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

View More author details

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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Finding Business Opportunities with Market Insights

In recent years, the word insight has been used with more frequency among innovation market testers. Most of the time it’s utilized without a clear definition, sometimes implying that there are hidden patterns in the data that is being utilized, or it can be used in the context of business to create new sources of revenue streams, to define more clearly the conditions and preferences of a given market, or how the different customer preferences vary across different geographies or groups.

In this chapter, we will use search engine trends to analyze the performance of different financial assets in several markets. Overall, we will focus on the following:

Gathering information about the relative performance of different terms using the Google Trends data with the Pytrends package
Finding changes in the patterns of those insights to identify shifts in consumer preferences
Using information about similar queries...