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 have learned how to perform conjoint analysis, which is a statistical tool that allows us to undercover consumer preferences that otherwise would be difficult to determine. The way in which we performed the analysis was by using OLS to estimate the performance of different combinations of features and try to isolate the impact of each one of the possible configurations in the overall perception of the client to undercover where the consumer perceives the value.

This has allowed us to create an overview of the factors that drive users to buy a product, and even be able to predict how a new combination of features will perform by using ML algorithms.

In the next chapter, we will learn how to adjust the price of items by studying the relationship between price variation and the number of quantities sold using price elasticity.