Predictive Retention Modeling
Predictive Retention Modeling is a computational analytical framework that distinguishes itself by leveraging historical behavioral and demographic data to forecast the likelihood of individual member retention or churn within a defined period, using statistical and machine learning techniques.
110
Publications
5.2K
Citations
301
Authors
165
Institutions
Publications per year
2017–2026
44
110
1–5 of 110
301
5
h-index
3
h-index
3
h-index
3
h-index
3
h-index
1–5 of 301
165
University of California, San Francisco
San Francisco, United States
5 h-index
14 publications
478 citations

Deakin University
Burwood, Australia
3 h-index
5 publications
108 citations
Universitat de València
Valencia, Spain
3 h-index
15 publications
205 citations
Purdue University West Lafayette
West Lafayette, United States
3 h-index
5 publications
97 citations
RMIT University
Melbourne, Australia
3 h-index
6 publications
218 citations
Arizona State University
Tempe, United States
2 h-index
5 publications
429 citations
University of Pennsylvania
Philadelphia, United States
2 h-index
2 publications
130 citations
Fraunhofer Institute for Intelligent Analysis and Information Systems
Sankt Augustin, Germany
2 h-index
3 publications
94 citations
1–8 of 165
8 publications · 261 citations
8
h-index
CSR · 3 publications · 257 citations
3
h-index
2 publications · 67 citations
2
h-index
2 publications · 185 citations
2
h-index
2 publications · 324 citations
2
h-index
1–5