Published on
July 16, 2021
The fashion industry went under a significant transformation during the previous year.
Between April and June 2020, when worldwide coronavirus infections peaked, production in Europe's apparel sector decreased by 37.4% compared to the same time in 2019.
Fashion products had the most drastic reduction in sales, with a 43.5% drop.
In order to get back on their feet, fashion companies had to reimagine their models and turn to more advanced technologies.
AI for fashion has come up as one of the technologies that provide standardized information and easier product discoverability.
Visual AI is the new trend in the fashion industry that it’s here to stay.
Artificial intelligence (AI) is a technology that aids computers in interpreting information in a manner comparable to that of the human brain.
Machine learning algorithms allow the machine to make judgments based on the information it has gathered.
Computer vision is an artificial intelligence area that teaches computers to analyze and comprehend images.
Machines can reliably detect and categorize things using digital pictures from cameras and videos, as well as deep learning models, and then react to what they observe.
Visual AI combines the capabilities of computer vision with artificial intelligence.
This clever technology recognizes pictures and processes data using image processing and deep learning to turn it into practical knowledge.
The retail AI industry was valued at $2,306.8 million in 2018 and will increase to $23,426.3 million by 2026, according to a pre-pandemic study analysis released by Fortune Business Insights.
The main innovation drivers are computer vision and machine learning for fashion.
Visual AI is used in a variety of industries, including finance, security, and transportation.
In fashion e-commerce, visual AI offers a new level of consumer experience. Here are some of its uses:
After Google debuted its Search by Image function in 2011, the visual AI revolution began.
This was the first time people could submit an image instead of keywords and receive a list of photos that were comparable.
Pinterest was one of the first companies to use the technology, launching Pinterest Lens in 2017.
While the firm looked at how its visual search tool was used, they discovered that 80% of their users started with visual search when buying.
Pinterest chose to include shoppable pins in visual search results after demonstrating the popularity of visual search on their site. If the searcher has previously taken a photo of the desired item, this choice suggests that they have a strong desire to buy.
In order to make the search experience more user-friendly, many companies are now employing AI in fashion.
Unless the user searches according to the item's particular attributes, a simple keyword search rarely yields the exact thing the user was seeking for.
What happens if a consumer sees a poofy dress in an image and wants to buy it? They're looking for a poofy dress. They'll never locate the identical skirt they saw in the photo this way, though.
If only they could utilize the photo to find the same skirt... This is exactly what visual search provides.
Customers may use AI in e-commerce to discover the goods they desire just by submitting a snapshot of it.
So, anytime their favorite artist wears the prettiest dress, all they have to do is snap a picture and find a similar one online.
With the highly reliable results that existing algorithms can generate, it's easier for customers to look for and select what best suits their preferences and budget.
Suggested read: 3 Ways Visual AI Is Transforming The Retail Customer Journey & Meeting Their Ever-Growing Needs
Despite the fact that visual search is becoming more popular, keyword searches are still the most common way to find items online.
Adding appropriate tags to items can enhance their discoverability and lead to a significant rise in order volume.
When users search for a product, the search results are a series of pictures with descriptive tags describing the product's attributes.
Your consumers would not be able to discover a particular item you're offering in the search results if you missed tagging it.
You must identify your items with tags that are relevant to the qualities they possess if you want your buyers to locate them fast.
When people search for a "leather jacket," your e-commerce store's search engine will discover and show goods with this tag.
Manual tagging is a viable option.
However, this is a time-consuming and inefficient procedure, especially if your business has a considerable number of items.
The more accessible and more efficient alternative to manual tagging is automatic tagging.
Automatic product tagging is a method in which AI systems arrange and identify product pictures based on their characteristics.
Deep learning-based image identification algorithms automate and execute the tagging process without the need for human intervention.
This use of artificial intelligence in fashion e-commerce may also help you customize your online retail business.
Each tag click teaches the algorithm something new. You might be able to figure out what your consumers want to see on your website if you combine this data with data-driven past behavior.
Suggested read: The Complete Guide for Automatic Product Tagging in E-Commerce
Organic traffic comes from unpaid sources and has a big impact on how well your website ranks in search results.
This is a crucial part of your business expansion. Optimizing your website for search engines is one approach to increase organic traffic.
Search Engine Optimization (SEO) is a set of criteria that you must follow in order to rank higher.
Visual AI is a fantastic tool to increase the search engine ranking of your product pictures.
Your items will be tagged with keyword-friendly tags thanks to automatic tagging. Visitors will find keyword-friendly tags more accessible and clickable, which will boost your site's SEO.
The larger the relevancy of the tag, the better the chance of ranking higher.
Suggested read: How Automatic Tagging is boosting SEO, SEM, and Conversions with Automatically Enriched Product Data
Sephora helps customers find their perfect eye shadow just by scanning their faces.
At the same time, North Face uses IBM Watson’s cognitive computing technology to collect data and offer customers the ideal clothing pieces for their activities.
Moreover, Neiman Marcus leverages the power of visual AI to provide the visual search feature to its customers so they can find products quickly.
AI technology has progressed significantly since its inception, and we can now see it benefiting nearly every industry.
By using AI in your fashion e-commerce business, you will be able to adjust your website's search to the needs of your consumers.
The more data you give them, the more intelligent visual AI systems get, resulting in consistently more trustworthy information for customers.
The relevance of fashion visual AI and edge computing in delivering seamless commerce and optimizing customer experiences has risen substantially as a result of COVID-19.
We're on the verge of changing the way people shop.
Now is the right moment to use Visual AI to improve your consumers' experience.
The Visual AI package from Pixyle may help you take your e-commerce fashion store to the next level.
Make your product offering more discoverable and relevant to increase sales.
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.
