Companies across almost every industry are looking to optimize their marketing campaigns. In this Jupyter Notebook, we’ll explore a marketing campaign optimization problem that is common in the banking and financial services industry, which involves determining which products to offer to individual customers in order to maximize total expected profit while satisfying various business constraints. You’ll learn how to formulate a mathematical optimization model of the problem (using machine learning predictive response models as parameters) and solve it using the Gurobi Optimizer. 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.
Access the Jupyter Notebook Modeling Example
Click on the button below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.
How to Run the Example
To run the example the first time, choose “Runtime” and then click “Run all”.
All the cells in the Jupyter Notebook will be executed.
The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.
You can also modify and re-run individual cells.
For subsequent runs, choose “Runtime” and click “on “Restart and run all”.
The Gurobi Optimizer will find the optimal solution of the modeling example.
Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.
