By Alistair Goodman
Digital ad targeting was built on the ability to target efficiently and measure ROI across sites and apps, greatly assisted by the availability of third-party user data, third-party cookies and mobile IDs. But, next year, Google will stop using third-party cookies to track users in Chrome, unless they opt in to sharing data. Apple has greatly reduced the number of mobile identifiers that can be used by advertisers — again, unless the user intentionally opts in. The industry has been looking at first-party data as a winning bet for individual businesses and to augment alternative identifiers — but you need an unrealistic amount of opt-ins to make up the gap we’re losing in traditional identifiers.
This is a problem. Attribution, lift studies, retargeting, the ability to build lookalike audiences — these processes and more are dependent on a scale of deterministic data we are unlikely to ever see again. We need to re-invent our approach to targeting and measurement and to recognize the centrality of machine learning to solving these problems.