Logo image
Back
Demand forecasting with user-generated online information
Journal article   Open access   Peer reviewed

Demand forecasting with user-generated online information

Oliver Schaer, Nikolaos Kourentzes and Robert Fildes
International journal of forecasting, v 35(1), pp 197-212
01 Jan 2019
DOI: https://doi.org/10.1016/j.ijforecast.2018.03.005
url
https://doi.org/10.1016/j.ijforecast.2018.03.005View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

Business & Economics Economics Management Social Sciences
Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Metrics

7 Record Views
38 citations in Web of Science
44 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Economics
Management
Logo image