Pricing is the process whereby a business sets the price at which it will sell its products and services, and may be part of the business's marketing plan.
Product pricing is an essential component of any organization. It is the result of much thought. For various product types, there are many methods for estimating prices. Certain products have a high degree of cost sensitivity, meaning that even a small adjustment to the price can have a significant impact on sales. Conversely, certain products—such as some medications—have sales that are mostly independent of their value; these are typically luxury goods or needs.
Price elasticity of demand(EPD), or elasticity, is the degree to which the compelling desire for something changes as its price changes. People generally lose desire for products that increase in price. Even with a slight price increase, buyers' demand for some products, however, may decline significantly, whereas for other things, their want may remain relatively unchanged even with a significant price increase. This sensitivity of sales to changes in price is known as elasticity in economics. More specifically, price elasticity expresses the percentage change in quantity required in the event of a one percent price rise, all other factors being equal.
Mathematically speaking, the price elasticity of demand is defined to be the percentage change in quantity demanded, q,divided by the percentage change in price, p. The formula for the price elasticity (ǫ) is:
In this machine learning pricing optimization project, I will use a cafe's data to determine the best prices for its products based on the elasticity of their prices in the past and their past sales Finding the best price will require first calculating each item's price elasticity. By using specific data from a cafe, this technique can be expanded to price any kind of goods. The previous products will be the main focus of this machine learning retail price optimization research.
In this project, I shall look at the sales of the items of a cafe. This cafe sells burgers, coke, lemonade and coffee. I will figure out the optimal prices to set for these items. If the price is set too high, the sales will drop & the price is set to low, then the margins will decrease. I will set the sweet spot that will give us the maximum profit.
The data is contained in three CSV files.
Cafe - Sell MetaData.csv This file has details about sales made by the cafe.
Columns: Sell ID, Sell Category, Item ID, Item Name
Cafe - Transaction - Store.csv This file contains information about transactions and sale receipts of the cafe.
Columns: Calendar Date, Price, Quantity, Sell ID, Sell Category
Cafe - DateInfo.csv This has date information corresponding to the transactions performed.
Columns: Date, Year, Holiday, Weekend, School Break, Temperature, Outdoor
Gaining insight into consumer behavior via sales data is essential for any company looking to expand. It helps to determine the appropriate price for each product, in addition to improving the quality of the products. For example, goods that the general public considers to be luxury goods are sold for excessively high costs, at this dynamic pricing Python project, I will estimate the price of various food items at a café using historical sales data. I will also discover more techniques for price optimization, such as demand-based pricing, perceived value pricing, cost-less pricing, and competition-based pricing. I will also learn about price elasticity in this project, which is an important idea in pricing estimation.