The Bank Revenues analytics case study. The complete collection of analytics cases is available from Collection: Analytics Case Study Library.
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Key ideas:
The log transformation, stepwise regression, regression assumptions, residuals, Cook’s D, interpreting model coefficients, singularity, Prediction Profiler, inverse transformations.
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.
From Building Better Models with JMP® Pro, Chapter 4, SAS Press (2015). Grayson, Gardner and Stephens. Used with permission.
