What does it mean for AI search to “understand” customers?

Sometimes we just need someone to listen; to make us feel understood. We’ll turn to a trusted friend or partner because we know they’ll hear us out and they truly “get” our needs.

That kind of personal connection is a marketer’s dream. Companies want their target customers to feel understood.

What does that mean in practice? When your potential customers head to your homepage search bar and enter a query, do they feel like they’re being accurately heard? Or are they annoyed because their search engine results pages are off base?

You just didn’t understand…

It used to be that keywords entered in an ecommerce search box produced similar results for most users. Now, search systems possess divergent levels of query understanding: some are hamstrung by older, keyword-matching-only methods, while others have the modern bells and whistles to serve up “intuitive” search results matched with what people need.

The ability to accurately understand human queries is a quantum leap for search engines. AI search — also referred to as smart search and cognitive search — is much better at understanding than traditional keyword search. That’s because AI enables more-relevant results that are adjusted in real time based on factors such as browsing history, the informational intent associated with the words, and high-performing content from similar searches. This upgraded type of search is able to go from simply matching keywords with content to understanding people’s desires and showing them exactly what they have in mind.

But what does it mean for a search engine to actually understand — to get the essence of — a person’s item-seeking needs? Can a search system understand humans’ desires as well as humans and offer up the most relevant suggestions for web pages?

With AI, for all user intents and purposes, that’s a definite yes.

The secrets of smart search

Behind the effectiveness of AI search optimization in recommending quality content lie machine learning and natural-language processing.

Machine learning is machines completing tasks without being programmed for doing specific tasks but rather to recognize patterns and make predictions. A machine uses algorithms to analyze received data and find patterns and connections in it. It refines search results for relevance based on factors such as a shopper’s past purchases and the intent behind their queries, all without human intervention. It learns from data in real time, which means it can continually optimize its knowledge.

Based on the ways people talk, often using ambiguous terms and synonyms, having machines accurately understand humans’ needs from their search queries has been a complex challenge. The good news is that AI-aided programs can detect language patterns and identify relationships between words to understand exactly what people want. How? Natural language processing (NLP, a subset of machine learning) eliminates many potential misunderstandings by interpreting the user search intent of specific search terms.

How AI search can improve different use cases

Aiding ecommerce sites

Online shopping is arguably the most celebrated arena where AI search can transform the user experience into a thing of beauty. Did you know that 43% of people on retail websites are likely to go straight to the search bar when they show up at an online store? Good AI-powered search is imperative. If you have AI-driven analytics reviewing your shopper interactions, purchase history, browsing habits, and other customer-journey touchpoints, you can translate their behavior and preferences into strong marketing strategy and personalization efforts that could very well boost your revenue.

Freeing up customer support agents

How many times have you tried to access an online store’s support center, maybe checked the FAQs, only to get cranky and resort to calling the 800 number? Intelligent search can help here, too, understanding what people want before they throw up their hands. When people ask a conversational shopping bot a question, for instance, a competent search engine can quickly locate the best answer in the documentation; no need to escalate to a human.

Overhauling site and app search

Geo search, also referred to as location-based or local search, provides search results according to the person’s physical location. When that process is also guided by intelligent search functionality, people are very likely to find what they need. That means businesses and organizations ranging from restaurants to retail outlets can benefit.

One example is AllTrails, a hiking-focused site that features hand-curated trail maps, photos, reviews, and search filters that help people receive the best suggestions. AI allows the site to cater to local residents’ desires while also making it easy for travelers from afar to find cool experiences. The site’s search feature accurately interprets each situation and offers up the right recommendations.

Understand your customers

Struggling to provide the search results your target audience increasingly expects? You can fix that with our AI search, which combines AI semantic search with traditional keyword search to give your customers the best-possible results in milliseconds.

Give your users the personalized shopping journeys they want so that they can then give you what you want: higher conversion rates and increased revenue. Contact us and let’s discuss!


About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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