The course is designed for professionals in marketing, analytics, and data science who are interested in learning how to leverage data to improve marketing outcomes.
The course covers several key topics, including market segmentation, targeting, positioning, and pricing. It also delves into customer lifetime value, retention, and acquisition. Additionally, the course covers various tools and techniques for data analysis, such as regression analysis, cluster analysis, and decision trees.
Throughout the course, students will learn how to use data to make informed marketing decisions. They will also learn how to develop marketing strategies that are data-driven and based on insights gleaned from analytics.
By the end of the course, students will have a solid understanding of the fundamentals of marketing analytics. They will also have practical experience using analytics tools and techniques to solve real-world marketing problems.
Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions.
Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions.
This course, developed at the Darden School of Business at the University of Virginia, gives you the tools to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns. You'll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources.
You can follow my posts in Twitter, @rajkumarvenk, and on linkedin: https://www.linkedin.com/in/education-marketing.
The Marketing Analytics course by Rajkumar Venkatesan on Coursera is divided into 5 weeks, which include:
Week 1: Leveraging User Generated Content
10 videos (Total 35 min), 2 readings, 3 quizzes
Course Overview 2m
Why Marketing Analytics? 4m
Introduction to the Marketing Process 2m
Airbnb Marketing Process 7m
Airbnb's Strategic Challenge 1m
Airbnb's Marketing Strategy with Data 3m
Using Text Analytics 3m
Utilizing Data to Improve Marketing Strategy 3m
Takeaways: Improving the Marketing Process with Analytics 3m
Course Overview & Requirements 10m
Use Discussion Forums to Deepen Your Learning 10m
Practice Quiz on the Marketing Process 15m
Practice Quiz on Data Analytics Basics 18m
Week 1 Quiz: the Marketing Process 33m
Week 2: Metrics for Measuring Brand Assets
12 videos (Total 46 min)
Intro to Metrics for Measuring Brand Assets 1m
Snapple and Brand Value 5m
Developing Brand Personality 4m
Brand Personality: Red Bull 1m
Developing Brand Architecture 5m
Brand Architecture: Red Bull 3m
Brand Architecture: Etch A Sketch 2m
Measuring Brand Value 10m
Measuring Brand Value: Key Points 1m
Revenue Premium as a Measure of Brand Equity 5m
Calculating Brand Value: Snapple 5m
Takeaways: Measuring Brand Value 1m
Practice Quiz on Brand and Brand Architecture 12m
Practice Quiz on Calculating Brand Value 24m
Week 2 Quiz on Measuring Brand Assets 30m
Week 3: Customer Lifetime Value
11 videos (Total 45 min)
Customer Lifetime Value (CLV) 4m
Customer Lifetime Value: Netflix 2m
Calculating CLV 7m
Understanding the CLV Formula 2m
Applying the CLV Formula: Netflix 6m
Extending the CLV Formula, Part1 7m
Extending the CLV Formula, Part2 3m
Using CLV to Make Decisions: IBM 3m
CLV: A Forward Looking Measure 3m
Takeaways: CLV 1m
Practice Quiz on CLV 30m
Week 3 Quiz on CLV 30m
Week 4: Marketing Experiments
Ever wonder how much you have to cut prices to drive the most sales? Or which advertisement copy is more effective in customer conversion? Do an experiment! Experiments allow you to understand the effectiveness of different marketing strategies and forecast expected ROI. This week, we'll explore how to design basic experiments so that you can assess your marketing efforts and invest your marketing dollars most effectively. We'll help you avoid a gap between your test results and field implementation, and explore how web experiments can be implemented cheaply and quickly. By the end of this module, you'll be able to design and conduct effective experiments that test your marketing campaigns--and then use the results to make future marketing decisions.
13 videos (Total 60 min), 3 readings, 5 quizzes
Determining Cause and Effect through Experiments 3m
Designing Basic Experiments 6m
Designing Before - After Experiments 5m
Designing Full Factorial Web Experiments 4m
Designing an Experiment: Etch A Sketch 2m
Analyzing an Experiment: Etch A Sketch 8m
Analyzing an Experiment: Betty Spaghetty 5m
Projecting Lift 2m
Calculating Projected Lift: Betty Spaghetty 6m
Pitfalls of Marketing Experiments: Betty Spaghetty 3m
Maximizing Effectiveness: Nanoblocks 8m
Takeaways: Marketing Experiments 1m
Spreadsheet with Formulas 10m
Transformation of Marketing at the Ohio Art Company (abridged) 15m
MBTN Assessments 10m
Practice Quiz 1 on Designing Experiments 9m
Practice Quiz 2 on Calculating Break Even and Lift 12m
Practice Quiz 3 on Projecting Lift 9m
Week 4: Marketing Experiments Quiz 30m
Week 5: Regression Basics
17 videos (Total 95 min)
Using Regression Analysis 2m
What Regressions Reveal 5m
Interpreting Regression Outputs 9m
Multivariable Regressions 6m
Omitted Variable Bias 5m
Using Price Elasticity to Evaluate Marketing 7m
Understanding Log-Log Models 6m
Marketing Mix Models 7m
Takeaways: Regressions 1m
Course Conclusion 1m
Interview with Jennifer Chick, VP Marketing, Hilton Worldwide - Part1 7m
Interview with Jennifer Chick, VP Marketing, Hilton Worldwide - Part2 4m
Interview with Paul Hunter, Head of Operations, dunnhumby, on Using Data - Part1 7m
Interview with Paul Hunter, Head of Operations, dunnhumby, on Using Data - Part2 6m
Interview with Paul Flugel, VP of Global Marketing Performance - Part1 6m
Interview with Paul Flugel, VP of Global Marketing Performance - Part2 8m
Practice Quiz: Regressions 30m
Week 5 Quiz on Regression Analysis 30m
As a former student of the Marketing Analytics course by Rajkumar Venkatesan on Coursera, I have a positive overall impression of the course. Below are some more detailed insights about the course.
The course covers a variety of topics related to marketing analytics, including customer segmentation, targeting, positioning, pricing, and marketing research. Each week, students watch pre-recorded video lectures that explain the concepts and provide real-life examples of their application in marketing.
In addition to the lectures, the course includes weekly assignments that require students to apply the concepts learned in the lectures to real-world datasets. These assignments often involve using data analysis software like R or Excel to manipulate, analyze, and visualize the data. The assignments are challenging, but not overly difficult, and the course provides ample resources to help students complete them successfully.
One of the strengths of the course is its instructor, Rajkumar Venkatesan. He is a highly respected academic and practitioner in the field of marketing analytics, and his expertise is evident throughout the course. He is engaging, knowledgeable, and responsive to students' questions and concerns.
Another strength of the course is the emphasis on practical skills. Students learn not only the theoretical concepts behind marketing analytics but also how to apply those concepts to real-world problems. The course provides numerous opportunities for students to practice using data analysis software and to develop their analytical and communication skills.
Overall, I found the Marketing Analytics course to be a valuable learning experience that has helped me to develop new skills and knowledge relevant to my career in marketing. I would highly recommend it to anyone who is interested in learning about marketing analytics or data-driven marketing.
At the time, the course has an average rating of 4.7 out of 5 stars based on over 6,250 ratings.
What you'll time:
After completing the Marketing Analytics course by Rajkumar Venkatesan on Coursera, learners can expect to gain several skills and knowledge, including:
Ability to understand and apply marketing analytics concepts: The course covers a wide range of marketing analytics concepts, including market segmentation, targeting, positioning, and pricing, as well as customer lifetime value, retention, and acquisition. By the end of the course, students will have a deep understanding of these concepts and be able to apply them to real-world marketing problems.
Data analysis skills: The course teaches students how to use various tools and techniques for data analysis, such as regression analysis, cluster analysis, and decision trees. These skills are critical for analyzing customer data and understanding consumer behavior, which can help inform marketing decisions.
Data visualization skills: Data visualization is an essential component of marketing analytics. The course covers different data visualization tools and techniques and teaches students how to present their findings and insights in a clear and concise manner. This skill is valuable for communicating results to stakeholders and making data-driven decisions.
Ability to develop data-driven marketing strategies: The course emphasizes the importance of using data to develop marketing strategies. Students will learn how to analyze customer data to identify patterns and trends, and use these insights to create targeted marketing campaigns. They will also learn how to evaluate the effectiveness of these campaigns and adjust their strategies accordingly.
Business acumen: The course provides a broader perspective on marketing analytics and its role in the business world. Students will learn how marketing analytics can be used to drive business outcomes, such as revenue growth, customer retention, and market share. They will also gain an understanding of how marketing analytics fits into the broader business landscape.
Overall, completing the Marketing Analytics course will equip students with the skills and knowledge necessary to make data-driven marketing decisions and develop effective marketing strategies. These skills are in high demand in today's data-driven business environment and can help students advance their careers in marketing, analytics, or data science.
Rajkumar Venkatesan is a well-known professor and expert in the field of marketing analytics. He is currently a Professor of Business Administration at the Darden School of Business at the University of Virginia, where he teaches courses on marketing analytics and marketing research.
Dr. Venkatesan is also a prolific researcher, having published numerous articles on marketing analytics in top-tier academic journals. His research focuses on customer lifetime value, customer acquisition and retention, and marketing-mix optimization, among other topics. His work has been widely cited in the academic literature and has had a significant impact on the field of marketing analytics.
In addition to his academic work, Dr. Venkatesan is also a sought-after speaker and consultant. He has worked with numerous companies to help them improve their marketing strategies using data and analytics. His expertise in marketing analytics has been recognized by several prestigious awards, including the INFORMS Society for Marketing Science Practice Prize and the John D.C. Little Best Paper Award.
Overall, Dr. Venkatesan is a highly respected and influential figure in the field of marketing analytics. His research and teaching have helped to shape the field, and his work has had a significant impact on the way companies approach marketing. Students who take his Marketing Analytics course on Coursera can be confident that they are learning from one of the foremost experts in the field.
The Marketing Analytics course by Rajkumar Venkatesan on Coursera has several requirements for learners, including:
Basic familiarity with statistics: Students are expected to have a basic understanding of statistical concepts such as correlation, regression, and hypothesis testing. This includes knowledge of probability theory, sampling distributions, and statistical inference.
Proficiency in Microsoft Excel: The course requires students to use Excel for data analysis and visualization. Students should be proficient in using Excel functions and formulas to perform basic data manipulations and create charts and graphs.
Access to data analysis software: Students will need access to data analysis software such as R or SAS. The course provides guidance on how to use these tools, but students should have basic familiarity with one of these tools.
Familiarity with marketing concepts: Students should have a basic understanding of marketing concepts such as market segmentation, targeting, positioning, and pricing. This includes knowledge of consumer behavior, marketing research methods, and marketing strategy.
Strong analytical and critical thinking skills: The course requires students to analyze data and develop insights based on their analysis. Students should have strong analytical and critical thinking skills to succeed in the course. This includes the ability to identify patterns, interpret results, and draw conclusions.
Strong written and verbal communication skills: The course requires students to present their findings and insights in a clear and concise manner. Strong written and verbal communication skills are essential for success in the course. This includes the ability to write clear and concise reports, create effective presentations, and communicate results to non-technical audiences.
Time commitment: The course is designed to be completed over 5 weeks, with an estimated time commitment of 2-5 hours per week. Students should have the time and dedication to complete the course assignments and participate in online discussions.
Overall, the Marketing Analytics course is designed for students who have a strong foundation in statistics, Excel, marketing concepts, and data analysis software, as well as strong analytical and communication skills. Students who meet these requirements and are interested in learning how to use data and analytics to inform marketing decisions will benefit from the course.