Optimierung für Sprachsuche mit dem Knowledge Graph für Ihre Marke

The rise of voice search hasn't taken place in a vacuum — it's intertwined with a major paradigm shift in consumer search behavior as a whole. Advances in AI have made search engines "smarter," enabling them to process more complex natural language queries, and to provide answers to these questions with a higher degree of specificity.

As a result, people are being trained to expect a direct answer to any question they ask, and that's true whether they're searching by text or by voice. Text-based queries look increasingly like conversational, voice-based queries, and consumers are going to continue searching both ways.

As search engines provide more and more direct answers, in the form of rich results at the top of the search engine results page (SERP), it's no surprise that people are often finding the information they want directly on the SERP. But there's good news around this shift. The essentials of how to optimize for voice search have never been more closely related to best practices for winning in search, period. And both depend heavily on building your brand knowledge graph.

How does your brand knowledge graph help you win in voice search?

Google, Amazon, et. al. haven't issued a guide to exactly how their voice assistants determine a preferred answer, but there are a few things we do know. At present, the primary strategy for winning a share of voice search is 1. ranking in featured snippets and 2. being able to answer multi-dimensional queries in general.

Building a brand knowledge graph is what allows you to do this. A brand knowledge graph is invaluable when it comes to "speaking the language" of search engines. Because search engines use a knowledge graph with bi-directional, flexible relationships, your brand needs to structure and store its data in the same way. Search engine knowledge graphs can then draw on the personal knowledge graph that you maintain — allowing you to manage your information at scale and deliver the answers customers want via third-party voice-based search experiences.

You can find all of our advice on ranking for featured snippets, in particular, here. But whether or not a search engine showcases a featured snippet for the type of natural language query your customer is making via voice, you need to be able to answer that question. This means you must think about the types of questions your customers would be most likely to ask via voice, and then ensure that you have structured the information on your website so that it's interrelated and marked up in such a way that it's intelligible to a voice assistant as the answer.

For example, if your customer performs a voice search for "Italian restaurants near me that are open now," the answer depends on diverse information: type of cuisine (Italian), near me (address), and hours (of that individual restaurant). With a knowledge graph, you can define the relationships between all these entities so that an AI-powered voice assistant can answer this question accurately — by recommending your restaurant. As a result, your organization increases its chances of being the answer for that high-intent voice query.

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