Audible Tests Using Prime Video Data for Audiobook Recommendations

In a move signaling tighter integration between Amazon's services, Audible is rolling out a test that will leverage Prime Video viewing data to offer personalized audiobook recommendations. The company aims to connect the dots between users' streaming and listening habits.

The new "Based on what you watched recently on Prime Video" carousel within the Audible app will surface relevant audiobook titles inspired by a customer's Prime Video activity. Around half of users with both an Audible membership and Amazon Prime subscription will see these recommendations during the test phase, with no opt-out option for now.

The recommendation engine uses collaborative filtering techniques to predict fitting audiobook selections based on what a user is streaming on Prime Video and what other customers with similar tastes have listened to. Factors like storylines, genres, authors and general preferences are analyzed.

Audible says the feature was prompted by observable spikes in audiobook consumption following book-to-screen adaptations. For instance, two weeks post the "Reacher" series premiere, author Lee Child's catalog saw nearly 80% more daily listening minutes. Jenny Han's books experienced over 10x higher numbers after "The Summer I Turned Pretty" hit Prime.

Audiobooks Find Wider Audience

While catalyzing content discovery synergies is the stated goal, the move can also be viewed as a strategic response to emerging competition in the audiobook arena from rivals like Spotify. The music streaming giant recently added audiobook functionality, becoming the second largest provider behind Audible's dominance.

"There is a natural synergy between TV, movies, and books...we're excited about the potential of this feature to help listeners dive deeper into worlds they love," stated Andy Tsao, Audible's Chief Product & Analytics Officer.

As audiobooks find a wider audience, leveraging Amazon's powerful data streams allows Audible to cross-pollinate recommendations between complementary content verticals. For viewers becoming readers and vice versa, these personalized suggestions could uncover the next literary obsession.