Case Study: Co-viewing

Publisher Profile

• A major children’s streaming service that monetizes their videos via advertising.
• Many advertisers interested in purchasing inventory on this platform are hoping to reach not just the children watching, but also the parents viewing with them.
• The publisher was challenged to find a vendor that could provide reliable census-level exposure data for a reliable co-viewing calculation.

Challenges

Every publisher wants to monetize viewers across all their platforms, including streaming, in a clear, consistent way. For this reason, good exposure data to feed into a co-viewing algorithm is important to the publisher so that they can sell against the full set of viewers that they reach. One impression could reach three people: publishers want credit for those views and advertisers want insight into who they are reaching.

On linear TV, co-viewing is determined using survey-based ratings that are calculated to create a “co-viewing coefficient.” Co-viewing coefficients are a probability of how many people were watching a show in a household at one time based on survey feedback, so it’s not an accurate view of the total reach of an ad since ratings data isn’t census level. For digital endpoints, there is an opportunity for deduplicated, census-level exposure data that can be matched to co-viewing algorithms.

Solutions

In this example, the publisher already had Conviva deployed across their digital endpoints. Through the Conviva Stream ID™, Conviva mapped their exposure data to households and then ingested co-viewing coefficient data from a partner to match with actual exposure logs.

Benefits

The result was a reliable view of the audience size that could be used to better monetize the inventory. By using Conviva’s Advertising Insights, the publisher solved their co-viewing challenge.

With Conviva, publishers can unify their anonymous and logged-in traffic and map it across channels, content, devices, demographics, geographic metadata, and more for the household identity needed to effectively capture linear TV ad dollars.

Publishers can also use Stream ID™ for marketing and advertising use cases such as improving content recommendation, audience targeting, streaming measurement, and gaining deep audience insights.

Results

3x higher match rates for consumption to household

90% of streams mapped to a household

20% increase in revenue by accounting for additional audience measured through co-viewership

Learn more about how Advertising Insights can help you understand your audience to drive more revenue.