Welcome to the world of "clean rooms", often used with tricky consumer data like this [1].
I recently was a Tech PM for a large ad/marketing agency and we utilized them for effectiveness of movie goers for a large studio. Essentially, we wanted to see who saw our ads on social media _AND_ set top boxes _AND_ searched for the movie title in particular Zip codes.
Obviously highly specific data that fingerprints a single user wouldn't be given to us by Meta, Comcast, and Google (first-party data), but we can ship that data to a "clean room" who will venn-diagram it together to get us our ultimate numbers, per Zip code, to find effectiveness/reach.
Wal-mart being a first party with both of their doors (retail and pharmaceutical) presumably can do this all themselves with their own data scientists looking at register receipts.
I recently was a Tech PM for a large ad/marketing agency and we utilized them for effectiveness of movie goers for a large studio. Essentially, we wanted to see who saw our ads on social media _AND_ set top boxes _AND_ searched for the movie title in particular Zip codes.
Obviously highly specific data that fingerprints a single user wouldn't be given to us by Meta, Comcast, and Google (first-party data), but we can ship that data to a "clean room" who will venn-diagram it together to get us our ultimate numbers, per Zip code, to find effectiveness/reach.
Wal-mart being a first party with both of their doors (retail and pharmaceutical) presumably can do this all themselves with their own data scientists looking at register receipts.
[1] https://digiday.com/marketing/data-clean-room/