AI shopping assistants and semantic search: The new frontier of online shopping

The evolving landscape of ecommerce search

When customers need to find something on your website, they’re probably going straight to your search bar. Search is one of the most critical components of your website and yet, people are frequently dissatisfied with ecommerce search. Only 12% of U.S. consumers claim that they find exactly what they are looking for when they use the search bar to find a product. Irrelevant results and an overwhelming number of product variations can make the search experience dissatisfying for online shoppers.

This is changing, though, with advancements in the era of generative AI. Now, your search bar can understand what your customers are actually looking for, beyond just the words that they type. In this post, we’ll explain why traditional search methods fail to achieve the desired results, outline how AI technology is transforming the way people shop online, and an industry-shaping development from Amazon. Technology is changing the ecommerce landscape faster than ever and it’s important not to get caught flat-footed. Here’s what you need to know about changes to search.

Traditional search misses the moment

Search abandonment and its impact on retailers

It likely won’t come as a surprise to find out that ecommerce shoppers are dissatisfied with the search bars of most ecommerce sites. We’ve all experienced that feeling of frustration when we know just what we’re looking for, but it’s buried under rows and rows of irrelevant search results. It’s a significant problem for the industry. According to recent research, search abandonment costs retailers over $2 trillion annually across the globe with $234 billion of lost revenue in the U.S. alone. 

A poor search experience can tarnish your brand’s reputation. Customers expect precise results and 78% view a brand differently when these expectations aren’t met. A bad search experience can even deter consumers from returning in the future. With mounting competition in the ecommerce space, brands often only have one opportunity to impress potential customers. And, getting search right pays off. When searchers find what they’re looking for, 92% purchase that item and 78% buy at least one additional item with an average of 3 additional items purchased after a successful search. On the other hand, 53% of consumers abandon the website entirely when they have an unsuccessful search. Not having capable search functionality is one of the most costly mistakes an ecommerce retailer can make.

Inconsistent and underperforming search functionalities

Despite their potential to drive improved business outcomes and customer satisfaction, many ecommerce websites lack advanced search capabilities. Take product-specific search terms as an example: 71% of websites struggle with searches that don’t feature the exact words listed in a product’s title or description. Searching for “sofa” may return hundreds of results while searching for “couch” may return only a handful despite the likelihood that customers are looking for the same types of products for either query. Additionally, 42% of ecommerce websites don’t fully handle search queries that include non-product or feature-based terms. This can prove problematic as each customer searches differently. Research shows that 60% of users will leave a website if they can’t quickly find what they’re looking for.

AI comes to the rescue

Natural language processing and intent understanding

To see how AI can address the pervasive issues with ecommerce search, it’s important to establish an understanding of natural language processing (NLP) and semantic search. NLP utilizes machine learning to decipher the intent of what a customer is searching. Significant amounts of writing and text are used to “train” the system so that it can comprehend the nuance of written language, including synonyms, slang, typos, and more. This type of search function is also known as semantic search, as it is can understand the quirks of user-written queries.

Traditional keyword search, which matches the words used in the search query to the words included in product titles and descriptions, is largely incapable of adequately handling imprecision and more descriptive language. A good example is a complex phrase like “long floral dress with short sleeves.” Traditional search technology might return a plethora of dresses, some with floral prints and some with short sleeves. The user would then need to further refine their search using search filters or she might even take a “round trip” and try an entirely new search query. However, an AI-powered semantic search engine would understand that the customer is only interested in dresses that feature both floral patterns and short sleeves. This means much more relevant search results and a higher likelihood that the user finds what she’s looking for before abandoning the website altogether.

AI-enabled personalization in ecommerce

Further improving the ecommerce shopping and search experience is the ability to utilize AI to generate beautifully tailored search results and product recommendations. This technology can utilize data like browsing history, past shopping behavior, and personal preferences. When your system has a clearer picture of each individual user, it can prioritize results that closer match that person’s style.

Enhanced personalization goes beyond search results, too. You know that section on product pages that says, “People who bought this also bought these?” It’s often populated with fairly irrelevant products because people who bought that pair of pants are just as likely to have gone and bought an unrelated item from the website. AI-powered personalization can create a curated list of additional products that go well with the original. Now, instead of other random items from across the website, that list may feature a number of tops that go perfectly with those pants.

Better personalization can have a positive impact on business outcomes. By showing tailored, relevant search results, businesses can reduce cart abandonment. By providing timely additional product recommendations, companies can increase their average order value (AOV) and average number of items purchased.

Soon, an advanced search bar may not be enough

Many ecommerce companies have begun to implement this advanced search functionality into their own search bars. Massive retailers like Amazon and Walmart may develop their own proprietary NLP algorithms that are tailored for their specific catalogs. However, even smaller ecommerce retailers have been able to implement this technology through third-party software and APIs. Tools like Vantage Discovery’s advanced search engine can take an existing catalog and structure its data to enable semantic search and personalized product recommendations.

However, as is often the case with new technologies, the landscape is changing rapidly. Already it seems that semantic search may be table stakes for competing in ecommerce. Big brands are looking ahead to the next generation of search and discovery. This next era is likely to be one driven by even more tailored personalization and conversational search through the use of chatbots and, unsurprisingly, Amazon is one of the first movers in the space.

The rise of AI shopping assistants

Amazon’s Rufus is here

Earlier this year, Amazon announced Rufus, its AI shopping assistant. Delivered in a chatbot format, users can talk with Rufus to help find the products they need, easily identify the features of a product, outline the differences between products, and even more. This is setting a new standard of excellence for ecommerce user experience. Now, shoppers can utilize Rufus to personalize each shopping experience and quickly find the Amazon offerings that meet their needs. Soon, the days of multiple “round trips,” or conducting a search, not finding what you need, trying a different search, and repeating that process until you either find what you’re looking for or give up entirely, will have passed.

We predict that Rufus is going to have a positive effect on conversion rates, or the percentage of site visitors that end up purchasing a product. Further, Rufus will apply pressure on the broader ecommerce industry to step up its game with similar features at the risk of losing potential business to Amazon.

An example of shopping automation

For a second, imagine that you’re looking for a camping backpack for an upcoming trip. You’ve never purchased one before and you don’t even know what to look for in a good backpack. Historically, you’d likely do some preliminary Google searches for blog articles that discuss the merits of various types of backpacks. With an AI shopping assistant, you can go straight to the ecommerce site and initiate a conversation. The chatbot may tell you that you should consider the material that the backpack is made of, its volume, its straps, and its storage compartments. Then, you tell it which of those you want to prioritize and it’ll show you a variety of product offerings that fit what you’re looking for. Curious what others think? You can ask the chatbot for user reviews of the options you’re considering. Finally, you’ve found just the perfect option, all without leaving the website, running multiple search queries, or scouring product pages for the information you need.

As you can see, AI shopping assistants have the potential to fundamentally change the way that we shop. With the rise of ChatGPT and other AI chatbots, consumers are increasingly comfortable engaging with these systems to uncover information and simplify workflows. It is only a matter of time before they become ubiquitous in the ecommerce space, which has long struggled with poor shopping experiences. Although shopping online is much easier than going into a store, it has been impossible to provide the type of hand-holding that consumers can get from sales associates in brick and mortar establishments at scale. That is, until now.

AI is the key to future-proofing ecommerce search

Implementing advanced search functionality like semantic search and developing AI shopping assistants are imperative to keep up with the trends. Fortunately, you don’t need to start hiring a team of machine learning engineers to make this a reality. Companies like Vantage Discovery offer search and personalization solutions that you can leverage without developing your own tech. We can take your existing catalog and product information and create a search engine that incorporates all the functionality we’ve highlighted in this article. If you want to learn more about how we can enhance your customers’ experience on your website and drive improved conversion rates, average order value, and more, book a demo with us today! It’s not too early to make sure you’re ready for the ecommerce experiences of tomorrow.