Efnisyfirlit

• 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

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