6% Sales Boost: How Marketing Mix Models Optimize Budgets

Marketing Mix Models (MMM) are crucial for optimizing marketing budgets. They help businesses understand how different channels contribute to sales, allowing for strategic budget allocation. Using Python’s pymc-marketing package, analysts can train models, validate them, and calibrate them for better insights. The package also supports running “what-if” scenarios through response curves, which show how sales respond to different levels of spend in each channel. For instance, a response curve can illustrate where diminishing returns begin for a channel like social media.

Linear programming, particularly Sequential Least Squares Programming (SLSQP), is used to solve complex budget optimization problems under constraints. An example scenario showed that by reallocating budget from digital channels to TV, sales could increase by 6% without increasing the overall budget. This demonstrates the power of MMM in not just understanding but also in actively improving marketing strategies through data-driven decisions.

Source: towardsdatascience.com