents our highly
accurate churn prediction method. We designed this method to take into account
each individual user's distinct usage period in churn prediction. As presented
in the paper, this approach was able to achieve 96.6% churn prediction accuracy
on a real game business. In addition, the paper shows that other existing churn
prediction algorithms are improved in prediction accuracy when this method is
applied.
