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

Understanding Customer Preferences with Conjoint Analysis

Conjoint analysis is a well-known approach to product and pricing research that identifies customer preferences and makes use of that knowledge to choose product features, evaluate price sensitivity, anticipate market shares, and foretell consumer acceptance of new goods or services.

Conjoint analysis is often utilized for all sorts of items, including consumer goods, electrical goods, life insurance policies, retirement communities, luxury goods, and air travel, across several sectors. It may be used in a variety of situations that revolve around learning what kind of product customers are most likely to purchase and which features consumers value the most (and least) in a product. As a result, it is widely used in product management, marketing, and advertising.

Conjoint analysis is beneficial for businesses of all sizes, even small local eateries and grocery stores.

In this chapter, you will learn how to understand...