Overview of Search features

Sitecore Search offers a range of features to enhance the search experience for visitors to your website. By leveraging the following features, you can optimize search results, create filtering and sorting options, and improve the overall relevance of the displayed content.


You can configure most of these features in the Customer Engagement Console (CEC) and then optionally override them or pass additional settings at runtime.


A filter is a search component that you can use to limit the type of content and attributes you want to be returned. Depending on your use case, filters can be facets, that is, visible to your visitors, or they might be used behind the scenes to only show a subset of results. An example of filters working behind the scenes is if you create a filter based on the blog_status attribute, and create a rule to show only blog_status=published blogs when users in a specific context search for a term.


A facet is a dynamic search interface component that creates categories based on attribute values. With facets, users can easily filter search results using those categories. Facets can change based on a visitor's query.

Facets are helpful when you have a large content inventory based on filters like content type, reviews, and related products, for example.


With personalization, Search customizes results for a specific visitor. When you enable personalization, Search uses machine learning to understand visitor affinities and past interactions and uses that data to customize results.

Questions and answers

Search uses machine learning to generate question-and-answer pairs that match queries sent by users. You can use these question-and-answer pairs to, for example, add a people also asked section in your search results.

Search ranking

Search uses its proprietary algorithm to rank potential results. However, you can use the ranking feature to apply custom ranking on top of this.

Here's how this feature works: Search first internally ranks items and assigns them a relevancy score. Then, based on the custom ranking you configure, it adds to the relevancy score.

For example, you might want Search to give more importance to items that were clicked the most in the past week. You can do this using custom ranking.

The following diagram shows how ranking works for a result set of six items:

Diagram showing how ranking changes search result order.

Semantic search uses natural language processing and machine learning to consider query context and the relationship between words.

Semantic search is helpful when keyphrases have more than one word.

For example, consider the search keyphrase ginger lemonade. A keyword search might show you results like how to make sparkling ginger lemonade or Lemonade recipes from around the world. A semantic search, on the other hand, might give you results like Crowd-pleasing summer beverages in addition to results for ginger lemonade. This is because semantic search looks at the words ginger and lemonade and understands that the visitor is possibly looking for summer-related beverages.

Sorting options

The sorting options feature enables search results to be arranged in ascending or descending alphabetical order of some option.

For example, you have a result set returning information about six movies. Each movie has a title and a year of release.

The following diagram shows how sorting works when you sort by title or by year of release:

Diagram showing how sorting options change the order of search results.

Suggestions blocks

Suggestions blocks provide relevant suggestions in the search bar by auto-completing search queries, getting typo-corrected search results, and suggesting relevant content.

For example, if a visitor types the word headless, the search bar might auto-complete the input to show terms such as headless commerce, headless architecture, and so on.

Similarly, if a visitor types the word heafless, Search might display the sentence Did you mean "headless"? with a link that, when clicked, runs the query again with the corrected term.

Textual relevance

Textual relevance refers to how strong a potential match is compared to the visitor's search query. When you configure textual relevance, you tell Sitecore Search where in the content it needs to look for matching terms, and the relative importance it needs to give different content areas.

For example, you might configure your Search implementation to look for search results in the titles and descriptions of your content items. If a visitor searches for the term artificial intelligence, Search will look for that term only in the titles and descriptions of the content items. Matching content items might include a blog titled What is AI?, an article titled AI is transforming our world, or a news article with the phrase AI-driven analytics in the description.

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