Digital Marketing Analytics Essentials
Master data-driven marketing decisions using advanced analytics tools, AI, and machine learning for actionable business insights.
Master data-driven marketing decisions using advanced analytics tools, AI, and machine learning for actionable business insights.
This comprehensive course explores digital marketing analytics tools and techniques for leveraging big data in business decision-making. Students learn to analyze customer data, web analytics, and marketing actions to derive actionable insights. The curriculum covers SEO strategies, A/B testing, recommendation systems, and AI/machine learning applications. Participants develop skills in assessing managerial problems, analyzing data, and creating compelling narratives for actionable results. The course emphasizes practical applications across various industries, teaching students to leverage customer transactions, surveys, and advertising data effectively.
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What you'll learn
Master SEO and SEM strategies with KPI analysis
Implement effective web analytics and A/B testing
Develop recommendation systems for marketing
Apply machine learning and AI in marketing decisions
Conduct advanced text and image analysis
Understand attribution modeling and marketing mix
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
The course explores six key areas of digital marketing analytics. Students learn about SEO and SEM strategies with focus on KPIs and keywords, web analytics including A/B testing methodologies, recommendation systems development, machine learning and AI applications in marketing, text and image analysis techniques, and attribution modeling. The curriculum combines theoretical knowledge with practical applications to help students make data-driven marketing decisions.
SEO and SEM Strategies
Module 1
Web Analytics and Testing
Module 2
Recommendation Systems
Module 3
Machine Learning and AI Applications
Module 4
Text and Image Analysis
Module 5
Attribution and Marketing Mix Modeling
Module 6
Fee Structure
Instructors
5 Courses
Leading Expert in Digital Marketing Analytics and Social Media Research
Dr. Michael Trusov serves as Dean's Professor of Digital Marketing and Analytics at the University of Maryland's Robert H. Smith School of Business, where he directs the MS in Marketing Analytics program. After completing his PhD from UCLA Anderson School of Management along with master's degrees in Computer Science and Business Administration, he has established himself as one of the most influential researchers in digital marketing with over 8,200 citations. His work spans multiple areas including social media marketing, search engine optimization, and consumer behavior analytics, earning numerous accolades including the 2020 Consumer Research in Practice Award and recognition among Top 50 Most Productive Researchers in Marketing. Through his teaching of Digital Marketing Analytics and development of innovative courses combining theory with practical applications, he helps students master the complexities of modern marketing analytics. His expertise particularly shines in analyzing social network dynamics and online word-of-mouth marketing, demonstrated through his highly cited research on social media influence and consumer behavior. Prior to academia, he spent seven years in software development and IT consulting, specializing in marketing automation and e-commerce solutions. Currently serving as Associate Editor for both Journal of Marketing Research and Journal of Marketing while maintaining active research in artificial intelligence applications and text analysis, he continues to bridge cutting-edge marketing research with practical industry applications.
5 Courses
Leading Expert in Digital Marketing Analytics and AI Applications
Dr. Liye Ma serves as Associate Professor of Marketing at the University of Maryland's Robert H. Smith School of Business, where he combines expertise in artificial intelligence with digital marketing research. After completing his PhD from Carnegie Mellon University's Tepper School of Business and degrees in Computer Science from Tsinghua University and Brown University, he has established himself as an influential researcher with his most-cited work garnering over 596 citations. His research focuses on analyzing consumer-firm interactions across digital platforms, developing statistical and machine learning methods to understand consumer behavior in the digital economy. Through his teaching of Marketing Research Methods and Data Science in both undergraduate and MS Marketing Analytics programs, he helps students master the complexities of modern marketing analytics. His expertise particularly shines in developing AI and machine learning applications for marketing, demonstrated through his influential work on consumer behavior analysis and digital marketing optimization. Prior to academia, he gained industry experience as a Senior Project Leader at Oracle Corporation and Development Lead at Wellington Management Company. His research has earned multiple accolades including being a finalist for the John D.C. Little best paper award and the Paul Root best paper award, while receiving research grants from the Marketing Science Institute and Wharton Customer Analytics Initiative.
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