Understanding the Coveo Relevance Maturity Model

The Coveo Relevance Maturity Model​™ (CRMM) can help you visualize your journey towards predictive relevance:

Coveo Relevance Maturity Model™

Siloed and federated search don’t apply to a Coveo search solution. The Coveo engine uses end-users data to pertinently rank search results.

Siloed search solutions only offer single source search, which constrains users to access each silo of information separately to achieve their search tasks. In addition, a siloed search doesn’t predict what users are searching for. Therefore, users have to explicitly request what they need to obtain quality results.

Federated search solutions allow users to access many sources of data to find the information they need. Even if this kind of solution interrogates several data sources, it fails to rank the search results according to relevance. This can be frustrating when facing a vast information ecosystem since relevant items can appear at the bottom of the search result list.

Stage 1: Secured and Unified Ranking

Like federated search solutions, search results in Coveo are consolidated from many sources. However, Coveo unifies and ranks these search results according to the sources’ access controls and follows security implementations (see Coveo Cloud Management of Security Identities and Item Permissions).

Unified ranking ensures the most relevant results surface at the top by applying a ranking process that filters irrelevant items (see Index Ranking Phases).

Stage 2: Content Navigation

While unified ranking ensures the most relevant results are at or near the top of the list, your users may want to filter and further dissect their results. The JavaScript Search Framework allows you to conveniently customize your search interface to meet your users’ needs (see Leveraging the Coveo JavaScript Search Framework).

This implementation permits building tabs and facets with which your end users interact to narrow down the initial search results. Unified ranking combined with faceted navigation allows users to quickly take advantage of comprehensive results and easily find the answers they’re seeking.

Stage 3: Tunable Relevance

At this stage, you’re already providing information to your users based on their context. You’re also gathering insights on the way they behave with your search solution (see Examining Usage Analytics Data).

Based on the data you analyzed from your users’ behavior, you will likely discover areas that may need improvement in your search solution. To overcome this lack of relevance, you can use the Query Pipelines page of the Coveo Administration Console, from which you can manually optimize your search results relevance and your search experience.

This powerful tool allows you to modify your end-user queries without having to use any code. From the Coveo Administration Console, you can create rules to improve the relevance of your search solution such as boosting specific items and creating synonyms (see Setting up Your Query Pipeline).

Stage 4: Contextual Relevance

At this stage, you’re more than simply responsive to your end-user experience. In fact, Coveo Machine Learning (Coveo ML) now helps you to proactively act on your users’ queries by suggesting items in the result list based on the similarity of context.

In other words, the manual tuning you made to improve your search experience at Stage 3 is further refined by receiving additional contextual inputs at the point of the search query (see Automatic Relevance Tuning).

Stage 5: Contextual Suggestions

Contextual suggestions bring Coveo ML to another step. When reaching this point, a model can proactively provide information to users based on the understanding of what they’re currently trying to achieve.

The main difference between the contextual relevance of Stage 4 and the contextual suggestions of Stage 5 is the predictive aspect. In other words, the capacity to evaluate probable future outcomes based on an understanding of the end users and their context.

The model now uses the search behavior of other users to predict the intent of a specific user by providing them with immediately relevant query suggestions (see Query Suggestions and How Does Intelligent Term Detection (ITD) Work?).

Stage 6: Self-Learning Predictive Recommendations

This is the stage where you get the most out of Coveo ML and maximize your positive business impact through a relevant search solution. At this point, you deliver an effortless search experience by providing your end users with key elements they need at a given moment, without them having to send further queries.

Coveo ML uses search navigation data to anticipate what a specific user will need next to accomplish their tasks by providing event recommendations (see Event Recommendations).

What’s Next?

The next article in this guide outlines the steps to conduct a successful search project (see Search Project Overview).

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