It occurred to me that I could simply enter a single static user_id value, say ‘0’, which would be functionally the same as no user_id. The metrics are naturally already in our DW so that should fulfill the other end of the exp

and its not uncommon to use GrowthBook for just the reporting side

and let us know if you have any thoughts on making a transition from Adobe Test and Target to GrowthBook easier.

Any guidance on before/after testing would be helpful. My hacks so far are unsuccessful. My report still wants to pull exposures and only takes the first record of a user_id, so using a single static ID is not really working. I don’t even really care about exposures. Would I treat this scenario like a holdout experiment?

Ok, after dissecting the queries, it’s clear that the opposite approach is better, to seed an exposures table with all known user IDs. I’m using a single timestamp for all of them, one experiment name of course, and I tried one variation name, and ran the queries manually in my DW, so now I’m getting a main_sum and main_sum_squares. But when I rerun the exp results in GB, I still get ‘no data’ for all fields in my goal metrics regardless of statistics engine (probably b/c I’m only including one variation? I am ignoring conversion windows.) I know there are more queries or calcs happening behind the scenes, but I’m wondering if I do need to include two variation_ids in my seed exposures table after all, and how that will play out for this type of scenario given my variations are essentially time-based. Seems to borrow bits from feature flag and holdout experiments without fitting neatly into either. (And, FTR, I totally get it, this is not the standard use case, and we will undoubtedly have more standard use cases/exps that do follow more normal setups.)

if you click on the 'view queries' you should see the results that GrowthBook is getting back