Demand forecasting with user-generated online information |
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Institution: | 1. Leeds University Business School, Leeds, United Kingdom;2. Bangor University, Bangor, United Kingdom;1. Catholic University of the Sacred Heart, Via Necchi 5, 20123 Milan, Italy;2. School of Management, University of Bath, Bath BA2 7AY, United Kingdom;1. Lancaster University, UK;2. University of Bath, UK;3. University of Bradford, UK;1. Northwestern University, Medill School of Journalism, Media, Integrated Marketing Communications, 1845 Sheridan Road, Evanston, IL 60208-2101, United States;2. Cornell University, Samuel Curtis Johnson Graduate School of Management, 452 Sage Hall, 14853 Ithaca, NY, United States |
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Abstract: | 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. |
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Keywords: | Google trends Social media Leading indicators Product life-cycle Search traffic Electronic word-of-mouth |
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