From categorization to standardization, artificial intelligence promises to upend retail’s present and future, starting with big data.
There’s something the apparel retail industry doesn’t want to talk about. Consumers are spending less on clothing. It doesn’t take an analyst to spot that this is problematic for retailers. To make matters worse, rent prices are rising, product cycles are getting shorter and fast-fashion retailers — with their low prices and enviably speedy reactions — are eating up market share.
The outcome is a record number of store closures this year and street upon street filled with those red discount banners.
Really, retail can’t be blamed: So much change has been thrust upon this industry in the last 10 years. The way people shop has been entirely reinvented, the way products are purchased has been overhauled, demands around delivery have ramped up to “within the hour” and even the once revered format of seasonal runway shows, fashion’s Super Bowl, are being challenged.
AI meets retail sales
This is where artificial intelligence promises to upend retail’s present and future, starting with big data. While every retailer has their own internal data, there had previously never been a way to understand what was going on in the industry outside of their own sales. There was no reliable way to know exactly when a new denim trend kicked off, to view which styles sold best for a competitor or understand how to price newly arrived stock.
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But with the advent of the Internet and ecommerce, there’s now the data, processing capabilities and technical expertise available to collect and analyze this information. Data scientists, now one of the most in-demand jobs in retail, play a vital role in organizing and making use of the world’s apparel information.
Data scientists,help automate recommendations for subscription brand StitchFix, provide personalized clothing suggestions for The North Face’s customers and give retailers invaluable real-time updates when new products arrive, sell out, get discounted or make any movement at all, worldwide. Retailers can now ensure they have the right products for their target market, that new inventory is timed perfectly, and that items are priced ideally to sell.
However, while large datasets inspire awe and huge budgets are dedicated to building them with a view to fix all problems, the data is only valuable when it’s meaningful. That’s where machine learning came in.
Machine learning adds meaning
Using machine learning, retailers can capitalize on market data to understand and anticipate consumer behaviors and trends. For example, British online fashion retailer ASOS, uses machine learning to recommend similar products to shoppers. It also analyzes which items and sizes are returned most often, to improve the customer experience and reduce costs.
The apparel sector is a visual and descriptive industry. Aspects such as texture, pattern, color and fit have a lot to do with why things are popular or not. A database not only needs to store information on products, but to make that data insightful, it needs to understand what those items represent.
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The first hurdle is categorization. How a retailer describes a product that consumers consider to be similar can vary wildly. Data scientists need to teach our machines to not only continually build upon their understanding of the language of apparel, but to actually see what was within an image.
The need for data scientists, deep learning and neural networks
Software needs to view a product photograph, know which parts of the picture were the model, identify the background and differentiate it from the garment being retailed. This could mean separating a long-sleeved polo shirt from a short-sleeved polo shirt, isolating a belt worn over jeans, or knowing what in the database was technical sportswear, versus athleisure.
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Standardizing the data in this way is transforming the industry as for the first time, retailers can run a direct comparison of their product assortment alongside every one of their competitors’ merchandise. They can address the latest consumer trends and capitalize on current best sellers. And they can launch a new product or enter new markets with unprecedented visibility.
The next frontier of AI for data scientists is using deep learning and neural networks to the point of revealing what will happen next. AI is clearly being applied in new ways across the entire retail product lifecycle – from design to purchase. Astute retailers will continue to tap into the latest AI advancements to elicit these data points and insights to their advantage.
