Academics

Applications are currently accepted on a space-available basis. Before starting an application for the fall 2019 semester, please contact us at masters@rhsmith.umd.edu or via phone at 301-405-2559 to check availability.

The Master of Science in Marketing Analytics program can be completed in 10 or 16 months. 

Required Courses

Elective Courses

Curriculum

10-Month Program

Fall

Market-Based Management (3)

Statistical Programming (3)

Advanced Marketing Analytics (3)

Business Policies & Ethics (2)

Customer Analysis (3)

Business Communication (2)

Spring

Data Science (3)

Marketing Research & Analytics (3)

Action Learning Project (2)

Web Analytics (2)

Business Communication II (2)

CRM Analytics (2)

Marketing Forecasting (2)

16-Month Program

Fall

Market-Based Management (3)

Statistical Programming (3)

Advanced Marketing Analytics (3)

Business Policies & Ethics (2)

Business Communication (2)

Spring I

Data Science (3)

Marketing Research & Analytics (3)

Action Learning Project (2)

Business Communication II (2)

CRM Analytics (2)

Spring II

Customer Analysis (3)

Business Policies & Ethics (2)

Pricing Analytics (2)

Web Analytics (2)

Required Courses

BUMK720: Market-Based Management
Credits: 3

The purpose of this course is to introduce students to the fundamentals of marketing. This course combines lectures, readings, case analyses and a competitive simulation. The cases will be presented in groups to help students develop analytical writing and presentation skills. A significant part of the course involves the MARKSTRAT simulation which teaches students to make marketing decisions in a simulated competitive environment based on a wealth of marketing data and metrics.

BUMK724: Customer Analysis
Credits: 3

Focuses on the analysis of customer decision-making and how marketing strategy can be used to influence those decisions. The framework used is the buyer behavior model, in which concepts from psychology, sociology, and economics are applied to individual and organizational purchase decisions. Marketing strategies of leading firms in consumer products, technology, and services (including internet services) are analyzed using a variety of case study formats.

BUMK726: Statistical Programming
Credits: 3

Provides students with a foundation in probability and statistics with a focus on business applications. It also gives students a foundation for thinking in both likelihood and Bayesian frameworks. The course teaches students the basics of SAS, as well as its use in statistical analysis and statistical programming. Also addressed are basic SAS language structure, data management, OLAP, enterprise miner, statistical analysis, writing procedures.

BUMK742: Advanced Marketing Analysis
Credits: 3

The analysis of marketing data needed for profitable marketing decisions. Advanced methods of marketing analysis for marketing decisions, including choice and count data models, joint analysis of consumers choice, quantity and timing decisions, mixture and mixture regression models, and conjoint analysis, all using data-based cases and SAS software. Applications are in the areas of strategic marketing, marketing segmentation, eye tracking for advertising effectiveness, new product development, sales promotion analysis, pricing, design of marketing mix, and direct marketing.

BUMK744: Marketing Research and Analysis
Credts: 3

A review of primary data collection methods for marketing data. Students will learn how to design effective confirmatory research. Both direct methods such as surveys and indirect methods such as experiments will be covered. In this hands-on course, students will design and conduct research with target customers, analyze the data, and then present their results to decision makers.

BUMK746: Data Science
Credits: 3

This class provides an introduction to the basic concepts of database management and an overview of the various sources of in house DM data. Students will learn how to work with click stream, scanner panel and social media data. Geo-demographic datasets will be discussed and explored, and techniques for data-fusion will receive ample attention. Segmentation of customers, selection and targeting of prospects, cross-selling and up-selling are issues that are dealt with.

BUMK760: Business Policies & Ethics
Credits: 2

The standards of business conduct, morals and values as well as the role of business in society. Students will consider the sometimes conflicting interests of and claims on the firm and its objectives.

BUMK762: Business Communications
Credits: 2

A study of the standards of business conduct, morals and values as well as the role of business in society. Students will consider the sometimes conflicting interests of and claims on the firm and its objectives.

BUMK764: Business Communications II
Credits: 2

BUMK776: Action Learning Project
Credits: 2

This is a project based course where students will analyze marketing data, report findings and provide appropriate recommendations.

Elective Courses

BUMK758R: CRM Analytics
Credits: 2

BUMK766: Web Analytics
Credits: 2

Examines the process of developing, implementing and analyzing strategies for successfully marketing a variety of existing and potential products and services on the Internet. Special attention is devoted to the tools and techniques unique to the electronic media.

BUMK773: Pricing Analytics
Credits: 2

The economic and behavioral aspects of pricing and the evaluation of innovative pricing practices such as price matching, customized pricing, bundle pricing and product line pricing. The course will cover both B2B, B2C, online and offline markets. Instruction will be through a mix of case studies, pricing simulation games, hands-on exercises, practitioner guest lectures and discussions.

BUMK774: Market Forecasting
Credits: 2

In this course, students will learn a number of market forecasting techniques, both quantitative and qualitative, for a variety of different contexts. The majority of this course focuses on quantitative modeling techniques based on established statistical methods. We will also cover non-statistical methods that are often used when empirical data is scarce. This is a very hands-on class where students will apply the forecasting methods learned to real data. By the end of the course, each student will have a portfolio of models for a variety of forecasting scenarios.

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