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Streaming platforms have attracted vast audiences. Now we need data to keep them engaged.

A robot hand points at a streaming UI progress bar that ends in a sparkle as two people look on.

Illustration by Dave Cole / Getty / Shutterstock / The Current

Capturing — and maintaining — audience engagement can make or break your platform. With a plethora of services to choose from, and a seemingly endless scroll of content, it’s a challenge for streaming services to break through the noise and remain top of mind for consumers.

Our collective success across the industry in drawing in hundreds of millions of viewers means that we have to serve an ever-increasing diversity of interests through the same product surface area as we did when we had smaller audiences with more focused interests.

So how can platforms build a strategy that captivates and retains attention, while balancing a deep library? This strategy is threefold: address audience engagement through content, leverage machine learning and data, and improve the advertising experience.

Using content to drive audience engagement

A good content programming mix of movies and TV series catering to diverse audience preferences leads to multiple user touchpoints and can drive user engagement. When audiences engage with a service that matches these preferences — whether that is AVOD or FAST channels — it better resonates with viewers and influences user retention.

Content acquisition should be done through a viewer-first lens to ensure compelling and highly anticipated titles for all consumers. In fact, according to Gen Z Insights, 88% of Gen Z viewers pick streaming services based on content. But it’s important that it is a variety. While Tubi’s The Stream data reported that 74% of Gen Z and millennial streamers prefer original titles, according to Mashable, existing shows topped the charts in 2023, indicating a clear need for new and original topics balanced with library titles to maintain audience engagement. Tubi data also found that 96% of Americans are interested in streaming shows that are 10+ years old.

In conjunction, the product has to be built to both deliver user delight and, for machine learning to work, high quality signal on user preferences. By leveraging advanced machine learning, platforms can enhance their programming mix, tailoring content to audience preferences. Programming recommendations serve as a good use case here. With a deep understanding of Tubi audiences via signals from personalization, machine learning is capable of matching and prioritizing external programming with Tubi audience fit. In aggregate, this could help identify potential movies and series targets for content acquisition. On a larger scale, these algorithms can also determine what is culturally relevant – tracking and highlighting external trends to create buzz around new or existing content to drive conversations beyond the streaming platform.

Leveraging machine learning in decision making

A study from Nielsen found that viewers spend an average of over 10 minutes per session deciding what to watch, and one in five who couldn’t find something compelling to watch turned to another activity. As a vast majority of viewers watch content based on search recommendations according to Scientific American, having strong algorithms to support personalization can speed up the decision process and avoid user drop off.

With machine learning applied to audience segmentation and content personalization, proprietary algorithms can offer a unique experience catered to viewers’ preferences and habits. This helps them discover relevant programming faster and reduces doom scrolling to find suitable titles.

From there, it’s important to constantly test various ways to keep audiences engaged. At Tubi, that has come to life with modifications for specific audiences — like Tubi en Espanol and Tubi Kids.

With these algorithms in place, that data can then be leveraged to meaningfully drive growth and build connections. A deep understanding of audiences can identify programming gaps and opportunities to invest, while allowing the exploration of new audience cohorts, new content categories, and new platform features. Having a data-driven strategy, with data-driven decision making at all levels, allows you to optimize the audiences’ experiences.

Improving the advertising landscape

By prioritizing content, leveraging machine learning and cultivating a better advertising experience, streaming platforms can redefine audience engagement at the forefront of the future of streaming.

Large scale platforms and services within an incredible library have a unique opportunity to personalize viewer engagement based on the programming genres and titles you know they enjoy. Unsurprisingly, the more engaged a user is on your platform, the more you can personalize their experience — and the more time they are likely to spend on the platform, which is a win for everyone.

This op-ed represents the views and opinions of the author and not of The Current, a division of The Trade Desk, or The Trade Desk. The appearance of the op-ed on The Current does not constitute an endorsement by The Current or The Trade Desk.