Search Engines Don’t Just Support AI-Powered Services, They Are AI-Powered Services

How search engines use AI

Investing in an AI strategy is top of mind for many brands, and it’s good that people across from industries and consumer segments are paying attention to the continual advancements in machine learning technology and artificial intelligence — especially when AI is projected to contribute $15.7 trillion to the global economy by 2030. 

But all that buzz can leave many of us feeling fuzzy on the details, particularly when it comes to AI as it relates to search experiences.

It’s not always clear how exactly AI impacts the answers people receive in response to the questions they’re asking online. And particularly with the rise in popularity of voice services in recent years, some may be under the impression that voice assistants (like Alexa, Siri, and Google Assistant) and other new, sophisticated interfaces (like Google’s Image Analysis Tool) may act as layers of AI-powered services that simply pull information from search engines.

How do search engines use AI?

Similar to voice assistants and image analysis tools, search engines utilize AI to provide their services. According to Mike Kaput at The Marketing AI Institute, here are some of the ways search engines use AI:

  • Quality Control
    To counter the efforts of less-than-scrupulous SEO practitioners (think: keyword stuffing, cloaking, invisible text), search engines have updated their algorithms and use AI to identify high quality content and separate it from low quality spam.

  • To Create Ranking Algorithms
    Search engines use AI to improve their ranking algorithms — specifically, they use Learning to Rank algorithms, which teach machines to create an optimal list from a set of possible outcomes, and learn from each of the variables over time.

  • To Understand Search Queries
    Natural language processing (NLP), a type of AI that teaches computers to understand written language, has become critical to search engines — helping them to determine the user intent behind search queries.
     As reported by The Marketing AI Institute, a University of Washington study examined Yandex (the world’s fourth largest search engine) and discovered that Yandex developers had taken their users’ previous searches and used NLP and machine learning to optimize future searches — creating personalized search results for users that improved click through rate by approximately 10%.

What does this mean for your marketing strategy?

You can’t control the many services that consumers are using to ask questions relevant to your business. As AI changes, the ways that search engines and other AI-powered services use your information to provide answers will also change. 

But you can control the amount, quality, and structure of the information you make available to these services. Search engines use a brain-like database, called a knowledge graph, to help search results align more closely to user intent. The knowledge graph is a structured collection of facts with flexible relationships established between entities. 

By creating your own similarly structured database, or knowledge graph, you will set yourself up to provide all the necessary information about your business to search engines and other AI-powered services when they look for it, helping more consumers find answers when and where they ask questions — and making your brand truly AI-ready.

Learn how your business can build its own knowledge graph with Yext.

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