Search and ye shall find. But search with an AI-powered search platform and ye (or ye customer) shall find, learn, and even discover!

Although most vendors say their search platform is fortified with AI, “AI-powered” isn’t a phrase that should be used lightly. So what does AI-powered search really mean and what is the value it can generate for your business? Lucidworks’ Senior Solutions Architect, Karthik Chelladurai explains, “Basic search is matching the text of your search term with the text in the document database. AI-powered search allows us to bring in multiple dimensions of the user and data available to produce the most relevant results.”

Let’s zoom out to understand what those additional dimensions are, how they impact the user in their search journey, and how AI-powered search can create immense value for businesses.

Back to Basics: What Is Search?

Think of basic search as an ecosystem that takes your query, scans through all the information available, and then presents the items that have an exact text match to the keywords you entered. For example, if you go on and search “iPads” you expect the site to go through its product catalog and show you iPads. But what if you actually wanted to view iPad covers? Or what if it shows you the iPad 3, which you already purchased from the site? Or what if you put a space in between “i” and “pad” and you get the dead end “No Results Match your Query” pop-up or alternatively return results for pads of paper or mouse pads?

There are so many things that can create friction and prevent users from finding information they need, including missed opportunities to make smarter recommendations and a failure to learn from user behavior to better serve them and others the next time.

Put User Data to Work for a More Valuable Experience

Many search platforms, such as Google Search Appliance (GSA), don’t learn much from an individual user’s behavior or search history; you’ll be given the same results as anyone else who searched for those same words on the site, regardless of your previous queries and clicks. While collecting data on user behavior is already common practice for many companies, they’re missing out on the next important step: learning from the data in real-time to produce more relevant results and recommendations based on things like user location, search history, and the behavior of users’ similar to them.

AI-Powered Search by the numbers:
• 6% of e-commerce visits that include engagement with AI-powered recommendations drive 37% of revenue, Salesforce
• One day per working week (19.8% of work time) is wasted by employees searching for information to do their job effectively, Interact

What Exactly Is AI Doing for Search?

Systems are already tracking an incredible amount of inputs considering that most of what we do online is driven by search. Even apps you don’t think of as ‘search’ rely on it at their core. The value of tools like Craigslist, Zillow, Amazon, streaming radio, and relies entirely on their ability to easily search and find relevant information. AI-powered search provides the next generation of search result relevance that learns from user behavior in real-time as they’re searching to help bridge the gap between human and computer language.

“When we think of AI-powered search we’re referring to how we take user interaction data and wrap it into search to improve relevancy, improve poor queries, misspellings, etc.,” explains William Tseng, Lucidworks Regional Director, Sales Engineering. “Basically we’re building a search solution that empowers users to define what’s important to them.”

The value of AI-powered search is the constant loop of information that happens in the background of the user’s journey; it informs smarter recommendations in digital commerce, enables more personalization within the experience, and saves time for knowledge workers who rely on locating documents to do their jobs.

Building Blocks Supporting AI-powered Search

Norbert Krupa, Lucidworks Senior Solutions Engineer says, “AI has become a hyped-up term that can mean different things to different people. For me, AI-powered search means learning from the user to deliver the next best action, and the capability of the system to auto-tune results based on what it learns from users.” Here are a few examples of the building blocks behind AI-powered search that help a search app learn and improve:

Signals Boosting for More Relevant Results

The more data available to an AI-powered search engine, the more relevant results it can return to a user. Aggregate behavior such as click-throughs, conversions, and queries teach the engine which content is most relevant, making traditional keyword search smarter. AI-powered search leverages these signals to learn which results your users see as the most relevant for your more popular queries.

It is also able to learn what kind of product characteristics generally matter the most across all queries through building out machine-learned ranking models. AI-powered search weighs these models, in addition to other similar users’ behavior, location, and more, to calculate and present the most relevant results. Read more on how machine-learned ranking models result in better search results here…

Personalization and Recommendations that Understand Your Individual Users

According to a study from Infosys, 74 percent of consumers get frustrated with product information that’s not personalized. For example, if you just purchased an iPad and then searched “screen protector,” AI-powered search will rank iPad screen protectors higher than the Pixel 3 screen protectors. The engine is interpreting your query in the context of what it knows about you.

Recommendations rely on that same logic to suggest complementary items at checkout that you did not search for, but are still relevant product suggestions based on your behavior. An AI-powered platform can update these recommendations in real time, which can have a major impact on conversions and average order value. Read more on the power of recommendations in retail here…

Smarter Results Through Semantic Understanding

AI can power semantic search, which is a more nuanced and domain-specific understanding of what users are typing in and what those words mean within each user’s query and context. For example, synonym discovery and misspelling detection allow us to find the best smokehouse whether we search BBQ, barbecue, or even berbeque.

Clustering and classification techniques train the engine to understand different words that can be a part of the same category, ie, purse and handbag, sneakers and tennis shoes, outerwear and coats. Semantic Knowledge Graphs enable the engine to understand entities, disambiguate phrases with multiple potential meanings, and to gain a nuanced understanding of the user’s intent in order to perform a conceptual search instead of just text-based matching. Additional natural language processing (NLP) techniques allow us to talk to Siri like we talk to our friends “What’s the weather in San Francisco today?” and have her reply “Here’s the weather for San Francisco today.” (You’ll probably want a light jacket.) Read more on the power of classification, clustering, and semantic search here…

One more important thing to note: AI-powered search is best when kept transparent. Black box solutions where you have to trust a one-size-fits-all algorithm don’t allow you to control or customize results to fit your specific needs. Fusion’s AI-powered search puts relevancy in the control of the owner to make it easy to get “under the hood” and see the mechanics at work to tune results and business rules to best serve your customers.

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