Marketing Campaign Optimization

This Jupyter Notebook describes a marketing campaign optimization problem that is common in the banking and financial services industry.

 

Marketing Campaign Optimization

This Jupyter Notebook describes a marketing campaign optimization problem that is common in the banking and financial services industry. The problem is formulated using the Gurobi Python API and solved using the Gurobi Optimizer. We assume that key parameters of the mathematical optimization model of the marketing campaign problem are estimated using machine learning predictive response models. The marketing campaign optimization problem entails determining which products to offer to each customer in order to maximize total expected profit while satisfying various business constraints

This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models. The reader should also consult the documentation of the Gurobi Python API.


 

Request a Gurobi Evaluation License or Free Academic License

Modeling examples are coded using the Gurobi Python API in Jupyter Notebook. In order to use the Jupyter Notebooks, you must have a Gurobi License. If you do not have a license, you can request an Evaluation License as a Commercial User or download a free license as an Academic User.

 

Commercial Users: Free Evaluation Version Academic Users: Free Academic Version

 


 

Access the Jupyter Notebook Modeling Example

Click on the button below to be directed to GitHub where you can download the repository for the Marketing Campaign Optimization Jupyter Notebook modeling example.

 

Marketing Campaign Optimization

 


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