• Understand core concepts of marketing analytics, including how data-driven analysis can inform decisions in key marketing domains.
• Apply quantitative and qualitative methods to marketing problems: use appropriate analytical techniques to analyze marketing data and generate insights.
• Design and conduct marketing analytics projects: formulate a marketing question, select suitable analytical tools, and utilize software to analyze data in order to answer that question.
• Critically evaluate analytical results and assumptions when making marketing recommendations.
• Communicate insights effectively: present the findings of marketing analytics in a clear, professional manner by translating data analysis results into actionable recommendations.

Marketing decisions are increasingly data-driven. This course introduces students to advanced Marketing Analytics, which links classic marketing concepts to analytical methods. Students will learn how marketing strategy and the marketing mix (promotion, product, price, and place) can be supported by quantitative and qualitative analyses, and how insights can be generated from data to guide managerial decision-making.

The course begins with an introduction to marketing strategy. Students revisit the concepts of segmentation, targeting, and positioning (STP) and learn how to identify customer groups using data. Techniques such as cluster analysis and related methods are applied to uncover distinct market segments. Students will practice how to evaluate and interpret these segments, and how segmentation can form the basis for effective targeting and positioning.

The next part of the course follows the structure of the marketing mix (the 4Ps) and demonstrates how analytics can be applied in each area:

• Promotion: Students examine how the effectiveness of marketing communications and campaigns can be measured. Key methods include A/B testing to assess campaign variations. The module also introduces text and sentiment analysis of customer reviews, social media, and other unstructured data formats, allowing firms to track how promotions influence customer attitudes.
• Product: Students are introduced to conjoint analysis, a method widely used in practice to measure customer preferences for product features. They learn how to design and analyze conjoint studies, and how results can guide product design, feature prioritization, and innovation decisions.
• Price: The course covers different pricing strategies and focuses on price elasticity of demand as a key concept. Students learn how to estimate price elasticities from sales data, use different regression models to derive demand curves, and apply these insights to practical pricing problems.
• Place (Distribution): Distribution will be analyzed in terms of customer journeys across multiple touchpoints. Students learn how to evaluate performance across physical stores, e-commerce, and digital channels. The central method here is attribution modeling, which assigns credit for conversions to different touchpoints along the customer journey.

The course incorporates emerging methods, including the use of Generative AI for marketing analytics. Students will learn how AI tools can support and administer the analyses, while also reflecting on their limitations and ethical challenges.

• Classic and basic theory
• Teacher’s own research
• Methodology
• Models

• Data collection
• Analysis
• Discussion, critical reflection, modelling
• Activities that contribute to new or existing research projects
• Students conduct independent research-like activities under supervision

Chapman, Chris, and Elea McDonnell Feit. R for marketing research and analytics. Vol. 67. New York, NY: Springer, 2015.

Additional literature will be shared via Canvas before the course starts.