CHURN TIME PREDICTION ON CASUAL GAME USER
Churn time prediction, or the time when user stops using a service, can be used as an information to plan churn prevention. On casual game X, churn time is defined as when user does not play game for 10 days in a row. To predict churn time, survival analysis which is mainly used to predict event tim...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/43465 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Churn time prediction, or the time when user stops using a service, can be used as an information to plan churn prevention. On casual game X, churn time is defined as when user does not play game for 10 days in a row. To predict churn time, survival analysis which is mainly used to predict event time, is used. This experiment used CRISP-DM as frame-work and users’ activity log as data source. The methods used are statistic method Cox, survival ensemble with conditional inference tree, SSVM, and Cox Neural Network. Experiment shows that the C-Index and IBS of survival ensemble with conditional inference tree have the best value out of the other methods, which is C-Index with 0.899 and IBS with 0.052. The mean of churn time prediction of the best model shows that users tend to stops playing game X after 6 days. From this information, game developer can plan how to prevent churn, such as weekly challenge. |
|---|