Published on
September 21, 2022
To help shoppers find that perfect piece of clothing that fits their styles, more fashion retailers are turning to artificial intelligence (AI).
As one of today’s emerging trends whose popularity is growing, AI is able to translate huge sizes of data into actionable insights that help retailers make data-driven and informed decisions. It is essential to connect with technology and become more data-driven to understand where the consumer is going.
A study by IBM predicted that 80% of retail companies would be using AI within the next three years, which means AI is here to stay.
If you’re thinking about creating a fashion e-commerce website or you’re on the lookout for technological trends that will boost your existing e-commerce store, make sure you keep on reading!
Below, we’re going to take a look at all the different ways AI can help fashion retailers make improvements in their business.
By analyzing customers’ behaviors, AI can make relevant product recommendations to shoppers.
This information shouldn’t surprise you, as you’ve surely purchased a product that popped up in your recommendations while you were looking for something else.
Retailers can learn plenty of things about their customers’ purchases and preferences using the data collected with AI technology.
This allows fashion retailers to customize their offering based on customer preferences and sell targeted products.
Suggested read: The What, Why, and How of Product Recommendation Engines
AI uses top styling principles and the latest trends in fashion to show “Complete the look” recommendations for each product in a fashion store.
Oftentimes, customers want to get items of clothing but aren’t sure how to style them.
These types of recommendations show customers how they can wear different products together.
An example of this is Forever 21, where once you scroll down the item you’re currently looking at, you will find a “Complete the Look” section with other items you can combine.
Every time a shopper sees the item they wanted is currently out of their size or out of stock, they leave your e-commerce store, and you lose money.
The solution for this is an AI algorithm trained with behavior patterns and customer data that will offer similar product recommendations.
One example of an online fashion retailer that uses AI for relevant product recommendations is Free People.
Once you discover a product, the product recommendation engine suggests similar products in the “Similar Items” section.
Suggested read: How do recommendation engines for fashion eCommerce work?
As you’ve surely heard by now, customization is a bulletproof way to win over your shoppers’ hearts.
Consider investing in AI that will analyze information from uploaded images, detect patterns in photos and suggest similar products that will meet the preferences of your customers.
An online fashion company that has excelled at doing this is Thread. Thread has an online virtual stylist that applies Artificial Intelligence to help people dress better.
After you submit your measurements, preferred stores, styles, budget, and information about your current wardrobe, the stylist suggests clothes you can buy from their partner’s catalogs.
The fashion company collaborates with luxury clothing brands like Burberry, as well as high street retailers such as Zara.
Another great example of an AI fashion stylist is Nodstorm, where at the bottom of each item, you get style ideas for other pieces you can purchase.
If the customer support department of your fashion store answers questions too slowly, you will end up with disappointed customers leaving your site.
According to statistics, live chat interactions result in a 10% increase in average order value.
However, having a customer support team with hundreds of employees can be really pricey.
If you want to keep costs low but not have to sacrifice the quality of the services you provide, consider integrating an AI-powered chatbot.
Chatbots are one of the most popular AI applications in the fashion industry, and they have become increasingly mature with the progress of machine learning models and algorithms.
This has allowed chatbots to assist customers at different stages of their buyer journey.
Chatbots help customers find items on the site, notify them about new items, remind them about discounts, and offer products similar to what they’ve picked.
Thanks to AR and AI-based mobile apps, customers can try out a product before they purchase it and see how it would look on them.
Nowadays, augmented reality (AR) in fashion is gaining popularity.
According to Business Insider, 75% of customers are expecting retailers to offer an AR experience.
Retailers can better predict the demand for different products and avoid shortages with the help of AI.
Fashion stores can analyze various events such as trends, weather patterns, and customer behavior in real-time.
As a result, they can make more informed decisions when it comes to their supply chain management.
Using AI, retailers are now able to offer better deals, get more customers, and increase sales by coming up with the best pricing strategies.
Fashion stores can get insights into customer buying behavior, pricing of competitors, and demand levels.
Consequently, this will allow for better predictions in terms of future market and customer behaviors, and better strategies when it comes to pricing.
Adjusting the prices according to the wishes of the customers and demand for products is one of the best ways of coming out with a pricing strategy, and AI can help with this..
AI offers multiple solutions to fashion retailers to effectively address the struggles of shoppers and deliver an amazing customer experience that will set them apart from their competitors.
There’s no question that the fashion industry will continue to reimagine the shopping experience, supply chain, and internal business structures.
However, with AI technology, retailers will be able to do it in a much faster, cost-effective, and more creative way.
To find out what Pixyle can do for your business, book a demo with us!
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.
How to develop solutions that help shoppers find what they are looking for.