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 web analytics

For e-commerce, understanding user base behavior is fundamental. Data from web analytics is frequently shown on dashboards that can be altered on the basis of a user persona or time period, along with other factors. These dashboards are then used to make product and market decisions, so the accuracy of this data is of paramount importance. The data can be divided into groups, including the following:

  • By examining the number of visits, the proportion of new versus returning visitors, the origin of the visitors, and the browser or device they are using (desktop vs. mobile), it is possible to understand audience data
  • Common landing pages, commonly visited pages, common exit pages, the amount of time spent per visit, the number of pages per visit, and the bounce rate can all be used to study audience behavior
  • Campaign data to understand which campaigns have generated the most traffic, the best websites working as referral sources, which keyword...