Despite the importance of good quality product discovery for eCommerce, there are still challenges that keep retailers from creating superior discovery experiences. 

Today’s biggest challenges with product discovery for eCommerce include:

• Good product discovery requires good product data. 
  
  
• Manually tagging data has limitations when trying to get richer product data, from high labor costs to slow, inefficient tagging, to inevitable human errors. Therefore, when product data is poor, retailers are doomed from the start.
  
  
• Product discovery journeys aren’t designed with shoppers front-of-mind.
  
  
• Designing amazing product discovery experiences requires constant testing, which is a full time job for engineers and designers. Most eCommerce websites can’t afford to commit to testing new discovery journeys in-house because there are more important, burning issues to resolve.
  
  

Be strategic: Well-designed discovery widgets, with proper placement, can be beneficial for both shoppers and retailers. While shoppers benefit from better discovery, retailers can gather good data about shopper behavior, and use the data for further optimization.

A good product discovery experience is constantly inferred based on user input, which requires someone to monitor and optimize product discovery based on user input.

At YesPlz, we specialize in solving eCommerce product discovery problems, with auto-product tagging and pre-built widgets that are based on hundreds of hours of user interviews. Our machine learning constantly learns from user behavior, and optimizes the results.

Leading fashion retailers and brands have partnered with us to improve their product discovery.

Partner with YesPlz for product discovery 

Tip 1: Utilize smart product tagging to improve product discovery for eCommerce

Smart product tagging (also known as AI tagging) is a method of tagging products using computer vision. Fashion-trained AI can quickly scan a product image and identify the key product attributes, extracting them to then build other discovery tools. 

Why is smart product tagging important?

Our research shows that shoppers tend to browse products with an open search mindset, with specific attributes in mind, so all product attributes should be accurately tagged. That way, shoppers can find the product they're looking for when using filters or recommendations.

Another key benefit of smart product tagging: accurate search results, with no surprises. We can’t talk about product discovery without mentioning the need for highly accurate search results. Smart product tagging reduces inaccurate search results, making discovery smoother. 

And, smart product tagging eliminates double-tagged/similar names that can confuse shoppers, and makes it easier for them to find what they are looking for

Smart product tagging sets up retailers to be able to use other eCommerce product discovery tools in the YesPlz ecosystem–but the first step is accurately tagging the product catalog.

Without fashion AI, tagging is a long, tedious, and inaccurate manual process with many mistakes. 

Tip 2: Create product discovery for eCommerce applications: filtering, search, & recommendations.

Smart Product Filtering:

Tap into the Visual Nature of Shopping with a Virtual Mannequin Filter

Shopping is visual in nature–yet, most product filters are text-based, missing the opportunity to tap into visual cues. 

The Virtual Mannequin Filter, by YesPlz AI, is an example of a visual search filter that is powered by AI, and allows shoppers to filter products on a virtual mannequin, using visual cues.

When shoppers can easily filter, the product discovery process is easier, leading to increased average cart sizes.

Use Fashion AI to Create Dynamic Filtering 

Shoppers still prefer to search within their own parameters (ie silhouette, color) even when in a product discovery mindset. 

Implementing dynamic filters is a best practice in eCommerce, but can be difficult to build because of complex computations. 

With fashion AI, we can build automated dynamic filters that empower shoppers to personalize their search.

Occasion filters that are powered by Fashion AI tap into product discovery that shoppers want

Occasion or theme filters are a great way to help shoppers discover products, and get inspired. Occasions can include: work, night-out, daytime, casual, wedding guest (and much more).

Although occasion filters are a best practice in eCommerce (The Good), they’re difficult to implement manually because of different interpretations of what a specific occasion means.
Because fashion is subjective, everyone has a different opinion based on their own personal experience

How do we solve this problem?

 By first, crowd-sourcing definitions for occasion with real customer answers, then using fashion AI to build occasion filters based on the product attributes that constitute a specific occasion

When occasion filters are accurate, they boost product discovery by allowing shoppers to explore within a set of parameters, making discovery more intuitive.

Highly effective product recommendations:

Using fashion AI to create highly effective product recommendations is another way to improve product discovery for eCommerce. AI-powered product recommendations can keep customers on-site for longer, resulting in higher basket sizes, and increase overall conversions. A well-trained AI algorithm can return product recommendations that are relevant to the original search query.

At YesPlz, we’ve built You Might Also Like and Frequently Bought Together recommendations for top retailers, who saw an increase in conversions. 

You Might Also Like recommendations draw from the takeaways from YesPlz user interviews: shoppers value silhouette, cuts, and vibes. We match product attributes that go further than simply matching a product title. Instead, we identify the core product attributes.

Frequently Bought Together recommendations draw from shopper behavior and patterns, connecting shopper choices to groups with similar interests. 

Enhanced site search inspires shoppers to interact different with products:

Text search is often considered to be “separate” from product discovery, but they’re closely related. Although a shopper may input a specific search term, she is still looking to discover new products closely related to her text search.

With YesPlz fashion AI, we’ve created 3 ways to make text search an integral part of the product discovery experience:

1. Include popular trending keywords to make shoppers aware of new products to optimize text searches

2. Auto-populated search suggestions give shoppers new ideas to power their search. Customers who land on an autocomplete page suggestion are six times more likely to convert than those who don’t.

3. Including product image previews is another way to spark discovery, because we’re tapping into the visual nature of shopping, leading shoppers to make new connections between what they see and what they want.

Tip 3: Inspiration as part of product discovery for eCommerce, using the GPT Fashion Stylist:

AI Styling that can also show off the full richness of a retailer’s product catalog, with GPT fashion stylist. 

Now we can interpret shoppers’ intentions by understanding natural language, thanks to modern technologies. That means, GPT Fashion Stylist can do more than exact keyword matching search. A machine now produces natural language and makes styling suggestions based on a shopper’s preferred style parameters.

Fashion AI is what powers the GPT Fashion Stylist, YesPlz’s styling assistant that combines ChatGPT with in-house fashion AI to give curated product recommendations for any prompt. 

Another key aspect of product discovery is giving shoppers the opportunity to discover the full depth of a product catalog. AI-powered styling gives retailers the opportunity to show off lesser-known items in their catalog, while still answering shopper prompt questions.

AI styling boosts product discovery by allowing shoppers to interact with brands in a totally unprecedented way. Retailers can learn from these conversational interactions and refine the product discovery process to reflect shopper behavior.

With curated products based on favorited items and the ability to remember conversational history, shoppers are more likely to discover new products that reflect their unique tastes, leading to improved product discovery for eCommerce. 

The Time is Now to Improve Product Discovery for eCommerce with Fashion AI

Modern technology can process millions of data points in seconds, and is capable of making personalized recommendations, therefore creating highly relevant product discovery.

However, it still requires work to train a machine to be a fashion domain expert, and to find the right use cases to apply the technology to shoppers.

We’re seeing more leading fashion retailers now spending time to design unique product discovery experiences for their shoppers as a way to stay ahead of the competition. 

Now, more than ever, we have technology, especially breakthroughs with LLM, so we can do a much better job helping shoppers to have delightful shopping experiences. 

Contact us for a demo and see how product discovery options work for your eCommerce shopping experience. 

Schedule a free 20-min demo