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Streaming Provider Drives 18x Higher Tune-In with People-Based Advertising

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Amidst seemingly endless options for TV viewing from cable networks and streaming services, it’s not easy keeping viewers’ attention. While major hits like Stranger Things or Game of Thrones have fans tuning, tweeting, and talking about episodes every week, cable networks and streaming services are struggling to find the right strategy to effectively drive tune-in and capture new audiences for new show premieres.

One of our clients, a major streaming provider, came to Viant with the challenge of re-engaging lapsed viewers. With people increasingly consuming content across multiple devices, we recommended a cross-device campaign in order to reach this client’s audiences across the devices they use. For this campaign, we also tested two unique segments: a people-based segment leveraging first-party data, and a probabilistic segment composed of a lookalike audience. This was done in order to prove the value of first-party data and show it in comparison to probabilistic, cookie-based data.

People-Based Approach

People-based advertising enables brands to use first-party data to paint full 360 degree views of their customers: from the devices they own, to their online and offline behaviors and purchases.  Armed with this kind of persistent data, people-based advertising empowers brands to connect with consumers with the right message at the right time.

Leveraging Viant’s people-based DSP, powered by Adelphic, this streaming content provider reached known viewers who had watched the OTT service once but never returned. Viewers were specifically targeted with custom smartphone ads when they were connected to their household IP address, and therefore most likely to be near their smart TV, to drive immediate tune-in.

Probabilistic Approach

Probabilistic targeting is another form of reaching people, which relies on cookies or proxies to form an estimated user profile. Unlike a people-based approach where the identity of the consumer is known, probabilistic targeting only gives you a “probable best guess” at the person behind the device.

In this case, to test the accuracy of the people-based campaign, the client also ran a campaign against a probabilistic segment of households deemed likely to engage with their content. As opposed to targeting known past viewers of the show, this audience segment consisted of lookalikes modeled against characteristics including general smart TV ownership and behavioral mobile/ web data.

So, which segment performed best?

Using the Viant Data Analytics Platform and our TV tune-in measurement capabilities, the client was able pinpoint which ad exposure drove viewership lift, in addition to other advanced attribution data.

The people-based targeting approach was the clear winner, achieving an 18x higher tune-in compared to the probabilistic campaign. By using Viant’s  people-based DSP, powered by Adelphic, and matching the client’s known viewer IP list with Viant’s first-party database, the client was able to programmatically deliver ads to identifiable past viewers. The people-based approach drove a total of 31,472 unique TV users, and achieved an 18% tune-in rate over a 15-day conversion window. In comparison, the probabilistic approach drove a 1.14% conversion rate.

In addition, the people-based segment also consumed the most content, watching roughly 25 minutes per tune-in compared to the probabilistic segment’s average of about 4 minutes. Overall, the people-based campaign drove 17% of the total time watched during the campaign and 2.5% of the total time watched for the month.

Using the right mix of strategies, streaming providers and networks can attract larger, more loyal audiences to watch premieres, live sports and music events, and more. As we saw for this client, running a cross-device campaign using a mix of first-party data and IP targeting effectively drove higher tune-in and for longer periods of time.

See the full case study here.

 

 

  • #demand-side platform
  • #people-based advertising
  • #probablistic modeling
  • #tune-in
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