Evaluation and Optimal Calibration of Purchase Time Recommendation Services

dc.contributor.author Buchwitz, Benjamin
dc.date.accessioned 2019-01-02T23:57:55Z
dc.date.available 2019-01-02T23:57:55Z
dc.date.issued 2019-01-08
dc.description.abstract Price Comparison Sites enable customers to make better – more informed, less costly – buying decisions through providing price information and offering buying advice in the form of prediction services. While these services differ to some extent, they are comparable regarding their prediction target and usually monitor every arbitrarily small price decrease. We use a large data set of daily minimum prices for 272 smartphones consisting of 198,560 daily price movements from a Price Comparison Site to show that the standard prediction setting is not optimal. A custom evaluation framework allows the maximization of the achievable savings by altering the calibration of the forecasting service to monitor changes that exceed a certain threshold. Additionally, we show that time series features calculated in a calibration period can be used to obtain precise out of sample estimates of the saving optimal forecasting setting.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.227
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59627
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd 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 Decision Analytics, Mobile Services, and Service Science
dc.subject Buying Recommendation
dc.subject E-Commerce
dc.subject Predictive Analytics
dc.subject Price Comparison Sites
dc.subject Threshold Forecasting
dc.title Evaluation and Optimal Calibration of Purchase Time Recommendation Services
dc.type Conference Paper
dc.type.dcmi Text
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