Demographic Data: Key to Ad Targeting on Streaming Platforms, Say Marketers

Digiday’s multi-part series on ad-supported streaming services is part of their CMO Strategies series and covers various aspects of these platforms. The series includes an overview of platform offerings such as pricing, plans, and ad options, examines advertisers' budget allocations and ad placements, and uncovers ad attributes that are important to marketers on streaming platforms.

Advertisers prefer premium or targeted audience reach capabilities over higher total audience reach because it allows them to focus on specific parts of the market they are targeting. This targeted approach can lead to a higher return on investment as marketing budgets are spent more efficiently on consumers who are likely interested in or fit the demographic for their products.

According to Digiday’s survey, the most important first-party data types for ad targeting on streaming platforms are:

• Demographic information (age, income level) - considered most important by 65% of respondents.
• Geographical information (city, region) - chosen by 47% of respondents.
• Websites visited and actions taken (purchases, browsing patterns) - almost equally important as geographical information with 43% of marketers selecting this response.

Ad-supported streaming platforms collect first-party data for ad targeting in various ways:

• Platforms owned by larger parent companies, like Google’s YouTube and Disney’s Hulu and Disney+ Basic (with ads), have access to large reservoirs of first-party data across media properties.
• NBCUniversal’s NBCUnified consolidates first-party data across all its media properties, including Peacock, and uses a data clean room.
• Smart TV-based streaming services like The Roku Channel and Samsung TV Plus collect data during device registration and use automatic content recognition (ACR) technology built into smart TVs.

To address consumers' data privacy concerns, platforms can use several strategies:

1. Transparency - Clearly informing consumers about the data being collected and its usage.
2. Control - Giving consumers control over how their data is used and shared.
3. Value Exchange - Demonstrating the value provided to consumers in exchange for their data, such as personalized content recommendations.