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609 KB, PDF document
Research output: Working paper
Research output: Working paper
}
TY - UNPB
T1 - Demand forecasting with user-generated online information
AU - Schaer, Oliver
AU - Kourentzes, Nikolaos
AU - Fildes, Robert Alan
PY - 2018/2/26
Y1 - 2018/2/26
N2 - 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.
AB - 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.
KW - Google Trends
KW - Social Media
KW - Leading indicators
KW - Product life-cycle
KW - search traffic
KW - electronic word-of-mouth
M3 - Working paper
SP - 1
EP - 41
BT - Demand forecasting with user-generated online information
PB - Lancaster University Management School
CY - Lancaster
ER -