Cited by 174Customer churn and segmentationCustomer Service Quality and LoyaltyData Mining and Machine Learning Applications

Social network analysis for customer churn prediction

Verbeke, Wouter, Martens, David, Baesens, Bart·2014·Applied Soft Computing

Integrity signals

Registry

Crossref · 10.1016/j.asoc.2013.09.017

Cites

48 of 48 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Filling missing data improved performance instead of dropping them.

    “Nimmagadda, Sravya, Akshay Subramaniam, and Man Long Wong.(2017) argued that filling the missing data improved performance instead of dropping them [58].”

    From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [58]

  • Nimmagadda et al. used log-normalization or quantile normalization to build a prediction model other than 0-1 normalization.

    “Nimmagadda, Sravya, Akshay Subramaniam, and Man Long Wong.(2017) used techniques such as log-normalization or quantile normalization to build a prediction model other than 0-1 normalization [58].”

    From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [58]

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Authors
Verbeke, W., Martens, D., & Baesens, B.
Affiliations
  • Vrije Universiteit Brussel (BE)
  • University of Antwerp (BE)
  • Vlerick Business School (BE)
Corresponding author
Verbeke, Wouter
Venue
Applied Soft Computing
Publisher
Elsevier BV
Published
2014-01
Volume
14
Pages
431-446
ISSN
1568-4946 (ISSN-L)
OpenAlex
W2001706356
Cited by
164
References
49
Citations by year
2026: 14 · 2025: 12 · 2024: 10 · 2023: 9 · 2022: 14 · 2021: 17 · 2020: 10 · 2019: 15 · 2018: 13 · 2017: 24 · 2016: 12 · 2015: 10 · 2014: 4
FWCI
25.73
Type
journal article
Language
en
Open access
closed
Locations
  • Applied Soft Computing (publishedVersion)
  • Lirias (submittedVersion)
  • VUBIR (Vrije Universiteit Brussel) (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
customer churn prediction, social network analysis, relational learning, skewed class distribution, parallel modeling, customer retention, telecommunications customers, call detail records

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