Machine learning is having a big impact on fashion retail.

We recently caught up with senior data scientist Joe Berry at retail tech company Edited and asked him about trends in this area, as well as how the Edited product uses machine learning to help set retail strategy.

(N.B. If you’re interested in marketing applications of AI, Econsultancy’s Supercharged conference takes place in London on May 1, 2018 and is chocked full of case studies and advice on how to build out your data science capability. Speakers come from Ikea, Danske Bank, Just Eat, Age UK, RBS and more)

Econsultancy: How does your product use machine learning? Is it chiefly for product categorisation?

Joe Berry: As the retail industry is highly visual, product categorisation is one of the most critical aspects where machine learning is used. How a retailer describes a product that customers consider to be similar can vary wildly, creating a lot of inconsistencies that make it hard for businesses to analyse information.

Edited builds systems that review millions of individual items every day to accurately and consistently categorise them. To perfectly categorise a garment, we look at more than just the words used to describe an item (text recognition). We need our machines to process and understand images as well as text. This entails knowing which parts of the picture are the model, identifying the background and differentiating it from the garment being retailed.

These tasks are often complex, as they often require 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, for example.

Standardising 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.

edited

E: Will machines ever make pricing and merchandising decisions autonomously?

JB: The Edited product is about using machine learning to make better decisions in their retail strategies – and this includes approaches around pricing, assortments, merchandising and other specific insights. Machine learning represents a reliable way of categorising data and spotting patterns in data without a risk of making biased decisions. The more data a company can tap into, the better it can understand patterns based on past performance and trends.

However, in order for machines to fully replace humans, computers would have to be fed information such as margins and inventory strategy, which are not only complex but also highly specialised making it difficult to generalise. The approach we use at Edited is to ensure that retailers have access to the world’s available data organized in a way where they can make strategic decisions based on variables suitable for their business.

E: What are the significant current trends in fashion ecommerce UX?

JB: When a customer enters a physical store, there are clear ways that retailers can maximize the shopper experience to influence a sale.

For online stores, the customer experience is centered around ease-of-use and convenience. E-commerce can optimise conversion by adding image-based classifications to extract information from pictures, or make product searches much easier.

ASOS, for example, has added an additional product categorisation in a more colloquial way, which better reflects how a shopper might refer to an item. By listing jeans in special category as “high waisted”, “ripped”, “cropped and ankle”, ASOS makes it easier for customer to find precisely what they want without having to browse hundreds of products with otherwise very basic categorisation.

asos search

ASOS colloquial categorisation

Another great UX example used across multiple retailers is adding customer-styled images next to the product image. ModCloth, for example, has an “Explore & Shop Outfit Photos” section where customers can see the product fit on other customers, which aims to boost purchases and reduce the rate of customer returns or exchanges.

modcloth ugc

ModCloth user-generated outfit photos on product pages

E: Do you think visual search will have a big impact on the industry?

JB: Using machine learning to understand images will always be a key functionality within retail. The vast majority of decisions within the industry are made with some form of visual input. Whether that’s a shopper deciding which dress to buy, or a buyer detecting the latest trends from the high street, we all rely on visual input when it comes to retail. At Edited, part of our focus is to create systems that comprehend visual aspects of the industry in a way that is synonymous with human expectation.

Related reading: