MKT 511: Marketing Analytics
Contents
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
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Recommended prerequisites
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| Forms of teaching and learning | Contact hours | Independent study time |
|---|---|---|
| Lecture | 2 SWS | 7 SWS |
| Exercise class | 2 SWS | 6 SWS |
| ECTS credits | 6 |
| Graded | yes |
| Workload | 180h |
| Language | English |
| Form of assessment | Written exam (90 min) |
| Restricted admission | no |
| Further information | – |
Examiner Performing lecturer |  | Prof. Dr. Florian Stahl Prof. Dr. Florian Stahl |
| Frequency of offering | Spring semester |
| Duration of module | 1 semester |
| Range of application | M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., LL.M., MAKUWI, MMDS |
| Preliminary course work | – |
| Program-specific Competency Goals | CG 1, CG 4 |
| Literature | Chapman, Christopher N., McDonnell Feit, Elea (2015): R for Marketing Research and Analytics. The book is available for free from the URL: https://link.springer.com/book/10.1007/978-3-319-14436-8 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 outline | Introduction 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 |