MKT 511: Marketing Analytics

Due to the digitalization of consumers' life as well as corporate functions and processes, companies today have vast amounts of different types of data, not only on an aggregated level but increasingly also on an individual level. But the amount of available data and information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. This course builds on the theory and foundations of marketing analytics and focuses on practical application by demystifying the use of data in marketing.

Learning outcomes
This course gives you the tools to measure brand and customer assets, understand various analytical approaches from statistics to machine learning as a way to evaluate and optimize marketing actions and campaigns. You'll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources.

Necessary prerequisites

Recommended prerequisites

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS7 SWS
Exercise class2 SWS6 SWS
ECTS credits6
Graded yes
Form of assessmentWritten exam (90 min)
Restricted admissionno
Further information
Performing lecturer
Prof. Dr. Florian Stahl
Prof. Dr. Florian Stahl
Frequency of offeringSpring semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., LL.M., MAKUWI, MMDS
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4
LiteratureChapman, Christopher N., McDonnell Feit, Elea (2015): R for Marketing Research and Analytics. The book is available for free from the URL:
Grigsby, Mike (2018) Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques
Katsov, Ilya (2018) Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations
Course outlineIntroduction in Marketing and Marketing Analytics
Consumer and Customer Analytics: Analyzing and Predicting Individual-level Preferences and Brand Choice
Binary Brand and Product Choice
Multinomial Brand and Product Choice
Markov Models
Analyzing and Modeling Purchase Quantity and Timing
Market Analytics: Analyzing and Predicting Aggregated Demand and Competition
Product Sales
Market Basket Analysis
Forecasting New Product Sales
S-Curves (New Product Sales Over Time)
Neural Networks
Considering Trends and Seasonality
Brand Sales and Market Share
Market and Customer Segmentation
RFM Models
Classification Trees
Latent Class Analysis
Collaborative Filtering
Marketing Management: Increasing Efficiency of Marketing and Competitive Advantage through Analytics Customer Management
Customer Relationship Management (CRM) Analytics
Customer Journey Analytics
Brand Management
Measuring Brand Perception Using Big Data
Brand Audit through Social Listening
Marking Strategy: Increasing Efficiency of Marketing Instruments Pricing Analytics
Dynamic Pricing
Multi-Channel Pricing