Marketing Analytics: A Machine Learning Approach

✍ Scribed by A. Mansurali, P. Mary Jeyanthi

Publisher: CRC Press/Apple Academic Press
Year: 2023
Tongue: English
Leaves: 367
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

With businesses becoming ever more competitive, marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume, Marketing Analytics: A Machine Learning Approach, enlightens readers on the application of analytics in marketing and the process of analytics, providing a foundation on the concepts and algorithms of machine learning and statistics. The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow, giving them to tools to make better business decisions.

This volume gives a comprehensive overview of marketing analytics, incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics, including segmentation and targeting analysis, statistics for marketing, marketing metrics, consumer buying behavior, neuromarketing techniques for consumer analytics, new product development, forecasting sales and price, web and social media analytics, and much more.

This well-organized and straight-forward volume will be valuable for marketers, managers, decision makers, and research scholars, and faculty in business marketing and information technology and would also be suitable for classroom use.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
About the Editors
Table of Contents
Contributors
Abbreviations
Introduction
Preface
1. Introduction to Marketing Analytics
2. Statistics for Marketing
3. Evolution of Data Analytics
4. Segmentation and Targeting Analysis
5. Important Marketing Metrics: A Snapshot
6. Consumer Buying Behavior
7. Understanding Consumer Behavior Using Market Basket Analysis
8. Neuromarketing Techniques for Consumer Analytics
9. New Product Development
10. Natural Language Processing for Branding
11. Forecasting Sales and Price
12. Sales Prediction and Conversion
13. Role of Supply Chain Analytics in Marketing Analytics
14. Web and Social Media Analytics
15. Marketing Analytics and Its Applications
Index

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