Mark is the head of marketing at machine learning DSP Intelligent Optimisations, and has 16 years of experience working on both the media agency and client side of performance marketing. Mark is also the founder of media charity Tribe and is an associate lecturer in digital media at two leading Higher Education institutions in the U.K. and U.S.
PA.O: Which matters more – data or creativity, and why?
Mark Doyle: They both matter is the short answer. It’s very tempting to reduce every discussion to a simple binary proposition, but in reality, the situation isn’t as clear-cut as that. There is an implication (in the question) that the one has replaced the other, or is far more important. Programmatic digital marketing hasn’t made creative advertising obsolete at all. Data-driven targeting and creatively driven advertising work best together. A great deal of marketing budget is going into programmatic right now, and quite rightly, as it has proved to be a very cost effective and powerful way to reach people. But advertising also needs to be engaging and attractive. Otherwise, engagement rates with a poorly executed ad can suffer, even when targeted and delivered programmatically.
But creativity is more than just an attractive ad, and data is more than just collecting terabytes of information about your customers online. Without an effective way to engage with your audience in real time, to understand their changing needs and to drive new prospects into your sales funnel, brands are in danger of missing the opportunity that programmatic presents. Advertisers need to find the right partner to help with both your creative and data needs. And whilst there are many excellent creative agencies, advertisers will struggle to achieve scale and relevancy without the right programmatic partner.
PA.O: Do you believe that data analytics and creativity are inherently, at odds?
Mark Doyle: For the reasons outlined above we, at IO, believe they are complementary and, if used correctly in conjunction, deliver better results than either can deliver separately. If no one engages with your ad because it’s dull and uninspiring, or fails to articulate the offer in a creative and engaging way, then you won’t have the data to qualify your potential audience properly. It’s not optimal to go to enormous lengths to prospect and qualify a potential new customer using data analytics, only to then serve them an ad which doesn’t engage! Like most things in digital marketing, A/B testing can contribute important learnings to a campaign, and in respect to both creative ads and machine-driven algorithms, can be used to qualify and enhance relevancy in the message shaped for the audience.
PA.O: Will viewability ever be resolved? If so, how and who should be held accountable?
Mark Doyle: Yes, and it can be resolved relatively easily – if you use a machine learning programmatic solution. Admittedly, we at IO are biased in our belief that Machine Learning enables advertisers to learn what is working and discard what does not, in real time and at scale. So, if a site is assessed by the (IO) machine and found not to be conducive, (for example, if the ad slot is not viewable) then the machine rapidly learns not to deliver an ad to that slot again. This automatic biasing out of underperforming advertising opportunities quickly eradicates unviewable ad slots from a campaign. In terms of accountability – all elements on the supply side (media owners, ad exchanges and ad-tech providers) hold a responsibility to reduce the impact of nonviewable ads for advertisers. This is an industry-wide issue, and IO is fully supportive of initiatives such as MRC, endorsed by all leading trade bodies, which is an industry-led response to the problem.