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Sunday gleich Funday? Die Analyse und Prognose von Zeiteffekten im Online-Kundenverhalten im Versicherungsbereich mittels Machine Learning
Wolf, Lukas | Galehr-Fritsch, Elisabeth | Steul-Fischer, Martina
Zeitschrift für die gesamte Versicherungswissenschaft, Vol. 113 (2024), Iss. 1 : pp. 105–125
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Lukas Wolf, M.Sc. (Korrespondierender Autor), Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Betriebswirtschaftslehre, insb. Versicherungsmarketing, Lange Gasse 20, 90403 Nürnberg.
Elisabeth Galehr-Fritsch, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Lange Gasse 20, 90403 Nürnberg, Deutschland
Martina Steul-Fischer, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Lange Gasse 20, 90403 Nürnberg, Deutschland
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Abstract
Customers act differently depending on the time of day, day of the week, or season of the year. However, the influence of such temporal factors on customer behavior has hardly been researched so far. This study investigates the extent to which time effects exist in the online behavior of insurance customers (click behavior, insurance purchases, device choice) and whether time series models can predict specific customer behavior based on these time effects. The study uses a comprehensive clickstream dataset of a German insurer, which contains detailed mouse clicks of all website visitors over a period of two years. It shows that significantly fewer customers visit the website or purchase insurances on weekends and public holidays. The day-of-the-week effect is more pronounced when stationary devices are used compared to mobile devices. In addition, the model comparison shows that machine learning models in particular enable accurate predictions about online customer behavior in the insurance sector. A thorough understanding of online customer behavior, coupled with accurate predictions based on customer data and temporal factors, is a crucial element in the optimal planning of marketing strategies and enhancing the customer experience.