上面的分析结果可以看到，在holiday(节假日)的midnight（晚间）的挂断率是最高的:0.158，而在PM2(下午第二档)的weekday(工作日)日的挂断率是最低的：0.1144
但是这些值或许还不是我们所期望的，比如老总发话了，要将挂断率保持在0.1以下，该如何调整呢，其实基于上一篇我们神经网络算法已经分析出来，平均应答率这个因素对于挂断率这个指标影响是非常大的，我们可以通过调整这个值来减小挂断率这个值的大小，提高服务水平，比如我们可以减少%90或者80%的平均应答时间，我们来预测以下这样产生的挂断率的值为多少。
我们调整上面的数据源视图的语句，增加两项：
然后将这个语句调整值数据源视图中，利用上述方法来预测下减少到90%的平均应答时间，它的挂断率是多少，我们直接写DMX语句进行查询：
SELECT t.[Shift], t.[WageType], Predict([FactCallCenterReturn].[Service Grade]), PredictProbability([FactCallCenterReturn].[Service Grade]) From [FactCallCenterReturn] PREDICTION JOIN OPENQUERY([Adventure Works DW2008R2], \'SELECT [Shift], [WageType], [AvgCalls], [AvgIssues], [AvgOperators], [AvgOrders], [Last90TimePerIssue] FROM (SELECT DISTINCT WageType, Shift, AVG(Orders) as AvgOrders, MIN(Orders) as MinOrders, MAX(Orders) as MaxOrders, AVG(Calls) as AvgCalls, MIN(Calls) as MinCalls, MAX(Calls) as MaxCalls, AVG(LevelTwoOperators) as AvgOperators, MIN(LevelTwoOperators) as MinOperators, MAX(LevelTwoOperators) as MaxOperators, AVG(IssuesRaised) as AvgIssues, MIN(IssuesRaised) as MinIssues, MAX(IssuesRaised) as MaxIssues, AVG(AverageTimePerIssue) as AvgTimePerIssue,(AVG(AverageTimePerIssue)*0.9) as Last90TimePerIssue, (AVG(AverageTimePerIssue)*0.8) as Last80TimePerIssue FROM dbo.FactCallCenter GROUP BY Shift, WageType) as [Shifts for Call Center] \') AS t ON [FactCallCenterReturn].[Wage Type] = t.[WageType] AND [FactCallCenterReturn].[Shift] = t.[Shift] AND [FactCallCenterReturn].[Calls] = t.[AvgCalls] AND [FactCallCenterReturn].[Issues Raised] = t.[AvgIssues] AND [FactCallCenterReturn].[Level One Operators] = t.[AvgOperators] AND [FactCallCenterReturn].[Orders] = t.[AvgOrders] AND [FactCallCenterReturn].[Average Time Per Issue] = t.[Last90TimePerIssue]