Feature flag versions are automatically calculated when they include percentage targeting rules. Flag versions that do not include percentage targeting rules will not be automatically calculated but can be calculated on-demand at any time via the Recalculate metrics button.
The duration between updates scales with the length of the version since the older the experiment, the less likely that the data collected in the last few hours can move the metric.
At the beginning of a version, calculations are run every 15 minutes for definitions updated in the past hour. The time between these calculations increases incrementally through the duration of a version. Feature flags will be updated every hour for the first 12 hours and then alternate hours until it has been running for 24 hours. The calculation schedule will then move to daily until day 14 of a flag version. Final automated calculations will be run on day 21 and day 28 of a flag version.
You can manually recalculate at any time, which is particularly useful if you add or update metrics. You can also see the last calculation time on the Metrics impact tab.
If you are using fixed horizon, as a best practice you should establish experimental review periods. Making conclusions about the impact of your metrics during set experimental review periods will minimize the chance of errors and allow you to account for seasonality in your data.
Perhaps, you see a spike in data on certain days of the week. It would be against best practice to make your product decisions based on the data observed only on those days, or without including those days. Or, a key event, such as arriving for a restaurant reservation, may not happen until a few weeks after the impression.
We will always show your current metrics impact, the review period has no direct impact on the metrics, neither the ingestion of events nor the recalculation of metrics. It's there as a guideline for users to provide a caution against making a decision too early or without accounting for seasonality, even if the card shows as statistically significant.
Experimental review periods are a common practice for sophisticated growth and experimentation teams. For some experimentation teams, no decisions can be made until the experiment has run for a set number of days.
As it’s an organization-side setting, whatever you set may or may not be applicable for a specific experiment. That’s why the warning says it MAY not be conclusive. In other words, if it’s set at 14 days, in many cases the results on day 15 are probably as accurate as the results on day 14 (or 16 or 17 or….). The exception would be if there is a very specific cadence to the results.
This article provides more information on reviewing your metrics cards.