Here’s how they’re doing it: The Problem with Traditional Retail Analytics Traditionally, fashion houses and brands kept vital information like sales records and inventory details in-house. WWD; 2019. According to the Renewal Workshop co-founder, technology professionals will be expected to not only trace the origins of pieces of clothing, but also build out analytics tools for sustainability. From the moment a customer signs up for the service and selects their favorite clothing options, the system goes to work, analyzing their choices and suggesting relevant items accordingly. Apart from having to meet the demands of “fast fashion” - turnaround time from the ramp to stores - retailers must also price items correctly, know when to reduce them, stock enough of the right styles, colors, fabrics and sizes, and ensure that stores are well supplied and operate efficiently. Imagine how much money, time and effort the company and its designers have saved by using the underlying data they collect to forecast trends based on customer preferences, rather than making the products and sending them out to retailers only to have them lose money. Companies providing AI-powered data analytics services to fashion brands and retailers use machine learning algorithms – the branch of artificial intelligence to create executable solutions for how to ensure maximum sales by optimizing product assortment, efficient allocation and a great customer experience.8. 2020;6(3):102-104. Companies’ first challenge in collecting and analyzing internal data is data silos that is isolation of data created by different departments or units within a company and without data integration, data cannot be used effectively. To implement data analytics in the existing fashion programs, collaboration with technology partners who provide AI-powered data analytics services to fashion brands and retailers is needed. Skorupa J. A potential future research direction is discussed in the Conclusion. Lately, advancements in data analytics, machine learning, and computing power, the value of utilizing artificial intelligence (AI)-based software or applications has been well acknowledged by fashion brans and retailers who want to apply a data-driven decision-making approach to develop more efficient fashion design, merchandising, and marketing strategies. Fashion is one the fastest growing and evolving industries, with new trends and consumer behaviour constantly changing. permits unrestricted use, distribution, and build upon your work non-commercially. Data is abundant in the current fashion business environment as sales data, product information, and consumer data are constantly collected and analyzed. WWD; 2013. Prescriptive analytics helps fashion retailers sift through the numbers. Artificial intelligence (AI) is a combination of technologies including natural language processing, computer visions, machine learning and deep learning algorithms, VR/AR/MR technologies, and more. It also tracks sales performance via wholesale business and the brands’ own stores and online channels. Geek meets chic: Four actions to jump-start advanced analytics in apparel. Due to environmental impact, more consumers and fashion brands are turning to the concept of “slow fashion” and away from the long and costly manufacturing process. Data is abundant in the fashion and retail industry. The system then goes through the second tier of clothing options to create nine different data-built designs which are then sent to the design team as blueprints. WWD; 2018. The roles of data analytics in the fashion industry. In particular, fashion data analysts may have a degree in the STEM fields; but they also need knowledge in fashion merchandising, fashion retailing, and fashion consumer behavior to predict trends and to gain consumer insight better.15 A good understanding of fashion fundamentals and data analytics should be considered as essential competencies by fashion brands and retailers when they are looking for new talents these days. The Future of Fashion and Big Data In addition to using data to understand customer needs and shopping behavior, data science is also being used to forecast a product’s “shelf-time” on the website, and advise the customer if it’s going to sell out soon. Not every eCommerce website has its presence in multiple countries, nor is every fashion site … Data analytics can analyze the impact different seasonal trends have on the buying behavior. The concept of big data includes analysing voluminous data to extract valuable information. 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