Highly Effective Marketing Analytics: A Practical Guide to Improving Marketing ROI with Analytics


Highly Effective Marketing Analytics, a highly practical and pragmatic how-to book, infuses analytics into marketing to help improve marketing performance and raise analytics IQ for companies that have not yet had much success with analytics. The author illustrates, step by step, many innovative, practical, and cost-effective methodologies to solve the most challenging problems facing marketers in today’s highly competitive omnichannel environment. Readers will learn the following from this book:
‧ Why has marketing analytics not yet kept the promise? How can organizations take advantage of marketing analytics to overcome budget constraints and become analytically competitive?
‧ What are strategic level, managerial level, and tactical level segmentation strategies and how to develop them?
‧ How to create customer profiles, customer personas, customer journeys, and a 360-degree customer view to gain deeper insights into customers.
‧ Why Mu’s 541-Rule can significantly improve response rates and profitability of direct mail campaigns.
‧ How to make print catalogs pro?table again in the digital era.
‧ How to take e-mail marketing to a different level of sophistication and success.
‧ Why loyalty programs failed and how to revamp a failed or low-performing loyalty program.
‧ How to calculate and apply different customer lifetime values to solving real-world problems.
‧ How to use marketing mix modeling (MMM), multi-touchpoint attribution models, and promotional lift analysis to better allocate marketing budgets into a wide range of marketing mediums.
Written in an applied, concise manner, and requiring no programming knowledge, this book is an easy read for both marketers and analytics practitioners who want to compete more effectively in the workplace.


About the Author(s)

Mu Hu

Mu Hu is the founder of Revenue Insights Corp., a Dallas, Texas-based data-driven and metrics-focus consulting firm known for its practical, no-nonsense approach to proving and improving the…

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Pub Date

December 30, 2019





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