Deep and Shallow Model for Insurance Churn Prediction Service

A Survey on Churn Analysis in Various Business Domains

“…The financial and insurance industries also predict customer churn. Zhang, Rong, et al (2017) stressed the need to build churn prediction models and prevent churn, referring to high customer acquisition costs and high customer values in the insurance industry [11]. Chiang, Ding-An, et al (2003) mentioned that customer values were high in the online financial market, and created a churn scenario according to the financial product selection and customers' financial product selection sequence using the Apriori algorithm [35].…”

“…In general, there have been few papers mentioning feature engineering know-how about churn. However, Zhang, Rong, et al (2017) shared useful information when building an algorithm to predict churn from log data [11].…”

“…Zhang, Rong, et al (2017) also used latent feature modification for churn prediction. They classified features into a memorization feature and a generalization feature depending on data attributes [11]. The memorization feature refers to data that is likely to be a latent feature among time series data.…”

“…They summarized the features using the memorization and generalization techniques described in feature modification, and increased churn predictability by combining the deep learning model and the traditional machine learning model. Zhang, Rong, et al (2017) compared the traditional machine learning model and the deep learning model in customer churn prediction problems in the insurance industry [11]. Fig.…”

“…However, these studies are limited to the telecommunications field, and the log data used for the churn analysis do not include time series features, retention and survival, and KPI (Key Performance Indicator) features. There were also papers applied to services using various deep learning model-based churn analysis techniques in terms of computer science [10] [11]. However, these studies are limited to the deep learning algorithm, and lack underlying models and parameter description.…”