Marketing Measurement and Analytics: An Introduction (2024)

Table of Contents

  1. Overview
  2. Publication details
  3. Summary
  4. Key concepts and frameworks
  5. Who it's for
  6. Edition history
  7. How this book compares
  8. Related podcast episodes
  9. FAQs
  10. Related Terms
  11. Related Books
  12. Sources

Overview

Marketing Measurement and Analytics: An Introduction is a 2024 book by Greg Kihlström, published by Mercury Learning and Information Systems, an imprint of De Gruyter Brill, that teaches marketers how to tie what they measure to the goals the business cares about, and then how to collect data, build dashboards, run tests, and interpret the results. It is written as a textbook-style introduction, with a recurring case study, recap quizzes at the end of each of its five parts, and a glossary of measurement formulas, and it’s suited both to self-study and to classroom use. AI-based measurement and analysis methods are threaded through every part rather than confined to one chapter.

See also: Key Performance Indicator (KPI) · A/B Testing · Incrementality · Data-Driven Attribution (DDA)

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Publication details

FieldDetail
Full titleMarketing Measurement and Analytics: An Introduction
AuthorGreg Kihlström
ForewordNone listed (the book has a Preface by the author)
SeriesNone listed
PublisherMercury Learning and Information Systems, an imprint of De Gruyter Brill
Publication dateDecember 26, 2024 (ebook); January 1, 2025 (paperback); copyright 2024
EditionFirst
FormatsPaperback, ebook (PDF and EPUB)
ISBN978-1-5015-2314-4 (paperback); 978-1-5015-2042-6 (ebook); 978-1-5015-2043-3 (VitalSource ebook)
DOI10.1515/9781501520426
Length270 pages (index begins at page 241)
Where to buyDe Gruyter Brill, Agile Brand Guide product page, Amazon, VitalSource
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Summary

The book’s starting argument is that most marketing measurement problems are alignment problems. Teams report what their tools make easy to report, and the result is a set of channel metrics that nobody outside marketing can connect to revenue, retention, or whatever the company is actually trying to do. Part 1, Aligning Measurement to Business Goals, is about fixing that. Its seven chapters cover terminology, a hierarchy of goals and measurements, the difference between business KPIs and marketing KPIs, how to choose metrics, single-channel versus multi-channel measurement, a short statistics primer for marketers, and (a chapter that would not have appeared in a measurement book five years earlier) how to measure the quality of an AI implementation and its models.

Part 2 introduces the book’s own marketing measurement framework: why to invest in one, what its components are, and how AI-based tools fit into it. Part 3 covers data collection, from deciding what data is needed through single- and multi-channel collection to a sustainable collection plan. Chapter 14 addresses collecting data in an AI-driven environment, where models consume and generate data at a rate that changes what “collection” means.

Part 4, Measurement and Testing, is the longest part and the most hands-on. It opens with building a marketing dashboard, then moves through hypothesis formation, AI-based prediction and hypothesis development, the statistics of testing, how to construct and run a single-channel test, what happens to single-channel tests in a multi-channel world, and multi-channel measurement itself. Part 5 closes the loop: analyzing results, using generative AI for analysis, interpreting results, and building a habit of experimentation and continuous improvement.

Each part ends with a recap quiz, with answers in Appendix B. Appendix A is a glossary of selected marketing measurements and their formulas. A case study recurs across the parts so that the framework, data plan, dashboard, and tests are shown applied to one organization rather than as isolated examples.

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Key concepts and frameworks

Hierarchy of goals and measurements. Chapter 2’s structure for connecting company-level objectives down through marketing goals to the metrics a team reports. Originates with Kihlström.

Business KPIs versus marketing KPIs. Chapter 3’s distinction, and a recurring theme: a marketing KPI is useful only if it can be traced to a business KPI.

The marketing measurement framework. The book’s own framework, introduced in Part 2, covering the components a measurement program needs and how AI-based methods fit into each.

Single-channel tests in a multi-channel world. Chapter 20’s treatment of why a clean single-channel A/B test can mislead when customers move across channels, and what to do about it. Related Martechipedia entries: A/B Testing, Multivariate Testing (MVT), Incrementality.

Measurement of AI implementation and model quality. Chapter 7 extends the measurement discipline to the AI tools themselves.

Generative AI for analysis. Chapter 24 covers using LLMs to summarize, query, and interpret marketing data, with its limits.

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Who it’s for

A marketing analyst or manager who can pull reports from GA4, HubSpot, or a BI tool but has never been asked to justify which numbers matter. A marketing leader who needs a shared vocabulary with finance before a budget review. An instructor looking for a measurement text with built-in quizzes and a case study. The statistics chapters (6 and 18) assume no prior coursework.

Readers who want measurement specifically for customer experience programs, rather than marketing, should see Kihlström’s Meaningful Measurement of the Customer Experience (2nd edition, 2024).

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Edition history

EditionYearPublisherNotes
Marketing Measurement and Analytics: An Introduction2024Mercury Learning and Information Systems / De Gruyter BrillFirst edition. Ebook December 2024, paperback January 2025.

Predecessor title, not an edition of this book:

TitleYearPublisherRelationship
The Agile Brand Guide: Marketing Measurement & Analytics2023The Agile BrandShort guide; out of print. Expanded into this book.
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How this book compares

TitleAuthorYearFocusBest suited for
Marketing Measurement and Analytics: An IntroductionGreg Kihlström2024Goal alignment, measurement framework, data collection, testing, analysisMarketers building a measurement program from the ground up
Meaningful Measurement of the Customer Experience, 2nd ed.Greg Kihlström2024Measurement for CX programsCX leaders
Marketing Technology PlatformsGreg Kihlström2025Platform categories including measurement tools (Part 5)Teams choosing measurement platforms
Marketing Analytics: A Practical GuideMike Grigsby2022Statistical methods for marketing problemsAnalysts wanting more math
Lean AnalyticsAlistair Croll and Benjamin Yoskovitz2013One metric that matters, by business modelStartups and product teams
Trustworthy Online Controlled ExperimentsKohavi, Tang, and Xu2020Rigorous A/B testing at scaleExperimentation teams
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FAQs

Is this a statistics book? No, though it includes two chapters of statistics for marketers (chapters 6 and 18). The focus is on which metrics to choose and how to run a measurement program, not on statistical theory.

Does it cover attribution? Multi-channel measurement is covered in chapters 5, 12, 20, and 21. The book treats attribution as one approach within multi-channel measurement rather than as a standalone topic.

What’s the recurring case study? A single organization followed through the book so that each concept (goal hierarchy, framework, data plan, dashboard, tests) is shown applied in one context. The publisher description confirms the device; the organization’s details are in the book.

Does it have exercises? Each of the five parts ends with a recap quiz, with answers in Appendix B. Appendix A is a glossary of measurements and formulas.

How does AI figure into it? Chapters 7, 10, 14, 17, and 24 each address AI: measuring AI implementations, AI tools in the framework, data collection in AI-driven environments, AI for prediction and hypothesis development, and generative AI for analysis.

How is it different from Meaningful Measurement of the Customer Experience? This book is about marketing measurement generally. Meaningful Measurement is about measuring customer experience programs specifically.

Who published it? Mercury Learning and Information Systems, an imprint of De Gruyter Brill.

How long is it? 270 pages, 26 chapters in five parts, plus two appendices.

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  1. Key Performance Indicator (KPI)
  2. Objectives and Key Results (OKR)
  3. A/B Testing
  4. Multivariate Testing (MVT)
  5. P-Value
  6. Minimum Detectable Effect (MDE)
  7. Null Hypothesis
  8. Incrementality
  9. Data-Driven Attribution (DDA)
  10. Predictive Analytics
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Sources

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