Managing Uncertainty in Marketing Analytics (Coursera)

We eschew specialized software and provide learners with the foundational knowledge they need to develop sophisticated marketing models in a basic spreadsheet environment. Topics include the development and application of Monte Carlo simulations, and the use of probability distributions to characterize uncertainty.

Course 2 of 6 in the Foundations of Marketing Analytics Specialization.

Syllabus

WEEK 1

Randomness and Probability

Module 1 focuses on developing an understanding where randomness appears in marketing problems. You will learn basic rules for calculating the probability of outcomes. We will also examine how these rules can be applied to determine the value of information

WEEK 2

Conducting Monte Carlo Simulations in Excel

Building on the basics of randomness and probability discussed in Module 1, we examine the use of Monte Carlo simulations for incorporating randomness into business problems. Using Microsoft Excel, we will build a tool that conducts a Monte Carlo simulation. We will use this tool to evaluate the best course of action for a particular business problem.

WEEK 3

Using Probability Distributions to Model Uncertainty

In Module 3, we look at the use of probability distributions as a means of characterizing uncertainty. We initially look at how uncertainty is incorporated into a general decision making framework. We then turn our attention to different probability distributions that can be used to model uncertainty, depending on the nature of the data. We examine the application of these probability distributions to assess the likelihood of events using features within Microsoft Excel.

WEEK 4

Application: Designing Extended Service Warranty Plans

Building the the discussion of probability distributions in Module 3, we apply this knowledge to a specific application: the design of extended service warranty plans. We provide an overview of the business problem and discuss how to incorporate uncertainty in customers' use of the warranty plan using the Poisson distribution. Using Microsoft Excel, we design a spreadsheet tool that enables a user to adjust features of the service plans. By comparing firm profit under different scenarios, we investigate how different features of the service plan result in risk being shared by the consumer and the firm.