@inproceedings{DegeleGorrHaasetal.2018,
  author    = {Degele, Jutta and Gorr, Anna and Haas, Katja and Kormann, Dimitri and Krauss, Sascha and Lipinski, Paulina and Tenbih, Muhammet and Koppenh{\"o}fer, Christine and Fauser, Jan and Hertweck, Dieter},
  title     = {Identifying E-scooter sharing customer segments using clustering},
  series = {Conference proceedings ICE/IEEE ITMC : 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) : Stuttgart, 17.06. - 20.06.2018},
  booktitle = {Conference proceedings ICE/IEEE ITMC : 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) : Stuttgart, 17.06. - 20.06.2018},
  publisher = {IEEE},
  address   = {Piscataway, NJ},
  isbn      = {978-1-5386-1469-3},
  doi       = {10.1109/ICE.2018.8436288},
  pages     = {8},
  year      = {2018},
  abstract  = {Free-floating e-scooter sharing is an upcoming trend in mobility, which has been spreading since 2015 in various German cities. Unlike the more scientifically explorend car sharing, the usage patterns and behaviors of e-scooter sharing customers are yet to be analyzed. This presumably discovers better ways to attract customers as well as adaptions of the business model in order to increase scooter utilization and therefore the profit of the e-scooter providers. As most of the customer's journey, from registration to scooter reservation and the ride itself, is digitally traceable, large datasets are available allowing for understanding of customers' needs and motivations. Based on these datasets of an e-scooter provider operating in a big German city we propose a customer clustering that identifies four different customer segments, which enables multiple conclusions to be drawn for business development and improving the problem-solution fit of the e-scooter sharing model.},
  language  = {en}
}