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Kathryn Surchek and Rajkumar Venkatesan

Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more The course is ideal for practicing and aspiring marketing professionals who want to grow their knowledge, develop their career portfolio, and improve the effectiveness of their marketing campaigns. However it will also be of interest to anyone looking to learn the basics of marketing analytics.

Topics Covered

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Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more The course is ideal for practicing and aspiring marketing professionals who want to grow their knowledge, develop their career portfolio, and improve the effectiveness of their marketing campaigns. However it will also be of interest to anyone looking to learn the basics of marketing analytics.

Topics Covered

  • The marketing process
  • Metrics for measuring brand assets
  • Customer lifetime value
  • Marketing experiments
  • Regression basics

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Reviews summary

Foundational marketing analytics overview

According to students, the Marketing Analytics course offers a solid foundational understanding of key concepts for those new to the field. Many find the lectures clear and easy to follow, making complex topics like regression basics approachable. The course is particularly praised for its real-world applicability, with learners appreciating how concepts connect to marketing scenarios, enabling immediate application of strategies like Customer Lifetime Value (CLV) models. However, some experienced professionals note that the initial modules can be too basic, suggesting it might lack advanced depth for those already familiar with statistical methods. While it provides a good overview of metrics, some wish for more hands-on practice with specific analytical tools.
Perfectly suited for those new to the subject.
"Perfect for beginners like me who need to understand the core concepts without diving too deep into the math."
"Lectures were clear and easy to follow, even for complex topics like regression basics. The instructor simplifies concepts well."
"As someone new to analytics, I found this course to be very approachable and easy to understand."
Provides a clear and comprehensive introduction.
"A great introduction to marketing analytics. It laid a solid foundation without being overwhelming."
"Covers essential topics well, giving a broad understanding of marketing analytics."
"The course gave me the essential concepts of marketing analytics that I needed to start in this field."
Connects theory to immediate business use cases.
"The course does an excellent job of connecting theory to real-world marketing scenarios. I can immediately apply the CLV models to my work."
"Found the marketing experiments module particularly useful. It provided actionable strategies I could implement."
"I found the content highly practical for understanding marketing analytics in my current role and apply it directly."
Good overview, but limited on practical tools.
"Provides a good overview of key metrics, but I wish there was more depth on specific tools or advanced analytical methods."
"Could use more in-depth coverage on how to actually implement these analyses using software, that was a missing piece for me."
"Some sections felt a bit rushed, especially the more technical parts, requiring re-watching videos multiple times to grasp."
May feel too basic for experienced professionals.
"As an experienced marketer, I found some of the initial modules too basic, but the later content on experimentation and regression was valuable."
"If you're already familiar with statistical modeling, this course might feel a bit slow and introductory."
"I was hoping for more advanced techniques and case studies, but it seems to be more of an overview than in-depth analysis."

Career center

Learners who complete Marketing Analytics will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 37 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Marketing Analytics.
This book, written by the famous brand consultant David Aaker, goes through a step-by-step process of what brand relevance is and how to attain it. It is written for marketing executives and provides tactical advice on how to achieve results.
Provides a comprehensive and current introduction to the foundations of linear regression analysis. It is widely used as a textbook in academic institutions and is suitable for gaining a broad understanding. The latest edition includes new material on generalized regression techniques and updated examples.
Provides a comprehensive guide to creating a profitable customer base through CLTV. It covers the entire customer lifecycle, from acquisition to retention to churn.
A practical guide to implementing CLTV strategies in your organization. It includes case studies, worksheets, and templates to help you get started.
Provides a practical and engaging introduction to regression analysis and other statistical methods, focusing on real-world data and research questions. It's suitable for a broad audience and emphasizes the importance of data visualization and model checking.
A solid reference book that covers a wide range of theoretical aspects in regression analysis. While extensive, some users note the code examples might be in a less common dialect. It valuable resource for deepening understanding and is often used at the undergraduate and graduate levels.
Emphasizes the practical application of regression analysis through numerous examples. It's an excellent resource for understanding how to perform regression and interpret the results, making it suitable for both broad understanding and practical application. The focus is on exploratory data analysis rather than just theory.
Offers in-depth coverage of regression analysis and generalized linear models with a focus on applications, particularly in the social sciences. It balances theoretical concepts with practical examples and is suitable for both students and researchers. The latest edition includes new chapters on mixed-effects models.
A more advanced text covering a wide range of statistical learning techniques, including various forms of regression. It provides a deeper theoretical understanding and key reference for researchers and professionals in the field. It is more mathematically rigorous than 'An Introduction to Statistical Learning'.
Offers a comprehensive overview of marketing analytics techniques, covering data collection, data analysis, and data visualization. It provides step-by-step instructions and real-world examples to help readers understand how to use data to improve their marketing campaigns.
Focuses on developing a comprehensive strategy for regression modeling, covering various types of regression models and emphasizing the importance of model validation and interpretation. It's a valuable resource for practitioners and researchers building complex regression models.
This practical guide provides a step-by-step approach to designing and conducting marketing experiments, covering key concepts such as hypothesis testing, experimental design, and data analysis.
A long-standing and well-regarded text that provides a thorough introduction to applied regression analysis. It balances theory and examples and has been a popular choice for practitioners for many years. While not the most recent, its content remains relevant for a solid understanding.
Offers a comprehensive overview of web analytics, covering topics such as traffic analysis, conversion optimization, and social media analytics. It good choice for readers who want to learn more about the specific techniques used to analyze web data.
This textbook offers a practical and accessible overview of brand measurement and management, with a focus on current trends and best practices. It is suitable for undergraduate and postgraduate students in marketing.
Written by two renowned brand experts, this book combines theoretical insights with real-world case studies to provide a comprehensive overview of brand management. It explores various aspects of brand measurement and evaluation.
A classic book on CLTV that provides a framework for understanding and managing customer relationships. It's a must-read for anyone interested in the topic.
A more accessible follow-up to 'Mostly Harmless Econometrics', this book focuses on key methods for causal inference, including regression-based approaches, with a strong emphasis on intuition and examples. It's an excellent resource for understanding how to use regression to answer causal questions.
While not solely about regression, this book crucial resource for understanding causal inference, a key application of regression analysis in many fields. It provides a rigorous framework for thinking about causality and is essential for anyone using regression to draw causal conclusions.
Provides a practical introduction to statistical concepts relevant to data science, including regression. It focuses on the 'how' and 'why' using R and Python, making it accessible to those with a programming background but limited statistical training. It's a good resource for applying regression in a data science workflow.
Provides a comprehensive overview of brand equity measurement techniques and their applications. It is geared towards brand managers, researchers, and students seeking to understand how to measure and manage brand value.
Thomke, a professor at Harvard Business School, draws on extensive research and case studies to demonstrate the value of experimentation in business decision-making.
Delves into nonparametric regression and generalized additive models, which offer more flexible approaches to modeling relationships between variables compared to traditional linear regression. It's suitable for those looking to explore more advanced and flexible modeling techniques.
Provides a historical perspective on branding and discusses the evolution of brand identity in the digital age. It is suitable for anyone interested in understanding the role of brands in society.
Considered a classic in the field of linear models, this book provides a rigorous theoretical treatment of the subject, which forms the foundation for linear regression. It's a valuable reference for those seeking a deep mathematical understanding of linear models.
Explores the psychological and emotional aspects of branding. It emphasizes the importance of creating meaningful connections with customers through storytelling and brand experiences.
Covers the principles and techniques of conversion optimization, a subtopic of marketing experiments that focuses on improving the conversion rate of website visitors.
Challenges traditional marketing approaches and discusses the need for brands to adapt to changing consumer behavior in the digital age.
Emphasizes the importance of emotional connections in branding. It provides practical strategies for creating brands that resonate with customers on an emotional level.
Explores the psychological principles that influence brand perception and behavior. It provides practical insights on how to use psychology to create more effective brands.
Presents 22 principles that the authors believe are essential for successful branding. It practical guide for marketers and entrepreneurs seeking to build strong brands.
This practical guide provides a comprehensive overview of the brand-building process, from brand strategy development to brand measurement and evaluation. It is suitable for both beginners and experienced brand managers.
Focuses on the practical aspects of testing business ideas through experimentation, with a focus on rapid and iterative testing.

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