The Future of Fashion: How Artificial Intelligence is Transforming the Apparel Industry

Annual AI spending is predicted to grow to $7.3 billion by 2022. Machine learning in retail is on the rise, so it’s no surprise that AI is becoming an integral part of technology in the apparel industry.

Here are five major ways that artificial intelligence is transforming the future of fashion.
 

Examples of Artificial Intelligence in the Apparel Industry

 
Here are five major ways that artificial intelligence is transforming the future of fashion.
 

1. Manage inventory

 
Accurate inventory management is a huge pain point for apparel brands. Retailers need to keep enough stock to keep business moving but not so much that it drains cash reserves through unsold product.

To do this, you need to be able to predict demand. Machine learning algorithms use historical data to make predictions and choices. AI-powered tools for demand forecasting use these algorithms to help solve the age-old industry pain point.

These tools can help retailers reduce forecasting errors by up to 50% while reducing inventory by 20-50%.

When it comes to planning for Q4, it’s never too early to start thinking about your approach. Put your process on paper so you can assess if there are opportunities to streamline or automate time-consuming tasks.
 

Liz Adamson, Founder & Lead Consultant at Egility

 

As Liz Adamson, Founder & Lead Consultant at Egility, said, “The time to plan for Q4 is now. Reviewing last year and year to date sales data, forecasting inventory levels, and planning POs and shipments to make sure you are fully stocked for the holiday rush.”

“But it’s not just about inventory levels. You should also be auditing your product pages, making sure that they are in top shape and ready for the Q4 traffic. Make sure you are fully utilizing your copy, images and Enhanced Brand Content and have great content that will convert clicks to purchases.”

 

2. Connect with customers

 
Many fashion retailers use AI chatbots, also called smart assistants, to connect with customers and provide product recommendations. This scalable method of customer service can help retailers save money on customer service staff while building customer loyalty.

For example, Levi’s uses a chatbot — the Levi’s Virtual Stylist — to help their customers choose the perfect pair of jeans.

 

 

The Virtual Stylist helps shoppers find the fit, rise, and stretch that works best for them. In addition to providing a custom shopping experience, the bot helps reduce returns by matching shoppers with the right size before purchase.

Tommy Hilfiger is also a leader in the chatbot trend for fashion brands. Their Facebook Messenger chatbot offers a personalized and interactive shopping experience.

Users can browse the brand’s latest collections, as well as share their personal style preferences for style advice and product recommendations. The chatbot also uses machine learning language processing technology to reply to customer queries.

 

3. Tailor recommendations

 
In order to keep costs low, brands need to better predict customer preferences by gathering and analyzing purchase data. Using this data alongside AI and machine learning allows fashion retailers to create clothes that customers want to buy.

Fashion subscription startup Stitch Fix uses AI to tailor clothing and accessory recommendations to its subscribers.

 

Image result for stitch fix ai

 

Stitch Fix’s AI tools analyze a customer profile, then sort through millions of combinations of clothing to handpick items that fit the customer’s taste and budget.

Stitch Fix delivers a personalized selection of clothes to customers each month; customers keep what they love, then return the rest. Stitch Fix collects feedback to provide even more tailored recommendations the next month.

 

4. Reduce returns

 
Returns are a pain for shoppers and retailers alike, especially in the fashion industry.

According to IHL research, retailers lose about $642.6 billion every year from preventable returns.

“Provide clear details in advance, pictures, descriptions and anything else the customer needs to make a good buying decision. It’s better to make wise decisions about operations than wish [returns] would go away.”

– Jeremy Bodenhamer, Co-Founder and CEO of Shiphawk

Clothing returns take approximately 3x longer to inspect than other verticals, making operational costs high. Even worse, apparel that is returned in anything less than perfect condition can be considered damaged and unsellable.

AI can help retailers personalize the shopping experience, leading customers to make more informed purchase decisions. In addition to improving customer satisfaction, this reduces the return rate and saves retailers money.

 

Image result for asos

 

For example, online fashion retailer ASOS uses AI to recommend clothing sizes to shoppers based on what they’ve purchased — and kept — in the past.

This data helps inform future size recommendations, which in turn provides a better customer experience, reduces return rates, and saves on return processing costs.

 

5. Improve product discovery

 
Another AI-powered retail trend, visual search makes it easier than ever for shoppers to discover and purchase the products they want. Shoppers simply snap and upload a photo of the product they want, then AI identifies the pictured product (or similar ones) across multiple sites and retailers.

One example is Google Lens, which allows mobile users to snap a photo of a product, then find (and buy!) similar styles through Google Shopping, right from their smartphone.

Similarly, Pinterest’s Lens feature uses AI technology and the camera of the Pinterest App to search for visually similar pins.

According to Evan Sharp, co-founder and head of product at Pinterest, “Sometimes you spot something out in the world that looks interesting, but when you try to search for it online later, words fail you.”

“You have this rich, colorful picture in your mind, but you can’t translate it into the words you need to find it. At Pinterest, we’ve developed new experimental technology that, for the first time ever, is capable of seeing the world the way you do. Now there’s a way to discover ideas without having to find the right words to describe them first.”

 

pinterest advertising

 

To benefit from visual search and make their products more discoverable, fashion retailers should make sure that their product photos are high-quality and up-to-date.

 

Looking forward

 
It’s clear that the future of fashion will be shaped in large part by advancements in AI and machine learning technology. Retailers will likely continue to leverage artificial intelligence to increase efficiency, save on costs, and, ultimately, create a best-in-class shopping experience for customers.

Moving forward, AI will continue to give brands a competitive advantage in the crowded fashion industry — and we can’t wait to see what’s next.

About the AuthorTara graduated from the University of New Hampshire with a B.S. in Journalism / Business. Her passion for creative publishing and quality reporting landed her work opportunities at several companies in Massachusetts, New York and California. She is a leading voice behind CPC Strategy’s Blog. See all posts by this author here.