CART® Regression for Predict Average Age Of Ticket

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

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

Example

The age of a ticket measures the length of time an unresolved ticket is open if not resolved on first contact.

In this worksheet, Ticket Age is the response. Schedule Adherence and Number of Agents are the continuous variables. Call Center Location and Department are the categorical variables. The predictors may explain differences in ticket age.

C1	C2	C3	C4-T	C5-T
`Ticket Age`	`Schedule Adherence`	`Number of Agents`	`Call Center Location`	`Department`
17.5	60	3	East	Billing
16.3	80	4	West	Billing
14.5	76	6	East	Customer Service
15.0	80	6	West	Customer Service

How-to

Choose Solutions Modules > Functions > Customer Contact Center KPIs, then select Launch.
Under Ticket Resolution, select Average age of ticket.
Select Predict average age of ticket, then click OK.
Select CART® Regression, then click OK.
In Responses, enter the column that contains the ticket age 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 ticket age. The predictors are also called X variables.
In Categorical predictors, enter the categorical classifications or group assignments that may explain or predict changes in ticket age. The predictors are also called X variables.
Click OK.

Tip

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