Data-driven marketing decision-making refers to measuring, managing, and analyzing marketing performance data to facilitate market planning, improve marketing effectiveness, and optimize return on investment (ROI). It encompasses a range of methodologies, technologies, and tools that enable organizations to generate actionable insights from historical and real-time data. By leveraging marketing analytics, firms can make data-driven decisions to refine their marketing strategies, allocate resources more efficiently, and achieve competitive advantages.

Marketing analytics is an invaluable tool for international businesses, offering many applications that inform and enhance decision-making processes. From market entry strategies to customer engagement, from optimizing the marketing mix to risk management, analytics provides the empirical basis for developing and refining effective international marketing strategies. Given international markets' complex and dynamic nature, a sophisticated approach to marketing analytics is beneficial and essential for success.

This course covers the core issues companies face when entering international markets and how data-driven decision-making can help resolve these issues.

Typical questions which will be answered in this module include (but are not limited to):

• How can I segment the 'right' customers?
• Are all my customers profitable?
• How do I set the price in foreign markets? How can I measure the willingness to pay?
• How can I measure brand value or brand equity in foreign markets?
• How can I develop an effective promotional campaign?

R will be used throughout the course. The R programming language is a powerful tool for conducting marketing analytics, offering a robust platform for data manipulation, statistical analysis, visualization, and machine learning. With a wide array of packages and community support, R is well-suited for tackling the complex challenges of marketing analytics in domestic and international contexts. No prior knowledge of R is required.

By completing this module, students should be able to

1. Understand Marketing Analytics Fundamentals: Gain a foundational understanding of marketing analytics concepts, tools, and methodologies, specifically in an international context.

2. Comprehend Data Types and Sources: Understand various types of data relevant to international marketing, such as customer data, transactional data, social media metrics

3. Master Data Import and Export: Learn to import and export data using R from various sources like spreadsheets, databases, and APIs.

4. Develop Data Cleaning Skills: Acquire skills in data cleaning, transformation, and preparation using R packages like dplyr and tidyr.

5. Perform Exploratory Data Analysis (EDA): Become proficient in conducting EDA to summarize and visualize data, using packages such as ggplot2.

6. Conduct Customer Segmentation: Learn to apply clustering algorithms and RFM analysis to segment international customers.

7. Execute Predictive Modeling: Gain expertise in implementing predictive models like logistic regression, decision trees, and machine learning algorithms for applications like churn prediction and sales forecasting.

8. Optimize Marketing Mix: Acquire the ability to use regression analysis and attribution modeling to evaluate and optimize the marketing mix in international markets.

9. Develop Hypothesis Testing Capabilities: Learn to conduct statistical hypothesis testing, including A/B testing, to validate marketing strategies.

10. Enhance Communication Skills: Improve the ability to communicate analytical findings effectively to both technical and non-technical audiences through presentations and reports.

11. Cultivate Teamwork: Gain experience in collaborative work, especially in interpreting data and making collective data-driven decisions.

12. Foster Critical Thinking: Develop the ability to critically evaluate data sources, analytical methods, and findings, particularly in the context of international marketing challenges.

This course will be taught as a blended module. Hence, students may miss only one session on campus. In addition to the lab sessions, online coaching sessions will be offered. 

A multi-method approach is used to achieve the learning outcomes. Specifically, the following teaching methods will be used in this course:

•    Pre-readings
•    Online Lectures or Online Expert Talks
•    Class Participation
•    Individual Assignment
•    Group Assignment
•    Case Studies
•    Online Quizzes

Grades are distributed as follows:

• Coding Exercises (Individual) - 5 x 5 % = 25 %
• Online Quizzes Statistics (Individual) - 5 x 5 % = 25 %
• Case Study: Written Report (Group)  - 40 %
• Peer Review - 10 %

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arne.floh@wu.ac.at