Journal article
Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective
Journal of service research : JSR, Vol.19(2), pp.123-141
05/2016
Metrics
30 Record Views
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Abstract
Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms' ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques.
Details
- Title
- Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective
- Creators
- Ali Tamaddoni - Deakin UniversityStanislav Stakhovych - Monash UniversityMichael Ewing - Deakin University
- Publication Details
- Journal of service research : JSR, Vol.19(2), pp.123-141
- Publisher
- Sage
- Number of pages
- 19
- Identifiers
- 991013134160502368
- Copyright
- © The Author(s) 2015.
- Academic Unit
- Faculty of Business, Law and Arts
- Language
- English
- Resource Type
- Journal article