Wayne L. Winston is John and Esther Reese chaired Professor of Decision Sciences at the Indiana University Kelley School of Business and will be a Visiting Professor at the Bauer College of Business at the University of Houston. He has won more than 45 teaching awards at Indiana University. He has also written numerous journal articles and a dozen books, and has developed two online courses for Harvard Business School.

Introduction xxiii I Using Excel to Summarize Marketing Data 1 1 Slicing and Dicing Marketing Data with PivotTables 3 Analyzing Sales at True Colors Hardware 3 Analyzing Sales at La Petit Bakery 14 Analyzing How Demographics Affect Sales 21 Pulling Data from a PivotTable with the GETPIVOTDATA Function 25 Summary 27 Exercises 27 2 Using Excel Charts to Summarize Marketing Data 29 Combination Charts 29 Using a PivotChart to Summarize Market Research Surveys 36 Ensuring Charts Update Automatically When New Data is Added 39 Making Chart Labels Dynamic 40 Summarizing Monthly Sales-Force Rankings 43 Using Check Boxes to Control Data in a Chart 45 Using Sparklines to Summarize Multiple Data Series 48 Using GETPIVOTDATA to Create the End-of-Week Sales Report 52 Summary 55 Exercises 55 3 Using Excel Functions to Summarize Marketing Data 59 Summarizing Data with a Histogram 59 Using Statistical Functions to Summarize Marketing Data 64 Summary 79 Exercises 80 II Pricing 83 4 Estimating Demand Curves and Using Solver to Optimize Price 85 Estimating Linear and Power Demand Curves 85 Using the Excel Solver to Optimize Price 90 Pricing Using Subjectively Estimated Demand Curves 96 Using SolverTable to Price Multiple Products 99 Summary 103 Exercises 104 5 Price Bundling 107 Why Bundle? 107 Using Evolutionary Solver to Find Optimal Bundle Prices 111 Summary 119 Exercises 119 6 Nonlinear Pricing 123 Demand Curves and Willingness to Pay 124 Profit Maximizing with Nonlinear Pricing Strategies 125 Summary 131 Exercises 132 7 Price Skimming and Sales 135 Dropping Prices Over Time 135 Why Have Sales? 138 Summary 142 Exercises 142 8 Revenue Management 143 Estimating Demand for the Bates Motel and Segmenting Customers 144 Handling Uncertainty 150 Markdown Pricing 153 Summary 156 Exercises 156 III Forecasting 159 9 Simple Linear Regression and Correlation 161 Simple Linear Regression 161 Using Correlations to Summarize Linear Relationships 170 Summary 174 Exercises 175 10 Using Multiple Regression to Forecast Sales 177 Introducing Multiple Linear Regression 178 Running a Regression with the Data Analysis Add-In 179 Interpreting the Regression Output 182 Using Qualitative Independent Variables in Regression 186 Modeling Interactions and Nonlinearities 192 Testing Validity of Regression Assumptions 195 Multicollinearity 204 Validation of a Regression 207 Summary 209 Exercises 210 11 Forecasting in the Presence of Special Events 213 Building the Basic Model 213 Summary 222 Exercises 222 12 Modeling Trend and Seasonality 225 Using Moving Averages to Smooth Data and Eliminate Seasonality 225 An Additive Model with Trends and Seasonality 228 A Multiplicative Model with Trend and Seasonality 231 Summary 234 Exercises 234 13 Ratio to Moving Average Forecasting Method 235 Using the Ratio to Moving Average Method 235 Applying the Ratio to Moving Average Method to Monthly Data 238 Summary 238 Exercises 239 14 Winter's Method 241 Parameter Definitions for Winter's Method 241 Initializing Winter's Method 243 Estimating the Smoothing Constants 244 Forecasting Future Months 246 Mean Absolute Percentage Error (MAPE) 247 Summary 248 Exercises 248 15 Using Neural Networks to Forecast Sales 249 Regression and Neural Nets 249 Using Neural Networks 250 Using NeuralTools to Predict Sales 253 Using NeuralTools to Forecast Airline Miles 258 Summary 259 Exercises 259 IV What do Customers Want? 261 16 Conjoint Analysis 263 Products, Attributes, and Levels 263 Full Profile Conjoint Analysis 265 Using Evolutionary Solver to Generate Product Profiles 272 Developing a Conjoint Simulator 277 Examining Other Forms of Conjoint Analysis 279 Summary 281 Exercises 281 17 Logistic Regression 285 Why Logistic Regression Is Necessary 286 Logistic Regression Model 289 Maximum Likelihood Estimate of Logistic Regression Model 290 Using StatTools to Estimate and Test Logistic Regression Hypotheses 293 Performing a Logistic Regression with Count Data 298 Summary 300 Exercises 300 18 Discrete Choice Analysis 303 Random Utility Theory 303 Discrete Choice Analysis of Chocolate Preferences 305 Incorporating Price and Brand Equity into Discrete Choice Analysis 309 Dynamic Discrete Choice 315 Independence of Irrelevant Alternatives (IIA) Assumption 316 Discrete Choice and Price Elasticity 317 Summary 318 Exercises 319 V Customer Value 325 19 Calculating Lifetime Customer Value 327 Basic Customer Value Template 328 Measuring Sensitivity Analysis with Two-way Tables 330 An Explicit Formula for the Multiplier r 331 Varying Margins 331 DIRECTV, Customer Value, and Friday Night Lights (FNL)333 Estimating the Chance a Customer Is Still Active 334 Going Beyond the Basic Customer Lifetime Value Model 335 Summary 336 Exercises 336 20 Using Customer Value to Value a Business 339 A Primer on Valuation 339 Using Customer Value to Value a Business 340 Measuring Sensitivity Analysis with a One-way Table 343 Using Customer Value to Estimate a Firm's Market Value 344 Summary 344 Exercises 345 21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making 347 A Markov Chain Model of Customer Value 347 Using Monte Carlo Simulation to Predict Success of a Marketing Initiative 353 Summary 359 Exercises 360 22 Allocating Marketing Resources between Customer Acquisition and Retention 347 Modeling the Relationship between Spending and Customer Acquisition and Retention 365 Basic Model for Optimizing Retention and Acquisition Spending 368 An Improvement in the Basic Model 371 Summary 373 Exercises 374 VI Market Segmentation 375 23 Cluster Analysis 377 Clustering U.S. Cities 378 Using Conjoint Analysis to Segment a Market 386 Summary 391 Exercises 391 24 Collaborative Filtering 393 User-Based Collaborative Filtering 393 Item-Based Filtering 398 Comparing Item- and User-Based Collaborative Filtering 400 The Netflix Competition 401 Summary 401 Exercises 402 25 Using Classification Trees for Segmentation 403 Introducing Decision Trees 403 Constructing a Decision Tree 404 Pruning Trees and CART 409 Summary 410 Exercises 410 VII Forecasting New Product Sales 413 26 Using S Curves to Forecast Sales of a New Product 415 Examining S Curves 415 Fitting the Pearl or Logistic Curve418 Fitting an S Curve with Seasonality 420 Fitting the Gompertz Curve 422 Pearl Curve versus Gompertz Curve 425 Summary 425 Exercises 425 27 The Bass Diffusion Model 427 Introducing the Bass Model 427 Estimating the Bass Model 428 Using the Bass Model to Forecast New Product Sales 431 Deflating Intentions Data 434 Using the Bass Model to Simulate Sales of a New Product 435 Modifications of the Bass Model 437 Summary 438 Exercises 438 28 Using the Copernican Principle to Predict Duration of Future Sales 439 Using the Copernican Principle 439 Simulating Remaining Life of Product 440 Summary 441 Exercises 441 VIII Retailing 443 29 Market Basket Analysis and Lift 445 Computing Lift for Two Products 445 Computing Three-Way Lifts 449 A Data Mining Legend Debunked! 453 Using Lift to Optimize Store Layout 454 Summary 456 Exercises 456 30 RFM Analysis and Optimizing Direct Mail Campaigns 459 RFM Analysis 459 An RFM Success Story 465 Using the Evolutionary Solver to Optimize a Direct Mail Campaign 465 Summary 468 Exercises 468 31 Using the SCAN*PRO Model and Its Variants 471 Introducing the SCAN*PRO Model 471 Modeling Sales of Snickers Bars 472 Forecasting Software Sales 475 Summary 480 Exercises 480 32 Allocating Retail Space and Sales Resources 483 Identifying the Sales to Marketing Effort Relationship 483 Modeling the Marketing Response to Sales Force Effort 484 Optimizing Allocation of Sales Effort 489 Using the Gompertz Curve to Allocate Supermarket Shelf Space 492 Summary 492 Exercises 493 33 Forecasting Sales from Few Data Points 495 Predicting Movie Revenues 495 Modifying the Model to Improve Forecast Accuracy 498 Using 3 Weeks of Revenue to Forecast Movie Revenues 499 Summary 501 Exercises 501 IX Advertising 503 34 Measuring the Effectiveness of Advertising 505 The Adstock Model 505 Another Model for Estimating Ad Effectiveness 509 Optimizing Advertising: Pulsing versus Continuous Spending 511 Summary 514 Exercises 515 35 Media Selection Models 517 A Linear Media Allocation Model 517 Quantity Discounts 520 A Monte Carlo Media Allocation Simulation 522 Summary 527 Exercises 527 36 Pay per Click (PPC) Online Advertising 529 Defi ning Pay per Click Advertising 529 Profi tability Model for PPC Advertising 531 Google AdWords Auction 533 Using Bid Simulator to Optimize Your Bid 536 Summary 537 Exercises 537 X Marketing Research Tools 539 37 Principal Components Analysis (PCA) 541 Defining PCA 541 Linear Combinations, Variances, and Covariances 542 Diving into Principal Components Analysis 548 Other Applications of PCA 556 Summary 557 Exercises 558 38 Multidimensional Scaling (MDS) 559 Similarity Data559 MDS Analysis of U.S. City Distances 560 MDS Analysis of Breakfast Foods 566 Finding a Consumer's Ideal Point 570 Summary 574 Exercises 574 39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis 577 Conditional Probability 578 Bayes' Theorem 579 Naive Bayes Classifier 581 Linear Discriminant Analysis 586 Model Validation 591 The Surprising Virtues of Naive Bayes 592 Summary 592 Exercises 593 40 Analysis of Variance: One-way ANOVA 595 Testing Whether Group Means Are Different 595 Example of One-way ANOVA 596 The Role of Variance in ANOVA 598 Forecasting with One-way ANOVA 599 Contrasts 601 Summary 603 Exercises 604 41 Analysis of Variance: Two-way ANOVA 607 Introducing Two-way ANOVA 607 Two-way ANOVA without Replication 608 Two-way ANOVA with Replication 611 Summary 616 Exercises 617 XI Internet and Social Marketing 619 42 Networks 621 Measuring the Importance of a Node 621 Measuring the Importance of a Link 626 Summarizing Network Structure628 Random and Regular Networks 631 The Rich Get Richer 634 Klout Score636 Summary 637 Exercises 638 43 The Mathematics Behind The Tipping Point 641 Network Contagion 641 A Bass Version of the Tipping Point 646 Summary 650 Exercises 650 44 Viral Marketing 653 Watts' Model 654 A More Complex Viral Marketing Model 655 Summary 660 Exercises 661 45 Text Mining 663 Text Mining Definitions 664 Giving Structure to Unstructured Text 664 Applying Text Mining in Real Life Scenarios 668 Summary 671 Exercises 671 Index 673