The waning of digital ad identifiers like cookies and Apple’s IDFA is prompting a new focus on advertisers and publishers gathering first-party audience data.
Whilst it may seem easier to manage that direct, there is a school of thought that it is actually going to make things rather complicated.
In this video interview with Beet.TV, Jeremy Hlavacek, chief revenue officer of IBM Watson Advertising, explains why – and what the answer may be.
Making transparency visible
Hlavacek says it is vital to ensure “transparency” in the ad supply chain. But he acknowledges people in the industry are confused about what that means – anything from algorithmic “black boxes” to ad margin taken by partners.
He advocates a two-step response:
- “Make sure you have first-party data relationships. Connected to all this is consumer privacy and making sure that consumers understand how their data is being used and that it’s being used in a reasonable and really increasingly legally compliant way as states and governments develop more and more laws in this area.”
- “The next thing that’s going to happen, logically, is that you will have many large data sets that are in disparate places, collected in different ways. There will be a number of different first-party data sets that are going to need to be sorted out and analysed by marketers.”
First-party overwhelm
That’s when things are going to get hard to manage, Hlavacek reckons.
“We’re moving to a first-party data model where you’re going to have data coming in a consumer privacy safe way, but it’s going to come from a lot of sources,” he says.
“It’s going to come from publishers. It’s going to come from ad tech companies. It’s going to come from marketers.
“So, you’re bringing together all these data streams from a variety of different places. They’re all going to be about the same consumer, about me or you, but they’re going to need to be rationalised and organised.”
AI to the rescue?
Artificial intelligence can help, Hlavacek says.
What is AI really? More specifically, machine learning – as a key pillar of AI – involves algorithms, trained on data sets to identify patterns, predicting future events from similar streams of input data.
In particular, AI can make meaning of large unstructured data sets.
As AI gets used, however, it will be important that consumers have sight of what is happening.
“I have a lot of sympathy for consumers who are trying to figure it out,” says IBM’s Hlavacek. “We want them to understand the value exchange, that those advertising experiences ultimately create the open internet and create free content.”
