CART® Regression for Predict Average Handling Time

Use CART® Regression to use complex relationships with multiple predictors to predict call handling time.

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

Example

Handling time is the total time an agent spends on a ticket, including related administrative duties, contact time, and after-call work time.

In this worksheet, Handling Time is the response. Schedule Adherence and Call Volume are the continuous variables. Call Center Location and Department are the categorical variables. The predictors may explain differences in call handling time.

C1	C2	C3	C4-T	C5-T
`Handling Time`	`Schedule Adherence`	`Call Volume`	`Call Center Location`	`Department`
42.3	60	30	East	Billing
48.3	80	18	West	Billing
45.5	76	15	East	Customer Service
50.1	80	20	West	Customer Service

How-to

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

Tip

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