Employee churn prediction
Integrity signals
Registry
Cites
Statistics
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?
Through such observation, the expected net business value can be increased by predicting customer churn rates and providing possible churn customers with incentives to retain them from leaving.
“And through such observation, the expected net business value can be increased by predicting customer churn rates and providing possible churn customers with incentives to retain them from leaving [16] [23].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [23]
Cites
Counting Your Customers: Who-Are They and What Will They Do Next?
Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987) · Management Science · doi:10.1287/mnsc.33.1.1 · Cited by 662 · see citing works
The Impact of Marketing-Induced Versus Word-of-Mouth Customer Acquisition on Customer Equity Growth
Villanueva, J., Yoo, S., & Hanssens, D. M. (2008) · Journal of Marketing Research · doi:10.1509/jmkr.45.1.48 · Cited by 559 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310 · see citing works
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113290 · Cited by 304 · see citing works
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms
Punnoose, R., & Ajit, P. (2016) · International Journal of Advanced Research in Artificial Intelligence · doi:10.14569/ijarai.2016.050904 · Cited by 244 · see citing works
“Saradhi and Palshikar [18] To compare data mining techniques for predicting employee churn Naïve Bayes, Support Vector Machines, Logistic Regression, Decision Trees and Random Forests Support Vector Machines”
MethodA 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
“(2011) conducted research on churn to reduce retraining costs when employees churned and to prove employee value in the human resources field [50].”
BackgroundMethodFutia, G., & Vetrò, A. (2020) · Information · doi:10.3390/info11020122 · Cited by 94 · see citing works
de la Llave, M. Á., López, F. A., & Angulo, A. (2019) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2018.1531781 · Cited by 21 · see citing works
- Authors
- Saradhi, V. V., & Palshikar, G. K.
- Affiliations
- Tata Consultancy Services (India) (IN)
- Corresponding author
- Saradhi, V. Vijaya
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 38
- Issue
- 3
- Pages
- 1999-2006
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2048244076
- Cited by
- 188
- References
- 2
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 21 · 2023: 26 · 2022: 28 · 2021: 20 · 2020: 11 · 2019: 6 · 2018: 15 · 2017: 11 · 2016: 9 · 2015: 4 · 2014: 3 · 2013: 2 · 2012: 5
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- employee attrition, customer churn, customer retention, machine learning comparison, job satisfaction
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
