Data, Analytics and AI for Marketing Strategy

The lectures on “Data, Analytics and AI for Marketing Strategy” 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