Marketers must make the best decisions based on the information presented to them. Rarely will they have all the information necessary to predict what consumers will do with complete certainty. By incorporating uncertainty into the decisions that they make, they can anticipate a wide range of possible outcomes and recognize the extent of uncertainty on the decisions that they make. In Incorporating Uncertainty into Marketing Decisions, learners will become familiar with different methods to recognize sources of uncertainty that may affect the marketing decisions they ultimately make. 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.

How will customers act in the future? What will demand for our products and services be? How much inventory should we order for the next season? Beyond simply forecasting what customers will do, marketers need to understand how their actions can shape future behavior. In Developing Forecasting Tools with Excel, learners will develop an understanding of the basic components of a forecasting model, how to build their own forecasting models, and how to evaluate the performance of forecasting models. All of this is done using Microsoft Excel, ensuring that learners can take their skills and apply them to their own business problems.

How do consumers see your brand relative to your competitors? How should a new product be positioned when it’s launched? Which customer segments are most interested in our current offerings? For these questions and many others, surveys remain the tried and true method for gaining marketing insights. From one-off customer satisfaction surveys to brand tracking surveys that are administered on a continuous basis, they provide the information that marketers need to understand how their products, services and brands are seen by consumers. In Analytic Methods for Survey Data, learners will become familiar with established statistical methods for converting survey responses to insights that can support marketing decisions. Techniques discussed include factor analytics, cluster analysis, discriminant analysis and multi-dimensional scaling. These techniques are presented within the STP (Segmentation, Positioning, Targeting) Framework, enabling learners to apply the analytic techniques to develop a marketing strategy. It is recommended that you complete the Meaningful Marketing Insights course offered by Coursera before taking this course.

Note: This course would require using XL Stat, an Excel Add-on that students would need to purchase. XL Stat offers a 30-day free trial, so students could complete this course without incurring additional expense.

Social media not only provides marketers with a means of communicating with their customers, but also a way to better understand their customers. Viewing consumers’ social media activity as the “voice of the consumer,” this session exposes learners to the analytic methods that can be used to convert social media data to marketing insights. In Introduction to Social Media Analytics, learners will be exposed to both the benefits and limitations of relying on social media data compared to traditional methods of marketing research. Partnering with a leading social media listening platform, this course provides learners with the foundational skills of social media listening including the creation of monitors and common social media metrics. Moving beyond social media listening, this course shows learners how social media data can be used to provide insights into market structure and consumers’ perceptions of the brand. Learners will have the opportunity to assess data and discern how to "listen" to the data by watching video lectures and completing activities, practice quizzes, discussion boards, and peer assessments.

This capstone project will give you an opportunity to apply what we have covered in the Foundations of Marketing Analytics specialization. By the end of this capstone project, you will have conducted exploratory data analysis, examined pairwise relationships among different variables, and developed and tested a predictive model to solve a marketing analytics problem. It is highly recommended that you complete all courses within the Foundations of Marketing Analytics specialization before starting the capstone course.