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Forecasting Fashion Consumer Behaviour Using Google Trends

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Forecasting Fashion Consumer Behaviour Using Google Trends social sciences $€ £ ¥ Article Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends Emmanuel Sirimal Silva 1,* , Hossein Hassani 2 , Dag Øivind Madsen 3 and Liz Gee 4 1 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 2 Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran; [email protected] 3 Department of Business, Marketing and Law, School of Business, University of South-Eastern Norway, Bredalsveien 14, 3511 Hønefoss, Norway; [email protected] 4 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; [email protected] * Correspondence: [email protected] Received: 22 February 2019; Accepted: 29 March 2019; Published: 4 April 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 1. Introduction The emergence of big data and advances in big data analytics led to the creation of Google Trends (Choi and Varian 2012), a tool and source for analysing big data on web searches across the globe. Given that 70% of luxury purchases are estimated to be influenced by online interactions (D’Arpizio and Levato 2017), Google Trends has the potential to play a pivotal role in the developments of big data in fashion. Fashion consumers are actively using Google, looking for ideas, finding the best designs, and buying with a tap (Boone 2016). In 2016 alone, Google was receiving more than 4 million search queries per minute from 2.4 billion internet users and was processing 20 petabytes of information per day (Wedel and Kannan 2016). Data analytics on such online behaviour lets Google predict the next big fashion trend (Bain 2016) with Google’s Online Retail Monitor indicating that in 2018, fashion has Soc. Sci. 2019, 8, 111; doi:10.3390/socsci8040111 www.mdpi.com/journal/socsci Sustainability 2019, 11, x FOR PEER REVIEW 2 of 24 Soc. Sci. 2019, 8, 111 2 of 23 lets Google predict the next big fashion trend (Bain 2016) with Google’s Online Retail Monitor indicating that in 2018, fashion has seen the highest growth in searches, boosted by overseas shoppers seen(especially the highest from the growth EU) inseeking searches, access boosted to UK by brands overseas online shoppers (Jahshan (especially 2018). Such from trends the EU)are positive seeking accessfor the to fashion UK brands industry online in (Jahshan the UK 2018 which). Such is trendsexperiencing are positive considerable for the fashion uncertainty industry with in theBrexit UK whichlooming is experiencingon the horizon. considerable uncertainty with Brexit looming on the horizon. Google TrendsTrends is is a gooda good example example of howof how big databig da canta becan exploited be exploited and visualised and visualised in a user-friendly in a user- style.friendly The style. term The big term data big itself data struggles itself struggles to find ato universal find a universal definition definition and it can and mean it can different mean different things tothings different to different people people (Marr (Marr2015; 2015;Hassani Hassani and Silva and Silva2015b 2015b).). Nevertheless, Nevertheless, most most researchers researchers agree agree on buildingon building upon upon the threethe three defining defining dimensions dimensions of Big of Data Big (3Vs) Data as(3Vs) introduced as introduced by Laney by(2001 Laney): volume, (2001): varietyvolume, and variety velocity. and velocity. Figure1 below Figure summarises 1 below summaris the 3Vses to the give 3Vs the to reader give the an reader indication an indication on how big on datahow wasbig data initially was thought initially to thought be expanding. to be expanding. Today, the definition Today, the of bigdefinition data has of evolved big data (much has evolved like the size(much of biglike data the size which of hasbig onlydata gottenwhich biggerhas only as predictedgotten bigger by Varian as predicted 2014) and by nowVarian includes 2014) 5Vs,and withnow additionalincludes 5Vs, Vs beingwith additional veracity (which Vs being accounts veracity for (which the quality accounts of the for data) the andquality value of (whichthe data) accounts and value for analytics(which accounts on data) for (Hassani analytics and on Silva data) 2018 (Hassani). and Silva 2018). Figure 1. The 3Vs of big data (Soubra (Soubra 20122012).). It is no secret thatthat GoogleGoogle Trends areare increasinglyincreasingly influencinginfluencing businessbusiness decision-makingdecision-making in a variety of industries (see, for example, YuYu et al. 2019;2019; Siliverstovs and and Wochner Wochner 2018;2018; ZhangZhang et et al. al. 20182018)) given its ability to act as a leading indicator for forecasting key variables of interest. The fashion industry tootoo cancan benefit benefit from from the the exploitation exploitation of Googleof Google Trends Trends for forecastingfor forecasting fashion fashion variables, variables, from predictingfrom predicting future future purchase purchase decisions, decisions, to determining to determining the effectiveness the effectiveness of marketing of marketing campaigns campaigns and forecastingand forecasting online online consumer consumer brand brand engagement. engagement. Moreover, Moreover, there is there a need is fora need more for conclusive more conclusive research whichresearch evaluates which evaluates whether bigwhether data in big the data form in of the Google form of Trends Google can Trends help predict can help actual predict sales actual for fashion sales brands.for fashion Whilst brands. finding Whilst the answerfindingto the this answer problem to this is beyond problem the is scope beyond of our the research, scope of there our research, is reason tothere believe is reason that thisto believe could that be the this case, could since be the evidence case, since suggests evidence that Googlesuggests searches that Google can predictsearches other can typespredict of other economic types activity of economic such as activity real estate such sales as real and estate prices sales (Wu and Brynjolfssonprices (Wu and 2015 Brynjolfsson), exchange rates2015), ( Bulutexchange 2018 rates), UK (Bulut cinema 2018), admissions UK cinema (Hand admissions and Judge (Hand2011), stockand Judge market 2011), volatility stock (Hamidmarket andvolatility Heiden (Hamid 2015), inflationand Heiden expectations 2015), inflation (Guzman ex 2011pectations) and tourist(Guzman arrivals 2011) (Bangwayo-Skeete and tourist arrivals and Skeete(Bangwayo-Skeete 2015). In addition, and Skeete there 2015). could In beaddition, several ther alternatee could research be several avenues alternate which research are waiting avenues to bewhich explored are waiting not only to from be explored a fashion not design, only buying from a and fashion merchandising design, buying perspective, and merchandising but also from Soc. Sci. 2019, 8, 111 3 of 23 a fashion management perspective (see, for example, Madsen 2016), particularly in evaluating the success of marketing and social media campaigns. We subscribe to Gordon’s (2017) view that Google Trends can be a metric for online consumer behaviour, as more than 75% of the world’s internet searches are conducted on Google (Net Market Share 2019). Therefore, we believe that the fashion industry should consider relevant Google Trends as ‘fashion consumer Google Trends’ which according to McDowell(2019) can enable brands to identify consumer patterns and profit from them. Here, it is worthwhile to define Google Trends for the reader. In brief, as one of the largest real time datasets currently available (Rogers 2016), recording Google search data from 2004 to present (Choi and Varian
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