Book Image

The Art of Data-Driven Business

By : Alan Bernardo Palacio
Book Image

The Art of Data-Driven Business

By: Alan Bernardo Palacio

Overview of this book

One of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get. The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company. You’ll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you’ll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you’ll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods. By the end of this Python book, you’ll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you’ve learned in the real world.
Table of Contents (17 chapters)
Part 1: Data Analytics and Forecasting with Python
Part 2: Market and Customer Insights
Part 3: Operation and Pricing Optimization


In this chapter, we dived into the relationship between the price of an item and the number of items being sold. We studied that different items have different demand curves, which means that in most cases, a higher price leads to the least items being sold, but this is not always the case. The relationship between price and quantity being sold can be modeled using price elasticity, which gives us an idea of how much the number of products being sold will be reduced by a given increase in the price.

We looked into the food truck sales data in order to determine the best price for each one of their items and we discovered that these items have different elasticities and that for each item, we can determine the price, which will maximize the revenue.

In the next chapter, we will focus on improving the way we bundle and recommend products by looking at how to perform a Market Basket analysis to recommend meaningful products that are frequently bought together.