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Demand forecasting with user-generated online information
Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, United Kingdom.
Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, United Kingdom.ORCID iD: 0000-0003-0211-5218
Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, United Kingdom.
2019 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 35, no 1, p. 197-212Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 35, no 1, p. 197-212
Keywords [en]
Electronic word-of-mouth, Google trends, Leading indicators, Product life-cycle, Search traffic, Social media
National Category
Other Mechanical Engineering Other Civil Engineering
Identifiers
URN: urn:nbn:se:his:diva-18239DOI: 10.1016/j.ijforecast.2018.03.005ISI: 000454976000014Scopus ID: 2-s2.0-85047984721OAI: oai:DiVA.org:his-18239DiVA, id: diva2:1398740
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved

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Kourentzes, Nikolaos

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
  • html
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