The Impact of Big Data on the Fashion Industry
The Impact of Big Data on the Fashion Industry | Ruth Kelly
What is Big Data?
Watson (2017) defines big data as “the collection, storage, and analysis of high volume, velocity, and variety data”. Big data continues to have a powerful impact on industries across the globe, with Marr (2018) asserting that big data and the Internet of Everything has led us to the cusp of the 4th Industrial Revolution.
Adoption of big data analytics is increasing worldwide amongst larger organisations in particular. Over 50% of executives surveyed by Dresner Advisory Services in 2017 stated that their organisations had adopted the use of big data (Watson, 2019). Big data analytics provides industries with numerous benefits such as the ability to analyse consumer intent, to predict buying patterns and to manage supply chains and production.
Another example is the Polo Tech Shirt launched by Ralph Lauren in 2013. The Polo Tech Shirt retails for $295 and was designed with the aim of creating wearable technology that captured biometric data such as heart rate and activity levels. Benefits for the wearer include personalised workouts crafted just for them based on their data. Speaking about the Tech Shirt, David Lauren noted that this technology would help the company to identify and respond to new needs amongst consumers (Marr, 2016).
Marr (2016) states that the big data phenomenon is here to stay and will represent a new normal in just a few years’ time. It certainly seems that way for the fashion industry! In 2021, Ralph Lauren launched The RL Virtual Experience, a new way for shoppers to virtually shop and step through Ralph Lauren’s flagship stores from the comfort of their own homes (DeAcetis., 2020). The big data analysis possibilities are endless. As users step through the virtual store, they are no doubt providing invaluable information to Ralph Lauren such as their navigation of the store; the rails, colours and styles they are most attracted to along with the impact of price point on their purchasing behaviour.
While big data offers invaluable insights for the fashion industry, it can also present challenges such as the protection and anonymization of data along with the need for specific technologies and highly skilled analysts to extract value from the data (De Mauro, Greco and Grimaldi, 2016). Furthermore, according to Rynsel, Gantz and Rydning (2017), not all data is advantageous to industry, and companies must learn to streamline and filter big data, focusing on data that offers value to their organisation and consumers.
Keywords: big data, fashion industry, technology
Bibliography:
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Du, Y., 2019. Data Analytics And Applications In The Fashion Industry: Six Innovative Cases. University of Rhode Island.
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The Importance of Big Data for the Fashion Industry | Louise Grant
ReplyDeleteAs there is an expanding amount of competition in the fashion industry, the role of big data is becoming increasingly important. It offers a service to fashion designers to produce and market their clothing brands with a precise level of accuracy. Jain, Bruniaux, Zeng and Bruniaux (2017) states how it is increasingly being used in trend forecasting, supply chain management, analysing customer behaviour, preferences and emotions.
Big data allows opportunities for smarter and more accurate decision making, providing that it is managed successfully. Fienberg (2013) explains that “big data is fundamentally about categorization and segmentation.”
The importance of data has been notably acknowledged by fashion professionals to improve sales and margins as fashion brands and retailers need to develop, manufacture, and sell styles that resonate with their consumers (Oh, 2020). Fashion markets are now trying to optimize data analytics to improve their marketing strategy through personalization for direct customers.
Amazon has introduced a device based on data analytics that helps the customer gain fashion insights on their looks. The Echo Look feature includes Style Check that offers advice and suggestions on outfits that compliment particular looks for the customer. The analysis of which becomes more accurate and improves over time, given these suggestions are precise. Fowler (2017) indicates how this type of feedback is processed to optimize the algorithms to offer the customer a more satisfying Style Check service.
Do customers understand just how much of their data is being used by large organisations? | Marcus O Halloran
ReplyDeleteAccording to Watson, (2017) Organizations now collect massive amounts of raw data and store it in HDFS on Hadoop or other platforms. The storage of such data is referred to as a ‘Data Lake’.
An example of a company who stores information in this form is Burberry. British fashion brand Burberry is one of the most famous luxury labels in the world, offering premium quality products, recognizable designs, heritage, exclusivity, and a global reputation, Du,(2019). Du tells us how Burberry began a process of ‘Clienteling’ which analyses customer data in exchange for a personalised service.
Burberry launched the ‘Customer 360’ programme which used RFID tags to build the customers profile and give a more relevant shopping experience. This was based on social media searches, preferences and previous purchases (Marr, 2021). This form of data collecting is widely used by all major brands in 2021.