Starting with the way designers create and how businesses sell their products, big data plays an important role across the fashion supply chain. The fashion business benefits everyone on the earth in some manner, but big data is changing the way apparel is advertised and sold to different sorts of customers right now.
Big data and relevant analytics are quickly becoming a necessary aspect of the fashion business. Big data is becoming more prevalent in trend predictions, consumer behavior, preferences, and even emotions. Big data is now used by businesses to develop new strategies by personalizing the customer experience and allowing the customer to take the lead – and that is significant.
In any high-level sector, including fashion, big data is critical to success. Big data is rapidly being utilized in the fashion sector for “trend forecasting, supply chain management, consumer behavior research, preferences, and emotions,” according to a recent study. According to Business of Fashion (BoF), over 75% of fashion businesses will invest in AI by 2020, which makes sense given that customers are predicted to spend close to $1 trillion on e-commerce purchases.
How is big data used in the fashion industry?
Fashion is a very sensitive sector to shifting consumer expectations, as well as one that is evolving toward greater customization. Buyers want a variety of alternatives according to their personal style preferences, situations (e.g., work, social attire), and cultural and musical influences. As the desire for customization grows, so does the need for mass customization of clothes to avoid excess inventory.
Consumer Expectations and Big Data
- Manufacturers are already obtaining data on certain categories utilizing web data (obtained via sales, market research, and social media comments, and purchase analytics).
- Fabric selection is intertwined with emotions, textural and structural choices, and seasonal preferences.
- Design is another important area that relies on big data, and it is impacted by human emotion, context, cultural influences, themes, and other factors. Data about the physique of the consumers, which may be obtained in 2D or 3D form, is very important for fashion firms. For 2D, the dimensions or sizes of the objects purchased or sought are sufficient. Body scanners are used in 3D to better comprehend both size and body type.
- Color (warm vs cool, soft vs firm, pastel vs bold, and so on) and technical design (necessary sewing, weaving, knitting, and so on) decisions are also influenced by the information gathered.
Making Crucial Decisions
Fashion firms can make critical decisions about online and offline buying using data acquired from AI and other sources. For example, firms like Glossier and Warby Parker, both of which started as online merchants, used customer data to select the best locations for their physical stores. In exchange for taking surveys about the things they want, customers are urged to sign up for discounts and important information. To increase customization, savvy businesses are now offering ‘personal shopper’ services.
Using Big Data to Create Big Brands
Major businesses are jumping on the big data bandwagon in the fashion sector, and they’re doing it quickly.
- Amazon is one of the frontrunners, having attempted to establish how people actually measure up earlier this year. This vast database of body kinds is essentially a pool of big data gathered by Amazon to better understand how bodies evolve over time. This data has the potential to accomplish incredible things for online merchants, especially given they handle over 40% of product returns when garments don’t fit. Consumers will be more delighted if they can fast get that much-desired ‘perfect fit’ before purchasing a garment online.
- Big businesses like Zara and Ralph Lauren are in the driver’s seat, thanks to big data. Zara’s success is largely due to the company’s continual usage of big data, which involves collecting and analyzing consumer preferences and selections to strategically construct a brand based on what people genuinely desire, or rather, purchase the most. While traditional sales reports were previously the norm in every retail organization, big data now serves as the backbone of Zara and other major businesses. Market researchers examine Zara’s in-store data, which is collected from roughly 100 global regions, to identify exactly what their customers want. The data is analyzed and sent on to Zara’s in-house designers, who then create outfits “for the people, for the people,” to use a famous slogan.
- Ralph Lauren, like Zara, has embraced big data, employing consumer data analytics to examine and anticipate future trends, including customers’ preferred fabrics, colors, finishes, and, perhaps most importantly, pricing. When Ralph Lauren linked up with OMsignal, an innovative biosensing garment maker, the Polo Shirt became known as the “PoloTech Shirt.” The PoloTech, which was created for active sportsmen, is a single huge sensor that collects real-time data on the wearer’s direction and movement, as well as physiological data including heart and breathing rates, steps walked, and calories burnt. The data from the shirt is sent to the cloud, where it is processed using appropriate algorithms. This form of wearable technology can collect a wide range of consumer data to forecast new fashion trends outside of the running track, analyzing customer and wearer data from any clothing, at any time and in any location.
Product Demands Predictions
Predicting the sorts of things with high sales potential is quite straightforward for some fashion businesses. For example, consider businesses that specialize in festival apparel for major events throughout the world, such as Coachella, Glastonbury, or the New Orleans Jazz Festival. Because they are seasonal and theme-based, making predictions in this area is simple. Essential festival clothing has a one-of-a-kind wow factor. Items worn are frequently influenced by the 1960s and 1970s. Crocheted shirts, high boots, huge sunglasses, fringed purses, and the like are always a hit. Predicting some more fluid or ad hoc events, on the other hand, can be significantly more difficult. As a consequence, Rue La La and MIT recently partnered to strengthen AI forecasts, resulting in a 10% boost in income.
Influencers and Big Data
More than just identifying essential colors, trims, and styles will be done with predictive technology. It will also assist marketers in identifying influencers with whom they may form partnerships before the latter’s ‘boom.’ Since social media has grown omnipresent, finding influencer partners is crucial in the modern fashion market. “The average earned media value per $1 spent on Influencer Marketing was $5,20,” according to data from 2018. This indicates a return on investment of more than 5 times.” She also points out that micro-influencers, who cost fashion companies far less than macro-influencers, are becoming crucial in marketing since they have solid ties and a high level of trust with their audiences.
Big data is unquestionably a game-changer for both fashion producers and customers, who are impatient for change. Big data is a fast-paced sector of innovation, and companies that make data-driven decisions will stay ahead of the competition. To be considered even viable, much alone trustworthy, large data must be acquired and analyzed by specialists. While big data cannot replace a designer’s originality, intuition, or ingenuity, it may help them focus on their creativity while still creating new and interesting fashion trends that, since they are based on customer preferences, can become global phenomena. Big data can provide advice and direction to the fashion sector, eventually assisting designers in creating the right product, at the right price, at the right time.
