I have a dataset consisting of products, clients, price policy, discounts, quantities, and net sales. The task as put in words by the business is quantity vs price. I have noted a few observations from looking at the dataset :
Discounts: Discounts nullify the effect of any change in the Price policy. So in the end the net sales don't follow this variation. And i observe this for so many client-product pairs.
Seasonality : Variation of quantity for client-product pairs simply follows a seasonality pattern and its not driven by any of the timeseries variables in the dataset. ( I should statistically verify this for now i just did a visual check).
Because at this point i dont see any logic behind how discounts are decided for the clients. Hence there is literally no affect on net sales vs price changes.
How should I model this ? Is this even a machine learning problem because there is simply no causal relation between the variables. If not Price vs Demand then what other things can I propose to the business ?
Edit : 1. Product-client scatter plots UNITARY_NET_SALES VS QUANTITY
- Timeseries plot for a particular product
Price_list and Discounts have the same behaviour. So whenever the business increases the prices they increase the discounts too, hence the overall affect on net sales is none. And Quantity simply follows a seasonal pattern.
75% of Quantity is less than 8 units !
Thank you !