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Let WPSolr Enterprise manage your conversions by leveraging AI to personalize the most relevant search results to each one of your users.
The AI search personalization engines in use are trained on your users’ data and deliver impressive results (you can take a look at their case studies).
WPSolr Enterprise makes use of word-class third party personalization engines.
WPSolr Enterprise is compatible with the following AI search personalization engines:
On any WordPress & Woocommerce website, WPSolr Enterprise can connect to your engine, index any products (or orders, pages, posts, etc…) and deliver the most relevant product search personalization in record time.
WPSolr Enterprise allows you to define the type of customization you want and select the search fields to display them.
User events are notoriously difficult to manage. No more with WPSolr Enterprise.
Other solutions require an external event storage like Google Analytics. This leads to a great complexity, and an issue with your visitors privacy.
This is why WPSolr Enterprise takes care of events internally:
– Use of first party cookies to track visitors / customers
– Track events like clicks in results with its own javascript
– Send events to each engine directly with their javascript APIs. No intermediary.
– Only an internal user ID is generated, stored in a cookie, and sent to the APIs.
By collecting user events, such as clicks on results, search systems can better understand individual preferences and tailor suggestions accordingly.
This personalization helps in delivering more relevant content or products to users, which increases satisfaction and engagement.
As more user data is collected, these systems can update their models to refine the accuracy of their search results.
This means that the system evolves with the user’s changing preferences, potentially keeping the search fresh and closely aligned with user interests.
Personalized and accurate search can lead to higher levels of engagement.
Users are more likely to interact with a platform if they feel it consistently meets their needs and interests.
When users receive highly relevant search, their overall satisfaction with a service tends to increase.
This satisfaction can translate into longer session times and more frequent returns to the platform, enhancing user retention.
Search personalization systems help users discover content and products they might not have found on their own.
By analyzing user events, these systems can highlight hidden gems that are tailored to user tastes but are outside of their usual browsing or purchasing patterns.
User events provide direct feedback on the system’s performance.
For example, if a search leads to a conversion or a long interaction, it indicates a successful search.
Conversely, if search results are consistently ignored, it might suggest the need for adjustments in the recommendation algorithms.
Analyzing user events can yield insights beyond improving search.
Understanding how users interact with different types of content can inform business strategies, content development, marketing campaigns, and more.
With machine learning algorithms, personalized search systems can automatically adapt and scale based on incoming user event data.
This automation allows for handling large volumes of data and users without necessitating manual intervention, making the system both scalable and efficient.
