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Hi Matt - The reason we suggest waiting is so you can use the Google Client ID.

Thanks @fresh-football-47124. Relatedly, we're noticing that because of this issue, the trackingCallback wasn't always sending the

experiment-viewed

event to GA since in some instances the

gtag()

wasn't loaded yet, which we're adjusting. However, we can still see in the GB dash that there are more reported users in the experiment population cohorts and higher reported Metrics than we're able to see in GA. Is GB logging this anywhere we can access? And, if so, would we be able to retroactively fill GA for the users GB has accounted for but are missing in GA as a result of the

gtag()

not loading fast enough for these missing users?

the data is coming from BigQuery

In a perfect world these numbers would match. But I guess the question is as it relates to a specific example: GrowthBook dash reports 73.9k users were exposed to our Variation for an experiment. As an example, we have a trial_starts Metric attached to this experiment. GB dash reports 163 trial_starts for this Variant. When gathering data to compare from GA, the number of users exposed to this Variation is 69.9k, and trial_starts are 93. If all data is stored in Big Query, then GB and GA should have equal reporting. But, GB is serving the experiment to all users, and this is independent of the trackingCallback missing the

experiment-viewed

event send to GA from the

window.gtag

load, so what causes that large of a discrepancy? Does GrowthBook store experiment data in Google Big Query in more ways than via the

window.gtag

?

no, GrowthBook is modular, and the experiment reporting is entirely based off your data (BigQuery)

What's the mechanism by which BigQuery collects information like

variation_id

from GrowthBook?

from the trackingCallback method

Have you seen other customer instances of GB reporting higher numbers than a GA query for certain metrics?

its usually lower, as we do some deduping and data quality checks

That's what I would expect as well, having it report near double a GA query is puzzling

we give the raw query, you could run it yourself and debug it if you like

We also have our Metrics queries running a date range as

BETWEEN '{{startDateISO}}' AND '{{endDateISO}}'

In an instance where we set up a Phase 2 for an existing experiment, I assume the results of our Metrics in the dash automatically adjust for the date range we have set for the Phase selected?

If I select Phase 2 in the results tab and Update Data, the results for the Metrics tracked should be from Dec 15th-today, right?

Yes. I suggest you look at the queries we produce, by clicking the three dot menu on the results tab and clicking View Queries. There you'll see some of our date filtering logic and how our queries pull data from BQ to produce the numbers in the dash. I'm happy to help you understand them if you have questions.

Perfect, thanks! Very helpful, exactly what I was looking for