Marketing and management in insurance: impact of innovations measures

Abstract

Nowadays insurance industry has huge innovation potential. Several key vectors for developing the concept of insurance tech include machine learning, business analytics, consumer protection rules, Big Data, artificial intelligence, neural networks, blockchain, and telematics. Technological innovations become widespread only when a community that supports them emerges, and COVID-19 has rapidly accelerated the changes that were already in full swing to a greater extent than any other factor. COVID-19 has helped reinforce the story and illustrate the results that technologies achieve on a large scale. Modern marketing and management approaches in insurance are viewed as an activity to optimize and control the insurance company's innovation and marketing activities. It would allow taking a strategically advantageous position in the insurance market. There are two kinds of insurance marketing: structural and commodity. Structural marketing could help to solve the problem of the economic efficiency of the activity of insurance companies. Commodity marketing helps to improve financial activity and, as a result, to increase profitability. This article summarizes the arguments and counterarguments within the scientific discussion on the place and prospects marketing and management in insurance (strategies, functions, principles) in the context of key innovation metrics. The study's primary purpose is to confirm the hypothesis about the functional link between the level of innovative development of the country and key insurance determinants as drivers for transformation in marketing strategies of insurance companies. In this regard, the array of input data is presented in the form of seven independent variables (regressors), six of which denote innovation measures, one is control variable, and five dependent variables (regressands), which identify the insurance sector. The study of the impact of innovation metrics on the insurance sector of the country in the article is carried out in the following logical sequence: 1) the formation of an array of input data; selection of relevant indicators using Principal Component Analysis; 2) formalization of functional relationships between variables by constructing five-panel Multifactor regression models with Random Effects; and 3) interpretation of the obtained results. Seventeen countries of Central and Eastern Europe were selected as the object of the study for the period from 2004 till 2019. The study empirically confirms the above hypothesis, which is evidenced by the following identified dependences. Key insurance determinants depend on innovation fluctuations. The most significant positive influence on the dependent variables is exercised by the Innovations index, Research and development expenditure, and Patent applications by residents. The study results could be helpful for insurance companies that provide new insurance technologies and seek to optimize activities to support innovative development. The main directions of marketing and management in insurance should be considered from two positions applying new technologies in insurance marketing and introducing new insurance products or services.