Binary Logistic Regression for Predict Abandonment Rate

Use Binary Logistic Regression to use multiple predictors to predict whether a ticket is likely to be abandoned or resolved.

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

Example

Abandonment rate is the percentage of customers that disconnect or abandon a ticket or call before they can work with an agent.

In this worksheet, Ticket Status is the response. The response event is Abandoned. Hold Time is a continuous predictor, and Call Center Location is a categorical predictor.

C1-T	C2	C3-T
`Ticket Status`	`Hold Time`	`Call Center Location`
Resolved	58	East
Resolved	45	East
Resolved	50	West
Abandoned	102	West

How-to

Choose Solutions Modules > Functions > Customer Contact Center KPIs, then select Launch.
Under Service Level, select Abandonment rate.
Select Predict abandonment rate, then click OK.
Select Binary Logistic Regression, then click OK.
In Response, enter the binary variable that contains the abandonment data. Binary variables are categorical variables that have two possible levels, such as pass/fail or true/false. The response is also called the Y variable.
In Response event, select the value that represents an abandoned ticket.
(Optional) In Frequency, enter the column that contains the counts that correspond to the response and predictor values in the row.
In Continuous predictors, enter the continuous variables that may explain or predict whether a ticket is abandoned. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether a ticket is abandoned. The predictors are also called X variables.
Click OK.

Tip

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