Usage Space Sampling for Fringe Customer Identification

dc.contributor.author Ling, Kunxiong
dc.contributor.author Thiele, Jan
dc.contributor.author Setzer, Thomas
dc.date.accessioned 2020-12-24T19:20:28Z
dc.date.available 2020-12-24T19:20:28Z
dc.date.issued 2021-01-05
dc.description.abstract With large numbers of available customers, it is often essential to select representative samples for reasons of computational cost reduction and upstream advanced data analytics. However, for many analytical procedures, the usage behavior observed from a smaller sample of customers must indicate well the fringe of usage and its relation to extreme product loads. Due to the high complexity of technical or service systems, it remains challenging to minimize the number of samples while sufficiently capturing the fringe customers. With the availability of data related to usage behavior, we consider a sampling method to address this problem by analyzing the customer usage space before sampling, then separately sampling fringe and core customers, and weighting the samples afterwards. Experimental results show that the method can identify fringe customers with significantly fewer, yet reproducible samples, while maintaining the distribution representativeness of customer population to a large extend.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.212
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70824
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject convex hull
dc.subject fringe customer
dc.subject sampling
dc.subject service analytics
dc.subject usage space
dc.title Usage Space Sampling for Fringe Customer Identification
prism.startingpage 1748
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