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Marketing Analytics

MKT 511

Lecturer Prof. Dr. Florian Stahl
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Course Format Lecture and Exercise
Credit Points 6 ECTS
Hours per Week 4
Semester Spring
Language English
Registration no registration required
Accepted Participants Mannheim Master in Management, Mannheim Master in Business Research (MMBR), M.A. Culture and Economy / Business, M.Sc. Business Education, M.Sc. Business Informatics, M.Sc. Business Mathematics, M.Sc. Economics, Diplom Business Administration

    Further Information

  • Brief Description

    Companies are currently spending millions of dollars on data-gathering initiatives, but few are successfully capitalizing on all this data to generate revenue and increase profit. Converting data into increased business performance requires the ability to extract insights from data through analytics.

    Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI) of marketing efforts. With a profound understanding of marketing analytics, marketers will be more efficient and will improve the performance of their marketing actions and minimize wasted marketing dollars.

    This course covers the three pillars of analytics – descriptive, predictive and prescriptive – within the marketing context. Students will be exposed to several methods such as linear regression, logistic regression, multinomial regression and machine learning methods (e.g., neural networks and Support Vector Machines). We will learn how to employ these methods for managerial decisions such as demand forecasting, pricing, and valuing customers.

    Overall, students will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable insights.

  • Course Outline

    The lectures on “Marketing Analytics” cover the following topics:

    Introduction in Marketing and Marketing Analytics

    • Difference between Normative/Prescriptive and Descriptive/Predictive 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 Network
      • 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
    • Advertising Analytics
      • Measuring Advertising Effectiveness
      • Data-Driven Media Selection
      • Attribution Models 
    • Attribution Modeling in Digital Marketing
      • Last & first touch and click
      • Holdout Testing
  • Lecture

    Lecturer Prof. Dr. Florian Stahl
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    Schedule
    Assessment Written Exam (100%)
  • Exercise

    Lecturer  
    Schedule