Despite the buzz, AI technology is just starting to transform our world, and that includes the way we shop. At the beginning of this decade, the AI market in the retail industry was valued at $3.75 billion. By 2028, that figure is expected to grow to over $31 billion. Nowhere is this change more dramatic than in fashion, which is the top online shopping category in the U.S.
According to McKinsey & Company, generative AI could increase profits for apparel, fashion and luxury retailers by $150 billion to $275 billion over the next three to five years. That means it’s imperative for fashion retailers to understand and implement this emerging technology.
So what does a successful AI-based online fashion store look like? Let’s explore how AI-powered shopping can help brands boost profits by improving customer experience — while empowering staff to work more efficiently.
AI Powers Personalization for Fashion Brands
Across online retail sectors, research shows that customers crave personalized shopping experiences — and they’re growing increasingly unwilling to settle for anything less. About two-thirds of customers say that personalization is simply a standard of service today. And when a customer views more personalized pages, the chances of a sale increase.
In fashion retail, personalization takes on extra importance. According to Gulnaz Khusainova, the founder of startup Easysize.me, personalization helps consumers sort through the multitudes of options in clothing stores to find the items that meet their needs. And those needs can be complex, Khusainova adds.
We buy clothing based on myriad factors: our personal style, practical considerations (like needing something to wear to work or to a special event), size and fit. Ticking all those boxes for a shopper is no easy task for online fashion companies.
Are Historical Trends Enough For Personalization?
Past purchases can give insights that help personalize the online shopping experience. For example, if a customer has previously purchased Nike running shoes and apparel, the retailer can display other Nike products on the homepage when this customer returns.
However, that’s not a foolproof approach to personalization.
As Khusainova points out, our fashion preferences are often not as fixed as those in other areas. Consumers who prefer red wine, will always choose red over white. Dog owners will consistently feed their dogs the same brand. But when it comes to what a person wears, that tends to fluctuate. Shopping might be influenced by weight changes, or a boss mandating a return to the office. Or when a person moves from chilly Minneapolis to tropical Miami.
In these situations, even recommendations a site makes based on your past shopping behaviors can feel irrelevant.
But that’s not the only problem with relying on purchase history to create personalized shopping experiences. Many shoppers prefer anonymity. So there’s no purchase history upon which to base recommendations. According to Coveo’s Ecommerce Relevance Report 2023, only 28% of respondents surveyed disagreed with the statement “When shopping online, I will only log in at checkout.” Another 32% agree with the statement “I always checkout as a guest.”
3 Ways AI Helps Fashion Brands Achieve Personalization
These challenges can make personalization tricky for fashion brands. That’s where retail intelligence software comes in. Here are three tools that AI-based fashion stores can use to improve the customer experience, and boost sales, through personalization.
AI Technology Enhances Site Search in Fashion Retail
The search box can be the launching pad for a personalized experience at an online fashion retailer, even if a shopper has never visited the site before. But its potential often goes untapped.
In a Coveo survey, 47% of respondents felt frustrated by search while shopping online. One common reason for this frustration? A lack of tailored search results. An AI-based online fashion store can help search feel more personal by harnessing the power of natural language processing (NLP). NLP makes it easier for a site to know what customers mean when they use everyday, conversational language, and even slang, to describe what they’re looking for.
An artificial intelligence search engine that uses machine learning can also decipher the customer’s user intent when they make a typo, which helps avoid the dreaded “zero results” message.
But what about the cold start problem we mentioned earlier? With AI-powered shopping, the machine learning model uses session data and product vectors to help determine intent. As the shopper starts looking around, the model leverages in-session actions to quickly build a greater understanding of what a customer might be looking for.
The experiences of Caleres, which owns and operates numerous popular shoe brands, show how machine learning can make search more effective for fashion brands. When Caleres’ brand switched to Coveo for their websites, conversion rates for customers using search soared above those from its legacy platform, which did not use AI.
AI Unlocks Deeper Personalization in Fashion Recommendations
Product recommendations are another way to make shopping for fashion items online feel more personal. When they’re done well, recommendations feel like getting tips from a style-savvy employee at a brick-and-mortar clothing store. Product recommendations are especially important to Gen Z and Millennial shoppers, according to Coveo’s 2023 Ecommerce Relevance Report.
But when they’re not executed well, recommendations actually do more harm than good. The Relevance Report found that almost one-quarter of shoppers cited “too many irrelevant recommendations” as a problem with online shopping.
Recommendations become more accurate, even for cold-start shoppers, when online fashion stores use artificial intelligence. Emerging technology in the fashion industry now means that brands can adjust recommendations in real time based on the customer’s behavior during a shopping session and how it compares with what past shoppers have done.
An AI-based online fashion store can also make recommendations based on what’s trending or even where a customer is located. In-session personalization also addresses the issue of logged-in customers whose fashion preferences have changed in some way since they last visited the site.
Besides improving the customer experience, AI-powered recommendations can also serve business priorities.
For example, if a customer is searching for a new swimsuit, an online fashion store that uses AI can automatically prioritize suits that have higher margins and cost less to ship. If a suit the customer likes is out of stock, the site can show similar options to keep the customer from abandoning their cart.
AI Chatbots Uplevel Customer Experience in Fashion
With advances in AI, fashion retail sites can get even closer to replicating the experience of being helped by a knowledgeable store employee — or even a personal shopper or stylist. Chatbots today have gone beyond basic tasks like helping customers track an order.
Now they can also make recommendations, answer questions and guide customers to the products that suit their needs. Large Language Models (LLMs) empower chatbots to deliver smarter, clearer and more on-brand answers.
Fashion brands that use chatbots include Victoria’s Secret, Louis Vuitton, Tommy Hilfiger and Burberry. And that list is only posed to grow. While retailers overall are expected to spend $12 billion on chatbots in 2023, that figure is expected to balloon to $72 billion by 2028.
AI Uncovers Valuable Data for Fashion Brands
So far, we’ve been talking about how emerging technology in the fashion industry drives sales by improving the customer experience. But AI can also boost the bottom line for fashion brands by providing information that drives smarter decisions.
Let’s take the problem of overproduction. It’s a big issue in fashion retail, reaching 30% to 40% each season. Overproduction isn’t just inefficient. Its wastefulness can also hurt the public’s perception of a fashion brand.
AI can help remedy the problem by enabling brands to more closely align supply with expected demand. For example, a fashion brand that uses AI on its website knows exactly when searches for swimwear rise and fall each year. They can adjust their inventory based on this knowledge.
Data from an AI-powered website can even shape new designs for fashion brands. Let’s say that a fashion retailer’s site sees a dramatic uptick in searches for “tomato girl aesthetic.” (Yep, that’s a thing.) Using this search data, plus other signals, the brand identifies an emerging fashion trend and tasks its designers with interpreting the trend for the brand’s audience.
(By the way, if you’re wondering if AI can actually create its own clothing designs, researchers at Amazon are working on it.)
AI Solutions for Your Online Brand
AI in the retail industry is rapidly evolving. Buying fashion items online looks and feels different than it did even just a few years ago, and forward-thinking fashion retailers are using emerging technology to make the shopping experience more and more personalized for their customers.
At Coveo, we’re excited about how our platform can power successful AI-based online fashion stores. Our retail intelligence software harnesses machine learning to deliver results including:
- A search experience that’s smooth and intuitive
- Relevant recommendations based on shopper context, shopper profile, product attributes or a combination
- Customization even for cold-start customers
- Highlighted products and promotions that drive business results
To learn more about how AI technology can transform online fashion brands, download our ebook, “Personalization in fashion.” In this free guide, you can explore some of the best examples of fashion innovators who are setting a new standard for personalization, and, in turn, driving revenue and customer loyalty.