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

Targeting decreasing returning buyers

One important aspect of businesses is that recurring customers always buy more than new ones, so it’s important to keep an eye on them and act if we see that they are changing their behavior. One of the things that we can do is identify the clients with decreasing buying patterns and offer them new products that they are not yet buying. In this case, we will look at consumer goods distribution center data to identify these customers with decreasing purchases:

  1. First, we will import the necessary libraries, which are the following: pandas for data manipulation, NumPy for masking and NaNs handling, and scikit-surprise for collaborative filtering product recommendation.
  2. We will explore the data to determine the right strategy to normalize the data into the right format.
  3. Once the data is structured, we will set up a linear regression to determine the clients with a negative slope to identify the ones with decreasing consumption...