A Python implementation of price optimization for maximizing revenue. Price and quantity are two fundamental measures that determine the bottom line of every business.
Price and quantity are two fundamental measures that determine the bottom line of every business, and setting the right price is one of the most important decisions a company can make. Under-pricing hurts the company’s revenue if consumers are willing to pay more and, on the other hand, over-pricing can hurt in a similar fashion if consumers are less inclined to buy the product at a higher price.
So given the tricky relationship between price and sales, where is the sweet spot — the optimum price — that maximizes product sales and earns most profit?
The purpose of this article is to answer this question by implementing a combination of the economic theory and a regression algorithm in Python environment.
We are optimizing a future price based on the relationship between historical price and sales, so the first thing we need is the past data on these two indicators. For this exercise, I’m using a time series data on historical beef sales and corresponding unit prices.
## load data import pandas as pd beef = pd ## view first few rows beef.tail(5
Business Intelligence and Data Science terms become very popular these days: It is undeniable that information is the foundation of any successful company and business entrepreneurs.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Learn how Big Data and Business Intelligence, both technologies helps the decision makers to make proper decisions that can help the organization to get advantages over their peers.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
A closer look at data analytics for data scientists. With a changing landscape in the workforce, many people are either changing their careers or applying to different companies after being laid off.