When was the last time you set foot into a physical store? If you’re like an increasing number of millennials, that answer is “I can’t remember.” Never shopping in a physical location might be an outlier, but the truth is, fashion eCommerce taps into a huge potential market. New types of retailers are choosing to forgo the overhead of a physical shop altogether. Of all possible eCommerce fields, fashion is the top growing vertical. It occupies the most significant chunk of the B2C eCommerce pie with a total global size of around $524.9 billion dollars in 2018 and a projected $835.8 billion by 2023.
If you’re in fashion, you can’t afford to operate the same way you always have. Customers demand more personalization and have less patience for user unfriendly sites. With thousands of other online retailers, how are you going to stand out? Let’s take a look at some of the most common challenges facing online fashion retailers and how AI visual technologies can overcome them.
Frustrating Searches - A.K.A Shopstration
Put yourself in your customer's shoes. You have a party coming up and a vision of the perfect thing to wear. You see in your mind a stunning red dress with balloon sleeves and a "cool back", but you don't know how to articulate what you want when searching for it on your favorite online store. You type in "red dress" on the website and get 17 pages of possibilities—mixed in with pages of irrelevant results. Or you type in "red dress with balloon sleeves and a cool back" and the search turns up nothing. Sure, the site's inventory is “technically searchable”, but instead of finding the item you really want, you get lost in the clutter. Product discovery shouldn’t be this hard. So what’s the problem?
Traditionally, search has been text-based, but fashion isn’t text-based; it’s visual. Online fashion retailers struggle with the traditional text-based tags because they're grossly inefficient at figuring out what the customer really wants. It’s a 20-year-old system trying to take on the unique visual world of fashion. Online shopping shouldn’t be a matter of typing the right combination of keywords. It should operate the way you do when you go into a store — through visual discovery.
AI Visual Search enables fashion searches the way shoppers envision their dream items and style—visually. This cutting edge technology deep tags items, not through the index, but through clearly defined style attributes. The tags are thorough, consistent, and refined based on your customer’s behavior.
The takeaway: Text-based search is limited in getting the job done in fashion. This is because fashion is about style, and style is visual. Thus, online fashion retailers need to give their customers an intuitive search based on visual cues, and the first step in this process is deep tagging your catalog to extract detailed style attributes. Simply put, when online fashion retailers deep tag, their shoppers get a more effective search because they are able to base their queries on detailed style attributes.
Making Sense of Fashion Data
Online business thrives on data, but if you don’t have an effective way to wrangle it, you're missing the potential. New online retail powerhouses like REVOLVE realize that online fashion isn't just about style. Retailers that find a way to utilize machine learning and the data obtained from this method better than their competitors have a serious competitive edge. Analyzing the attributes of style helps you utilize your data in a unique way. It helps on the business level and the personal level.
On the business side, style-based data allows you to make better predictions, not just for your business forecast but for potential trends on the horizon. In fashion, some are using predictive models to analyze things like demographics or location, but computer vision analyzes the fashion attributes themselves. Deep tagging allows a business to look at specific style attributes to make comprehensive predictions and analyze inventory in a way that complements what customers actually want.
On a personal level, it uses the same visual search navigation components for holistic pictures of your customers' personal styles. As AI learns, it uses the massive amounts of fashion data from attributes to refine search results, providing more relevant suggestions and predictions for what customers might want. It captures their “style DNA,” making your site more like a human and less like a warehouse.
The takeaway: AI-based search and machine learning allow you to understand and analyze detailed style attributes, something basic visual search isn’t doing, for richer experiences.
How AI in Fashion Retail is Changing the Game
AI and Computer Vision give online fashion retailers the power to overcome some of the key challenges they face. First, as these technologies automatically extracting detailed style attributes about their merchandise, online retailers are able to offer an intuitive visual discovery experience by allowing their shoppers to visually find products, instead of getting stuck in the clutter of endless search results.
Second, online fashion retailers can easily gather, analyze, and utilize the unique style-based attribution data. This not only enables a better, more intuitive user experience but also makes the forecasting and planning stages more effective and efficient.
Donde Search uses Computer Vision and AI to turn retail catalog images into a highly structured data-set that connects detailed style attributes about products to consumer shopping behaviors, thus improving merchandising, discovery, and personalization across e-commerce platforms. You can follow Donde on Twitter, LinkedIn, Facebook, and Instagram.