Loyola University Chicago

Quinlan School of Business

Digital Marketing Analytics

The Digital Marketing Analytics track is devoted to engaging emerging capabilities built on the foundation of creative, digital, and analytical marketing skill sets.

Combining business goals with marketing decisions, this increasingly critical marketing function leverages consumer, retail, and category trend data to help marketers make informed marketing and business decisions through database models, improved sales forecasting, and data-driven tactics that enhance consumer loyalty, market share, and profitability.

Possible careers paths include digital marketing analyst, data analyst, and marketing analyst. More outcomes →

Track Curriculum

The Digital Marketing Analytics track has a 12-course curriculum with 1 prerequisite course. The curriculum can be completed in 12-16 months.

Prerequisites (1)

In ISSCM 402 – Quantitative Methods II (Statistics Primer), the fundamentals of managerial statistics are presented. Topics include descriptive statistics, probability distributions, normal distribution, central limit theorem, estimation, hypothesis testing, and regression analysis.

The ISSCM 402 Quantitative Methods II course is required of all incoming MSM students and is offered every quarter.

Marketing Core (6)

This course develops a broad understanding of the marketing principles that undergird successful marketing strategies and marketing plans with special attention given to international and ethical considerations. 

Outcome:  Students use and apply marketing principles, strategic research, consumer analysis and target marketing to either a project or to case studies.

Prerequisites: MARK 460 and ISSCM 491
This course develops an understanding of the marketing research process and the role of survey research in this process.
Outcome: Students formulate research problems and a design research study, including the development of a questionnaire, selection of an appropriate sample, and data analysis.

Prerequisites: MARK 460; MARK 467 is recommended
This course develops an understanding of how advertising, sales promotion, public relations, personal selling and, in some cases, packaging decisions form a coordinated marketing communications plan.
Outcomes: Students apply the elements of integrated marketing communications and develop a coordinated marketing communications plan for a project or case study.

Prerequisite: MARK 460
This course develops an understanding of marketing problems in an international context, with particular attention given to the impact of international factors on consumers, competition, and marketing strategies. 
Outcome: Students apply the principles of marketing to solve marketing problems in an international context. Students analyze cases and identify optimal solutions to international marketing problems.

Prerequisite: MARK 460 (and recommended prior to MARK 464)
This course develops an understanding of consumer behavior before, during, and after the consumption process by exploring both the micro-level mental processes that have an impact on consumer decision-making, as well as macro-level cultural and social influences on consumer behavior. 
Outcomes: Students apply course concepts and theories to develop a consumer analysis and marketing strategies for a firm or nonprofit organization.  

Prerequisite: MARK 460
This course develops an understanding of the internet as part of an overall marketing strategy by considering the ways in which the internet has changed marketing and business. The course covers topics such as online consumer behavior, web analytics, online advertising, email, social media, mobile marketing, and search engine marketing (paid and organic). In addition to learning fundamental principles of digital channels, students will apply the learned principles in a class project such as creating a paid search campaign for a client, running a digital marketing simulation, writing a digital marketing plan, or conducting a social media audit.
Outcome: Students develop the power to act effectively by using technology in increasingly complex buying environments.

Ethics Requirement (1)

This course examines the ethical aspects of individual and corporate decision making in business and provides resources for making ethical decisions within the context of managerial practice. 
Outcome: Students will be acquainted with the concepts and principles of ethical reasoning that have been developed in ethical theory; be aware of the specific ethical issues that arise in management and of the ways in which these issues are commonly analyzed; and be able to make sound ethical and managerial decisions and implement those decisions within the context of an organization in a competitive marketplace.

Track Requirements (2)

This course uses database systems as the focus for studying concepts of data modeling and data manipulation. Procedures for creating, managing, sorting, and processing data are discussed. Concepts of relational database methods are covered as well as the issues that arise in managing information in a database and using it to support business processes.

Outcome: Understanding the development and use of business database systems.

This course develops an understanding of the development and use of databases for marketing, retrieval of appropriate data and analysis of that data to increase marketing effectiveness.

Outcome: The student will perform database manipulation and analysis of data. Analysis includes at least univariate analysis, cross-tabulation, creation of new variables, regression analysis and recency-frequency-monetary analysis.

Track Electives (Choose 3)

In this course the students will study how to use data analytics to learn about customer needs and improve targeting individual consumers. The course will encourage students to apply scientific methods and models to predict and respond to customer choices. This is the key part of learning Big Data. The term Big Data is viewed in the broad sense as it relates to various aspects of the consumer behavior, which may be captured, measured, and transformed to the digital form.

Through applications of statistical models to the analysis of the real-world databases, the students will learn how firms may use customer data to serve customers better.

Data Mining involves the search for patterns in large quantities of data. The fundamental techniques used in data mining include, but are not limited to, clustering, decision trees, neural networks, and association analysis.

Outcome: The student will be able to build models using an industry-standard package and interpret the results.

This course introduces the student to economic and business practices of a foreign country using the analysis of data, and on-site experiences. We will focus on business strategies, impediments, and challenges in light of the culture, politics, history and institutions of a selected country. We will interact with a variety of local people such as small business owners, firm managers, economists, journalists, and students, in order to inform our understanding and analysis.

Outcome: Students will gain knowledge and analytical skills that can assist them in facing the challenges of conducting business in global locations.

The amount of data that our world generates is growing at a torrid pace. Sifting through & making sense of these humongous mountains of data is crucial to ensuring business growth, success and to making scientific discoveries & advancements. Data visualization plays an important role in this process.

Outcome: Students will be able to process & visualize large amounts of data in order to enable efficient & effective analysis using industry standard software.

The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses are emphasized. Oracle tools will be used to demonstrate design, implementation, and utilization issues.

Outcome: students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses.

Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.

Outcome: To be able forecast business and economic variables to enhance business decisions.

* Upon completion of INFS 492, 494, 796 and any other two starred (*) courses, the student will earn a Business Analytics Certificate.