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The Art of Data-Driven Business

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

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804611036
Length 314 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

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 16. Other Books You May Enjoy

Creating client segments

Marketers can better target different audience subgroups with their marketing efforts by segmenting their audiences. Both product development and communications might be a part of those efforts. Segmentation benefits a business by allowing the following:

  • Creating targeted marketing communication on the right communication channel for each client or user segment
  • Applying the right pricing options to the right clients
  • Concentrating on the most lucrative clients
  • Providing better client service
  • Promoting and cross-promoting other goods and services

In this section, we will be preprocessing the data to be able to apply clustering methods for customer segmentation. The steps that we will apply to preprocess the data are set out here:

  • Encoding the categorical variables using a label encoder, which will transform them into numerical columns
  • Scaling features using the standard scaler to normalize the values
  • Applying principal...
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