About the Search Hub

Your Coveo license defines a limit on the monthly number of Search API calls for your organization. Performing a keyword search counts against this quota, but other user actions also trigger Search API calls. Moreover, calls to the Search API can originate from various locations in your search solution, and even from the Coveo Administration Console. Your organization Consumption (platform-ca | platform-eu | platform-au) dashboard lets you monitor query usage on a per search hub basis.

Content and search managers want to monitor the effectiveness of each of their search interfaces using metrics based on calls to Coveo Usage Analytics. In turn, they can use the insights derived from these metrics to carry out changes to their search interfaces, their query pipelines, or to the indexed content (see the articles in the Leverage Coveo Usage Analytics Coveo documentation section).

The Analytics section of the Coveo Administration Console navigation menu contains a wide array of reports and graphics, many of which you can filter on a search hub basis.

To get good analytics metrics, you need large amounts of good data. This is achieved by setting your search hub values early and properly.

Typical Coveo-powered search solutions include multiple search interfaces that may be used by different audiences and for different purposes. For example, a Coveo search solution may contain the following search interfaces:

Since audience and audience intent differ on these two types of search interfaces, a given query shouldn’t yield the exact same results (and in the same order) on both. Indeed, when you properly set up your search solution to use Coveo Machine Learning, a model associated with the community search page will gather user behavior analytics far different from the ones gathered by a model associated with the support agent Insight Panel. That’s why Coveo separates ML models and sub-models based on the search hub value.

Whether for Automatic Relevance Tuning (ART), Query Suggestions (QS), or Dynamic Navigation Experience (DNE), the effectiveness of a Coveo ML model driven feature depends on the amount and quality of usage analytics data gathered, and that begins with properly setting your search hub values (see Leverage Machine Learning).

Whether you build a Coveo Hive search page one rendering at a time or fast-track your way to a multi-rendering search page by inserting an Example Search Page, you need to insert the Coveo for Sitecore Analytics rendering in your Sitecore search page item. You set the Search Hub value in the Coveo for Sitecore Analytics data source.

Coveo Hive supports integrating a Coveo search page in a Sitecore item through the Example Hosted Search Page branch template. When you insert a branch from this template, a data source item is added as the deepest child of the branch. This data source contains the Search Hub field.

You may want to insert a Coveo Global Searchbox rendering in your website article headers and have this global search box provide query suggestions as the user types.

Set the Override search hub value in the Coveo Global Searchbox data source to match the search hub value of the search interface whose query suggestions you want to use.

Surfacing index data using direct server-side calls to the Search API should be done parsimoniously. This method not only involves writing code but it also doesn’t log usage analytics search events.

If you’re going to use this approach, you should tag your searches using the SearchHub property of the Coveo.Framework.SearchService framework QueryParams class, as illustrated in the Calling the Coveo Search API Using Server-Side Code code sample. This lets you either leverage machine learning acquired by the model associated with the given search hub or track your search consumption on a dedicated search hub name.