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Demand forecasting with user-generated online information

Research output: Working paper

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Demand forecasting with user-generated online information. / Schaer, Oliver; Kourentzes, Nikolaos; Fildes, Robert Alan.

Lancaster : Lancaster University Management School, 2018. p. 1-41.

Research output: Working paper

Harvard

Schaer, O, Kourentzes, N & Fildes, RA 2018 'Demand forecasting with user-generated online information' Lancaster University Management School, Lancaster, pp. 1-41.

APA

Schaer, O., Kourentzes, N., & Fildes, R. A. (2018). Demand forecasting with user-generated online information. (pp. 1-41). Lancaster: Lancaster University Management School.

Vancouver

Schaer O, Kourentzes N, Fildes RA. Demand forecasting with user-generated online information. Lancaster: Lancaster University Management School. 2018 Feb 26, p. 1-41.

Author

Bibtex

@techreport{5a1698f1589d46e69586c33e2b019be9,
title = "Demand forecasting with user-generated online information",
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.",
keywords = "Google Trends, Social Media, Leading indicators, Product life-cycle, search traffic, electronic word-of-mouth",
author = "Oliver Schaer and Nikolaos Kourentzes and Fildes, {Robert Alan}",
year = "2018",
month = "2",
day = "26",
language = "English",
pages = "1--41",
publisher = "Lancaster University Management School",
type = "WorkingPaper",
institution = "Lancaster University Management School",

}

RIS

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 -