You're reading from The Art of Data-Driven Business Transform your organization into a data-driven one with the power of Python machine learning

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781804611036

Length 314 pages

Edition 1st Edition

Languages

Python

Tools

Seaborn

Concepts

Data Science

Author (1):

Alan Bernardo Palacio

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Table of Contents (17) Chapters

Preface

1. Part 1: Data Analytics and Forecasting with Python

2. Chapter 1: Analyzing and Visualizing Data with Python FREE CHAPTER

3. Chapter 2: Using Machine Learning in Business Operations

4. Part 2: Market and Customer Insights

5. Chapter 3: Finding Business Opportunities with Market Insights

6. Chapter 4: Understanding Customer Preferences with Conjoint Analysis

7. Chapter 5: Selecting the Optimal Price with Price Demand Elasticity

8. Chapter 6: Product Recommendation

9. Part 3: Operation and Pricing Optimization

10. Chapter 7: Predicting Customer Churn

11. Chapter 8: Grouping Users with Customer Segmentation

12. Chapter 9: Using Historical Markdown Data to Predict Sales

13. Chapter 10: Web Analytics Optimization

14. Chapter 11: Creating a Data-Driven Culture in Business

15. Index

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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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