Distinct count on dynamic query using unrelated dimension tables

You can easily get the distinct count of customers for the current year using a measure like this:

DistinctCustomersCurrentYear =
DISTINCTCOUNT(MainTable[CustomerID])

For the lifetime distinct count, since you’re dealing with unrelated dimension tables, you can use a combination of DAX and context. Try creating a measure that calculates the distinct count while ignoring the slicers for the lifetime table:

DistinctCustomersLifetime =
CALCULATE(
    DISTINCTCOUNT(LifetimeTable[CustomerID]),
    ALL(MainTable)  // Ignores current year filters
)

To filter the lifetime data based on slicers, you can create a measure that checks for the selected products or stores from the slicers and then uses that context to filter the lifetime data:

DistinctCustomersLifetimeFiltered =
CALCULATE(
    DISTINCTCOUNT(LifetimeTable[CustomerID]),
    FILTER(
        LifetimeTable,
        LifetimeTable[StateID] IN VALUES(MainTable[StateID]) // Adjust as necessary
    )
)

If you’re using DirectQuery, you may be limited in how you pass slicer values. You might need to look into using stored procedures or creating a SQL view that combines these tables dynamically.

Finally, create visuals that show both measures side by side for comparison. This will help illustrate the differences between the distinct customer counts.