Business Managers of Advertising, Marketing, Retail, Banking, and Insurance Industries together with Professionals from the Data Science and Engineering disciplines can profit from content that employs a practical approach to the solution of a wide array of current and emerging business problems.
Customer Analytics and Insight
Customer Profiling: Demographic, Geographic, Income And other industry-specific attributes
Segmentation: Cluster Analysis Techniques and their application to Customer Classification
Psychographic Profiling: Personality Profiling models and Techniques
Analytics And Customer Insight: Holistic view of Customer, Organizational And Market data
Business Modeling
Customer Life Time Value: Time Value of Money concepts And Discounted Cash Flow models
Index Creation: Customer Rating And Grading techniques for Decision Support Systems
Credit Risk Modeling: Principals of Risk Analysis and guidelines for model creation
Product Life Cycle Modeling: Growth and Decay curves, model estimation and fitting
Customer Loyalty and Rewards Schemes: Analytical approaches to managing Customer base
Statistics and Data Analysis for Analytics (Computer Lab Practical using Excel)
Descriptive Statistics: Probability Concepts, Measures of Centrality and Dispersion, Correlations
Distributions from Data: Derive Histograms, Ogives, Empirical Cumulative Distribution Functions
Goodness of fit: Chi-Square Test
Time Series Analysis: Trend, Seasonality, Cyclical Variation and Decompositions
Forecasting: Single/Multiple Regressions and Exponential smoothing
Social Network Analysis and Marketing
Introduction to Network Theory: Actors, Links, Random Walks, and Influence Diffusion
Graph Theory Applications: Social Graphs, Bipartite Graphs And their Programs
Measures of Centrality: Leaders, Followers, Gatekeepers, Communities
Quantification: Estimate values for Actors and Network attributes needed for Target Marketing
Target Marketing: Exploiting SNA for Product Placement And Advertisement Diffusion
