Gracenote, the Nielsen content solutions division, has launched Gracenote Watch Prompts, an AI-powered dataset that equips global video platforms and services with programming facts intended to help influence consumer viewing behavior. Designed to be paired with user preference and consumption data, the new Watch Prompts aim at delivering personalized film and TV promotion, resulting in increased tune-in. According to Nielsen, 74 percent of U.S. consumers last year either didn’t know or only had a vague idea as to what they wanted to watch when starting a streaming session, “meaning a large majority are making on-the-fly viewing decisions.”

“The launch comes at a time when many surveys are reporting that consumers find it difficult to decide what to watch and traditional recommendation offerings aren’t very effective,” writes TV Technology, detailing how “a combination of Gracenote’s unmatched machine learning capabilities and human editorial expertise” resulted in the following Watch Prompts features:

• Critical facts: Prominent award wins and praise from renowned TV and film critics, providing evidence of content quality.
• Talent spotlights: Showcases popular actors and creators to appeal to viewer preferences.
• Content comparisons: Analogies based on thematically similar content presenting new frames of reference.

“Gracenote Watch Prompts complements both basic program metadata and video descriptors with additional information on individual TV programs and movies to drive viewer consideration,” Nielsen explains in its announcement.

For example, Watch Prompts could present the series “Succession” in the context of its “13 Emmy Award wins, including two in the prestigious ‘Outstanding Drama Series’ category, to educate a potential viewer on the show’s critical acclaim and motivate sampling.”

The “Barbie” movie page could describe the film as “‘Legally Blonde’ meets ‘The Lego Movie,’ providing a relatable analogy based on beloved films to pique interest,” according to Nielsen.

“Advanced machine learning techniques help to automate creation of content snippets based on the company’s unmatched video data on millions of program titles,” TENB writes, adding that “human editors with language, market and content expertise review outputs to ensure accuracy and quality, creating a feedback loop that helps the algorithms continually improve over time.”