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What is search intent?
Search intent is the reason behind a person's online search. It's about understanding what someone wants to find when they type a word or a phrase into a search engine and where they are in their buyer’s journey. Ensuring your content matches what people are looking for increases your chances of ranking higher.
This is a best practice box. The borders that appear in the editor and this explainer will not appear in the published article. Use the BP Body section below for best practices and use cases you'd like to call out.
- Informational keywords (like “choosing comfortable running shoes” or “how to pick a running shoe”) indicate people are looking to learn more about a topic, product, or service.
- Commercial keywords (like “adidas ultraboost vs nike react”) indicate people want to compare multiple products or services.
- Transactional keywords (like “adidas ultraboost for sale”) indicate people are ready to take action on a website, like purchasing a product or service.
- Navigational keywords (like “nike.com returns”) indicate people are looking to visit certain websites or pages
- Brand/Service Navigation: User seeks a specific brand, site, or known service. Not all branded prompts are of this type.
- Comparison: User directly compares alternatives.
- Education: User seeks to learn or understand.
- Other: The prompt doesn't fit neatly into the other categories.
- Pricing: User seeks cost information.
- Purchase: User shows intent to buy or take conversion action.
- Recommendations: User seeks suggestions or options (not direct comparison).
- Support: User needs help or customer service.
Read more about search intent in AI Search Performance reporting
End of bp box
Best Practices for Using Search Intent in Conductor Intelligence
Customers can refer to our best practice document in the in-app Learning Library.
What sets Conductor's Search Intent apart from other data providers?
Intrinsic Search Intent
Other vendors in the enterprise SEO space have search intent classifications, but we developed our own methodology using AI to not just recognize the words people use to search (what we call "reflected search intent"), but the meaning behind the words—the "intrinsic search intent"—which reflects more nuance and understanding of less common language usage, leading to more accurate and complete assessment of search intent.
When data providers focus on only reflected search intent, they can fail to account for how people use language in nuanced ways that may mean different things, but appear the same. This reliance on search term frequency leads to issues like difficulty differentiating Commercial intent from Transactional intent and the inability to recognize brand names in queries.
For example, when someone searches "best cleaners nearest", a data provider that uses only reflected search intent might identify the word "best" as an obvious indication of a Commercial intent.
However, using intrinsic search intent signals to determine intent might recognize that same potential Commercial intent but also recognize that the users could be searching for the nearest location for a business called "Best Cleaners".
By recognizing signals in queries that help identify intrinsic search intent, Conductor can provide a more nuanced understanding of what people might really want when they search. In the "best cleaners nearest" example, Conductor could provide both Commercial and Transactional as possible intents where other providers might determine one or the other. This expanded, nuanced understanding of intent can help you create a content strategy that doesn't neglect potential markets for your products or services.
Multi-language Support
Your audience probably searches in more than one language, so what good is search intent for only English? Conductor's AI solution for intent also supports non-English languages better than other providers that attempt to provide search intent.
Earlier approaches to determining search intent used natural language processing (NLP) models that were training on English libraries. Because it's more complicated to train an NLP model for each language, those English models were then applied to other languages, leading to intent classifications that didn’t accommodate the cultural differences and nuances inherent in unique languages.
The reason Conductor can support non-English languages better is that our solution, by nature, includes language understanding, and it not language-specific to begin with. So it's not just that we've "expanded" support to other languages, but it's multi-language from the ground up.
This means you can account for the cultural differences and nuances represented in different languages—so you always meet your customers on their terms, not just on yours.