CART® Regression for Predict Cost Per Ticket

Use CART® Regression to use complex relationships with multiple predictors to predict cost per ticket.

This example applies to the Customer Contact Center Module. For more information, go to www.minitab.com/customer-contact-center-module.

Example

Cost per ticket is the total operating expenses of the contact center divided by the number of tickets over the same time period.

In this worksheet, Cost Per Ticket is the response. Average Monthly Schedule Adherence and Average Monthly Customer Satisfaction are the continuous variables. Call Center Location and Issue are the categorical variables. The predictors may explain differences in cost per ticket.

C1	C2	C3	C4-T	C5-T
`Cost Per Ticket`	`Average Monthly Schedule Adherence`	`Average Monthly Customer Satisfaction`	`Call Center Location`	`Issue`
$17.09	68.3	7.6	East	Late payment
$22.45	75.5	6.7	East	Technical question
$19.45	63.2	7.6	West	Incorrect charge
$21.37	81.5	7.9	West	Technical question

How-to

Choose Solutions Modules > Functions > Customer Contact Center KPIs, then select Launch.
Under Utilization and Cost, select Cost per ticket.
Select Predict cost per ticket, then click OK.
Select CART® Regression, then click OK.
In Responses, enter the column that contains the cost per ticket data. The response is also called the Y variable.
In Continuous predictors, enter the columns of numeric data that may explain or predict changes in cost per ticket. The predictors are also called X variables.
In Categorical predictors, enter the categorical classifications or group assignments that may explain or predict changes in cost per ticket. The predictors are also called X variables.
Click OK.

Tip

For more information about this analysis, click Help in the main dialog box.