Please use this identifier to cite or link to this item:

Evaluation and Optimal Calibration of Purchase Time Recommendation Services

File Size Format  
0187.pdf 535.03 kB Adobe PDF View/Open

Item Summary

Title:Evaluation and Optimal Calibration of Purchase Time Recommendation Services
Authors:Buchwitz, Benjamin
Keywords:Service Analytics
Decision Analytics, Mobile Services, and Service Science
Buying Recommendation
Predictive Analytics
show 2 morePrice Comparison Sites
Threshold Forecasting
show less
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Service Analytics

Please email if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons