The Board Dashboard With Three Different MQL Counts

Dashboard filters only apply to reports that contain the same property and object context as the filter. If the pipeline card is built on Deals with a Deal Region property, it responds. If the MQL card is built on Contacts with Country instead of Region, or on Companies without a contact-level region, the filter cannot bind. A dashboard filter is not a global SQL WHERE clause; it is matched report by report.

The MQL gap is likely a definition and grain issue. Dashboard MQL counts may count contacts by Lifecycle stage = Marketing Qualified Lead, while campaign analytics counts campaign members or contacts associated with a campaign. A contact can be an MQL and belong to multiple campaigns, so campaign totals can differ. The date property also differs: Create Date versus campaign membership date. The dashboard may include contacts created this quarter but exclude campaign membership outside the quarter, or vice versa.

Three checks:

1. Open each card’s report and list its primary object, filter properties, and date property. Confirm whether Region exists on that object.
2. Reconcile one card to a raw contact list. Compare counts with and without the campaign association. Check whether the definition is lifecycle stage or campaign membership.
3. Check the business-unit and pipeline filters. In a multi-business-unit portal, default reports can include all business units unless the report explicitly filters. Also verify whether the 11 pipelines include internal, renewal, or test pipelines.

I would cut the 14 cards to six cards with explicit definitions. The board needs trend and conversion, not every campaign tactic.

Keep:

1. Qualified leads created - Contacts, filter Lifecycle stage = MQL, date property Create date. Label it “MQLs created.”
2. Pipeline created - Deals, date property Create date, filter to new-business pipelines, measure Deal amount. State whether it is gross or net.
3. Closed-won revenue - Deals, date property Close date, filter Closed Won and new-business pipelines, measure Amount.
4. Win rate - Deals, date property Close date, closed deals only, measure count won divided by count closed.
5. Source mix - Contacts or Deals, depending on the question, with an explicit “Original source” definition and a single date property.
6. Pipeline coverage - Open pipeline divided by remaining quarter target, with a snapshot date card and no date range filter.

For consistent filters, standardize on one Region property used by every card, preferably a company-level region copied to the deal and contact where needed. Apply report-level filters for business unit and pipeline set. Use dashboard filters only for Region and Owner, and test each card after applying the filter. Put the date property in the card title, for example “Closed-won (Close date).” Remove lifetime cards or move them to a separate historical dashboard. Add a footnote defining MQL, pipeline, and revenue.

I would reconcile by definition before touching the data. The dashboard probably shows all open deals in the selected pipelines, while the forecast report uses forecast category, forecast date, deal stage, and commit/best-case/pipeline classification. The dashboard may include deals with no forecast category, deals with a close date beyond the quarter, renewals, or deals in pipelines the forecast excludes. It may also include deals owned by users who are not in the forecast hierarchy.

Reconciliation steps:

1. Export both populations at deal level. Compare deal IDs and find the extras in the dashboard. Classify the extras by pipeline, forecast category, close date, and owner.
2. Rebuild the dashboard card with the same filters as the forecast: open deals, forecast category not blank, close date in the quarter, included pipelines, and active owners.
3. If the business wants a broader “all open pipeline” view, keep it but label it separately from “forecastable pipeline.” Do not present the broader number as the forecast.

Dashboard change: add two cards, “Forecastable pipeline” and “All open pipeline,” with a variance card showing the difference. Add a filter for forecast category and a footnote on the inclusion rules. Set a monthly reconciliation between the dashboard and the forecast report. This gives the VP a legitimate broad pipeline view without creating a false forecast comparison.