CASE STUDY: JMP025

Bank Revenue

From Building Better Models With JMP®Pro, Chapter 4, SAS Press (2015). Grayson, Gardner and Stephens. Used with permission. For additional information see https://www.jmp.com/en_us/academic/building-better-models.html

Key Concepts: Log transformation, stepwise regression, regression assumptions, residuals, Cook’s D, model coefficients, singularity, prediction profiler, inverse transformations

Authors

Dr. Jim Grayson

Augusta University

Sam Gardner

Eli Lilly

Mia Stephens

JMP

Objective

A bank wants to understand how customer banking habits contribute to revenues and profitability. Build a model that allows the bank to predict profitability for a given customer. The resulting model will be used to forecast bank revenues and guide the bank in future marketing campaigns.

Background

A bank wants to understand how customer banking habits contribute to revenues and profitability. The bank has customer age and bank account information, e.g., whether the customer has a savings account, whether the customer has received bank loans, and other indicators of account activity.

The Task

We want to build a model that allows the bank to predict profitability for a given customer. A surrogate for customer profitability available in our data set is the Total Revenue a customer generates through their accounts and transactions. The resulting model will be used to forecast bank revenues and guide the bank in future marketing campaigns.