With the help of pervasive connected computing, the scope and scale of the TV industry has dramatically changed over the past decade. Consumers now have a wide variety of choices in TV consumption with the establishment of many providers offering both subscription and advertising-funded services. TV viewing via OTT and on-demand, whether a set-top box, connected TV, mobile device, or computer, is becoming the dominant behavior.
TV operators are faced with several challenges to future income streams. Competition is fierce and consumer research suggests that monthly subscription pricing becomes highly elastic once it reaches modest monthly amounts (in comparison to traditional cable/satellite TV pricing) –leading to high churn.
Ad-funded business models are now the norm with nearly every major OTT provider offering an ad-tier subscription option and the emergence of FAST channels. Addressable advertising has become the big promise to both operators and advertisers. However, to fight off the tech giants, TV operators will have to deliver audience targeting on TV that matches the sophistication of online media.
Addressable TV advertising is the ability to serve up video ads to specific individuals, rather than large demographic groups. But the revolution has been slow to come to the boil and investment remains modest.
Deloitte Global predicts that addressable TV advertising, which allows different ads to be shown to different households watching the same program, will generate about US$7.5 billion globally in 2022. While this number has increased 40-fold growth in 10 years, it is a small percentage of the global US$153 billion TV ad market forecast for 2022. Clearly, addressable TV advertising has a long way to go.
One of the big factors holding back growth is the massive disparity between targeting capability on TV when compared to the established practices for digital.
Raising the bar for targeting attributes for TV
Targeting on TV is antiquated. Most TV advertising is still bought and sold using very basic demographic segments. As operators have moved into addressable advertising, most have initially sought to sell ad inventory using geo-targeting and by matching subscribers to purchased third-party lifestyle data to offer advertisers “off-the-shelf” targeting attributes. However, this doesn’t address advertisers’ demands for consistent online audience taxonomies (for cross-platform campaigns) or accuracy (i.e., based on actual consumer behavior).
The successful business case for addressable TV advertising really hinges on accurate, relevant, and recent behavioral audience targeting data, something that many in the industry still don’t appreciate.
With the rise of addressable advertising capabilities, there is an accelerating demand from advertisers for better targeting on TV using audiences that are consistent with media buying on the internet. Digital or online media has established the usefulness of the “affinity attribute,” albeit the death of cookies is causing a lot of re-thinking about how to target. Therefore, first-party data has become more important and federating this data is crucial (i.e., a consistent way of using it).
TV operators have a major advantage because they have the opportunity to compile and package their own first-party viewer data, which is a highly valuable asset to advertisers.
More sophisticated targeting attributes make an operator’s platform more attractive to TV advertisers by offering increased reach and enhancements, such as better viewer identification. Sophisticated targeting also attracts niche and premium advertisers into TV by enabling them to buy only the audiences that are relevant to their offerings.
TV operators need to step up their targeting capabilities in order to make their investment in addressable advertising pay back. Some TV operators recognize this and have recruited data science resources to build it. However, from my own experience, producing meaningful, predictive, and valuable targeting attributes from first-party data can be slow, expensive, and uncertain for an operator using manual analytical resources and inadequate metadata. And it’s an approach that’s hardly compatible with the fast-changing dynamics of viewer behavior.
In the online world, companies can more easily collect behavioral data and create affinity segments. In TV it is much more difficult. Collecting raw viewing data and transforming it into meaningful viewing records is challenging and modelling those viewing records to create and operationally manage affinity targeting attributes is even more difficult, especially if content metadata is weak.
So how can you build the right targeted audiences for proven ROI?
What is needed is a highly automated targeting attribute development and maintenance solution, capable of producing and adjusting a wide range of accurate, behavioral-based target audiences at the speed the advertising market demands. This is not a trivial thing to create.
The essential components of such a solution are rich content metadata, access to a large scale and scope of viewing data from which to derive standardized attributes, and an advanced AI platform to develop and maintain a suitable suite of target audiences that deliver proven ROI for advertisers.
The good news is that there are existing solutions that can rapidly produce a large set of accurate affinity attributes based on standard audience definitions from any TV operator’s first-party viewing data set. TV operators and advertisers can use the power of audience TV behavior to reach the right target audience at the right moment with the right message. Not only does this improve advertising spend effectiveness, but it should also go a long way to mitigate the potentially deadening effect of a blizzard of advertising messages on consumer satisfaction, engagement, and response.
[Editor’s note: This is a contributed article from ThinkAnalytics. Streaming Media accepts vendor bylines based solely on their value to our readers.]
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