PurposeDigital technology is revolutionizing insurance distribution allowing the insurer companies to reach customers via multichannel. The aim of this study is to segment potential customers of life insurance based on their information search, purchasing channels and personal characteristics in the digital environment.Design/methodology/approachThe study uses cross-sectional research survey. In total, 422 questionnaires were collected through a convenience sample of the Romanian population. The data was segmented based on consumer information touchpoints (online vs offline), purchase channel preference (offline by a professional vs online by a standardized platform) and personal characteristics (age, marital status and children).FindingsThe channel segmentation analysis revealed that information channel preferences are the most important clustering variables, followed by purchase channel preferences, marital status, having children and age. Four distinct segments were identified: young fully offliners (23.7%), mature fully offliners (31.5%), committed online searchers (23.2%) and cross-channel onliners (21.6%).Practical implicationsInsurance companies should adapt their communication and distribution strategy based on multichannel segmentation and should focus on digital touchpoints with costumers.Originality/valueFirstly, the paper reveals multichannel and hybrid segmentation for life insurance. Secondly, it extends the already studied retail channels with search engines and companies' websites. Thirdly, it extends the behavioural variables for channel segmentation with technology acceptance behaviour, attitude towards life insurance, knowledge about life insurance, attitude towards personal selling and quality appraisal of online information sources.
The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over 2%, and it is the result of the non-Pearson type auto-correlations in the index. These non-Pearson type correlations can be viewed also as functionally dependent daily volatilities, extending for a finite time interval. A generalized time-window shuffling method is used to show the existence of such auto-correlations. Their characteristic time-scale proves to be smaller (less than 25 trading days) than what was previously believed. It is also found that this characteristic time-scale has decreased with the appearance of program trading in the stock market transactions. Connections with the leverage effect are also established.
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