The Role of Data in CTV and OTT Advertising

You may remember when watching your favorite show involved tuning in at a specific time on television. Today, it’s more likely you’re on-demand streaming content via platforms like Netflix or Hulu, thanks to Connected TV (CTV) and Over-The-Top (OTT) streaming.

CTV involves watching internet-based content on a television screen, whereas OTT is content viewed on any device, including a TV, phone, or tablet. Remarkably, in 2020, 80% of U.S. households owned at least one connected TV device, demonstrating increased popularity.

This change is not a transient fashion. The COVID-19 pandemic kept many of us at home, leading to a surge in streaming. These habits formed during lockdown are proving to remain, even as we regain strides towards normalcy.

The emergence of CTV and OTT has also completely transformed the advertising industry. It provides advertisers with an innovative platform, allowing for precise targeting and measurement of ads. This article will highlight the crucial role data plays in this revamped advertising landscape.

Understanding Data Collection Strategies

Grasping CTV and OTT advertising requires a thorough understanding of data collection methods, specifically, probabilistic and deterministic strategies. These strategies power popular streaming platforms and influence the ads you encounter.

Probabilistic and Deterministic Methods

Probabilistic data collection predicts user behavior based on the likelihood that they belong to a certain group, using anonymous, aggregated data. It leverages patterns and correlations among a large group of users. In contrast, deterministic data collection uses known, identifiable data about a user, such as an email address or a device ID. This enables advertisers to link a specific user across different devices accurately. As an example, this data can include demographic data.

These methods have unique applications in the CTV and OTT space. For example, deterministic data can be used for precision-targeted advertising, serving you ads based on your specific interests. Probabilistic data, however, is useful for serving ads to groups of users with similar behavior patterns.

Practical Applications

Major streaming platforms use identifiers like RIDA, TIFA, AFAI, or VIDA for data collection and targeting. These identifiers, akin to labels assigned to your device, communicate your preferences to advertisers. This CTV measurement helps them build out target audience personas and perform consumer behavior analysis.

Consider this scenario: you frequently watch cooking shows. The data collection method registers this trend, and as a result, you start seeing more ads for cookware or recipe books. This is data collection in action!

  • RIDA, Roku’s ID for advertisers, is used by the Roku platform.
  • TIFA and AFAI serve similar purposes for Amazon Fire TV and Android TV, respectively.
  • VIDA, Vizio’s Inscape Automatic Content Recognition ID, collects deterministic data from smart TVs to provide detailed viewing data to advertisers.

Importantly, these identifiers respect user privacy and only collect data with user consent and while following consumer data privacy regulations, striking a balance between personalized advertising and privacy.

Data Types: First-party, Second-party, and Third-party Data

In the realm of CTV and OTT advertising, not all data is alike. Advertisers primarily use three types of data: first-party, second-party, and third-party data, each with its unique strengths and applications.

Deciphering the Data Types

First-party data is information collected directly from your audience or customers. This data is highly reliable as it comes straight from the source. For instance, a streaming service like Netflix gathers first-party data every time you watch a show or create a playlist. This data is crucial as it’s highly accurate and relevant to your brand.

Second-party data is essentially another entity’s first-party data. It’s shared between partners through a mutual agreement. For instance, an advertiser might buy second-party data from a streaming platform to understand the viewing habits of their target audience.

Third-party data is gathered by an entity that lacks a direct relationship with the user. This data is typically compiled from various sources and can supplement your first and second-party data. It offers an all-inclusive depiction of the tendencies and preferences of the user.

Real-world Applications

Consider this example. If you’re a fan of a popular sitcom on Hulu, the platform collects first-party data about your viewing habits, such as the time you typically watch, how long you watch, and your preferred shows.

Hulu might then sell this second-party data to a retail company looking to advertise their product to viewers of this sitcom. The retail company can then target their ads with precision to those likely interested.

Third-party data comes into play to expand the targeting. A data aggregator might have data on your shopping habits, your favorite brands, and your lifestyle. Integrating Hulu’s second-party data allows the retail firm to design a more impactful promotional campaign.

In CTV and OTT advertising, each data type contributes to a more comprehensive understanding of the audience, enhancing the targeting and effectiveness of your ads.

Refining CTV and OTT Advertising Tactics

In the world of CTV and OTT advertising, targeting audiences accurately presents a challenge. Traditional cookie-based tracking doesn’t work here, but that doesn’t leave marketers empty-handed. Instead, they’ve adopted innovative targeting solutions tailored to these unique platforms.

Alternative Targeting Solutions: IP and Contextual

The most popular alternative to cookies is IP targeting. This solution connects data with a user’s specific location using their IP address. It allows advertisers to deliver ads that are locally relevant and resonate with the user. An alternative method involves targeting based on context. This method focuses on the content being watched, not the individual viewer’s behavior.

Using Contextual Targeting

Contextual targeting employs sophisticated algorithms to analyze the subject matter of the content users are streaming. This allows it to serve ads related to that content. For instance:

  • While watching a cooking show, you might see an ad for a local grocery store or a new kitchen gadget.
  • Streaming a fitness workout could trigger ads for athletic wear or health supplements.
  • Enjoying a travel documentary might bring up ads for flight deals or hotel bookings.

These ads are likely to catch your attention because they’re linked to the content you’re actively engaged with. That’s the strength of contextual targeting in CTV and OTT advertising!

Comparing Linear TV, CTV, and OTT: An Examination of Cost, Reach, and Measurement

There is a clear shift from traditional linear TV to CTV and OTT, influenced by differing costs, reach, and measurement capabilities.

Cost Analysis

Linear TV advertising is often more costly due to high production and airtime charges. In contrast, CTV and OTT ads are typically more affordable. The cost-saving benefits of these platforms extend to production, with digital ads offering creative flexibility without the heavy expense.

Reach

Linear TV appeals to a broad audience but lacks the personalization of CTV and OTT platforms. Streaming platforms, with their interactive nature, cater to individual viewer preferences, enhancing engagement. With the rise of streaming services, advertisers have access to a larger audience.

Measurement Capabilities

CTV and OTT excel in measurement capabilities compared to traditional TV. Performance of linear TV ads is often estimated, while CTV and OTT platforms offer precise, real-time data. This data provides advertisers with insights into viewer behavior, optimizing campaigns.

  • Linear TV – Higher cost, broad reach, less precise metrics.
  • CTV and OTT – Affordable, personalized reach, real-time metrics.

Understanding the Future of Advertising

Connected Television and Over-The-Top advertising keeps growing, offering new possibilities. Unique data collection strategies and refined targeting techniques, combined with real-time metrics, allow advertisers to reach audiences in innovative ways.

Data drives these changes. This data is not just making personalized, compelling advertising attainable, but mandatory. The first step?  Having access to the data necessary to make the optimal strategic and tactical decisions for your CTV and OTT advertising. To learn more about how SMS data can help, contact us here.

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