TV Service Providers & OEMs
Ranker can help you redefine your approach to discovery by focusing on what your users really like.
Fan-Curated Content Galleries
Millions of people visit Ranker lists every day to find crowdsourced rankings of great TV shows and movies. Tap into the power of those lists by placing them directly within your UI.
Give users a far more interesting, enlightening, and engaging way to find what to watch next.
Organize titles in unexpected and intriguing ways, like “Great Villain Performances By Actors Who Always Play The Hero,” or “The Best Gen Z Shows.”
Rely on the recommendations of thousands of real fans to drive users to shows and movies they’ll really love.
Rankings as Qualifiers
A quantitative score is just a number. Rankings do more by conveying not just how popular a given title is, but what a viewer will enjoy about it.
Offer prospective viewers a multi-dimensional view of consumer sentiment that is both qualitative AND quantitative.
Put each title in context and compare it to titles that users might be more familiar with.
Create additional discovery paths by inviting users to check out the list a given title is ranked on — as well as any other shows or movies on the list.
“Fans Also Like…”
“What to watch next” recommendations are often based on simple viewership data, or limited to just one streamer’s library. Rely on Ranker to step these recommendations up a notch.
Our holistic view of consumer preferences spans 135K series, 600K movies and over 2 million celebrities, showing us connections that go way beyond one streamer’s catalog.
Your users don’t care what “viewers also viewed.” Base your recommendations on the shows and movies that are actively beloved true fans of every title.
Easily deploy Ranker recommendations within your existing UI via data feed or API.
Personalized Recommendations
Ranker’s proprietary affinity index identifies the strength of relationships between titles based on post consumption viewer sentiment data, putting the user at the center of the equation.
Build an accurate taste profile for new or existing users in seconds.
Surface personalized recommendations that go beyond the usual mood, genre, and theme-based categories
Give users fun discovery features that tap into the user’s state of mind at the point of decision making (Can’t Decide Guide, In The Mood, Pick Your Faves)