Developing strategies to retain organizational insurers using a clustering technique: Evidence from the insurance industry

Kambiz Shahroodi, Soroush Avakh Darestani, Samaneh Soltani and Adeleh Eisazadeh Saravani

Technological Forecasting and Social Change, 2024, vol. 201, issue C

Abstract: Formulating strategies to maintain policyholders is one of the main challenges for most insurance companies in Iran. The purpose of this article is to help marketing strategists of insurance companies predict insurees' churn and develop insurees retention strategies. Since the cost of maintaining an insurance policyholder is approximately one-eighth of the cost of attracting new ones, predicting their churn can help insurance companies adopt proper strategies in advance, which will definitely lead to saving marketing costs and maintaining the insurer's portfolio. Accordingly, the main question of this research is how to classify organizational insurees with the help of the clustering technique. This research is conducted in both qualitative and quantitative phases. In the qualitative phase, by conducting a semi-structured interview (interview protocol) with 15 experts in the insurance industry, the influential factors on policyholders' churn are identified. Then, based on the factors identified in the research literature and comparing them with the interview results, eight main factors are finalized. In the quantitative phase, in order to cluster the organizational insurees, 120 samples from the Iran Insurance Company are selected, and k-means is applied for clustering. Organizational insurees are divided into two groups according to the desired indicators. Using the results of clustering, insurees are divided into four groups, and effective marketing strategies are developed for each group. According to the results, the variable “health care insurance price” has the most effective role in separating the clusters at an error level of <0.01, and on the contrary, the variable “liability insurance amount” has the least important role at an error level of <0.978.

Keywords: Influential factors on churn; Insurance industry; Forecasting; Clustering; K-means technique (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162524000131
Full text for ScienceDirect subscribers only

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:eee:tefoso:v:201:y:2024:i:c:s0040162524000131

DOI: 10.1016/j.techfore.2024.123217

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().