Personalized search solution by Vue.ai uses AI to make search results accurate and personalized for every shopper.
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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.
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.
Personalized SearchTextual 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?)
Many businesses that have put personalization to work on their websites are sold on the concept. Engaged shoppers become loyal customers, which leads to more items being sold, return visits…what’s not to love?
Statistics bear this out. According to Evergage, 88% of marketers surveyed said their biggest motivator with personalization was to provide a superior customer experience. And almost all of them felt that personalization enhances customer relationships.
According to Adweek, marketers say personalization can raise revenues by up to 15%, plus, it can reduce customer acquisition costs by up to 50% According to Econsultancy, website guests who use search to locate items convert to customers at a rate 1.8 times higher than the average site visitor
Search personalization is considered a definitive win across industries. It can help not only from a business-growth perspective but with improving user engagement, for example, by increasing readership on a magazine website or improving the efficiency of customer support for a technical product. But in certain industries, it can have an outsized impact.
When most people think about how ecommerce has recently gone nuts, they think of the impact of COVID-19 forcing shoppers to steer clear of brick-and-mortar stores. The switch to browsing and buying online en masse—both for B2C and B2B shoppers—is looking pretty permanent. Online shopping is hot. People have gotten so comfortable with it that they’re adding items to their shopping carts that they never used to buy online.
Personalization on a retail website can take many forms, ranging from providing the most relevant search results to supplying on-target recommendations to simply remembering customers’ names and saving their item color and size preferences for the next time they log on.