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)
1
Part 1: Data Analytics and Forecasting with Python
4
Part 2: Market and Customer Insights
9
Part 3: Operation and Pricing Optimization

Predicting customer revenue

By utilizing the historical transactional data from our company, we are attempting to forecast the future revenue that we will get from our clients at a given time. Planning how to reach your revenue goals is simpler when you can predict your revenue with accuracy, and in a lot of cases, marketing teams are given a revenue target, particularly after a funding round in startup industries.

B2B marketing focuses on the target goals, and here is when historical forecasting, which predicts our revenue using historical data, has consistently been successful. This is because precise historical revenue and pipeline data provide priceless insights into your previous revenue creation. You can then forecast what you’ll need in order to meet your income goals using these insights. Things that will allow us to provide better information to the marketing teams can be summarized into four metrics before you start calculating your anticipated revenue:

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