Abstract
Abstract. In various German cities free-floating e-scooter sharing is an upcoming trend in e-mobility. Trends such as climate change, urbanization, demographic change, amongst others are arising and forces the society to develop new mobility solutions. Contrasting the more scientifically explored car sharing, the usage patterns and behaviors of e-scooter sharing customers still need to be analyzed. This presumably enables a better addressing of customers as well as adaptions of the business model to increase scooter utilization and therefore the profit of the e-scooter providers. The customer journey is digitally traceable from registration to scooter reservation and the ride itself. These data enable to identifies customer needs and motivations. We analyzed a dataset from 2017 to 2019 of an e-scooter sharing provider operating in a big German city. Based on the datasets we propose a customer clustering that identifies three different customer segments, enabling to draw multiple conclusions for the business development and improving the problem-solution fit of the e-scooter sharing model.
Highlights
In our cities nowadays, trends such as climate change, urbanization, demographic change, amongst others are arising
Vehicle free-floating sharing concepts are increasing around the world and in Germany, providing on-demand mobility for the customers while tackling the problem of crowded streets and lacking parking spots in metropolitan areas
Free-floating e-scooter sharing is a recent variation of this idea and can already be found in multiple big German cities ((EMMY, no date), (Stella Stadtwerke Stuttgart, no date), (‘MVV’, no date), (Düsseldorf, no date)) It combines the advantages of vehicle sharing with those of e-mobility and is supposed to reduce air and noise pollution in urban environments
Summary
Trends such as climate change, urbanization, demographic change, amongst others are arising. Vehicle free-floating sharing concepts are increasing around the world and in Germany, providing on-demand mobility for the customers while tackling the problem of crowded streets and lacking parking spots in metropolitan areas. Free-floating e-scooter sharing is a recent variation of this idea and can already be found in multiple big German cities ((EMMY, no date), (Stella Stadtwerke Stuttgart, no date), (‘MVV’, no date), (Düsseldorf, no date)) It combines the advantages of vehicle sharing with those of e-mobility and is supposed to reduce air and noise pollution in urban environments. We investigated the customer profiles and used data mining to cluster to build customer segmentation This enables the service provider to target a specific customer segment and develop new features to improve the value of the service for the specific customer segment. We followed the cross industry standard process for data mining model (CRISPDM Model) (Chapman et al, 2000; IBM, 2012) the following research question has been formulated: How can customers of mobility (e-scooter) service be segmented?
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