AI-Powered Personalized Search

Personalized search solution by Vue.ai uses AI to make search results accurate and personalized for every shopper.

The eCommerce industry’s Achilles’ heel is the search bar. There are enough stats and numbers to help retailers understand why the search bar is essential but not enough information about how to solve the problem of showing people exactly what they want in a way that doesn’t require constant fixes.

The search page is prime real estate on any site, and shoppers can move straight to the cart page or discover brands and products from this page. But random recommendations on this page serve little purpose if the recommended products don’t align with what the shopper is searching for, their intent, or style. So what should retailers do to ensure this Achilles heel doesn’t become fatal? How can their search on-site be robust, accurate, and give shoppers what they need without forcing them to go on an epic adventure?

The Ingredients For A Good Search. The dissonance between the shopper and retailer on the site search page is primarily because of how the page is viewed. Search from a shopper’s perspective is not just about typing in a query — it is also about sorting and filtering through the results, clicking on products, browsing the page, understanding the layout, gauging and analyzing styles, price, and reviews to find exactly what they want. What if AI could do all this for the shopper, and the only thing the shopper has to do is choose, click and convert?

What Makes Vue.ai’s Personalized Search Better | Vue.ai personalized search solution does three things that deliver accurate and relevant results every time to every shopper. It enhances product data to ensure better catalog coverage, accurate results, and highly efficient matching of shopper intent to the product. It also personalizes results based on individual shopper preferences. Thanks to an incredibly rich product and customer data, the solution can sort and recommend based on every shopper’s preference. The algorithm is also fully customizable and can be trained for different retail strategies and priorities, putting the control of the solution in retailers’ hands. This video explains how Vue.ai’s personalized search solution handles search queries on a site.

Shoppers who use personalized search as a primary way of finding products are 2–3 times more likely to buy. On the flip side, If they don’t find what they’re looking for on one site, they are going to bounce really fast to the next open tab where a different brand/marketplace is open for business. Vue.ai capitalizes on the high purchase intent behind eCommerce searches, by personalizing results based on individual shopper preferences. The AI-powered personalized search solution also enhances product data to ensure greater catalog coverage and more accurate results. This tailored search experience reduces bounce rate and improves conversions for eCommerce retailers.

When you need to find or buy something online, do you want to feel like the company “knows” you? Perhaps suggesting other items that you might like? When you type in a search query, do you like seeing the search engine’s “thoughtful” guesses at what you want? Some people find this type of guidance a bit alarming; you may even worry that your privacy is being compromised. But if you’re like most Americans, your answers to these questions are a resounding “Yes, yes, and yes.” Which is to be expected, because once you’ve gotten used to—and spoiled by—the experience of web personalization, it’s hard to imagine going back. 

How wonky was unpersonalized web searching in the past? According to a 2018 Internet Retailer report, customers’ main challenge with website search was that they were getting irrelevant search results, or results that were organized in the wrong order. People cited personalized search results as their #1 need.  Of course, there’s more to the AI-driven realm of personalization than customized search results. It also encompasses the practice of taking careful note of people’s interests and preferences as they’re surfing websites and browsing content, and then applying that knowledge to help them navigate and find what they want.

In its various forms, personalized website (and app) content creation is pretty much all the rage, and it’s here to stay. According to Instapage, 74% of customers find it frustrating when content has not been personalized for them. And a huge majority of consumers, 91%, are “more likely to shop with brands that recognize, remember, and provide them with relevant offers and recommendations,” says Accenture.  

So the ability to glean information about shoppers—for example, through their past searches and browsing history—and then tailor content to their needs, plus anticipate their queries (“predictive search”), obviously has a ton of merits for consumers. But what about for the companies and organizations that implement it on their websites? You guessed it: the prospective benefits of customizing a user’s search experience for companies are just as monumental, and in certain industries, they are transformational and phenomenal for the bottom line.

Let’s take a look at what’s behind the “hand-holding” shopping and browsing experiences being created by personalized search, and how companies can apply this AI wizardry to improve and  optimize their customers’ search experiences, expand their customer bases, increase their revenue, and grow their brands.

Thanks to the virtually limitless storage capacity of the Internet, there’s now much more information, and many more products, available and findable online. However, as the mountains of data and volumes of products have proliferated, consumers have found themselves inundated with and overwhelmed by the first-world problem of having “too many choices.”

In the midst of this information explosion, search technology came to the rescue, allowing  people to cut through the mountains of extraneous details and start making a virtual beeline for their desired items. 

Personalized Search

In an extensive paper published in 2020, “Search Personalization Using Machine Learning,” the researchers explain that most businesses have been tackling the problem of information overload by using a query-based search model to help people narrow their shopping choices. Unfortunately, however, the ability to let people search sites doesn’t go far enough. And if a searcher doesn’t find what they want fast enough (like in a few milliseconds) and gets overly annoyed, they may jump ship for another site. So the challenge for companies is figuring out how to make search work better for their potential customers.

Rank the most relevant results higher, or display them earlier while the person is searching, so that they don’t click the wrong results or have to scroll to the bottom of the list to find the right ones

The researchers are partial to the second method: “The optimal ordering of results within a list is an important problem because recent research has shown that position effects have a significant impact on consumers’ click behavior and firm profits (Narayanan and Kalyanam 2015, Ursu 2018),” they say.

The problem is that different query terms mean different things to people searching for them. The researchers cite an example of people entering the query “java” (De Vrieze, 2006). People could be looking for information on coffee, the Java programming language, or a vacation spot, the Java islands. The relevance values of documents are user specific, the researchers note, so rank ordering must be specific to the individual user intent and the search instance.

The solution? Personalized search and discovery. Yes, this growing field has privacy implications and presents some gray-area issues for companies, which makes it an ongoing topic of debate. Still, the potential pay-offs of user-specific search rankings are significant and inarguable. “Personalization of digital services remains the holy grail of marketing,” the researchers conclude. 

Machine learning is the secret sauce of effective personalized search. Based on parameters set by the organization, a search engine algorithm learns about particular users through observing and noting their behavior over time The first step in implementing search personalization is gathering user data, which has two phases: deciding which behavior to track and then capturing that behavior by sending events to a software program. Next, the business creates a personalization relevance strategy, which is simulated and tested; and finally, the relevance strategy is moved to production.

Textual relevance, matching functionality that takes into account typo tolerance, synonyms, natural language processing, and more

Another tool to consider is dynamic re-ranking, which, based on collective search data from a group of users, such as all shoppers on a site, shows site visitors trending results and categories.

Lastly, a robust personalization strategy wouldn’t be complete without considering recommendations, which, like a personal shopping assistant might do, encourage people to check out other items based on what they’ve shown interest in. (Looking for a pair of jeans? How about this T-shirt to go with them?)