August 28, 2020
Computer vision is one of the new technologies that make online retailers stay competitive and provide more superior customer experiences.
Moreover, computer vision has the ability to automate manual processes and therefore retain customers and reduce costs.
Computer vision refers to collecting information from images or multi-dimensional data through artificial systems.
The technology can process and image and understand what's on it. Then, artificial intelligence (AI) makes decisions based on the information obtained.
The goal is to automate the actions of the human visual system, cut costs, and save time.
Although still in its beginnings, computer vision progressively becomes part of our everyday life.
The technology is expected to expand rapidly during the next few years, impacting everyone's lives through its use in various industries.
Visual search, product recommendations, facial recognition, and inventory operations automation, are only some of the benefits retailers can enjoy by implementing computer vision.
In this article, we'll dive deep into the ways computer vision is transforming online shopping.
Visual search is one of the most common techniques of leveraging deep learning to improve the user experience.
Moreover, this product discovery option eliminates the drawbacks of text-based search.
As it's very challenging to use words to describe some search queries, visual search is the perfect solution that makes product discovery more convenient.
Thanks to deep learning algorithms, visual search can detect the features of the image and provide results with a similar style.
Visual search is still at its very beginnings, accounting for 27% of searches across ten significant search properties.
However, the technology has great potential to transform the way we search for products online.
In fact, 62% of millennials prefer visual search to other technologies.
They like the comfort of the process; they take a picture of the product, find it, and purchase it online.
eBay was one of the first online stores to adopt visual search, along with other computer vision features.
Home Depot, Alibaba, Forever 21, ASOS, Rakuten, and many others also recognized the technology's potential and used it to take customer experience to the next level.
This is one of the implementations of AI in fashion that are transforming the entire industry.
Suggested read: The Essential Guide to Visual Search in Fashion Ecommerce
Online shoppers are embracing the convenience visual search has to offer. It's now sure that this technology is going to transform the online shopping experience.
Instead of an option, it's becoming a requirement for online retailers who want to stay in the game.
Customers want to see the most relevant products that match their search, or the ones at reduced prices.
On the other hand, online retailers want to find ways to sell their website visitors more products.
Product recommendations are a win-win situation for both sides.
Pattern matching algorithms can examine a wide variety of e-commerce behaviors to evaluate the characteristics that drive positive recommendations.
A Barillance study notes that personalized product recommendations can dramatically increase the AOV (average value of the order).
Their study, carried out with 300 randomly chosen consumers, found that product recommendations constitute up to 31% of online shopping sales.
These numbers make the combination of computer vision and AI in fashion a real win-win.
Product recommendations are a tool that can show customers similar products to what they already searched. It can be beneficial for different situations.
For example, some visitors just come to your website and just search for random items.
This is when you can convert them into customers by offering them product recommendations.
For those who know what they want to buy, you could show them various alternatives for the same product.
Moreover, you can increase your revenue by offering complementary products to customers who've already decided what to buy.
Suggested read: The What, Why, and How of Product Recommendation Engines
Computer vision can automate customer service answers to questions that don't require a lot of effort.
These can be questions about colors, sizes, working hours, styles, etc.
Computer vision detects the object the customer sends us a picture.
Then, AI uses NLP (Natural Language Processing) to understand the question. Based on the language it has detected, the AI answers the question.
This is a handy feature that could save you a lot of resources.
You won't have to hire customer service representatives to perform these tasks manually, and your existing employees will be able to focus on actions that bring growth.
At the same time, the use of AI in fashion is creating a next-level customer experience.
Augmented reality incorporates computer-generated elements with real-world scenarios.
For example, this could be your online retail store's product placed on a background from reality.
This means that customers can try out fashion items with a photo of them or see how a chair would look in their living room.
In this case, computer vision locates and rotates the product correctly to make it look real in the scenery.
Aside from visual elements, sensory modalities like auditive or haptic can be added as well.
Customers are loving such interactive environments that make shopping possible without trying the product in person.
Retailers should implement exciting new features like AR to surprise customers with a more dynamic experience and raise sales.
Computer vision is just starting to show its abilities.
This technology is yet to become significant. Below are just some of today's uses:
Computer vision doesn't end here.
Cashier-free physical stores, security scans powered by AI, delivery in self-driving vehicles, and many other new discoveries are probably our future.
Pixyle’s computer vision-powered e-commerce tools are the right choice if you’re looking to upgrade your online retail store.
Transform customer experience by embracing the power of computer vision. Try our demo now.