Contentwise and Ranker Partner to Deliver Personalized Content Recommendations Powered by 1+ Billion Fan-Generated Votes

ContentWise, the AI-powered experience automation and personalization company, and Ranker, the leading source for crowdsourced rankings with over 1 billion consumer votes on a multitude of M&E topics, announced a partnership that combines Ranker’s rich movie and TV correlation data models with the ContentWise UX Engine. This integration enables operators to leverage the power of fan-based rankings and collective consumer opinion directly on their platforms, extending viewership signals beyond their customer base, and significantly enhancing recommendations precision.

Ranker’s proprietary consumer sentiment and affinity data, “Ranker Insights”, produces correlation data powered by over 1.3 billion fan votes. In the entertainment category specifically, Ranker’s holistic view of consumer preferences spans more than 52,000 editorially-curated polls which generate rankings across tens of thousands of TV shows, movies, characters, casts and celebrities. By collecting post-consumption opinion data at a massive scale, Ranker’s psychographic engine uncovers insights into consumer viewing preferences across the entertainment ecosystem, including the walled gardens of major streaming platforms.

Thanks to “Pluggability”, a new feature ContentWise officially unveiled last month for its UX personalization and content discovery platform, the collaborative correlation data models provided by Ranker can now be run within the UX Engine platform. This new feature, built on ContentWise Open Connector, allows operators to seamlessly integrate any external, bespoke recommendation model into the UX Engine. With Pluggability, operators have the freedom and power to use any AI personalization model with UX Engine, marking a significant advancement in content personalization.

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Pluggability and Ranker Insights enable unprecedented levels of recommendation accuracy and unique content discovery paths. In particular:

• Topical Homepage Carousels: Operators will have the ability to tap into Ranker’s vast catalog of editorially-curated lists organized by votable rankings to power home screen carousels directly within ContentWise’s UX Engine. These carousels present the user with topical content recommendations personalized to their tastes. Some Ranker list examples include:
  • The Absolute Funniest Movies Of All Time
  • Movies With The Best Soundtracks
  • Comedy Sequels That Might Be Better Than The Originals
  • Great Comedy Shows About the Workplace and Co-Workers
  • The 100+ Best Adult Animated Shows
  • The 50+ Best TV Talent Shows, Ranked By Fans
• “Fans Also Like” Carousels: On movie and TV show detail pages, Ranker’s data will power “Fans Also Like” carousels. These carousels leverage the strength of Ranker’s affinity correlations data and ContentWise machine learning capabilities to surface title-based recommendations that key off of shared user sentiment rather than program-level metadata. This advantage allows operators to circulate a wider selection of their catalog while maintaining relevancy.

“We are thrilled to be partnering with ContentWise to provide adaptable, data-driven solutions to their impressive clientele of video operators, digital publishers, and online retailers,” adds David Yon, SVP and GM of Ranker Insights. “Our Ranker Insights platform has made extraordinary strides toward solving the problems associated with targeted marketing, editorial, and merchandising practices over the past few years, and we are confident we can help make ContentWise’s already successful CX solutions perform even stronger.”

“The combined power of consumer sentiment data by Ranker and personalization technology by ContentWise brings fresh new discovery and recommendations use cases to our partner operators” comments Renato Bonomini, VP of Sales Engineering and Ecosystem at ContentWise. “On the editorial side, topics from Ranker Insights are directly available within UX Engine to support content curation. From the algorithmic perspective, ratings from Ranker Insights increase the quality of our recommendation models.”

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