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.
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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.
What You Will Learn
- How to build and define a brand architecture and how to measure the impact of marketing efforts on brand value over time
- How to measure customer lifetime value and use that information to evaluate strategic marketing alternatives
- How to design basic experiments so that you can assess your marketing efforts and invest your marketing dollars most effectively
- How to set up regressions, interpret outputs, explore confounding effects and biases, and distinguish between economic and statistical significance
Syllabus
WEEK 1
The Marketing Process
Welcome! We'll start with an overview of the marketing process and the transformational role of analytics. Then we'll walk through a case study. Ever heard of Airbnb? They're a powerhouse of the online community marketplace matching travelers to hosts. You'll see how they use analytics and the surprising results of their analyses.
WEEK 2
Metrics for Measuring Brand Assets
Firms spend millions on branding for one reason: It allows them to charge more for their products and services. In this module, we'll explore this valuable, if intangible, asset. We'll discuss how to build and define a brand architecture and how to measure the impact of marketing efforts on brand value over time. By the end of this module, you'll be able to measure and track brand value. So let's get started!
WEEK 3
Customer Lifetime Value
How valuable are your customers? That's a tough question that we'll show you how to answer in this module where we'll explore Customer Lifetime Value, or the future net value of a customer relationship. This forward-looking measure of the customer relationship helps you connect marketing strategies to future financial consequences and invest marketing dollars in the right place to maximize return over a customer's lifetime. By the end of this module, you will know how to measure customer lifetime value and evaluate strategic marketing alternatives based on whether they improve customer retention and lifetime value.
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.
WEEK 5
Regression Basics
Ever wonder how variables influence consumer behavior in the real world--like how weather and a price promotion affect ice cream consumption? In this module, we will take a look at regression and how it's used to understand that relationship. We will discuss how to set up regressions and interpret outputs, explore confounding effects and biases, and distinguish between economic and statistical significance. We'll finish the week with a series of interviews with real marketing professionals who share their experiences and knowledge about how they use analytics on the job.
