Big Data in fashion: transforming the retail sector

The potential impact and usefulness of analysing different types of data is rather apparent and obvious in numerically driven fields such as finance or insurance where companies have been early and enthusiastic adopters of Big Data. Although the fashion industry traditionally has relied heavily on intuition and creativity for direction in designing, buying and merchandising, it has also been playing around with Big Data for a few years now, with New Gen Apps (2017) asserting that the fashion industry can use Big Data for a number of different purposes, including market identification, trend analysis, understanding the consumer, converting high ticket purchases, lifting new designers, measuring influencers’ impact and improving cross-selling. While previous research has identified numerous beneficial opportunities related to the application of Big Data, this paper focusses specifically on how Big Data can be exploited by fashion retailers in practice. At a time when the highly volatile economic conditions are threatening the survival of fashion retailers, Big Data can potentially provide a much-needed competitive edge which can improve profitability and the chances of survival

The application of big data in fashion retailing: a narrative review

Big data continues to disrupt the fashion retail industry and has revolutionised traditional business models. Today, both leading fashion brands and new start-ups are using big data analytics to improve business operations and maximise profitability. The current paper aims to take stock of the literature on big data in fashion and concisely summarise the fashion industry’s current position. We uncover five main reasons that are driving the utilisation and application of big data analytics in the fashion industry. These are: 1) trend prediction; 2) waste reduction; 3) consumer experience, consumer engagement and marketing; 4) better quality control and the need for a world with fewer counterfeits; 5) shortening supply chains. We also identify key challenges which must be overcome as the most fashionable industry now seeks to model the fashion market and consumer behaviour with big data

Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends

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

The science of statistics versus data science: What is the future?

The importance and relevance of the discipline of statistics with the merits of the evolving field of data science continues to be debated in academia and industry. Following a narrative literature review with over 100 scholarly and practitioner-oriented publications from statistics and data science, this article generates a pragmatic perspective on the relationships and differences between statistics and data science. Some data scientists argue that statistics is not necessary for data science as statistics delivers simple explanations and data science delivers results. Therefore, this article aims to stimulate debate and discourse among both academics and practitioners in these fields. The findings reveal the need for stakeholders to accept the inherent advantages and disadvantages within the science of statistics and data science. The science of statistics enables data science (aiding its reliability and validity), and data science expands the application of statistics to Big Data. Data scientists should accept the contribution and importance of statistics and statisticians must humbly acknowledge the novel capabilities made possible through data science and support this field of study with their theoretical and pragmatic expertise. Indeed, the emergence of data science does pose a threat to statisticians, but the opportunities for synergies are far greater