Seven Perfect Fits for GenAI in Retail Fashion

England’s Mary Earps was among the breakout stars of the Women’s World Cup soccer tournament this past summer, creating a buzz across social media as her team rode her standout goaltending to the tournament’s final match.

As that buzz grew, so did the clamor to purchase replicas of her outfit. But none were available for sale. Here’s where a bit of forecasting help from generative artificial intelligence (GenAI) could have changed the narrative. 

By applying GenAI heuristically to social media and other channels during the World Cup, the big-name athletic wear company that owned jersey sales rights for the team could quickly have been alerted to the mounting buzz around Earps, then ramped up jersey production in response. 

Not only might the athletic wear company have avoided the public backlash that reportedly resulted when people discovered they could buy replica jerseys for many male goalies but not for Earps and other female keepers, it also could have reaped big sales benefits by identifying and riding the surging wave of interest in women’s soccer jerseys as a fashion statement (driven by players like Earps as well as tastemakers like Kim Kardashian). 

Soon after the controversy, the company did reportedly start selling Earps replica jerseys. And within weeks, companies like Adidas were riding the wave, too, with the launch of new fashion-focused adaptations of soccer attire.

Cases like this hint at the potentially massive and versatile role GenAI can play for fashion retailers, in marketing to and engaging with customers, identifying and shaping trends, and managing key aspects of their business. Here are areas where it could really prove valuable:

Trendspotting and forecasting consumer demand: The Earps jersey controversy illustrates genAI’s potential for helping companies monitor the buzz across multiple channels and heuristic events (the outfit worn by the star of a hit movie, for example, or people in fashion hot spots talking about the return of flare pants), identify the very lead edge of an emerging trend, and rapidly respond on the design and production sides.

Customer segmentation: Consumers want fashion brands to understand their preferences and deliver offers that cater to them in a meaningful, highly personalized way. AI can help them gather and connect data from a huge range of consumer touchpoints to create highly targeted and relevant marketing campaigns.

Virtual try-on: Instead of a person ordering multiple sizes of the same shoe because they’re not sure which size will fit, then having to return a couple of them, they could feed a couple barefoot images of their feet on a blank sheet of a specified size of paper, and let the embedded AI capabilities on the retailer’s website determine the best size for the person. Not only does that reduce return-related costs and hassles for retailer and consumer alike, it reduces the brand’s overall carbon footprint, an important consideration as companies face increasing scrutiny of their emissions. 

Revenue growth management: GenAI can help fashion retailers and CP brands on several revenue fronts. One is with pricing, where it can offer insight into pricing strategies that are most likely to succeed. The same goes for promotions. AI can reveal the types of campaigns that have been most successful and suggest reasons why, then provide suggestions on promotional strategies that are most likely to hit the mark. That includes strategies for cross-promotion across brands within a brand family, to try to turn consumers’ relationships with one brand into broader revenue-producing relationships.

Supporting the creative design process: As rich as the fashion industry is in creative design talent, AI can support the human creative process, such as by using parameters fed to it by a design team to quickly generate pattern options. This could significantly shrink development time. 

Content creation: GenAI could be used to generate product descriptive text and images (such as for a catalog) that aren’t based on actual photographs, and to adjust advertising product/model photographs to specific markets — keeping strictly to ethical standards that govern image usage, the rights of modeling talent, etc. This could reduce the carbon footprint and cost associated with flying models and crews to various locations for photo shoots.  

Supply chain efficiency, resiliency and sustainability: Based on certain parameters, GenAI can provide options for sourcing a material or component with a specific look and feel. It also can evaluate a huge range of suppliers, fabrics and raw materials based on factors like price, availability and carbon or CO2e (carbon dioxide gas equivalent) footprint, then provide recommendations that balance those factors.

Using blockchain technology and AI, a company can compile and analyze data sourced internally and from the supply chain to trace the origin of materials in an item of clothing, as well as the CO2e footprint associated with that item throughout its journey to the end consumer. Having this level of visibility into product-specific carbon footprint is critical because consumers weigh it more heavily in their buying decisions, and because we anticipate companies soon will be required to disclose that information to regulators in places like the U.S. and Europe. 

AI can indeed be a powerful and versatile tool for fashion retailers and CP brands, provided they have a few fundamental digital elements in place. Most important is a clean data foundation, where decision-makers have fresh operational, finance, supply chain, retail and sustainability data in front of them, in standardized format, with the intelligent tools to draw insight from it, along with the ability to report that data to regulators, partners and the public. The ability to share actual, auditable carbon footprint data with partners and across business networks and supply chains is a must. 

So is having a kind of internal “green ledger,” where companies can readily factor sustainability and carbon-reduction into their decision-making, so when consumers begin clamoring for a certain athlete’s replica jersey, brands can answer the call — and do so in an environmentally responsible way.