Pricing optimization
Finding the ideal price for a product can be very challenging. There are many factors that can influence the price and the behavior can be counterintuitive as lowering prices can raise revenue and vice versa. Further, the ideal price can be influenced by alternative products (both within the company) and from competitors as well as your own marketing efforts.
Overview
Predict the sales volume for a given product and use that information to find the optimal price.
TARGET
Price for each product.
Challenge
While most of the data will come from the pricing and sales database, you will need to aggregate this at a reasonable (weekly or monthly) level and identify the average selling price and how many discounts were in place. Information on competitive products price and discounts and external events during the same time period needs to be added. To this, any marketing campaigns should be included. Finally, inventory information should be merged. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.
Modeling techniques and libraries
Machine learning analysis
Build machine learning models to predict the target as a function of the independent variables. Use model interpretability packages to evaluate the impact of the independent variables on the prediction.
Packages:
- Sklearn
- ELI5
- LIME
- SHAP
Price optimization
Find the optimal product prices to maximize revenue.
Package:
- CVOXPT
- pyOpt
- scipy.optimize
