I want to share some context with you I am working for a pharmaceutical company and the business side needed to update the price for some products once a year. So I thought to develop a model to estimate price elasticity of demand. The justification it that indicating which products have a inelastic behaviour can guide them to prioritize products to increase price or which products are not interesting to have a price increase.
So I developed a model to calculate the price elasticity of demand of the client's portfolio using Double/Debiased Machine Learning. The code uses historical data of the last 2 years moving window so I can bring always a fresh value of elasticity at each run.
The feedback was positive but they asked to have a recomendation of price increase as well. My idea is:
- Select products that have elasticity coefficients below 1 cause they are inelastic and could receive price increases.
- For these products, estimate the revenue effect for a variety of price increases for the next year.
- Get the price that gives you the max revenue as price increase recommendation.
You think this idea is viable? The estimated decrease in demand is proportional to the price increases?