Revenue management is increasingly important in hospitality management. This course is for those interested in learning more about formulating tactics and strategies to maximize revenues for their organizations. The course will prepare students for roles responsible for financial performance and operation. Topics include history of revenue management, reservation systems, forecasting demand, inventory control, cost analysis, pricing strategy, channel management, revenue management tactics (i.e., overbook, discount allocation, and demand management and applications). Appropriate for any business application. Prereq: ADMN 960.

Marketing Analytics is the art and science of developing and utilizing quantitative marketing decision models to plan, implement, and analyze marketing strategies and tactics. The course is primarily designed for graduate students who have already acquired basic data analysis skills as well as principles of marketing. Using marketing cases and related exercises tied to Marketing Engineering for Excel (ME-EL), students will develop marketing plans in various decision contexts. Specifically, this course will introduce a wide variety of quantitative models to improve marketing decision making in such areas as market response, customer segmentation/targeting, product/brand positioning, new product development, and allocation of marketing mix expenditures. Prereq: ADMN 960.

This course will focus on modern predictive analytic techniques. Each module is designed to introduce a set of statistical techniques and their application to real data from various business fields. The course will focus on 4 broad topics 1) Finding the most appropriate model for the data, 2) selecting optimal set of predictors, 3) reducing dimensionality of the data, 4)improving prediction performance. Programming using R, open source software, is fundamental to the course. Prereq: ADMN 950 or permission.

This course is concerned with the final frontier of business analytics, and develops student knowledge of the uses of descriptive statistics and forecasts to find and suggest optimal courses of action, focusing on the development and use of optimization and Monte-Carlo simulation models for making quantitative business decisions. Optimization topics include linear and mixed-integer programming, network flow optimization, and nonlinear optimization. Application areas include operations & supply chain management, marketing, and finance. Prereq: ADMN 950.