This Jupyter Notebook describes a marketing campaign optimization problem that is common in the banking and financial services industry. The problem entails determining which products to offer to individual customers in order to maximize total expected profit while satisfying various business constraints. 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.
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.
For details on licensing or on running the notebooks, see the overview on Modeling Examples
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