JUMP Personalizer
Bring the power of AI to your video service and personalize your users’ experience.
Personalized Content Offering
Personalized Marketing
Performance Tracking
JUMP Personalizer is an AI Recommendation Engine able to provide Contextual Content Recommendations individualized to each user profile, to offer them an ultra personalized experience.
According to the needs of each video service, we offer different recommendations based on editorial use cases, personalized use cases and extended personalized capabilities.
Grow your customer lifetime value by user engagement
Better Conversion Rates
Improve your user’s experience and increase engagement and retention
Search Time Reduction
Boost acquisition by using predictions to identify the most likely candidates to convert into paying customers
Marketing ROI
Increase content consumption and activate low-engaged users
VIEWING TIME
Recommendations by genre availability in the platform
Profile the user preferences concerning genre preferences and affinity.
Continue watching
Permits the user to get back to where he left the content the last time he engaged in the platform.
Recently added
Gives the opportunity to the platform to promote its newest added content or showing off Real-Time Events such as Sports.
Most Popular -Top Content
Show the most popular article across the platform, in specific regions or in specific genres or types of content.
Recommended for You
Gives general recommendations for a specific subscriber/device ID.
Because you watched
Gives recommendations for related and similar content for specific items.
In – Player Recommendations
Ir order to keep the user engaged it displays the pop-up of watch next episode in series and for movies it displays similar item.
EXTENDED PERSONALIZED CAPABILITIES
Having our Personalizer put in place permits the organization to further extend it’s personalization capabilities to a personalized communication and marketing campaigns targeting.
We enrich the user base profiling, creating different types of Clusters filtering variables such as Engagement Level (Top, High, Moderate, Low,..), Content Type, Genre, Tags, Communities, Director Fans, among many others defined by the organization.
Users actively watching recommendations
Monthly Viewing Time
