• September 08 2022

Visual Recommendation Algorithms: The Next Big Thing in Fashion Ecommerce

Style is unique—everyone has their own preferences when it comes to what to wear.

Fashion retailers are tasked with helping shoppers find their ideal products. These include shirts, trousers, sunglasses, or any other fashion must-have.

How do they do that?

With visual recommendations, that’s how.

Retailers must embrace new technology to increase sales and continue to meet shopper needs. Let’s take a look at how visual ecommerce product recommendations can change the game for fashion ecommerce.

Why fashion ecommerce sites get recommendations wrong

Put simply, fashion sites get recommendations wrong because they don’t personalize suggestions to each shopper. They recommend products based on the site visitor’s demographic details, such as age, geography, and income.

For many types of ecommerce sites, generic recommendations just don’t work. Fashion is one example of this—demographic-informed suggestions rarely provide unique recommendations for individual users. 

AI-powered visual recommendation algorithms do much, much more—let’s take a look.

How do visual recommendation algorithms work?

Visual recommendation algorithms in ecommerce use AI vision and machine learning to provide product recommendations to your online shoppers. It goes further than identifying the items in the image, such as distinguishing a shirt from a sweater. It does so by considering more detailed information. This includes information on the product’s material, cut, color, and more.

AI recommendations consider the browsing and purchase history of your shoppers to provide more relevant recommendations. This leads to 8% more revenue per session. These recommendations help to drive sales in a number of shopper situations:

  1. Similar look: shoppers don’t always find exactly what they’re looking for in the first product they find. Visual recommendations help bring them one step closer by considering the product in question, alongside browsing history, to provide similar products online shoppers are more interested in.
  2. Shop the look: enable shoppers to easily identify and shop other items included on a product display page (PDP). For example, say the PDP is for a pair of shorts but the model is also wearing a bracelet and a pair of shoes. Visual recommendations give shoppers the chance to easily find these items in your product catalog by displaying them as product recommendations.
  3. Complete the look: AI vision can also offer complementary product recommendations based on style and occasion. This gives shoppers personalized recommendations for adding to their look with items like shoes, sunglasses, bags, and more.

Smart Recommendation Algorithms thus helps shoppers see and purchase their most preferred products immediately. They do these without having to browse the entire product catalog.

recommendation algorithms

How are visual recommendation algorithms different?

AI visual recommendations offer more accurate product recommendations to shoppers by considering purchasing and browsing history. This enables your ecommerce site to show more accurate, relevant recommendations to shoppers. This happens both on your website or in retargeting emails.

There are a number of ways that visual product recommendation systems outperform traditional, solely demographic-based recommendations.

Visual recommendations improve the customer experience

AI-powered visual recommendations provide better product suggestions to your customers. 86% of buyers will pay more for a better customer experience. Thus, optimizing all aspects of the buying process is a must for fashion ecommerce retailers.

Smart Recommendations will display products that are hyper-relevant to each individual shopper. This means you’re not just selling products. You’re also giving your customers a direct path to shop your complete brand look and lifestyle.

Visual recommendations increase ecommerce conversion rates

Using visual recommendations for products leads to more conversions than providing generic recommendations. Visual AI recognizes products within images and returns recommendations relevant to the specific item being viewed. 

The visual approach is a better indicator of customer intent. It has been proven to uplift click-through rate by 30%, and conversion rates by 1%-9%

Visual recommendations work faster

Visual Recommendations go further than text search and empower shoppers with an intuitive product discovery based on product images. This makes website navigation quicker and easier for shoppers—allowing them to find what they’re looking for, faster.

Additionally, AI-powered visual recommendations save you time, too. Manually tagging and organizing an entire product catalog is time-consuming and inefficient. Opting for a visual recommendation solution—like ViSenze—can reduce manual tagging effort by up to 45%—a win-win.

Visual recommendations don’t need customer data

Optimizing your ecommerce site with visual recommendations doesn’t require the same personal details that demographic-based recommendations require. Basically, you don’t have to secure the same personal information. You also don’t need to deal with the legal responsibilities associated with collecting and holding them.

Visual recommendations for your fashion ecommerce business

Ecommerce is changing, and brands need to keep up to continue meeting the evolving needs of online shoppers. Providing a better customer experience helps position your ecommerce brand as a leading brand. All in all, this leads to increased conversion rates and customer retention.

Moreover, getting started is easy.

Simply install the ViSenze app on the Shopify store, sign up to the ViSenze Discovery Suite platform, and get started. From there you can connect and import your product catalog. Then let the catalog enrichment solution work its product tagging magic! Lastly, publish the tags to your Shopify store to enrich the customer’s ecommerce site search experience. 

Download ViSenze on Shopify today! Henceforth, start benefitting from increased organic traffic, a lower drop-off rate, and increased AOV. Do all this while reducing manual tagging effort.