Marketing Analytics: Data-Driven Techniques with Microsoft Excel (2014)
Part I. Using Excel to Summarize Marketing Data
Chapter 1. Slicing and Dicing Marketing Data with PivotTables
Chapter 2. Using Excel Charts to Summarize Marketing Data
Chapter 3. Using Excel Functions to Summarize Marketing Data
Part II. Pricing
Chapter 4. Estimating Demand Curves and Using Solver to Optimize Price
Chapter 7. Price Skimming and Sales
Part III. Forecasting
Chapter 9. Simple Linear Regression and Correlation
Chapter 10. Using Multiple Regression to Forecast Sales
Chapter 11. Forecasting in the Presence of Special Events
Chapter 12. Modeling Trend and Seasonality
Chapter 13. Ratio to Moving Average Forecasting Method
Chapter 15. Using Neural Networks to Forecast Sales
Part IV. What do Customers Want?
Chapter 17. Logistic Regression
Chapter 18. Discrete Choice Analysis
Part V. Customer Value
Chapter 19. Calculating Lifetime Customer Value
Chapter 20. Using Customer Value to Value a Business
Chapter 21. Customer Value, Monte Carlo Simulation, and Marketing Decision Making
Chapter 22. Allocating Marketing Resources between Customer Acquisition and Retention
Part VI. Market Segmentation
Chapter 24. Collaborative Filtering
Chapter 25. Using Classification Trees for Segmentation
Part VII. Forecasting New Product Sales
Chapter 26. Using S Curves to Forecast Sales of a New Product
Chapter 27. The Bass Diffusion Model
Chapter 28. Using the Copernican Principle to Predict Duration of Future Sales
Part VIII. Retailing
Chapter 29. Market Basket Analysis and Lift
Chapter 30. RFM Analysis and Optimizing Direct Mail Campaigns
Chapter 31. Using the SCAN*PRO Model and Its Variants
Chapter 32. Allocating Retail Space and Sales Resources/a>
Chapter 33. Forecasting Sales from Few Data Points
Part IX. Advertising
Chapter 34. Measuring the Effectiveness of Advertising
Chapter 35. Media Selection Models
Chapter 36. Pay per Click (PPC) Online Advertising
Part X. Marketing Research Tools
Chapter 37. Principal Components Analysis (PCA)
Chapter 38. Multidimensional Scaling (MDS)
Chapter 39. Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis
Chapter 40. Analysis of Variance: One-way ANOVA
Chapter 41. Analysis of Variance: Two-way ANOVA
Part XI. Internet and Social Marketing
Chapter 43. The Mathematics Behind The Tipping Point