Course 2025-2026 a.y.

This course is designed around a problem-solving approach.

From the outset, students will be assigned to teams and tasked with analyzing and evaluating marketing strategy performance. Working collaboratively, each team will develop a marketing strategy supported by data analysis, with the goal of making informed business decisions. This structure allows students to apply the analytical tools and theoretical frameworks discussed in class to a real-world business problem. At the end of the course, each team will present its proposed solution.

To build the technical and analytical skills required for the course, the first sessions will review key statistical methods, including hypothesis testing and linear and non-linear regression models (i.e. logit). These techniques will be implemented using Python, which will be the main software tool for data analysis throughout the course. Students will then apply these methods to address key questions related to the design and assessment of marketing strategies.

The teamwork component is designed to reinforce the analytical and technical skills developed throughout the course. Each class session follows a structure that:

• Introduces a theoretical or analytical concept (e.g., strategies for customer acquisition)

• Engages students in a practical application of the concept (e.g., analyzing acquisition performance using real company or industry data)

• Encourages students to apply the acquired knowledge within their group project, in a new business context (e.g., analyzing customer acquisition for a potential partner company)

This structure provides students with the opportunity to integrate theory with hands-on experience through team-based learning.

Part I – Foundations and Analytical Tools

Topics covered in the first part of the course include:

• Review of statistical concepts and introduction to the software used for data analysis (i.e.  Python)

• Mapping and analyzing the customer journey and its phases

• Initial phase: customer acquisition, lean prospecting, and privacy-related challenges

• Use of field experiments to evaluate marketing strategy performance

• Managing customer heterogeneity in the evaluation of marketing effectiveness

• Development phase: customer development and retention

The course includes multiple lab sessions, where students work with real datasets and business cases to practice analytical techniques and strengthen their understanding of key concepts.

Part II – Marketing Strategy Simulation and Presentation

The second part of the course simulates the typical internal process of presenting and discussing marketing projects within a firm. Each group will participate in a role-playing exercise, alternating roles (e.g., acting as product or brand managers) to present and defend their marketing strategy in front of their peers and instructors.

Key Topics Covered

• The value of data and the design of data-driven marketing strategies

• Customer journey mapping

• Data-driven approaches to customer acquisition, development, and profiling

• Lead generation, prospecting, and privacy management

• Customer development and retention strategies

• Field testing and methods to analyze and manage customer heterogeneity in marketing response

• Evaluation of marketing performance and discussion of alternative strategies

Learning Outcomes

By the end of the course, students will have developed a solid understanding of the core principles of strategic marketing analysis. Specifically, they will acquire the following skills:

• Customer Journey Mapping: Students will be able to describe and illustrate the process of mapping the customer journey, including its key stages and customer-firm interactions.

• Use of Data Analysis Tools: Students will gain familiarity with key techniques for analyzing individual-level secondary data. They will be able to use analytical tools to describe, analyze, and predict behaviors relevant to marketing decisions.

• Data-Driven Strategy and Decision-Making: Students will learn how to design integrated marketing strategies grounded in data analysis, and how to translate insights into concrete, evidence-based decisions. Emphasis will be placed on the use of secondary data and field experiments to inform both strategic planning and tactical choices.

• Privacy, Fairness, and Data Responsibility: Students will develop a critical understanding of the factors that influence consumers’ willingness to disclose personal information, and explore the trade-offs between personalization and privacy. They will examine ethical and regulatory challenges related to data collection, including issues of algorithmic fairness and compliance with data protection frameworks (e.g., GDPR), and learn how to design marketing strategies that are both effective and responsible.

• Understanding Customer Heterogeneity: Students will acquire a deep understanding of the sources of customer heterogeneity and the statistical techniques used to analyze it.

In summary, by the end of the course students will be able to apply the acquired skills to analyze and interpret the customer journey, use data to inform marketing decisions, and manage customer privacy responsibly and ethically.

By the end of the course, students are expected to be able to:

• Map the customer journey by distinguishing between the different stages of the decision-making process (information search, purchase, and post-purchase) and the various available channels and touchpoints (e.g., physical stores, digital channels, web, mobile, social media, etc.).
• Use statistical tools to describe, analyze, and predict relevant user/customer choices.
• Develop data-driven marketing strategies, considering data analysis as a core input for decision-making.
• Make informed decisions supported by data analysis.
• Manage customer data with a focus on the acquisition phase, ensuring appropriate handling of data protection aspects.
• Understand customer heterogeneity and apply statistical techniques to analyze it.
• Work effectively in teams, collaborating and leveraging the analytical skills and techniques acquired during the course—particularly in analyzing new datasets and developing data-driven marketing strategies.
• Communicate clearly and effectively, presenting marketing analyses and solutions in a persuasive manner.

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments
• Interaction/Gamification

Teaching Methods and Learning Activities

• Guest Lectures by Industry Professionals: Company representatives will be invited to deliver in-class testimonials. These sessions are designed to highlight the connection between the theoretical frameworks and analytical tools covered in the course and their application in real-world managerial contexts.

• Hands-On Exercises and Case Studies: Students will work on exercises and case discussions based on real business problems and datasets. These activities provide opportunities to apply the data analysis techniques introduced in the course to concrete marketing challenges.

• Team Project – Marketing Strategy Evaluation: The core teamwork activity consists in solving a marketing problem, specifically evaluating the performance of one or more marketing strategies. The goal is to understand the factors behind the success or failure of a campaign and to identify the most effective strategy while accounting for customer heterogeneity. This project also helps students develop teamwork, time management, and project planning skills.

• Role-Playing Presentations: Each group will present its marketing plan and critically assess the marketing plans presented by peers working on different product categories. This activity is designed to strengthen students' effective presentation skills and their ability to provide constructive feedback and critical analysis of others’ work.

Grades are assessed as follows:
1. Individual exam: 50% of the overall grade;
2. Group project: 50% of the overall grade.

Final exam: A written test aimed at verifying:

• Knowledge of the models and analysis tools covered during the course.
• The ability to use these models and tools to address actual business problems.
• The ability to interpret outputs and metrics related to the data analysis techniques addressed during the course.
• The ability to develop a marketing strategy and evaluate its potential performance.

Group assignments: Students, divided into groups, must develop a marketing strategy for a product/service/business problem chosen by the instructors. To develop the strategy, they must analyze data and support their decisions with the conducted analyses. Additionally, the groups are required to act as discussants for the plans presented by their peers. The group assignment grade is valid for the three sessions of the academic year.

• Final exam: 100%

   

  Final exam: A written test aimed at verifying:

• Knowledge of the models and analysis tools covered during the course.
• The ability to use these models and tools to address actual business problems.
• The ability to interpret outputs and metrics related to the data analysis techniques covered during the course.
• The ability to analyze and interpret data to make decisions relevant to the development of a marketing plan.
• The ability to critically analyze a marketing case and suggest appropriate and well-motivated strategies.

Required Materials:

• Lecture slides

• Cases and exercises discussed in class

Required Readings:

• Lemon, Katherine N., and Peter C. Verhoef. "Understanding customer experience throughout the customer journey." Journal of Marketing, 80(6), 2016: 69–96.

• Krafft, Manfred, et al. "Insight is power: Understanding the terms of the consumer-firm data exchange." Journal of Retailing, 97(1), 2021: 133–149.

• Artea: Designing Targeting Strategies, Eva Ascarza, Ayelet Israeli, Harvard Business Review Case, September 25, 2020.

Suggested Readings (Optional for Further Exploration – Not Required for Class Attendance or Exam):

• Schwarz, Jason S., Chris Chapman, and Elea McDonnell Feit. Python for Marketing Research and Analytics. Springer Nature, 2020.

• Othellonia: Growing a Mobile Game, Ascarza et al., HBR, 2022.

• Ascarza, Eva. "Retention futility: Targeting high-risk customers might be ineffective." Journal of Marketing Research, 55(1), 2018: 80–98.

• Krafft, Manfred, et al. "Insight is power: Understanding the terms of the consumer-firm data exchange." Journal of Retailing, 97(1), 2021: 133–149.

• Blattberg, Robert C., Byung-Do Kim, and Scott A. Neslin. Database Marketing: Analyzing and Managing Customers. Springer, New York, NY, 2008. Chapters 20, 21, and 24.

• Rosenbaum, M. S., Otalora, M. L., & Ramírez, G. C. "How to create a realistic customer journey map." Business Horizons, 60(1), 2017: 143–150.

• Wedel, Michel, and P. K. Kannan. "Marketing analytics for data-rich environments." Journal of Marketing, 80(6), 2016: 97–121.

• Bradlow, Eric T., et al. "The role of big data and predictive analytics in retailing." Journal of Retailing, 93(1), 2017: 79–95.

• Verhoef, Peter, Edwin Kooge, and Natasha Walk. Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge, 2016. Chapters 1 & 2.

• Ascarza, Eva, Peter S. Fader, and Bruce G.S. Hardie. "Marketing models for the customer-centric firm." In Handbook of Marketing Decision Models. Springer, Cham, 2017: 297–329.

• Palmatier, Robert W., and Shrihari Sridhar. Marketing Strategy: Based on First Principles and Data Analytics. Macmillan International Higher Education, 2017. Chapter 2.

Required Materials:

• Lecture slides
• Cases and exercises uploaded on Blackboard 

• Lemon, Katherine N., and Peter C. Verhoef. “Understanding Customer Experience throughout the Customer Journey.” Journal of Marketing, 80(6), 2016: 69–96.

• Krafft, Manfred, et al. “Insight is Power: Understanding the Terms of the Consumer-Firm Data Exchange.” Journal of Retailing, 97(1), 2021: 133–149.

• Artea: Designing Targeting Strategies, Eva Ascarza and Ayelet Israeli, Harvard Business Review Case, September 25, 2020.

• Knott, Aaron, Andrew Hayes, and Scott A. Neslin. “Next-Product-to-Buy Models for Cross-Selling Applications.” Journal of Interactive Marketing, 16(3), 2002: 59–75.

• Blattberg, Robert C., Byung-Do Kim, and Scott A. Neslin. Database Marketing: Analyzing and Managing Customers. Springer, New York, NY, 2008. Chapters 20, 21, and 24.

• Othellonia: Growing a Mobile Game, Ascarza et al., Harvard Business Review, 2022.

Suggested Readings (Optional for Further Exploration – Not Required for Class Attendance or Exam):

• Schwarz, Jason S., Chris Chapman, and Elea McDonnell Feit. Python for Marketing Research and Analytics. Springer Nature, 2020.

• Ascarza, Eva. “Retention Futility: Targeting High-Risk Customers Might Be Ineffective.” Journal of Marketing Research, 55(1), 2018: 80–98.

• Rosenbaum, M. S., Otalora, M. L., & Ramírez, G. C. “How to Create a Realistic Customer Journey Map.” Business Horizons, 60(1), 2017: 143–150.

• Wedel, Michel, and P. K. Kannan. “Marketing Analytics for Data-Rich Environments.” Journal of Marketing, 80(6), 2016: 97–121.

• Bradlow, Eric T., et al. “The Role of Big Data and Predictive Analytics in Retailing.” Journal of Retailing, 93(1), 2017: 79–95.

• Verhoef, Peter, Edwin Kooge, and Natasha Walk. Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge, 2016. Chapters 1 & 2.

• Ascarza, Eva, Peter S. Fader, and Bruce G. S. Hardie. “Marketing Models for the Customer-Centric Firm.” In Handbook of Marketing Decision Models. Springer, Cham, 2017: 297–329.

• Palmatier, Robert W., and Shrihari Sridhar. Marketing Strategy: Based on First Principles and Data Analytics. Macmillan International Higher Education, 2017. Chapter 2.