BANK {gpk}

Bank Churn data set

Description

Businesses like banks which provide service have to worry about problem of 'Churn' i.e. customers leaving and joining another service provider. It is important to understand which aspects of the service influence a customer's decision in this regard. Management can concentrate efforts on improvement of service, keeping in mind these priorities.

Usage

data(BANK)

Format

A data frame with 245 observations on the following 20 variables.

Serial_Number

Serial Number

Response

Response (1\: deserter, 0\: Loyal)

Branch

Branch code

Occupation

Occupation of Customer

Age

Age in Years

Sex

Gender

Pleasant_Ambiance

Pleasant Ambiance ACT1

Comfortable_seating_arrangement

Comfortable seating arrangement ACT2

Immediate_attenttion

Immediate attenttion ACT4

Good_Response_on_Phone

Good Response on Phone ACT5

Errors_in_Passbook_entries

Errors in Passbook entries ACT10

Time_to_issue_cheque_book

Time to issue cheque book ACT14

Time_to_sanction_loan

Time to sanction loan ACT16

Time_to_clear_outstation_cheques

Time to clear outstation cheques ACT17

Issue_of_clean_currency_notes

Issue of clean currency notes ACT24

Facility_to_pay_bills

Facility to pay bills ACT26

Distance_to_residence

Distance to residence ACT28

Distance_to_workplace

Distance to workplace ACT30

Courteous_staff_behaviour

Courteous staff behaviour ACT31

Enough_parking_place

Enough parking place ACT32

Details

Explore the application of logistic regression and contingency tables for this data set.

Source

http://ces.iisc.ernet.in/hpg/nvjoshi/statspunedatabook/databook.html

Examples

data(BANK)