Making AI a friend, not a foe in TV planning
Opinion: Strategy Leaders
Nina Franck explores how AI can be a friend rather than a foe to help TV planning and strategy.
When thinking about AI your initial thoughts might be it is for digital media such as social media algorithms or lenses.
One of the channels that has not changed much in the past 20+ years in terms of planning and buying processes, at least in Germany, and yet, here we are using artificial intelligence not just for measuring TV, but more importantly for planning TV.
Don’t get me wrong, the channel itself has evolved a lot from a consumer point of view. Streaming, catch-Up, BVOD, AVOD, SVOD etc. all have entered the market.
More and more channels, especially niche channels have entered the linear TV market too, and the way linear and time-shift TV is being bought and consumed has changed.
Now planning and buying is evolving too, with the help of AI.
Where AI beats the human planners
Earlier this year All Eyes On Screens introduced AI to this channel in the German market. Large sample sizes and second-by-second data coming from telco data and an owned AI video-tracking allows granular measurement which has been done for nearly two-and-a-half years now. And this measurement data collected over a few years give us such a rich database that can make TV planning much easier and faster.
The core function of AI here is to calculate the optimum combination of adblocks that allow for building up maximum reach. This is what it is pretty good at and beats any human approach to do this particular part of the job.
However, it does not replace the need for a human who understands the channel and the consumer, or target audience, the challenge of a brand within their given means such as budget constraints, competitor’s activity and creatives as well as any TV strategy developed.
AI in TV planning allows the planner to run multiple TV tactic scenarios while the AI runs thousand of reach scenarios in the background to get the best outcome of reach within the briefed framework.
It is much more efficient and allows the planner to invest their time and effort in TV strategy, thinking about tactics and campaign scenarios to get the best our for their clients or your brand directly.
And there is more to it
The granular data level allows to identify TV consumption behaviour based on actual interests (think channels, genres and programmes) and time (think weekends only, just 3 hours max. per month, or 5 hours per day etc.). That means we are now able to build TV target audiences that are defined based on qualitative elements rather than falling back to a demographic proxy.
That allows you to get closer to the actual target audience and maximising reach within your relevant segment on a broadcast channel. And maximum reach is the driver of brand relevant tracking such as awareness.
These profiles can also build the relevant planning audience via AI.
I know that change can be daunting.
In the past agencies have been planning towards GRPs rather than reach. But a GRP is a theoretical figure that consists of net reach x Opportunities To See (OTS), and does not necessarily allow for planning towards maximum reach.
But we need to plan for maximum reach in order to increase awareness and to keep awareness up (see Byron Sharp’s How Brands Grow). And this new AI approach allows to evolve linear TV planning to evolve further to suit the needs of brands better.
The magic combination
A lot of frightening and damaging things can be done with AI, such as deep fakes and creating and spreading fake news, or infringing copyrights. I am with you.
Having AI in the shape of an ever-learning algorithm is important. But it needs to be combined with a connection of live availabilities of sales houses. And further external data, such as weather.
This combination will result in the magic formula needed to make AI our friend. It need not be seen as an enemy of human craft.
Nina Franck is an independent paid media strategy consultant and director of client success at All Eyes on Screens (formerly AdScanner).
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