Data feeds and APIs are changing the performance marketing landscape, enabling a whole new level of personalisation and context in advertising. Yet according to Neil Perkin, founder of digital consultancy Only Dead Fish and curator of Google Firestarters, we’ve only just begun to unlock their potential.
APIs bring scalability and speed to managing dynamic changes. With a basic use of data feeds, we might update a small number of variable aspects of our advertising – for example live pricing or stock information, or even to take account of other factors such as the current weather. So far though, we’re only scratching the surface of how we might use dynamic data to bring a whole new level of contextual relevance to advertising.
As the world becomes more digital, we’ll see an ever-increasing number of data sources and feeds, all of which may be used to better contextualise messaging. iProspect’s Alistair Dent describes this as using ‘structured data’ to talk to customers in more relevant ways. While the use of client and third-party data expands, agencies can start to construct networks of APIs that link up multiple data sources and dynamically update many different aspects of a brand’s advertising communication.
Kris Tait from performance marketing agency Croud envisages this world of possibility as one where machines augment what humans can do – a complementary combination of AI (artificial intelligence) and IA (intelligence augmentation, or amplification). Croud used dynamic data to make 297,000 changes to ads for their client Netflix over a six-month period, equivalent to 50,000 changes a month. Combining multiple feeds and data sources (including the film listings on Netflix) meant that ad elements could be continually updated. In a great example of the creative use of feeds, Croud even used a TV schedule API to upweight advertising for a garden seed client around times when programmes that had ‘gardening’ in the title were on air.
According to Visar Shabi of Brainlabs, our use of data feeds might become even more interesting when we consider applying machine learning. Brainlabs takes incoming information from feeds and applies a layer of machine learning in order to inform outgoing actions through APIs. In this way, they’re developing a model that can continuously update itself, for example by using prediction APIs. There can be little doubt that feeds and APIs will play a far more significant role in the future of marketing and advertising, and this is just one more fantastic example of how significant the scale of their potential is likely to be.
