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Index
Cover
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
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
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
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
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
Part VI: Market Segmentation
Chapter 23: Cluster Analysis
Clustering U.S. Cities
Using Conjoint Analysis to Segment a Market
Summary
Chapter 24: Collaborative Filtering
User-Based Collaborative Filtering
Item-Based Filtering
Comparing Item- and User-Based Collaborative Filtering
The Netflix Competition
Summary
Chapter 25: Using Classification Trees for Segmentation
Introducing Decision Trees
Constructing a Decision Tree
Pruning Trees and CART
Summary
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
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
Chapter 28: Using the Copernican Principle to Predict Duration of Future Sales
Using the Copernican Principle
Simulating Remaining Life of Product
Summary
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
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
Chapter 31: Using the SCAN*PRO Model and Its Variants
Introducing the SCAN*PRO Model
Modeling Sales of Snickers Bars
Forecasting Software Sales
Summary
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
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
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
Chapter 35: Media Selection Models
A Linear Media Allocation Model
Quantity Discounts
A Monte Carlo Media Allocation Simulation
Summary
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
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
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
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
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
Chapter 41: Analysis of Variance: Two-way ANOVA
Introducing Two-way ANOVA
Two-way ANOVA without Replication
Two-way ANOVA with Replication
Summary
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
Chapter 43: The Mathematics Behind The Tipping Point
Network Contagion
A Bass Version of the Tipping Point
Summary
Chapter 44: Viral Marketing
Watts' Model
A More Complex Viral Marketing Model
Summary
Chapter 45: Text Mining
Text Mining Definitions
Giving Structure to Unstructured Text
Applying Text Mining in Real Life Scenarios
Summary
Introduction
How This Book Is Organized
Who Should Read This Book
Tools You Need
What's on the Website
Errata
Summary
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