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Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends

Author

Listed:
  • Emmanuel Sirimal Silva

    (Centre for Fashion Business and Innovation Research, Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK)

  • Hossein Hassani

    (Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran)

  • Dag Øivind Madsen

    (Department of Business, Marketing and Law, School of Business, University of South-Eastern Norway, Bredalsveien 14, 3511 Hønefoss, Norway)

  • Liz Gee

    (Centre for Fashion Business and Innovation Research, Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK)

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.

Suggested Citation

  • Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    • Handle: RePEc:gam:jscscx:v:8:y:2019:i:4:p:111-:d:219992
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    2. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    3. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    4. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    5. Michal Kudlacek, 2021. "Individual vs. Team Sports—What’s the Better Strategy for Meeting PA Guidelines in Children?," IJERPH, MDPI, vol. 18(22), pages 1-10, November.
    6. Levent Bulut, 2018. "Google Trends and the forecasting performance of exchange rate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 303-315, April.
    7. Correa, Alexander, 2021. "Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 105-134, July.
    8. Dag Øivind Madsen, 2019. "The Emergence and Rise of Industry 4.0 Viewed through the Lens of Management Fashion Theory," Administrative Sciences, MDPI, vol. 9(3), pages 1-25, September.
    9. Geri Skenderi & Christian Joppi & Matteo Denitto & Marco Cristani, 2024. "Well googled is half done: Multimodal forecasting of new fashion product sales with image‐based google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1982-1997, September.
    10. Lihki Rubio & Alejandro J. Gutiérrez-Rodríguez & Manuel G. Forero, 2021. "EBITDA Index Prediction Using Exponential Smoothing and ARIMA Model," Mathematics, MDPI, vol. 9(20), pages 1-14, October.
    11. Jasmandeep Kaur & Priyanka Malik & Surabhi Singh, 2024. "Expressing Your Personality Through Apparels: Role of Fashion Involvement and Innovativeness in Purchase Intention," FIIB Business Review, , vol. 13(3), pages 318-330, May.
    12. Rahlff, Helen & Rinne, Ulf & Sonnabend, Hendrik, 2023. "COVID-19, School Closures and (Cyber)Bullying in Germany," IZA Discussion Papers 16650, Institute of Labor Economics (IZA).
    13. Yeong-Hyeon Choi & Kyu-Hye Lee, 2021. "Ethical Consumers’ Awareness of Vegan Materials: Focused on Fake Fur and Fake Leather," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    14. Chorong Youn & Hye Jung Jung, 2021. "Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry," Sustainability, MDPI, vol. 13(7), pages 1-17, March.
    15. Mohammad Reza Farzanegan & Mehdi Feizi & Saeed Malek Sadati, 2020. "Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran," MAGKS Papers on Economics 202017, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    16. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.

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