Early churn prediction with personalized targeting in mobile social games
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Verbraken et al. (2012) and Neslin et al. (2006) proposed formulas for calculating the net profit using churn rate, CAC, CLV, fixed operating cost, and incentive cost.
“Verbraken, Thomas, Wouter Verbeke, and Bart Baesens.(2012) and Neslin, Scott A., et al. (2006) proposed formulas for calculating the net profit using churn rate, CAC, CLV, fixed operating cost, and incentive cost [54] [55].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [55]
The AUC close to 1 indicates that the churn prediction model accurately distinguishes difference between the characteristics of churn customers and non-churn customers.
“Thus, the AUC close to 1 indicates that the churn prediction model accurately distinguishes difference between the characteristics of churn customers and non-churn customers [13] [45] [55] [95].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [55]
Cites
Social network analysis for customer churn prediction
Verbeke, W., Martens, D., & Baesens, B. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2013.09.017 · Cited by 164 · see citing works
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A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108 · see citing works
“(2017) created a model predicting churn in the study on game churn, gave churn prevention incentives by finding out and dividing probable churn customers into A/B groups, and demonstrated actual effects statistically [44].”
Background
- Authors
- Milošević, M., Živić, N., & Andjelković, I.
- Affiliations
- Union University (RS)
- University of Nis (RS)
- University of Belgrade (RS)
- Corresponding author
- Milošević, Miloš
- ORCID
- Milošević, Miloš
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 83
- Pages
- 326-332
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2608554340
- Cited by
- 83
- References
- 1
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 10 · 2023: 5 · 2022: 7 · 2021: 17 · 2020: 13 · 2019: 11 · 2018: 7 · 2017: 1
- FWCI
- 15.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- customer churn prediction, user retention, user activity monitoring, binary classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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