Marketing Analytics

Catalog description

Companies are currently spending millions of dollars on data-gathering initiatives, but few are successfully capitalizing on all this data to generate revenue and increase profit. Converting data into increased business performance requires the ability to extract insights from data through analytics. This course covers the three pillars of analytics – descriptive, predictive and prescriptive – within the marketing context. Descriptive Analytics examines different types of data and how they can be visualized, ultimately helping you communicate your findings and strengthen your team’s or organization’s decision making. Predictive Analytics explores the use of data for forecasting. You will learn to utilize various tools, including regression analysis, to estimate relationships among variables and predict future behavior. Prescriptive Analytics takes you through the final step — formulating concrete recommendations. These recommendations can be directed toward a variety of marketing actions, including pricing and social-platform outreach. Students will be exposed to several methods such as linear regression, logistic regression, multinomial regression, machine learning methods (e.g., neural networks and random forests). We will learn how to employ these methods for such managerial decisions as demand forecasting, pricing, and valuing customers. Overall, you will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable insights.