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• Title Page
• Copyright
• Contents
• Introduction
• Part I Using Excel to Summarize Marketing Data
• Chapter 1 Slicing and Dicing Marketing Data with PivotTables
• Analyzing Sales at True Colors Hardware
• Analyzing Sales at La Petit Bakery
• Analyzing How Demographics Affect Sales
• Pulling Data from a PivotTable with the GETPIVOTDATA Function
• Summary
• Exercises
• Chapter 2 Using Excel Charts to Summarize Marketing Data
• Combination Charts
• Using a PivotChart to Summarize Market Research Surveys
• Ensuring Charts Update Automatically When New Data is Added
• Making Chart Labels Dynamic
• Summarizing Monthly Sales-Force Rankings
• Using Check Boxes to Control Data in a Chart
• Using Sparklines to Summarize Multiple Data Series
• Using GETPIVOTDATA to Create the End-of-Week Sales Report
• Summary
• Exercises
• Chapter 3 Using Excel Functions to Summarize Marketing Data
• Summarizing Data with a Histogram
• Using Statistical Functions to Summarize Marketing Data
• Summary
• Exercises
• Part II Pricing
• Chapter 4 Estimating Demand Curves and Using Solver to Optimize Price
• Estimating Linear and Power Demand Curves
• Using the Excel Solver to Optimize Price
• Pricing Using Subjectively Estimated Demand Curves
• Using SolverTable to Price Multiple Products
• Summary
• Exercises
• Chapter 5 Price Bundling
• Why Bundle?
• Using Evolutionary Solver to Find Optimal Bundle Prices
• Summary
• Exercises
• Chapter 6 Nonlinear Pricing
• Demand Curves and Willingness to Pay
• Profit Maximizing with Nonlinear Pricing Strategies
• Summary
• Exercises
• Chapter 7 Price Skimming and Sales
• Dropping Prices Over Time
• Why Have Sales?
• Summary
• Exercises
• Chapter 8 Revenue Management
• Estimating Demand for the Bates Motel and Segmenting Customers
• Handling Uncertainty
• Markdown Pricing
• Summary
• Exercises
• Part III Forecasting
• Chapter 9 Simple Linear Regression and Correlation
• Simple Linear Regression
• Using Correlations to Summarize Linear Relationships
• Summary
• Exercises
• Chapter 10 Using Multiple Regression to Forecast Sales
• Introducing Multiple Linear Regression
• Running a Regression with the Data Analysis Add-In
• Interpreting the Regression Output
• Using Qualitative Independent Variables in Regression
• Modeling Interactions and Nonlinearities
• Testing Validity of Regression Assumptions
• Multicollinearity
• Validation of a Regression
• Summary
• Exercises
• Chapter 11 Forecasting in the Presence of Special Events
• Building the Basic Model
• Summary
• Exercises
• Chapter 12 Modeling Trend and Seasonality
• Using Moving Averages to Smooth Data and Eliminate Seasonality
• An Additive Model with Trends and Seasonality
• A Multiplicative Model with Trend and Seasonality
• Summary
• Exercises
• Chapter 13 Ratio to Moving Average Forecasting Method
• Using the Ratio to Moving Average Method
• Applying the Ratio to Moving Average Method to Monthly Data
• Summary
• Exercises
• Chapter 14 Winter’s Method
• Parameter Definitions for Winter’s Method
• Initializing Winter’s Method
• Estimating the Smoothing Constants
• Forecasting Future Months
• Mean Absolute Percentage Error (MAPE)
• Summary
• Exercises
• Chapter 15 Using Neural Networks to Forecast Sales
• Regression and Neural Nets
• Using Neural Networks
• Using NeuralTools to Predict Sales
• Using NeuralTools to Forecast Airline Miles
• Summary
• Exercises
• Part IV What do Customers Want?
• Chapter 16 Conjoint Analysis
• Products, Attributes, and Levels
• Full Profile Conjoint Analysis
• Using Evolutionary Solver to Generate Product Profiles
• Developing a Conjoint Simulator
• Examining Other Forms of Conjoint Analysis
• Summary
• Exercises
• Chapter 17 Logistic Regression
• Why Logistic Regression Is Necessary
• Logistic Regression Model
• Maximum Likelihood Estimate of Logistic Regression Model
• Using StatTools to Estimate and Test Logistic Regression Hypotheses
• Performing a Logistic Regression with Count Data
• Summary
• Exercises
• Chapter 18 Discrete Choice Analysis
• Random Utility Theory
• Discrete Choice Analysis of Chocolate Preferences
• Incorporating Price and Brand Equity into Discrete Choice Analysis
• Dynamic Discrete Choice
• Independence of Irrelevant Alternatives (IIA) Assumption
• Discrete Choice and Price Elasticity
• Summary
• Exercises
• Part V Customer Value
• Chapter 19 Calculating Lifetime Customer Value
• Basic Customer Value Template
• Measuring Sensitivity Analysis with Two-way Tables
• An Explicit Formula for the Multiplier
• Varying Margins
• DIRECTV, Customer Value, and Friday Night Lights (FNL)
• Estimating the Chance a Customer Is Still Active
• Going Beyond the Basic Customer Lifetime Value Model
• Summary
• Exercises
• Chapter 20 Using Customer Value to Value a Business
• A Primer on Valuation
• Using Customer Value to Value a Business
• Measuring Sensitivity Analysis with a One-way Table
• Using Customer Value to Estimate a Firm’s Market Value
• Summary
• Exercises
• Chapter 21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making
• A Markov Chain Model of Customer Value
• Using Monte Carlo Simulation to Predict Success of a Marketing Initiative
• Summary
• Exercises
• Chapter 22 Allocating Marketing Resources between Customer Acquisition and Retention
• Modeling the Relationship between Spending and Customer Acquisition and Retention
• Basic Model for Optimizing Retention and Acquisition Spending
• An Improvement in the Basic Model
• Summary
• Exercises
• Part VI Market Segmentation
• Chapter 23 Cluster Analysis
• Clustering U.S. Cities
• Using Conjoint Analysis to Segment a Market
• Summary
• Exercises
• Chapter 24 Collaborative Filtering
• User-Based Collaborative Filtering
• Item-Based Filtering
• Comparing Item- and User-Based Collaborative Filtering
• The Netflix Competition
• Summary
• Exercises
• Chapter 25 Using Classification Trees for Segmentation
• Introducing Decision Trees
• Constructing a Decision Tree
• Pruning Trees and CART
• Summary
• Exercises
• Part VII Forecasting New Product Sales
• Chapter 26 Using S Curves to Forecast Sales of a New Product
• Examining S Curves
• Fitting the Pearl or Logistic Curve
• Fitting an S Curve with Seasonality
• Fitting the Gompertz Curve
• Pearl Curve versus Gompertz Curve
• Summary
• Exercises
• Chapter 27 The Bass Diffusion Model
• Introducing the Bass Model
• Estimating the Bass Model
• Using the Bass Model to Forecast New Product Sales
• Deflating Intentions Data
• Using the Bass Model to Simulate Sales of a New Product
• Modifications of the Bass Model
• Summary
• Exercises
• Chapter 28 Using the Copernican Principle to Predict Duration of Future Sales
• Using the Copernican Principle
• Simulating Remaining Life of Product
• Summary
• Exercises
• Part VIII Retailing
• Chapter 29 Market Basket Analysis and Lift
• Computing Lift for Two Products
• Computing Three-Way Lifts
• A Data Mining Legend Debunked!
• Using Lift to Optimize Store Layout
• Summary
• Exercises
• Chapter 30 RFM Analysis and Optimizing Direct Mail Campaigns
• RFM Analysis
• An RFM Success Story
• Using the Evolutionary Solver to Optimize a Direct Mail Campaign
• Summary
• Exercises
• Chapter 31 Using the SCAN*PRO Model and Its Variants
• Introducing the SCAN*PRO Model
• Modeling Sales of Snickers Bars
• Forecasting Software Sales
• Summary
• Exercises
• Chapter 32 Allocating Retail Space and Sales Resources
• Identifying the Sales to Marketing Effort Relationship
• Modeling the Marketing Response to Sales Force Effort
• Optimizing Allocation of Sales Effort
• Using the Gompertz Curve to Allocate Supermarket Shelf Space
• Summary
• Exercises
• Chapter 33 Forecasting Sales from Few Data Points
• Predicting Movie Revenues
• Modifying the Model to Improve Forecast Accuracy
• Using 3 Weeks of Revenue to Forecast Movie Revenues
• Summary
• Exercises
• Part IX Advertising
• Chapter 34 Measuring the Effectiveness of Advertising
• The Adstock Model
• Another Model for Estimating Ad Effectiveness
• Optimizing Advertising: Pulsing versus Continuous Spending
• Summary
• Exercises
• Chapter 35 Media Selection Models
• A Linear Media Allocation Model
• Quantity Discounts
• A Monte Carlo Media Allocation Simulation
• Summary
• Exercises
• Chapter 36 Pay per Click (PPC) Online Advertising
• Defining Pay per Click Advertising
• Profitability Model for PPC Advertising
• Google AdWords Auction
• Using Bid Simulator to Optimize Your Bid
• Summary
• Exercises
• Part X Marketing Research Tools
• Chapter 37 Principal Components Analysis (PCA)
• Defining PCA
• Linear Combinations, Variances, and Covariances
• Diving into Principal Components Analysis
• Other Applications of PCA
• Summary
• Exercises
• Chapter 38 Multidimensional Scaling (MDS)
• Similarity Data
• MDS Analysis of U.S. City Distances
• MDS Analysis of Breakfast Foods
• Finding a Consumer’s Ideal Point
• Summary
• Exercises
• Chapter 39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis
• Conditional Probability
• Bayes’ Theorem
• Naive Bayes Classifier
• Linear Discriminant Analysis
• Model Validation
• The Surprising Virtues of Naive Bayes
• Summary
• Exercises
• Chapter 40 Analysis of Variance: One-way ANOVA
• Testing Whether Group Means Are Different
• Example of One-way ANOVA
• The Role of Variance in ANOVA
• Forecasting with One-way ANOVA
• Contrasts
• Summary
• Exercises
• Chapter 41 Analysis of Variance: Two-way ANOVA
• Introducing Two-way ANOVA
• Two-way ANOVA without Replication
• Two-way ANOVA with Replication
• Summary
• Exercises
• Part XI Internet and Social Marketing
• Chapter 42 Networks
• Measuring the Importance of a Node
• Measuring the Importance of a Link
• Summarizing Network Structure
• Random and Regular Networks
• The Rich Get Richer
• Klout Score
• Summary
• Exercises
• Chapter 43 The Mathematics Behind The Tipping Point
• Network Contagion
• A Bass Version of the Tipping Point
• Summary
• Exercises
• Chapter 44 Viral Marketing
• Watts’ Model
• A More Complex Viral Marketing Model
• Summary
• Exercises
• Chapter 45 Text Mining
• Text Mining Definitions
• Giving Structure to Unstructured Text
• Applying Text Mining in Real Life Scenarios
• Summary
• Exercises
• Index