# Main Logic

Finding the best markdown is a challenging game of balance. Low discounts will lead to a loss of market share and insufficient sales opportunities; if too high, products will sell out without maximizing profit.

![issues.png](6VJLoNQVurWd)

Using a demand forecasting model is a great way to integrate all available features about the product, the historical demand, the time of the year, etc.

![method.png](BYR5ZeJE8lIG)

By keeping the parameters constant, we can then play on the price and markdown features to explore the price elasticity of a given product. We can use this elasticity to find the local maximum for the metrics we try to maximize (usually profit).

As we rely on the Demand forecasting model for the optimization, the better its performances are, the more accurate the markdown optimization will be.

It's also important to check the model's sensitivity to the price/discount parameters as it is vital for good behavior of the markdown optimization.

