# Identification of customer groups in the German term life market: a benefit segmentation

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## Abstract

We run a benefit segmentation of 2017 insurance consumers in order to analyze the structure and heterogeneity of the German term life insurance market. The consumers’ preference information has been obtained through a choice-based conjoint (CBC) experiment and a subsequent hierarchical Bayes (HB) estimation routine. Drawing on their part-worth utility profiles, we first construct a diverse cluster ensemble, comprising a total of 1624 hierarchical and *k*-means solutions based on different linkage criterions and sensibly drawn starting points. Then, final group memberships are determined by means of consensus clustering. Our empirical results indicate that the market divides into three segments characterized by substantially different consumer types with distinct demands and needs. While the first group is clearly driven by the premium, the opposite holds true for the brand-loyal group. Additionally, the market is completed by a third segment with in-between preference structures. Hence, both brand insurers and companies with a lower reputation face consumer groups that almost perfectly fit their provider profiles. More specifically, by offering segment-oriented products, an efficient resource allocation is fostered and the basis for long-term business relationships is laid. This is becoming increasingly important, because ongoing regulatory efforts, low interest rates, and market entrances from InsuranceTech start-ups and tech giants aiming to utilize the market’s enormous hidden potential are changing the competitive environment significantly. A consequent alignment of important strategic decisions related to product innovations, pricing, and distribution channels to our identified consumer segments enables incumbents to maintain a stable and sustainable market share and profitability.

## Keywords

Benefit segmentation Term life insurance Consensus clustering## JEL Classification

C38 C83 G22 M31## References

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