Problem Statement
Currently, performance metrics are limited to 1-day time periods and intervals, which
creates too much noise for meaningful trend analysis and reporting. Daily data is
affected by holidays, releases, reverts, and other one-off events, making it difficult
to identify genuine performance improvements over longer timeframes. This prevents teams
from accurately measuring and reporting on performance changes for OKRs and quarterly
reviews, where consistent week-over-week or quarter-over-quarter comparisons are needed
to demonstrate real progress (e.g., "we reduced LCP by X% compared to last quarter").
Solution Brainstorm
No response
Product Area
Explore
Problem Statement
Currently, performance metrics are limited to 1-day time periods and intervals, which
creates too much noise for meaningful trend analysis and reporting. Daily data is
affected by holidays, releases, reverts, and other one-off events, making it difficult
to identify genuine performance improvements over longer timeframes. This prevents teams
from accurately measuring and reporting on performance changes for OKRs and quarterly
reviews, where consistent week-over-week or quarter-over-quarter comparisons are needed
to demonstrate real progress (e.g., "we reduced LCP by X% compared to last quarter").
Solution Brainstorm
No response
Product Area
Explore