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Effortlessly scale your sales funnel with AI automation.
Let WPSolr Pro+ automate your conversions by leveraging AI to recommend only the most relevant content to each one of your users.
The recommendation engines are trained in real-time on your users’ actions to deliver outstanding results. No man in the loop anymore.
Most of your products or blog posts are buried deep within your internal pages.
Recommendation widgets expose your entire catalog and bring your content closer to your homepage.
This is not ignored by Google’s crawlers, which begin indexing all your content at an accelerated rate.
This leads to visits to previously invisible content.
WPSolr Pro+ makes use of word-class third party recommendation engines.
WPSolr Pro+ is compatible with the following recommendations engines:
On any WordPress & Woocommerce website, WPSolr Pro+ can connect to your engine, index any products (or orders, pages, posts, etc…) and deliver the most relevant product recommendations in record time.
Place recommendations anywhere you want using shortcodes and widgets.
WPSolr Pro+ doesn’t place recommendations in predetermined locations, instead it offers the administrators total freedom on where they want each individual recommendation to appear using shortcodes.
Our ready-made templates are modern and seamless.
WPSolr Pro+ offers many recommendation templates for you to choose from.
They are made using Twig so you have total freedom when it comes to customization.
Models based on behavioral patterns.
This approach finds users whose tastes are similar to those of the target user and recommends items they have liked.
Example: “Similar users also liked” or “Others also purchased”
Models based on behavioral patterns.
This method calculates similarities between items based on user ratings and recommends items similar to those the user has already rated highly.
For example, if a user likes a certain book, the system might recommend other books that were liked by people who liked the original book.
Models based on attributes of items and users.
Content-Based models assess similarities between items or users by examining their attributes.
For instance, items may be deemed similar if they share categories, names, or text descriptions.
Different models are employed to handle various types of attribute data.
These models prove particularly beneficial in cold-start scenarios, where interaction data is scarce, such as with new items or users.
User events are notoriously difficult to manage. No more with WPSolr Pro+.
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 Pro+ takes care of events internally:
– Use of first party cookies to track visitors / customers
– Track events like clicks, add-to-basket, orders 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, purchases, or search queries, recommender 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 recommendations.
This means that the system evolves with the user’s changing preferences, potentially keeping the recommendations fresh and closely aligned with user interests.
Personalized and accurate recommendations 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 recommendations, 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.
Recommender 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 recommendation leads to a conversion or a long interaction, it indicates a successful recommendation.
Conversely, if recommendations are consistently ignored, it might suggest the need for adjustments in the recommendation algorithms.
Analyzing user events can yield insights beyond improving recommendations.
Understanding how users interact with different types of content can inform business strategies, content development, marketing campaigns, and more.
With machine learning algorithms, recommender 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.
