It's not as simple as that. There are multiple ways to figure out which ad to show to a user, but I think the most efficient way (development and compute) is to

It's not as simple as that. There are multiple ways to figure out which ad to show to a user, but I think the most efficient way (development and compute) is to put a user in segments (e.g. lives in area X, searched for Y, age Z, ...) and then do the bidding magic. There are tight tolerances for bidding--you want it to happen within a fraction of a second so the ad quickly loads for the user. Feeding purchase data is likely something that requires a bit of work, and I imagine the ad team at Amazon doesn't see enough value to prioritize it (especially since they make money either way--the advertisers pay for the ad with a higher CPC). One thing to keep in mind is that the purchase data is per-user, so it's different from the segment approach I was mentioning above. It would require a whole new schema* that is queried whenever bids are processed for an ad event. * Sorry for using the word schema here. It was used a lot on a team I was on in a similar context, and it bothered me at first but I got used to it. What I mean by schema is a "blob" with purchase data that is periodically processed by the bidding subsystem to add bid exclusions per user.