Presentation information

Oral presentation

General Session » [General Session] 6. Web Mining

[1E2] [General Session] 6. Web Mining

Tue. Jun 5, 2018 3:20 PM - 5:00 PM Room E (4F Queen)

座長:池田 和史(KDDI綜合研究所)

4:00 PM - 4:20 PM

[1E2-03] Churn Prediction using Deep Learning

〇Kunihiro Miyazaki1, Natsuki Murayama2, Yuki Yamamoto1, Fumiaki Ushiyama3, Shohei Ohsawa1, Yutaka Matsuo1 (1. School of Engineering, The University of Tokyo, 2. Dept. of Arts and Science, The University of Tokyo, 3. WealthNavi Inc.)

Keywords:churn prediction, Web mining, Robo-advisor

The number of companies using subscription business model is increasing, and churn prediction is getting a more important task. In existing research, various type of machine learning models have already been used, but churn prediction has to be trained by combining various data such as time series data and non-time series data, which has not been fully studied.

On the other hand, the technique of deep learning is still being developed, and one of its characteristics is that it can learn various data and models from end-to-end.

In this research, we propose a churn prediction model with deep learning using data of WealthNavi inc. which manages the service of Robo-adviser.

Specifically, we propose a method to learn time series data and non-time series data with one model.

In the experiment, the effectiveness of this method was demonstrated by obtaining the result exceeding the accuracy of the classifier of the existing research.