Hi Team, I've updated the LDM to connect date filter to first_visit_date, and then created the new_player metrics as below snapshot. The figure is incorrect. It should be 280 based on the filter. The link is: 31. Player Summary - GoodData Cloud

Thinking completed Based on your screenshots and metric definition, there are a few likely reasons why you are seeing 2,954 instead of 280: 1. The Fact Filter Shifts the Date Context to the Session/Fact Dataset Your metric is defined as:

SELECT count(PLAYER ID {label/PLAYER_ID}) WHERE Turnover_ps {fact/DTM_PLAYER_SESSION_DETAILS_AGG.TURNOVER} > 0

By adding

WHERE Turnover_ps > 0

, the metric context involves the

DTM_PLAYER_SESSION_DETAILS_AGG

fact table. • If that table connects to a general activity/session date dimension, or if filtering on this fact scopes the calculation to records where players had turnover in that period, it may evaluate against activity dates rather than strictly filtering players whose

first_visit_date

falls in the period. • Try checking the count without the fact filter first to see if the base count matches:SELECT COUNT({label/PLAYER_ID})In GoodData,

COUNT

automatically counts distinct values of the attribute [COUNT aggregation]. 2. The Dashboard Widget Date Dimension Mapping On a dashboard, the common date filter applies to a widget based on the date dataset configured on that widget [Primary vs Additional Filter]: 1. Edit the dashboard and click on the New Players # headline widget. 2. In the right-hand panel, check the Filter by date setting. 3. Ensure the date dimension selected for this widget is specifically your

first_visit_date

dataset (not another date dataset like Session Date or Transaction Date). 3. Resolving the Context with USING If

PLAYER ID

connects to both your player dimension (with

first_visit_date

) and a session/activity dataset, the calculation path may be ambiguous [Specifying COUNT Context Resolution with USING]. You can explicitly enforce that the count resolves through `first_visit_date`:

SELECT COUNT({label/PLAYER_ID}) USING {label/first_visit_date.date}

_(Replace

{label/first_visit_date.date}

with the specific date attribute or dataset identifier from your

first_visit_date

date dimension)._ Next Steps If the count remains incorrect after verifying these settings, resolving the exact relationship paths and data model requires inspecting your workspace directly. To help the Support team investigate, please add the following details to this thread: • The exact workspace ID (

a73625a4a0c54c408112daeb13296852

) and confirmation of permission to inspect via admin/impersonation. • The name/identifier of the specific date dimension you want to drive this KPI. • A description or screenshot of the relationships connecting

PLAYER ID

,

first_visit_date

, and

DTM_PLAYER_SESSION_DETAILS_AGG

in the LDM Modeler. Support Options • Community support (Startup & Professional): You are already in the right place! The GoodData Support team monitors this Slack and a team member will join the conversation when human review is needed, so please keep the discussion and post the requested details directly in this thread. You can also visit the Community Forums. • 24/7 Support (Enterprise plans): If your account tier includes 24/7 Support or this is a Severity 1 (L1) critical issue, you can also reach out via: ◦ Submit a Ticket ◦ Email:

support@gooddata.com

◦ Phone:

+1 (415) 200-0194

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Hi Cathy, Just an advice for better visibility 🙂 You can keep the discussion in a single thread and reply with the AI hashtag mentioned in the footer of every AI message if you want to keep discussion with our AI chatbot.

Thanks Julius, I'll just keep the same discussion in a single thread.