pRNN: A Recurrent Neural Network based Approach for Customer Churn Prediction in Telecommunication Sector

Jinlong Hu · Jian Yu Yang · Lei Lei · Minjie Huang · Runchao Zhu · Shoubin Dong

24 citations0/0 files storedIEEE International Conference on Big Data (BigData)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Predicting churning customers in advance allows marketers to retain existing and valuable customers, and to develop a customer churn predicting model is a key issue of customer relationship management in modern marketing. In this paper, a product-based Recurrent Neural Network (pRNN) approach is proposed for customer churn prediction in telecommunication sector. In the proposed model, RNN with long short-term memory units is used to learn sequential patterns from customer data changing over time, and the product operation is introduced before recurrent layer to learn high-order interaction between features. pRNN is applied on a real-world telecommunication dataset; experiment results demonstrate that pRNN significantly outperforms other comparison models.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 24 citations exist upstream.

References

2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2008Customer churn prediction using improved balanced random forests 2012A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models 2017Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors 2015Hidden Markov models for churn prediction 2012Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

Cited by

2020Lifelog Data-Based Prediction Model of Digital Health Care App Customer Churn: Retrospective Observational Study 2019Deep Learning Based Customer Churn Analysis

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.