Marketing Measurement and Analytics: An Introduction

by Greg Kihlstr m

Overview

This book is a comprehensive guide to aligning your marketing strategies with business objectives while embracing the latest innovations in data and AI-driven analytics. You’ll cover topics such as: aligning measurement to business goals by distinguishing between business and marketing KPIs and choosing the right metrics to guide your strategy; building adaptable systems that link organizational goals to actionable marketing tactics; exploring AI-based tools, predictive analytics, and generative AI to refine your data strategies; and analyzing results, run multi-channel tests, and create a culture of experimentation and optimization. Through a unique blend of expert insights, practical frameworks, and a recurring case study, this book brings theoretical concepts to life, making them actionable and relatable for any organization.

FEATURES
• Integrates cutting-edge AI technologies into your measurement processes
• Uses a recurring case study to demonstrate real-world applications of measurement concepts
• Analyzes results and runs multi-channel tests to create a culture of experimentation and optimization

Product Details

ISBN-13:	9781501523144
Publisher:	Mercury Learning and Information
Publication date:	12/19/2024
Pages:	244
Product dimensions:	7.00(w) x 9.00(h) x 0.50(d)

About the Author

Greg Kihlström is a best-selling author, speaker, and entrepreneur, and serves as a consultant to top companies on marketing technology, marketing operations, customer experience, and digital transformation initiatives.

Table of Contents

1: Exploring the Terminology
2: The Hierarchy of Goals and Measurements
3: Distinguishing Between Business and Marketing KPIs
4: Choosing the Right Marketing Metrics
5: Single-Channel vs. Multi-Channel Measurement
6: A Brief Overview of Statistics for Marketers
7: Measurement of AI Implementation and AI Model Quality
8: Investing in a Marketing Measurement Framework
9: Components of the Marketing Measurement Framework
10: Incorporating AI-Based Tools and Methods
11: Determining What Data Is Needed
12: Single- and Multi-Channel Data Collection
13: Creating a Sustainable Data Collection Plan
14: Collecting Data in an AI-Driven Marketing Environment
15: Creating a Marketing Dashboard
16: Beginning with a Strong Hypothesis
17: AI-Based Approaches to Prediction and Hypothesis Development
18: Statistical Considerations for Testing
19: Constructing and Running a Single-Channel Test
20: Single-Channel Tests in a Multi-Channel World
21: Multi-Channel Measurement
22: Introduction to Analysis and Improvement
23: Analyzing Your Results
24: Using Generative AI for Analysis
25: Interpreting Results
26: Experimenting, Refining, and Continuous Improvement
Epilogue
Appendices
Index