Improved search relevancy with Content Intelligence
VUE™ Content Intelligence Hub consolidates content from various structured and unstructured sources to provide a shared view of the organizational content. It offers auto-categorization, concept tagging, sentiment analysis, keyword and concept extraction using domain-specific machine learning models, natural language processing and word vectors. Enriched content is in turn more conducive to information discovery. VUE™ also provides a single source of truth for managing taxonomies and meta-data to disseminate within the organization and distribute to various downstream applications.
Instead of creating an unmanageable number of business rules within the keyword search engine to solve the problem of relevancy and recall, the Havas solution addresses the core of the problem i.e the quality of the content itself that drives search.
Understand customer’s intent using Artificial Intelligence
Search remains one of the greatest sales tools because it is the clearest expression of a customer’s intent. Without understanding the intent of the user’s query, it is not possible to provide an optimal response for their query. VUE™ offers domain-specific intent recognition and democratizes the inputs to the underlying machine learning models to the domain experts and non-data scientists. This brings an unparalleled level of transparency to Artificial Intelligence, something that marketers need to not only trust the machine, but to truly combine their domain expertise with the power of the machine.
Personalize the Cognitive Search Experience
VUE™’s Personalization Engine allows marketers to create relevant search experiences based on the customer’s intent, context and touchpoint through a self-service interface to manage the user experience on all digital channels. This targeting capability is used to customize the relevancy of the search results as well as the overall page experience for each unique customer.
Learn from user’s interactions
In order to ensure that the machine learning algorithm continues to accurately predict the user intent, VUE™ continuously monitors the effectiveness of the model predictions by comparing them with actuals. For instance, the intent of the customer’s search query is validated if the customer selects the relevant result. If the customer selects an unexpected result, the confidence value of the predicted intent for a specific query is lowered.