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How Advanced Analytics Are Shaking Up TV Ad Buying


For the last 50 years, brand advertising has focused on building awareness, while direct response used its power to increase sales.

But in a TV marketplace where change is constant, this dichotomy is falling by the wayside. As advanced TV analytics continues its rapid development, both disciplines will give way to a converged solution that builds business value for brands.

At the center of this change is the prowess of advanced TV analytics and its ability to home in on an optimal TV investment and generate a specific desired return. Wait, isn’t that just direct response? It would be if it weren’t for the fact that today you can build multistage regression models that account for behavior and revenue long after the ad impression runs – even attributed down to the station-daypart level. That goes far beyond the immediate measurement of direct response to start encompassing longer-term brand effects.

If longer-term effects of TV advertising can be robustly measured, we can toss the traditional “hope and a prayer” approach to brand media planning out the window. Econometric modeling was the first step, but was limited to annual or quarterly efforts. By the time you got the readout, the world had changed. Now results are actionable during the campaign.

When these newer analytics models are combined with ongoing brand studies, you have a completely converged solution that builds business value. Now all your TV advertising is accountable to both revenue and brand metrics, without closely resembling either brand or direct disciplines. The data driving TV decision-making is now coming from outside of each discipline’s traditional ecosystem.

You still have to choose one set of primary metrics to drive optimization, so do you optimize for improving preference or revenue? As these advanced models mature, revenue will rule with brand metrics being used to ensure things are moving in the right direction. The efficacy of properly managing a brand will also certainly reveal itself with the longer-term revenue measurement.

While direct-response media planners are used to having analytics drive decisions, brand media planners will have to deal with a process that turns its tradition on its head. That’s because advanced TV analytics reveal optimal brand media parameters unique to each product or service.

For example, with traditional brand media planning, there can be a lot of hand wringing over the optimal number of gross rating points (GRP) and target rating points necessary to be successful or “break through.” Advanced TV analytics reveal the GRP level that is optimal for each individual campaign with a given strategy. Now marketers can determine if they can scale to higher GRP levels while maintaining profitability or not – and allocate across channels more accurately.

Shifts in strategy can then be tested to see if the maximum profitable investment level can be increased to further scale the business. Optimal GRP levels become an analytics output rather than serving as an upfront planning parameter.

Accountability through advanced analytics will redefine how all media is planned and optimized, both in brand and direct-response disciplines. Service organizations, especially agency environments, are somewhat notorious for not evolving very quickly. This opens up opportunity for new entities. Whoever embraces this new world faster will capture larger market share.

Looking a little further ahead, these advanced models will become coupled with evolving programmatic TV platforms. We will one day have a TV planning, buying and analytics ecosystem that enables machine learning and more automated optimization.

Professional media planners and buyers will curate these systems to account for marketplace idiosyncrasies and continue to hone and validate results. Professional buyers will fill critical gaps where inventory requires human intervention to maximize value. Domain expertise will greatly accelerate the overall development and success of this model. No matter how it evolves, the ability to accelerate optimization and drive greater profitability and scale for brands will greatly improve.


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