How E-Commerce Personalization Can Boost Conversions by Using Visual AI

Published on

July 3, 2020

Site Search & Product Discovery
Shop the look for a girl wearing white tank top, jeans and black belt

Customers want an experience that meets their personal requirements.

Unfortunately, many brands still haven’t discovered the importance of personalization.

A Deloitte paper claims that customer expectations and customer experience often don’t meet.

In fact, 34% of customers feel like standard products don’t meet their expectations. 

This gap is hard to fill. When it comes to personalization, we often hear that if you don’t do it, you’ll lose the battle to your competitors.

It sounds like something urgent, scary, and like something you should do immediately. Others just stick to some basic forms of personalization, like displaying the name of the shopper on the landing page.

However, doing this means tackling only the surface.

Personalization is much more than just calling your shopper by their name.

Personalization is a strategy that improves the overall performance of your business.

You should consider it as a tool that improves the customer experience while improving your conversion rate

What is Visual AI?

Visual AI is one of the ways you can turn your personalization strategy into a serious metrics booster.

Visual AI is the capability of computers to analyze, process, and interpret image data.

Among other uses, this technology is used in automatic tagging, similar recommendations, and visual search engines, making customers more engaged during the buying process. 

As the human brain interprets visual information much faster than textual, the visual AI technology works the same way.

Visual AI is so powerful because it’s wired in the same way humans process visual information of what they see.

That’s why it’s very useful when it comes to making e-commerce stores user-friendly and responsive. 

Here’s how visual AI can boost the conversions of your e-commerce store: 

Valuable analytics with automatic tagging

Automatic product tagging uses advanced AI algorithms to eliminate manual product tagging by organizing and tagging the photos of the product catalog based on their attributes.

Such algorithms speed up the tagging process, making it streamlined and reducing the need for human interaction, thanks to Deep Learning. 

This process generates metadata for catalog assets. It basically scans the image, detecting the attributes that are related to particular keywords. 

The Deep Learning algorithms automatic tagging uses help you identify and understand the performance of your products and their attributes.

They generate valuable insights about the behavior of your website’s visitors, like preferences, demographic, and geographic characteristics. 

A greater number of descriptive tags enable you to get more information on what customers want and to make more personalized offers.

This way, you can detect and analyze trends, preparing your e-commerce store for future shoppers.

Furthermore, clean and accurate tags will minimize refunds as customers will see more quality information, and they won't be shocked when the fashion item is shipped to their doorstep.

Aside from personalization, this data will help you make smarter business decisions and reduce your business risks when it comes to design, prices, and buying processes.

You’ll also minimize the chances of ending up with overproduction, waste, and price subtraction.

In fact, IBM identified that 62% of retailers believe that the use of information and data analytics gives them a competitive advantage. 

Suggested read: How to get mind-blowing data analytics for fashion retailers with visual AI

Automatically generated tags for an ecru top, knitted long sleeves cardigan, and abstract skirt

Similar product recommendations for easier discovery

This feature is a must-have for your e-commerce store. It can make your offer more valuable, making your conversion rate higher, and increasing your sales. 

According to Barillance, product recommendations significantly improve the average order value of the store.

Moreover, the report also claims that product recommendations account for up to 31% of online retail revenues.

Online stores like Sephora and Asos are aware of the benefits similar product recommendations can bring, and they have already incorporated this feature into their online stores. 

Based on their behavior, this is a tool that shows website visitors the items they are most likely to purchase.

For example, they might be looking at a product they like, but the similar products section shows them even better versions of their product. 

They also might know what they want but don’t know which alternative to choose. Or, they could be interested in some complementary products to those they already purchased. 

Whatever the case, product recommendations are here to guide your shoppers through their website and help them make a decision easily.

Research by SalesForce with 150 million shoppers claims that shoppers who clicked a product recommendation spent an average of 12.9 minutes on the site, while those who didn’t click left after 2.9 minutes.

So, product recommendations are a factor that directly influences sales, with the ability to give your e-commerce store a serious boost. 

Suggested read: 6 Powerful Ways AI Can Help Fashion Retailers Provide Shoppers With Accurate Recommendations

More precise search results with visual search

Many users admire celebrities on the interior design of their homes or their fashion choices.

That’s why they often want to have the items they own. To achieve this, they search for similar items at more affordable prices.

Visual search is a very useful tool for this, with’s visual search engine having 87% accuracy in predictions.

This is a feature that allows website visitors to search for photos instead of keywords. They can use any images—screenshots, photos from the internet, or photos they’ve taken themselves.

Thanks to the AI engine, the technology has the ability to find a similar item within seconds. 

When it comes to fashion items, for customers, this is a much easier and convenient way to search than text-based search.

On the other hand, for retailers, this is an open door to external customer insights that show the latest industry trends and styles.

Based on the data, online retailers can create new designs, promote their best-selling items, and analyze customer behavior.

With machine learning algorithms applied, retailers can predict the behavior of every user and capture their attention much faster.

Suggested read: How Visual Search Strengthens Your Retail Brand and Increases Customer Loyalty

Process of visual search in three steps: image upload, image analysis, visual search top results

Personalization comes in all shapes and sizes.

However, what the modern customer needs are more advanced means of personalization that show them exactly what they want. 

Not every personalization type can hit the mark.

To be successful, it has to be done in a clever and targeted way, delivering relevant results

Don’t miss out on conversions and go beyond just displaying the user’s first name.

Move one step forward and build a smooth buying process your shoppers will love. 

Pixyle’s visual AI can take your e-commerce personalization to the next level. Improve product visibility, make your offering more relevant, and skyrocket your conversion rates. 

Try our demo now.

Product Discovery

How to develop solutions that help shoppers find what they are looking for.

Product Discovery

How to develop solutions that help shoppers find what they are looking for.

Product Discovery

How to develop solutions that help shoppers find what they are looking for.

Product Discovery

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