Preparation of Data. For price analytics, we will have to use historical pricing data which consists of demand at day or week or month level, price at which item was sold, actual price , discount, quantities sold etc. We have to be sure of the quality of this data before we use it. After this we will have to remove outliers from it . It will be great to also have stock level info such as opening and closing stock
Estimating the elasticity of price or demand elasticity of price is an important step in the pricing process. We will have to evaluate the right function to capture the customer demand. In a textbook based ideal market , the price elasticity function will be linear and a straight line. If a seller prices the price below market price, the demand for price will be very high and vice versa. In reality the price elasticity is not linear for many products. The analysts can try to fit functions in various types of elasticity curves such as linear, exponential, weibull etc. Whichever fits best the data can be considered.
Forecast the demand using the function found in step 2 using additive or multiplicative forecasting techniques based on the input data.
Once step 2 is determined, we can go ahead and set objective functions and constraints and optimise the price. The objective function can be revenue maximisation, profit maximisation , volume maximization etc. Whether the seller needs to clear out his inventory or need to increase the price than at which he is currently selling is a business call or input that has to come from the customer. This can be ascertained with the help of the data as well. But we need to have access to the entire historical data. I will be writing a separate chapter on these steps, hence not going deep into each topic.