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

Web Analytics Optimization

A data-driven marketing optimization is an analytical approach to marketing that values decisions that can be supported with trustworthy and verifiable data. It places high importance on choices that can be substantiated by empirical evidence, whether traffic sources, page views, or time spent per session. The effectiveness of data collection, processing, and interpretation to maximize marketing results are key components of the data-based approach’s success.

In this chapter, we will be learning about the following:

  • Understanding what web analytics is
  • How web analytics data is used to improve business operations
  • Calculating the user’s customer lifetime value (CLV) based on web analytics data
  • Predicting the user’s CLV based on this historical data

Let’s determine what the requirements will be for understanding these steps and following the chapter.

This chapter covers the following topics: