Published on
September 24, 2021
Fast fashion is gaining popularity in the fashion business, as it provides a broader range of clothing goods in a more timely and cost-effective manner.
To achieve this goal, fashion designers must produce a large number of different fashion goods in a short period of time.
Simultaneously, fashion merchants are focusing on leveraging technology to create and deliver items that are personalized to their customers' requirements and in line with the latest current trends.
Researchers from the University of California and Adobe proposed a method for AI to understand a person's style in 2017.
By analyzing data and producing customized messaging based on customers' purchase behaviors, they managed to produce computer-generated pictures of accessories that fit that style.
The technology, which serves as the foundation for outfit builders (a feature seen on some e-commerce sites), enables businesses to design personalized clothes and even forecast wider trends.
Referral systems may be able to assist online shops in determining what customers desire in addition to what they already have.
Since 2017, things have changed. Many companies have implemented AI as a way to deliver a personalized shopping experience and increase customer retention.
In November 2020, Google Cloud published research that claimed that retailers were aiming to employ AI in 10 various areas of their companies, ranging from demand forecast to customer loyalty programs and product personalization.
Statista, moreover, predicts that AI in retail will reach $23.32 billion by 2027.
The technology gained even more popularity during the Covid-19 pandemic. As it became risky for individuals to go out and buy, or even try on clothing, AI-enabled virtual fitting rooms came in handy.
Brands like ASOS, Macy's, and Adidas even started doing virtual photo-shoots. Clothing can be mapped onto people's bodies using AI, which can be models or potential consumers who input their own photographs to an app.
Visual similarities are also something that AI can leverage to provide a better customer experience.
In the next section, we’re giving an example of one of the ways you can leverage the power of AI-based algorithms.
Suggested read: How Is AI Going to Affect the Fashion Industry in 2024
Customers will leave your retail store whenever they find out that you don’t have the product they want or that you offered them the wrong size. And every time they leave your store, you lose revenue.
The solution is based on AI and computer vision, a combination that results in a sophisticated product recommendation engine.
Unlike existing AI algorithms that are trained on consumer data and behavior patterns, this artificial system gathers data from product pictures, analyzes features like color, pattern, and style, classifies them, and proposes additional things based on visual similarities.
Visual product recommendations represent a way for online stores to offer their visitors more alternatives and allow them to find products they’ll love.
For every product on a fashion retailer's online shop, AI utilizes visual recognition and key product characteristics to propose visually comparable alternatives.
Customers are led to various relevant product pages on the retailer's website when a product is out of stock or size. They may quickly discover what they're searching for without having to go through the process of redoing product searches, which might lead to dissatisfaction and abandonment of the site.
Relevant product recommendations boost client engagement and reduce the number of lost sales opportunities.
One of the first and most successful examples of integration of this technology was the partnership between Macy’s and IBM. Macy's teamed up with IBM Watson to incorporate personalized product recommendations with the help of IBM’s cognitive learning capabilities. When you begin to explore products, the "More Like This" section will recommend aesthetically related products. Filters such as color, patterns, style, and pricing can be used to generate similar product recommendations.
According to Broadridge, 77% of consumers have bought, suggested, or paid extra for a product that provides them with a customized experience.
The advantages that product recommendation engines have to provide much surpass any short-term costs they may cause. Let’s see some of them.
Your users want to find what they like. And they want to find it fast.
They don’t want to spend hours searching for products on your website.
Personalized product recommendations keep your visitors engaged for a more extended period of time. This means that they will stay longer on your website, reducing your fashion e-commerce store’s bounce rate.
The lower your bounce rate, the higher your Google ranking will be.
Offering more options makes it more likely for you to show a product that your visitor will like.
Personalizing the experience means showing your visitors what they are most likely to buy, which leads to higher conversion rates and more items in the product cart.
Don't let non-targeted offers cost you conversions; instead, utilize sophisticated product recommendation systems to boost your average order value.
Suggested read: 6 Tactics to increase your Average Order Value
Cart abandonment costs e-commerce businesses $18 billion each year in sales revenue.
Cart abandonment has become a hot issue that e-commerce businesses can no longer afford to ignore, with $4 trillion in products expected to be abandoned in digital carts alone next year.
Personalized product recommendations can help you fight this big e-commerce issue.
Exit-intent popups are a proven strategy for showing abandoning visitors items they're likely to be interested in just as they're about to depart your website.
Whatever product you offer should be based on their surfing history and personal interests for optimal conversions.
There are many opportunities to leverage a product recommendations engine to reduce cart abandonment. Before finding the most optimal one, it’s best to try and test several strategies.
To be a successful e-commerce store, you must provide consumers with the items they want based on collecting data about their preferences.
This straightforward idea applies to all sorts of industries, including fashion. By giving your consumers what they want, you may avoid storing up on items that no one is interested in.
Personalized product recommendations are also helpful in this situation.
They provide you with a thorough understanding of what your consumers are looking for. Then, armed with this knowledge, you can make an informed decision about which items to stock up on.
Website visitors may use product suggestions to help them discover exactly what they're looking for.
To leave a positive image that would entice customers to return, the online buying experience must be as seamless as possible.
You're missing out on too many conversions if you haven't gone the extra mile to customize your online retail shop. Furthermore, you're passing up an opportunity to build a seamless purchasing experience for your customers.
Improve your online offering by selecting the product suggestions that are most appropriate for your brand and target audience. Create a consumer journey that makes buying simple and enjoyable.
The similar recommendations engine by Pixyle.ai might help you tailor your online offering. You can improve the exposure of your goods, the relevancy of your offering, and the entire revenue stream of your business.
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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.
Pixyle AI generates e-commerce product data that enables brands, retailers and marketplaces to deliver exceptional product discovery experiences.
