Since the advent of programmatic buying in the media buying space, particularly in performance marketing, automation itself has been championed as the optimal method of improving efficiency while achieving scale.
We are now rapidly adapting advanced data science techniques, among which artificial intelligence is just one facet, in various aspects of media buying.
It is clear that operational gains from the use of AI are driving immediate value in deriving insights from data, rapidly developing data-driven media plans, and summarizing information from media documents. However, the most significant efficiencies are to be found in content and creative production at scale.
The game changer in using AI methods in creative software is less about full production and more about the ability to make rapid, meaningful changes based on multiple choices presented to the author. It is truly innovative in empowering humans and keeping them in the loop while revving up their productivity with AI.
Audience segmentation and targeting have benefitted from data science for quite a while now and will see increased sharpness at scale as AI gets integrated into platforms that apply optimization algorithms to campaigns.
Meanwhile, attribution modelling is being enriched significantly as it is increasingly powered by machine learning models and, in many cases, by more than one model, fueling the demand for ever-richer data sets in marketers’ quest for novel analytical outputs.
The media industry’s dependence on the use of AI will only intensify, but that should always be in the context of using AI as a powerful tool – or as an ecosystem of tools, as is often the case – treating is as a means to an end, serving, not dictating, brilliant strategy and creativity.