Demand forecasting with user-generated online information

Schaer, Oliver and Kourentzes, Nikolaos and Fildes, Robert Alan (2018) Demand forecasting with user-generated online information. Working Paper. Lancaster University Management School, Lancaster. (Unpublished)

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Abstract

Recently, there has been substantial research on augmenting aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies report increased accuracy, many exhibit design weaknesses including lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, which may change, as initially, consumers may search for pre-purchase information, but later for after-sales support. In this study, we first review the relevant literature and then attempt to support the key findings using two forecasting case studies. Our findings are in stark contrast to the literature, and we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better than when online information is included. Our research underlines the need for thorough forecast evaluation and argues that online platform data may be of limited use for supporting operational decisions.

Item Type:
Monograph (Working Paper)
Subjects:
ID Code:
123767
Deposited By:
Deposited On:
26 Feb 2018 14:50
Refereed?:
No
Published?:
Unpublished
Last Modified:
21 Sep 2020 23:57