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


In this chapter, we have learned to identify the clients that have a decreasing number of sales in order to offer them specific product recommendations based on their consumption patterns. We have identified the decreasing sales by looking at the slope in the historical sales in the given set of periods, and we used the SVD collaborative filtering algorithm to create personalized recommendations for products that customers are not buying.

As the next step and to improve the loyalty of existing customers, we have explored the use of the Apriori algorithm to run a market basket analysis and to be able to offer product recommendations based on specific products being bought.

In the next chapter, we will dive into how we identify the common traits of customers that churn in order to complement these approaches with a deeper understanding of our customer churn.