The Optimization module has been updated with improvements to the model and model class. We've cleaned up and made several updates, resulting in a cleaner and more updated Optimization module. You'll still find all the main features, including the listed models.

Multifactor Elasticity

The multifactor elasticity model forecasts the quantity that would be sold based on past transactions in the data. Moreover, some variations of prices to understand their impact on quantity have been made.

Pricefx built an elasticity model related to all the information you can get, all the features and fields in your data set, to understand the relationship between price and quantity for your specific case. This is used for Shelf Price positioning and to understand the dynamics of the market.

In this multifactor elasticity, the possibility to define the number of periods to forecast (up to 15 periods, meaning we can forecast up to 15 weeks) was introduced in the Paper Plane release as it is used for Markdown, another optimization accelerator.

Markdown is perfect when moving excess products in stock, when products of a specific kind need to be merged and to reduce stock. This model can also be used to position the best price to reduce the stock.

Additional Parameters

In the Paper Plane 11.0 release some additional parameters were added. The range of the prediction for elasticity can be defined. For example, one can consider a 20% change in price good enough for Shelf Price, but for Markdown, where the price can be aggressively decreased, it should probably go up to 50% or even 60%. 

By clicking the Model Parameter Configuration, you can tune the values and define the number of trials you would like to do, as well as the number of cross-validation and splits.

This means taking some data out, computing it, checking with the data that's been taken out, and seeing if that model makes sense or not. It's something you typically do at least once when you start working on it, and you probably have to update it every three to six months depending on your markets.

Model Training

In the next step, Model Training, there are several new refinements. The chart on Feature Importance that has not been there before, is showing the main drivers to define the forecast with all the aspects you may want to consider. There is also a new table with additional metrics to understand the accuracy of the model.

In the model predictions, you will see that there is a demand forecast for all, and for specific products. 

The main target of this model is to make a price change. So for the last week price, we make a price change for instance by 20% more and 20% less, and then we see what the model will predict. We fit on top of this a curve to understand what is the relationship between price and quantity.