The amount of content available to the average digital shopper is astronomical. On the other side, service agents must contend with an equally dense amount of information to deliver positive customer service experiences for shoppers. To help both parties navigate the ever-growing mass of content management and information, brands are turning to smart search to pick up the slack in such a densely packed world of information.

What Is Smart Search?

Smart search is a broad term that refers to search systems powered by machine learning (ML), natural language processing (NLP), and artificial intelligence (AI). It encompasses semantic search, intelligent search, and cognitive search. By combining these technologies, smart search can intuit what a user is looking for by considering their goals, history, and the theme of their search. In other words, smart search streamlines a customer or service agent’s ability to find what they seek.

How Does Smart Search Work?

Smart search relies on the combination of technologies listed above to create a picture of what the user is looking for as accurately as possible. Natural language processing allows smart search to create meaning out of the site search terms, even if they aren’t exact point-and-click queries.

AI and machine learning combine forces to determine the context around which a user is searching for something using signals – taking into account history and how it may inform their future goals. This is how brands can create highly personalized digital shopping experiences that replicate an in-store experience or empower service agents to find the materials they need to provide high-quality customer service on the fly when digging through their knowledge base.

What Are Table Stakes for Smart Search?

Smart site search is highly personalized and, therefore, dependent on the user. Even so, several capabilities should be a part of every smart site search strategy that should be considered:

Smart search should be conversational

A site search session is not typically a single query or single set of responses. It is a fluid conversation with an understanding of the bigger picture. The user often needs a specific goal, and they may need the perfect website search terms. Smart search fills those gaps by understanding how an average person would speak and respond appropriately and can still return accurate results.

Smart search should be natural

Similarly to the above point, people do not think in terms of “search queries.” They want to be able to simply input the words that will convey what they are looking for and hopefully find it in the results. Gone are the days of stop words, tokenizers, stemmers, or lemmatizers. Technologies like NLP make it easier for systems to understand what a user is trying to convey when communicating in the language they are familiar with. ML makes it easier for systems to improve on that communication.

Smart search should be personalized

Everyone has a unique set of goals when they are searching. With the near-limitless amount of answers to a single question, search personalization allows smart search to be a meaningful experience wherein the user feels understood. Personalization can be achieved when the system has a complete user picture: site search history, site search frequency, and more. These small data elements accurately represent the intention behind anything they may search for.

Smart search must be responsive

To run a natural and personalized smart search system, brands need to hear what the users are asking, understand their true intentions, and be able to serve those true intentions. This isn’t always as straightforward as it may seem. For example: is a click always a positive signal? Not always. What if the user goes back to double-check something? For this, systems must have virtuous cycles built-in – meaning consumption of queries, impressions, clicks, and other feedback should be part of the primary service API.

Curious about what smart search can do for your brand? Get in touch with us.

About Lila Schoenfield

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