How to maintain effective with digital privacy scrutiny and the GDPR/CCPA

With Europe’s GDPR and California’s CCPA, together with the fact that both Google and Apple are limiting the use of Device ID’s and cookies - the industry has to start working with other methods to ensure and maintain efficiency and effectiveness.

The apparent fact that the lack of accuracy the above brings, doesn’t mean a person can’t be ‘found’ and served a relevant ad. Here’s why:

On a normal day we receive more than 1 million ad requests per second. The requests come with metadata such as; date, time, location or a Device ID like Apple’s IDFA. We could use the IDFA to determine if we have served an impression on this device before. Also, if the device is located within a certain data-segment that we find relevant for the campaign in question.

Many vendors and buying networks rely on ID’s to find and target specific devices/individuals. However, from day 1, we have relied and successfully deployed machine learning mechanisms to determine if an ad request is suitable for a specific campaign. Essentially, each signal is automatically categorized and given a score - based on machine learning - to learn and apply the effect of each signal on campaign performance.

The combination of present variables gets a score based on historical data and live/actual campaign performance. This score will determine if we bid (and how high) on that specific ad-request - ‘looking’ at the campaign configuration and it’s goal.

The ad-requests described above are also know as ‘Limited Ad Tracking’ (LAT) requests. Being that the whole industry is moving towards LAT based impression requests, we feel we have been applying the right targeting vs privacy strategy from day 1. We are - and always have been - focusing on new ways to target and attribute.

Do you want to share ideas, have questions or simply want to discuss? Reach out!