Sequential one‐step estimator by sub‐sampling for customer churn analysis with massive data sets

Feifei Wang, Danyang Huang, Tianchen Gao, Shuyuan Wu and Hansheng Wang

Journal of the Royal Statistical Society Series C, 2022, vol. 71, issue 5, 1753-1786

Abstract: Customer churn is one of the most important concerns for large companies. Currently, massive data are often encountered in customer churn analysis, which bring new challenges for model computation. To cope with these concerns, sub‐sampling methods are often used to accomplish data analysis tasks of large scale. To cover more informative samples in one sampling round, classic sub‐sampling methods need to compute non‐uniform sampling probabilities for all data points. However, this method creates a huge computational burden for data sets of large scale and therefore, is not applicable in practice. In this study, we propose a sequential one‐step (SOS) estimation method based on repeated sub‐sampling data sets. In the SOS method, data points need to be sampled only with uniform probabilities, and the sampling step is conducted repeatedly. In each sampling step, a new estimate is computed via one‐step updating based on the newly sampled data points. This leads to a sequence of estimates, of which the final SOS estimate is their average. We theoretically show that both the bias and the standard error of the SOS estimator can decrease with increasing sub‐sampling sizes or sub‐sampling times. The finite sample SOS performances are assessed through simulations. Finally, we apply this SOS method to analyse a real large‐scale customer churn data set in a securities company. The results show that the SOS method has good interpretability and prediction power in this real application.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12597

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:71:y:2022:i:5:p:1753-1786

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().