Algorithms and Automation: A data-driven future for fashion

From personalized styling to trends and forecasting, data collection is reshaping the approach brands take to develop and more accurately predict what products their consumers want. Artificial intelligence (AI) and machine learning have become a growing trend in the fashion industry over the past years. They are helping companies make more strategic decisions during the product design and development processes. Companies are currently using it to forecast, predict trends, create sewing patterns, and offer personalized styling services. Briefly, artificial intelligence is a branch of computer science that focuses on creating computer-controlled intelligent machines that can perform tasks that require human reasoning and capabilities. Implementing automation in the fashion industry poses many solutions to production barriers; however, it is still worth questioning what challenges automating an industry built upon creative intuition will bring forth.

Using AI for Product Development, Marketing, Buying, and Merchandising:

During the design process of product development, material size, colours, fabrics, shapes, sizes, weights are all variables to be considered, plus their impact on the cost and efficiency of the rest of the chain. With collections of consumer and product data, AI provides a vehicle for brands to make more intelligent and strategic decisions for product development and expansion that align with their consumers’ wants and needs. By using algorithms with data from past profitability of products and their analytical performance amongst consumers, algorithms provide brands with the ability to analyze designs, predict future trends, and even develop sewing patterns. Considering that knowing what consumers want is the first step brands have to take in product development, AI makes it faster to rationalize data and allows designers to support their creative decisions accurately. When microtrend turnover and social media fads apply pressure on designers and brands to get product development right, AI and machine learning provide both parties with a glimpse of certainty into what will be most appealing to their consumers’ desires. By tracking design elements such as colour, size, fit, cut, and fabric, designers and brands can understand how past design elements have performed and indicators for future performance.

Marketing and merchandising teams also reap the benefits from AI and machine learning as they can provide teams with relevant data that can make forecasting inventory more accurate. According to Heuritech, McKinsey, and Business of Fashion, concerns with overstock or forecasting errors can be reduced by 20-50% with the help of AI trend detection. Intelligent technology can provide real-time snapshots data that can help detect shifting trends and stock performance while it happens. This gives buying and merchandising teams the power to establish proactive strategies that can adapt to the rising and relevant consumer demands, rather than relying on metrics and data on a season-to-season basis. While trend forecasting has traditionally been dependent on human analysis, this is one area of the industry that has the potential to become partially automated and more reliant on powerfully developed algorithms. FINESSE, a unisex clothing brand, uses algorithms and predictive analytics to predict trends, reduce fabric waste. Ramin Ahmari, CEO of FINESSE, explains that the company only produces what it knows will sell. Forecasting is carefully pre-estimated by how much demand there is based on data. FINESSE designs three potential drops of designs that shoppers get the opportunity to vote on based on trend analysis. The collection with the most votes is then sent to production, leaving the other two collections to be scrapped and only producing what consumers explicitly showed interest in. Interestingly, FINESSE brings to the table a simple yet complex perspective to the textile and garment waste problem that continues to plague the fashion industry.

Using AI for Consumer Experiences:

Using AI for consumer experience seems to be one-way brands are implementing such technology into their services. Personalized style recommendations, sizing, and clothing curations are ways that artificial intelligence and machine learning build a greater connection between brands and their customers. The most significant element at play is interaction; that is, brands can ask their customers questions and explore their purchasing patterns. Because customers can provide feedback, this essentially acts as a back and forth conversation about their shopping desires that can offer them curations of similar items they may be interested in. StitchFix and Wantable are two companies that provide personal styling services powered by AI technology. StitchFix is algorithmically driven and helps expert human stylists decide on garments that best match a client’s style and shopping patterns. Powered by data collection and customer profiles, StitchFix provides shoppers with a unique, tailored, and personal experience while also building a trusting and reliable image for their service. Similar to StitchFix, Wantable provides personalized styling recommendations using a combination of human experts and technology. Starting the process with a short style quiz, Wantable is reliant on interactive feedback. Once the style quiz is completed, shoppers can access the “stream,” or in other words, a curation of the latest arrivals and garments that match the input of the user. While placing an order is an optional next step, deliveries are packed and shipped within a week or two, and you won’t be billed for anything you return. But returning shipments and orders is something most brands try to avoid, and AI not so surprisingly has a solution to this concern as well. Aside from defects, unfit sizing is a significant motivation behind consumers returning a purchase and poses a problem for brands that predominantly rely on e-commerce. Aiming to take the uncertainty out of e-commerce buying, VirtuSize sees data as a driving force to create an online shopping experience free of sizing setbacks. Working with the Swedish School of textiles and Acne Studios as early clients, the company developed a service that allows consumers to compare item sizes to other items they already own and try items on with a digital silhouette feature.