Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends

Social Sciences, 2019, 8(4), 111; DOI/10.3390/socsci8040111

23 Pages Posted: 3 Mar 2020

See all articles by Emmanuel Sirimal Silva

Emmanuel Sirimal Silva

University of the Arts London - Fashion Business School

Hossein Hassani

Organization of the Petroleum Exporting Countries (OPEC)

Dag Øivind Madsen

University of South-Eastern Norway

Liz Gee

University of the Arts London - London College of Fashion

Date Written: April 4, 2019

Abstract

This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry — a British luxury fashion house — as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry.

Keywords: Google Trends; fashion; forecast; neural networks; singular spectrum analysis; big data

Suggested Citation

Silva, Emmanuel Sirimal and Hassani, Hossein and Madsen, Dag Øivind and Gee, Liz, Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends (April 4, 2019). Social Sciences, 2019, 8(4), 111; DOI/10.3390/socsci8040111, Available at SSRN: https://ssrn.com/abstract=3532100

Emmanuel Sirimal Silva (Contact Author)

University of the Arts London - Fashion Business School ( email )

20 John Princes Street
W1G 0BJ
United Kingdom

Hossein Hassani

Organization of the Petroleum Exporting Countries (OPEC) ( email )

Obere Donaustrasse 93
A-1020 Vienna
Austria

Dag Øivind Madsen

University of South-Eastern Norway ( email )

Bredalsveien 14
Hønefoss, Buskerud 3511
Norway

Liz Gee

University of the Arts London - London College of Fashion ( email )

20 John Princes Street
W1G 0BJ
United Kingdom

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