Data-Driven TV Is More Than Addressable And Connected

"On TV And Video" is a column exploring opportunities and challenges in advanced TV and video.

Today’s column is written by Tom Weiss, chief technology officer and chief data scientist at Dativa.

After decades of planning and measuring TV with only minor iterations of the same fundamental techniques and technologies, marketers now find themselves confronted with an influx of new TV advertising technologies vying for mindshare, adoption and budget.

Among them are addressable and connected TV, which are both positioned to play a prominent role in TV’s future. However, while networks, MVPDs, vendors and investors need to steer their media businesses and road maps for five to 10-plus years out, agencies and advertisers have brands to build and promote today.

For virtually all mid-sized to large brands, the scale and cost efficiencies of advertising within conventional linear TV delivery continue to reign supreme. That's the annoying kind where you and your neighbor see the same ad if you’re watching the same network. It composes more than 94% of available TV ad impressions, and it is the most cost-efficient, practical and pragmatic way for brands to advertise on TV today, and it will continue to be over the next several years.

Moreover, many new data tools in TV advertising can apply as equally to the annoying kind of TV advertising as they do to addressable and connected TV advertising. In particular, there's great potential for brands to improve the traditional TV ad buy by indexing digital audiences against linear TV viewership, also known as data-driven linear.

The emergence of smart-TV data sets has driven the indexing of digital audiences against linear TV. Smart-TV manufacturers, which don't have legacy data businesses to preserve or relationships with existing data providers to protect, made TV viewing data available to data brokers and others in the ad tech ecosystem to match against digital audiences. The result was the ability to identify TV programming with the highest concentration of a targeted audience. The beauty of this technique is it allows marketers to plan TV campaigns using precise audience segments rather than blunt-edged age and gender demographics.

Take an online travel aggregator that buys adults 25-54 years old but is ultimately trying to reach business travelers. Without indexed linear, the decision-making process for determining programs with a high composition of business travelers is likely a function of common sense and gut – business travelers may be more likely to watch business news and golf – and negotiating media suppliers on rates for reaching adults 25-54 years old.

However, with indexed linear TV, the online travel aggregator can define an audience composed of users with the Delta, United or American Airlines apps on their phones, users who have physically been in airports multiple times in the past 60 days or countless other digital audience segments. Networks, dayparts and programs are then objectively scored based on their composition of the business-traveler audience, and media can be justly secured based on index scores and cost.

The same TV viewing data and audience data can then be used to deliver a post-campaign report measuring the in-target views and reach, similar to how a traditional TV buy is today measured against adults 25 to 54 years old.

Herein lies an obstacle that pragmatic marketers need to sidestep. Guarantees against a target demo have long been a staple of TV advertising contracts, with research teams generating forecasted ratings and measurement companies providing actual GRPs, with those on the sell side pressured to deliver against these plans. For marketers to use the best digital audience for a given campaign, they’ll need to accept that media sellers do not yet have the forecasting and yield systems in place to guarantee linear TV delivery against any digital audience.

However, agencies and brands can still index digital audiences against TV to determine the networks, dayparts and programs they want and have the buy guaranteed off a legacy demographic.

For example, a pizza chain could take an audience composed of people with the Dominos app downloaded on their phones. From that it can determine which networks and dayparts most strongly index with those pizza-ordering customers on linear TV. It can then buy those dayparts from its usual linear TV suppliers, but transact using a legacy demo that suppliers are comfortable guaranteeing, such as adults 25 to 54 years old.

For agencies and brands seeking to execute an indexed linear campaign for the first time, they have to trust where the data is pointing. Agencies should vet the TV data and audience data efficacy upfront. Afterward, the data should drive the decision-making. While there are scenarios requiring constraints to be applied – no kids’ programming, for example – each constraint limits the opportunity to find the highest indexing programming. It takes trust in the tools, but broad-based restrictions, such as dayparts, day of week and network, should be narrowed down or eliminated outright to drive efficiency.

For many advertisers, audience-indexed linear TV is a powerful technique for enhancing their TV investments and will continue to be for the next several years. If advanced TV history has told us one thing, it’s that platform shifts take time. So, while marketers should invest to stay on top of new technologies, it’s even more critical to get value from media investments today.

Follow Dativa (@Dativa4Data) and AdExchanger (@adexchanger) on Twitter.

 

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