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Churn Prediction Using Dynamic RFM-Augmented Node2vec · Study

WorkChurn Prediction Using Dynamic RFM-Augmented Node2vec

Sandra Mitrović2017

Work2017

Churn Prediction Using Dynamic RFM-Augmented Node2vec

Sandra Mitrović · Bart Baesens · Wilfried Lemahieu · Jochen De Weerdt

10 citations·0/0 files stored·Lecture notes in computer science

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Customer churn and segmentation Online Learning and Analytics Advanced Graph Neural Networks

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References

2005Applying data mining to telecom churn management 2008Classifying the segmentation of customer value via RFM model and RS theory 2008Social ties and their relevance to churn in mobile telecom networks 2007Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression 2014Improved churn prediction in telecommunication industry using data mining techniques 2012A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data 2015Telco Churn Prediction with Big Data 1994Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program

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2017Scalable RFM-enriched Representation Learning for Churn Predictionother · 15 shared references 2019tcc2vec: RFM-informed representation learning on call graphs for churn predictionarticle · 13 shared references 2019Churn modeling with probabilistic meta paths-based representation learningarticle · 8 shared references 2021Research Trends in Customer Churn Prediction: A Data Mining Approachother · 7 shared references 2017On the operational efficiency of different feature types for telco Churn predictionarticle · 6 shared references 2023Churn prediction methods based on mutual customer interdependencearticle · 6 shared references

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Year2017

Typeother

Published2017-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations10

Files0 public / 0

ShelfCustomer churn and segmentation

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doi10.1007/978-3-319-71970-2_11

openalex_workW2742583698

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