We have an interesting situation which we're not quite sure how to address The data we're working with is a combination of our ETL'd Mixpanel events, and internal backend transactional databases A lot of the data we work with is "special category" - health/sexuality/similar - so in order to protect it without locking down our data warehouse, we have a pseudonymization process. Most data (Mixpanel events and some backend data) is held against a "pseudonym ID" rather than the regular user ID. Some data (e.g. Stripe records, basic user registration info, etc) does just use the normal user ID though. In order to make this processable in tandem with the pseudonymized data in our warehouse, we make a copy all the user ID-oriented records so they can be used with the pseudonym ID as well, hashing all the IDs etc, removing any identifying information. Critically, to prevent reidentification via ordering/matching of high-entropy fields, we truncate all timestamps in this process, usually to the day

Both of those options would work. Negative conversion delays are meant for cases where the logged experiment date might be after the metric conversion. Most commonly used for things like sessions, but it would work for this too. Just to clarify the "multiple variations" part, if the user sees multiple different variations (e.g. A and then B), we remove them from the analysis completely. If they just see a single variation (e.g. B), then the attribution Model determines how we treat multiple exposure events.

ah great okay, so it's pretty safe to truncate