The fashion industry is one of the biggest industries in the world, worth around USD $3 trillion as of 2018; about 2% of the world’s GDP. Artificial intelligence (AI) is driving the shift from catalog-based shopping to company and brand social media to improve the customer experience. Artificial intelligence is the simulation of human intelligence by machines to make tasks easy and quick to perform. The machine mimics human actions, as it is programmed in a way that it can think like a human and complete goals. Here are a few ways how AI is revolutionizing the fashion industry.
The fashion industry started using AI to fundamentally transform the way brands manufacture, market, and sell their products. With brands looking for new, more efficient ways to create awareness and demand for their goods, AI not only makes the marketing and selling of goods easier but also maximizes users’ shopping experiences as well as improving the sales system and enhancing the sales process through intelligent automation.
A number of luxury brands, including Dior, Macy’s, Nike, and Nordstrom, are using AI to help increase sales, promote goods, and enhance the customer experience. There are a couple of ways these brands include AI as a part of their marketing and management techniques.
Brands have come up with a more efficient way to connect directly with their consumers by using a bot to conduct a computerized chat over their website or application that makes shopping easier for the user.
For example, the Paris-based fashion brand Dior launched its beauty assistant, Dior Insider, which conducts chats through Facebook Messenger. It starts with greetings and a series of questions that allows the app to know the consumer’s needs and requirements. It was released in 2016 but disengaged in 2017 for unspecified reasons.
Some brands have launched personalized applications that aim to help the user have a shopping experience online that is similar to an in-store experience.
For instance, VF Corporation’s brand The North Face launched its Expert Personal Shopper in 2015, in collaboration with IBM Watson. Unlike with other online shopping websites, where one has to scroll through a number of products to find the item they are looking for, this app makes the work easier for the consumer by first asking a few questions then taking the information and curating a personalized set of recommendations for shopping.
With these types of apps, customers have a better shopping experience, and the data collected gives the brand a clear and more informative idea of their products and customers’ needs.
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PTTRNS.ai launched an AI-based shopping tool aimed at delivering smart interactions with customers. Consumers can upload a picture of the product and can easily find similar or the exact same product. This visual search tool is applicable to different product categories, ranging from clothes to shoes to bags.
Designers and manufacturers are involving AI in their production methods. For example, a computerized system for spotting defects in the fabric and color of the textile allows quality assurance, thus saving time.
Cognex ViDi, for example, is a vision-based platform tailored for fabric pattern recognition such as weaving, knitting, printing, beading, and finishing of textiles. The company claims to have manufactured the system in such a way that it is trained using predefined images of what a good textile looks like.
Designers use AI not only for quality assurance but also to manufacture the garments. Bulk production can be done using various technologies like computer-controlled lasers, knives, water jets, plasma, or ultrasound.
Also, automated sewing is spreading its wings in the manufacturing process, yet it’s still in its primitive stage. In 2019, ITMA introduced software in combination with supporting hardware named the Juki Advanced Network System (JaNet) that allows data collection on production activities where sewing machines are interlinked. Therefore, digital sewing machines have become an essential to provide sewing error detection in mass production.
Earlier, only e-commerce giants used AI to obtain analytical data regarding their sales and trends; but recently, both small and big brands have started incorporating machine learning to understand the market better by identifying the analytics and insights found in data related to everything from fashion trends and purchase patterns to inventory and forecasting.
An important arm of the huge fashion industry is fashion styling, one of the most interesting and important parts of the industry.
In this space, Stitch Fix is an online styling application that uses machine learning to personalize clothing items based on a customer’s size, budget, and style preferences. The AI is cleverly being used in the supply chain to make it more efficient, and optimization in the supply chain reduces transit time and shipping costs while providing updates about the inventory.
Another example is the AI styling service Intelistyle that provides styling advice such as complete look recommendations that is able to personalize shopping recommendations depending on previous purchases and recommend alternate products in case a particular item is out of stock.
With AI in the fashion market expected to grow at a CAGR of +40% in the next 7 years (2020–2027), the future of fashion is intelligent, as the use of AI is spreading in every part of the fashion industry. The use of AI in fashion is important in these times because it provides better experience and satisfaction to the customer. It is also shown in a survey by Capgemini that use of AI in the fashion retail industry can help save USD $340 billion by the year 2022.
As CEO of Bleacher Report, Howard Mittman, said, “Content is king but engagement is queen and she wears the pants.”
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