first call resolution

// T is the first dataset (not the second, aggregated).
// There must be a Date table in the model
// marked as Date table that joins to
// T on [contact_date] and the filtering is
// one-way. There also has to be a Customer dimension
// in the model that joins on CustomerId to
// T on [customer_id]. All columns of the fact table
// T must be hidden and slicing is only allowed via
// dimensions. If you want to slice by Issue, 
// you have to create a dimension Issue and join
// to T on [issue_id]. In the code below I've assumed
// that you won't be slicing by issues.
// Then you can create measures:

// This, for any slicing, will tell you
// how many rows are visible in the current
// context, which means "number of calls."
[# Calls] = COUNTROWS( T )

// This will tell you the percentage of
// customers visible in the current context
// that got their issue resolved in the first
// call.
[First Call Resolution %] =
var __existingCustIssueTable =
	SUMMARIZE(
		T,
		Customer[CustomerID],
		T[issue_id]
	)
var __custIssueWithOneCallResolution =
	SUMX(
		__existingCustIssueTable,
		( [# Calls] = 1 ) * 1
	)
var __custIssueTotal =
	COUNTROWS(
		__existingCustIssueTable
	)
RETURN	
	DIVIDE(
		__custIssueWithOneCallResolution,
		__custIssueTotal
	)

If you decide to introduce a separate Issue dimension, you'll need to adjust the code, especially what's under SUMMARIZE. Here's the code after the required changes for this scenario:

[First Call Resolution %] =
var __existingCustIssueTable =
	SUMMARIZE(
		T,
		Customer[CustomerID],
		Issue[IssueID]
	)
var __custIssueWithOneCallResolution =
	SUMX(
		__existingCustIssueTable,
		( [# Calls] = 1 ) * 1
	)
var __custIssueTotal =
	COUNTROWS(
		__existingCustIssueTable
	)
RETURN	
	DIVIDE(
		__custIssueWithOneCallResolution,
		__custIssueTotal
	)

By the way, your specification of the problem is a bit misleading. You cannot just count customers in the current context. You have to calculate the pairs "customer-issue" to get the correct percentage.

 

Best

D