{"product_id":"marketing-analytics-9781118373439","title":"Marketing Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHelping tech-savvy marketers and data analysts solve real-world business problems with Excel     Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI \u003c\/b\u003e\u003cb\u003eUsing Excel to Summarize Marketing Data 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 \u003c\/b\u003e\u003cb\u003eSlicing and Dicing Marketing Data with PivotTables 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnalyzing Sales at True Colors Hardware 3\u003c\/p\u003e \u003cp\u003eAnalyzing Sales at La Petit Bakery 14\u003c\/p\u003e \u003cp\u003eAnalyzing How Demographics Affect Sales 21\u003c\/p\u003e \u003cp\u003ePulling Data from a PivotTable with the GETPIVOTDATA Function 25\u003c\/p\u003e \u003cp\u003eSummary 27\u003c\/p\u003e \u003cp\u003eExercises 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 \u003c\/b\u003e\u003cb\u003eUsing Excel Charts to Summarize Marketing Data 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCombination Charts 29\u003c\/p\u003e \u003cp\u003eUsing a PivotChart to Summarize Market Research Surveys 36\u003c\/p\u003e \u003cp\u003eEnsuring Charts Update Automatically When New Data is Added 39\u003c\/p\u003e \u003cp\u003eMaking Chart Labels Dynamic 40\u003c\/p\u003e \u003cp\u003eSummarizing Monthly Sales-Force Rankings 43\u003c\/p\u003e \u003cp\u003eUsing Check Boxes to Control Data in a Chart 45\u003c\/p\u003e \u003cp\u003eUsing Sparklines to Summarize Multiple Data Series 48\u003c\/p\u003e \u003cp\u003eUsing GETPIVOTDATA to Create the End-of-Week Sales Report 52\u003c\/p\u003e \u003cp\u003eSummary 55\u003c\/p\u003e \u003cp\u003eExercises 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 \u003c\/b\u003e\u003cb\u003eUsing Excel Functions to Summarize Marketing Data 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummarizing Data with a Histogram 59\u003c\/p\u003e \u003cp\u003eUsing Statistical Functions to Summarize Marketing Data 64\u003c\/p\u003e \u003cp\u003eSummary 79\u003c\/p\u003e \u003cp\u003eExercises 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII \u003c\/b\u003e\u003cb\u003ePricing 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 \u003c\/b\u003e\u003cb\u003eEstimating Demand Curves and Using Solver to Optimize Price 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimating Linear and Power Demand Curves 85\u003c\/p\u003e \u003cp\u003eUsing the Excel Solver to Optimize Price 90\u003c\/p\u003e \u003cp\u003ePricing Using Subjectively Estimated Demand Curves 96\u003c\/p\u003e \u003cp\u003eUsing SolverTable to Price Multiple Products 99\u003c\/p\u003e \u003cp\u003eSummary 103\u003c\/p\u003e \u003cp\u003eExercises 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 \u003c\/b\u003e\u003cb\u003ePrice Bundling 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy Bundle? 107\u003c\/p\u003e \u003cp\u003eUsing Evolutionary Solver to Find Optimal Bundle Prices 111\u003c\/p\u003e \u003cp\u003eSummary 119\u003c\/p\u003e \u003cp\u003eExercises 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 \u003c\/b\u003e\u003cb\u003eNonlinear Pricing 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDemand Curves and Willingness to Pay 124\u003c\/p\u003e \u003cp\u003eProfit Maximizing with Nonlinear Pricing Strategies 125\u003c\/p\u003e \u003cp\u003eSummary 131\u003c\/p\u003e \u003cp\u003eExercises 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 \u003c\/b\u003e\u003cb\u003ePrice Skimming and Sales 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDropping Prices Over Time 135\u003c\/p\u003e \u003cp\u003eWhy Have Sales? 138\u003c\/p\u003e \u003cp\u003eSummary 142\u003c\/p\u003e \u003cp\u003eExercises 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 \u003c\/b\u003e\u003cb\u003eRevenue Management 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstimating Demand for the Bates Motel and Segmenting Customers 144\u003c\/p\u003e \u003cp\u003eHandling Uncertainty 150\u003c\/p\u003e \u003cp\u003eMarkdown Pricing 153\u003c\/p\u003e \u003cp\u003eSummary 156\u003c\/p\u003e \u003cp\u003eExercises 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII \u003c\/b\u003e\u003cb\u003eForecasting .159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 \u003c\/b\u003e\u003cb\u003eSimple Linear Regression and Correlation 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSimple Linear Regression 161\u003c\/p\u003e \u003cp\u003eUsing Correlations to Summarize Linear Relationships 170\u003c\/p\u003e \u003cp\u003eSummary 174\u003c\/p\u003e \u003cp\u003eExercises 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 \u003c\/b\u003e\u003cb\u003eUsing Multiple Regression to Forecast Sales 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Multiple Linear Regression 178\u003c\/p\u003e \u003cp\u003eRunning a Regression with the Data Analysis Add-In 179\u003c\/p\u003e \u003cp\u003eInterpreting the Regression Output 182\u003c\/p\u003e \u003cp\u003eUsing Qualitative Independent Variables in Regression 186\u003c\/p\u003e \u003cp\u003eModeling Interactions and Nonlinearities 192\u003c\/p\u003e \u003cp\u003eTesting Validity of Regression Assumptions 195\u003c\/p\u003e \u003cp\u003eMulticollinearity 204\u003c\/p\u003e \u003cp\u003eValidation of a Regression 207\u003c\/p\u003e \u003cp\u003eSummary 209\u003c\/p\u003e \u003cp\u003eExercises 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 \u003c\/b\u003e\u003cb\u003eForecasting in the Presence of Special Events 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding the Basic Model 213\u003c\/p\u003e \u003cp\u003eSummary 222\u003c\/p\u003e \u003cp\u003eExercises 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 \u003c\/b\u003e\u003cb\u003eModeling Trend and Seasonality 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Moving Averages to Smooth Data and Eliminate Seasonality 225\u003c\/p\u003e \u003cp\u003eAn Additive Model with Trends and Seasonality 228\u003c\/p\u003e \u003cp\u003eA Multiplicative Model with Trend and Seasonality 231\u003c\/p\u003e \u003cp\u003eSummary 234\u003c\/p\u003e \u003cp\u003eExercises 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 \u003c\/b\u003e\u003cb\u003eRatio to Moving Average Forecasting Method 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing the Ratio to Moving Average Method 235\u003c\/p\u003e \u003cp\u003eApplying the Ratio to Moving Average Method to Monthly Data 238\u003c\/p\u003e \u003cp\u003eSummary 238\u003c\/p\u003e \u003cp\u003eExercises 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 \u003c\/b\u003e\u003cb\u003eWinter’s Method 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eParameter Definitions for Winter’s Method 241\u003c\/p\u003e \u003cp\u003eInitializing Winter’s Method 243\u003c\/p\u003e \u003cp\u003eEstimating the Smoothing Constants 244\u003c\/p\u003e \u003cp\u003eForecasting Future Months 246\u003c\/p\u003e \u003cp\u003eMean Absolute Percentage Error (MAPE) 247\u003c\/p\u003e \u003cp\u003eSummary 248\u003c\/p\u003e \u003cp\u003eExercises 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 \u003c\/b\u003e\u003cb\u003eUsing Neural Networks to Forecast Sales 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRegression and Neural Nets 249\u003c\/p\u003e \u003cp\u003eUsing Neural Networks 250\u003c\/p\u003e \u003cp\u003eUsing NeuralTools to Predict Sales 253\u003c\/p\u003e \u003cp\u003eUsing NeuralTools to Forecast Airline Miles 258\u003c\/p\u003e \u003cp\u003eSummary 259\u003c\/p\u003e \u003cp\u003eExercises 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV \u003c\/b\u003e\u003cb\u003eWhat do Customers Want? 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 \u003c\/b\u003e\u003cb\u003eConjoint Analysis 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProducts, Attributes, and Levels 263\u003c\/p\u003e \u003cp\u003eFull Profile Conjoint Analysis 265\u003c\/p\u003e \u003cp\u003eUsing Evolutionary Solver to Generate Product Profiles 272\u003c\/p\u003e \u003cp\u003eDeveloping a Conjoint Simulator 277\u003c\/p\u003e \u003cp\u003eExamining Other Forms of Conjoint Analysis 279\u003c\/p\u003e \u003cp\u003eSummary 281\u003c\/p\u003e \u003cp\u003eExercises 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 \u003c\/b\u003e\u003cb\u003eLogistic Regression 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy Logistic Regression Is Necessary 286\u003c\/p\u003e \u003cp\u003eLogistic Regression Model 289\u003c\/p\u003e \u003cp\u003eMaximum Likelihood Estimate of Logistic Regression Model 290\u003c\/p\u003e \u003cp\u003eUsing StatTools to Estimate and Test Logistic Regression Hypotheses 293\u003c\/p\u003e \u003cp\u003ePerforming a Logistic Regression with Count Data 298\u003c\/p\u003e \u003cp\u003eSummary 300\u003c\/p\u003e \u003cp\u003eExercises 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 \u003c\/b\u003e\u003cb\u003eDiscrete Choice Analysis 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRandom Utility Theory 303\u003c\/p\u003e \u003cp\u003eDiscrete Choice Analysis of Chocolate Preferences 305\u003c\/p\u003e \u003cp\u003eIncorporating Price and Brand Equity into Discrete Choice Analysis 309\u003c\/p\u003e \u003cp\u003eDynamic Discrete Choice 315\u003c\/p\u003e \u003cp\u003eIndependence of Irrelevant Alternatives (IIA) Assumption 316\u003c\/p\u003e \u003cp\u003eDiscrete Choice and Price Elasticity 317\u003c\/p\u003e \u003cp\u003eSummary 318\u003c\/p\u003e \u003cp\u003eExercises 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 \u003c\/b\u003e\u003cb\u003eCalculating Lifetime Customer Value 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Customer Value Template 328\u003c\/p\u003e \u003cp\u003eMeasuring Sensitivity Analysis with Two-way Tables 330\u003c\/p\u003e \u003cp\u003eAn Explicit Formula for the Multiplier r 331\u003c\/p\u003e \u003cp\u003eVarying Margins 331\u003c\/p\u003e \u003cp\u003eDIRECTV, Customer Value, and \u003ci\u003eFriday Night Lights (FNL) \u003c\/i\u003e333\u003c\/p\u003e \u003cp\u003eEstimating the Chance a Customer Is Still Active 334\u003c\/p\u003e \u003cp\u003eGoing Beyond the Basic Customer Lifetime Value Model 335\u003c\/p\u003e \u003cp\u003eSummary 336\u003c\/p\u003e \u003cp\u003eExercises 336\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 \u003c\/b\u003e\u003cb\u003eUsing Customer Value to Value a Business 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Primer on Valuation 339\u003c\/p\u003e \u003cp\u003eUsing Customer Value to Value a Business 340\u003c\/p\u003e \u003cp\u003eMeasuring Sensitivity Analysis with a One-way Table 343\u003c\/p\u003e \u003cp\u003eUsing Customer Value to Estimate a Firm’s Market Value 344\u003c\/p\u003e \u003cp\u003eSummary 344\u003c\/p\u003e \u003cp\u003eExercises 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 \u003c\/b\u003e\u003cb\u003eCustomer Value, Monte Carlo Simulation, and Marketing Decision Making 347\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Markov Chain Model of Customer Value 347\u003c\/p\u003e \u003cp\u003eUsing Monte Carlo Simulation to Predict Success of a Marketing Initiative 353\u003c\/p\u003e \u003cp\u003eSummary 359\u003c\/p\u003e \u003cp\u003eExercises 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 \u003c\/b\u003e\u003cb\u003eAllocating Marketing Resources between Customer Acquisition and Retention 347\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModeling the Relationship between Spending and Customer Acquisition and Retention 365\u003c\/p\u003e \u003cp\u003eBasic Model for Optimizing Retention and Acquisition Spending 368\u003c\/p\u003e \u003cp\u003eAn Improvement in the Basic Model 371\u003c\/p\u003e \u003cp\u003eSummary 373\u003c\/p\u003e \u003cp\u003eExercises 374\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI \u003c\/b\u003e\u003cb\u003eMarket Segmentation 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 \u003c\/b\u003e\u003cb\u003eCluster Analysis 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClustering U.S. Cities 378\u003c\/p\u003e \u003cp\u003eUsing Conjoint Analysis to Segment a Market 386\u003c\/p\u003e \u003cp\u003eSummary 391\u003c\/p\u003e \u003cp\u003eExercises 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 \u003c\/b\u003e\u003cb\u003eCollaborative Filtering 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUser-Based Collaborative Filtering 393\u003c\/p\u003e \u003cp\u003eItem-Based Filtering 398\u003c\/p\u003e \u003cp\u003eComparing Item- and User-Based Collaborative Filtering 400\u003c\/p\u003e \u003cp\u003eThe Netflix Competition 401\u003c\/p\u003e \u003cp\u003eSummary 401\u003c\/p\u003e \u003cp\u003eExercises 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 \u003c\/b\u003e\u003cb\u003eUsing Classification Trees for Segmentation 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Decision Trees 403\u003c\/p\u003e \u003cp\u003eConstructing a Decision Tree 404\u003c\/p\u003e \u003cp\u003ePruning Trees and CART 409\u003c\/p\u003e \u003cp\u003eSummary 410\u003c\/p\u003e \u003cp\u003eExercises 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 \u003c\/b\u003e\u003cb\u003eUsing S Curves to Forecast Sales of a New Product 415\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExamining S Curves 415\u003c\/p\u003e \u003cp\u003eFitting the Pearl or Logistic Curve 418\u003c\/p\u003e \u003cp\u003eFitting an S Curve with Seasonality 420\u003c\/p\u003e \u003cp\u003eFitting the Gompertz Curve 422\u003c\/p\u003e \u003cp\u003ePearl Curve versus Gompertz Curve 425\u003c\/p\u003e \u003cp\u003eSummary 425\u003c\/p\u003e \u003cp\u003eExercises 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 \u003c\/b\u003e\u003cb\u003eThe Bass Diffusion Model 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing the Bass Model 427\u003c\/p\u003e \u003cp\u003eEstimating the Bass Model 428\u003c\/p\u003e \u003cp\u003eUsing the Bass Model to Forecast New Product Sales 431\u003c\/p\u003e \u003cp\u003eDeflating Intentions Data 434\u003c\/p\u003e \u003cp\u003eUsing the Bass Model to Simulate Sales of a New Product 435\u003c\/p\u003e \u003cp\u003eModifications of the Bass Model 437\u003c\/p\u003e \u003cp\u003eSummary 438\u003c\/p\u003e \u003cp\u003eExercises 438\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 \u003c\/b\u003e\u003cb\u003eUsing the Copernican Principle to Predict Duration of Future Sales 439\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing the Copernican Principle 439\u003c\/p\u003e \u003cp\u003eSimulating Remaining Life of Product 440\u003c\/p\u003e \u003cp\u003eSummary 441\u003c\/p\u003e \u003cp\u003eExercises 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 \u003c\/b\u003e\u003cb\u003eMarket Basket Analysis and Lift 445\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComputing Lift for Two Products 445\u003c\/p\u003e \u003cp\u003eComputing Three-Way Lifts 449\u003c\/p\u003e \u003cp\u003eA Data Mining Legend Debunked! 453\u003c\/p\u003e \u003cp\u003eUsing Lift to Optimize Store Layout 454\u003c\/p\u003e \u003cp\u003eSummary 456\u003c\/p\u003e \u003cp\u003eExercises 456\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 \u003c\/b\u003e\u003cb\u003eRFM Analysis and Optimizing Direct Mail Campaigns 459\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRFM Analysis 459\u003c\/p\u003e \u003cp\u003eAn RFM Success Story 465\u003c\/p\u003e \u003cp\u003eUsing the Evolutionary Solver to Optimize a Direct Mail Campaign 465\u003c\/p\u003e \u003cp\u003eSummary 468\u003c\/p\u003e \u003cp\u003eExercises 468\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 \u003c\/b\u003e\u003cb\u003eUsing the SCAN*PRO Model and Its Variants 471\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing the SCAN*PRO Model 471\u003c\/p\u003e \u003cp\u003eModeling Sales of Snickers Bars 472\u003c\/p\u003e \u003cp\u003eForecasting Software Sales 475\u003c\/p\u003e \u003cp\u003eSummary 480\u003c\/p\u003e \u003cp\u003eExercises 480\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 \u003c\/b\u003e\u003cb\u003eAllocating Retail Space and Sales Resources 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentifying the Sales to Marketing Effort Relationship 483\u003c\/p\u003e \u003cp\u003eModeling the Marketing Response to Sales Force Effort 484\u003c\/p\u003e \u003cp\u003eOptimizing Allocation of Sales Effort 489\u003c\/p\u003e \u003cp\u003eUsing the Gompertz Curve to Allocate\u003c\/p\u003e \u003cp\u003eSupermarket Shelf Space 492\u003c\/p\u003e \u003cp\u003eSummary 492\u003c\/p\u003e \u003cp\u003eExercises 493\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 \u003c\/b\u003e\u003cb\u003eForecasting Sales from Few Data Points 495\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredicting Movie Revenues 495\u003c\/p\u003e \u003cp\u003eModifying the Model to Improve Forecast Accuracy 498\u003c\/p\u003e \u003cp\u003eUsing 3 Weeks of Revenue to Forecast Movie Revenues 499\u003c\/p\u003e \u003cp\u003eSummary 501\u003c\/p\u003e \u003cp\u003eExercises 501\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 \u003c\/b\u003e\u003cb\u003eMeasuring the Effectiveness of Advertising 505\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Adstock Model 505\u003c\/p\u003e \u003cp\u003eAnother Model for Estimating Ad Effectiveness 509\u003c\/p\u003e \u003cp\u003eOptimizing Advertising: Pulsing versus Continuous Spending 511\u003c\/p\u003e \u003cp\u003eSummary 514\u003c\/p\u003e \u003cp\u003eExercises 515\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 \u003c\/b\u003e\u003cb\u003eMedia Selection Models 517\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Linear Media Allocation Model 517\u003c\/p\u003e \u003cp\u003eQuantity Discounts 520\u003c\/p\u003e \u003cp\u003eA Monte Carlo Media Allocation Simulation 522\u003c\/p\u003e \u003cp\u003eSummary 527\u003c\/p\u003e \u003cp\u003eExercises 527\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 \u003c\/b\u003e\u003cb\u003ePay per Click (PPC) Online Advertising 529\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Pay per Click Advertising 529\u003c\/p\u003e \u003cp\u003eProfitability Model for PPC Advertising 531\u003c\/p\u003e \u003cp\u003eGoogle AdWords Auction 533\u003c\/p\u003e \u003cp\u003eUsing Bid Simulator to Optimize Your Bid 536\u003c\/p\u003e \u003cp\u003eSummary 537\u003c\/p\u003e \u003cp\u003eExercises 537\u003c\/p\u003e \u003cp\u003e\u003cb\u003eX \u003c\/b\u003e\u003cb\u003eMarketing Research Tools 539\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 \u003c\/b\u003e\u003cb\u003ePrincipal Components Analysis (PCA) 541\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining PCA 541\u003c\/p\u003e \u003cp\u003eLinear Combinations, Variances, and Covariances 542\u003c\/p\u003e \u003cp\u003eDiving into Principal Components Analysis 548\u003c\/p\u003e \u003cp\u003eOther Applications of PCA 556\u003c\/p\u003e \u003cp\u003eSummary 557\u003c\/p\u003e \u003cp\u003eExercises 558\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38 \u003c\/b\u003e\u003cb\u003eMultidimensional Scaling (MDS) 559\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSimilarity Data 559\u003c\/p\u003e \u003cp\u003eMDS Analysis of U.S. City Distances 560\u003c\/p\u003e \u003cp\u003eMDS Analysis of Breakfast Foods 566\u003c\/p\u003e \u003cp\u003eFinding a Consumer’s Ideal Point 570\u003c\/p\u003e \u003cp\u003eSummary 574\u003c\/p\u003e \u003cp\u003eExercises 574\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39 \u003c\/b\u003e\u003cb\u003eClassification Algorithms: Naive Bayes Classifier and Discriminant Analysis 577\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConditional Probability 578\u003c\/p\u003e \u003cp\u003eBayes’ Theorem 579\u003c\/p\u003e \u003cp\u003eNaive Bayes Classifier 581\u003c\/p\u003e \u003cp\u003eLinear Discriminant Analysis 586\u003c\/p\u003e \u003cp\u003eModel Validation 591\u003c\/p\u003e \u003cp\u003eThe Surprising Virtues of Naive Bayes 592\u003c\/p\u003e \u003cp\u003eSummary 592\u003c\/p\u003e \u003cp\u003eExercises 593\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40 \u003c\/b\u003e\u003cb\u003eAnalysis of Variance: One-way ANOVA 595\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTesting Whether Group Means Are Different 595\u003c\/p\u003e \u003cp\u003eExample of One-way ANOVA 596\u003c\/p\u003e \u003cp\u003eThe Role of Variance in ANOVA 598\u003c\/p\u003e \u003cp\u003eForecasting with One-way ANOVA 599\u003c\/p\u003e \u003cp\u003eContrasts 601\u003c\/p\u003e \u003cp\u003eSummary 603\u003c\/p\u003e \u003cp\u003eExercises 604\u003c\/p\u003e \u003cp\u003e\u003cb\u003e41 \u003c\/b\u003e\u003cb\u003eAnalysis of Variance: Two-way ANOVA 607\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Two-way ANOVA 607\u003c\/p\u003e \u003cp\u003eTwo-way ANOVA without Replication 608\u003c\/p\u003e \u003cp\u003eTwo-way ANOVA with Replication 611\u003c\/p\u003e \u003cp\u003eSummary 616\u003c\/p\u003e \u003cp\u003eExercises 617\u003c\/p\u003e \u003cp\u003e\u003cb\u003eXI \u003c\/b\u003e\u003cb\u003eInternet and Social Marketing 619\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e42 \u003c\/b\u003e\u003cb\u003eNetworks 621\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMeasuring the Importance of a Node 621\u003c\/p\u003e \u003cp\u003eMeasuring the Importance of a Link 626\u003c\/p\u003e \u003cp\u003eSummarizing Network Structure 628\u003c\/p\u003e \u003cp\u003eRandom and Regular Networks 631\u003c\/p\u003e \u003cp\u003eThe Rich Get Richer 634\u003c\/p\u003e \u003cp\u003eKlout Score 636\u003c\/p\u003e \u003cp\u003eSummary 637\u003c\/p\u003e \u003cp\u003eExercises 638\u003c\/p\u003e \u003cp\u003e\u003cb\u003e43 \u003c\/b\u003e\u003cb\u003eThe Mathematics Behind \u003ci\u003eThe Tipping Point \u003c\/i\u003e641\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNetwork Contagion 641\u003c\/p\u003e \u003cp\u003eA Bass Version of the Tipping Point 646\u003c\/p\u003e \u003cp\u003eSummary 650\u003c\/p\u003e \u003cp\u003eExercises 650\u003c\/p\u003e \u003cp\u003e\u003cb\u003e44 \u003c\/b\u003e\u003cb\u003eViral Marketing 653\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWatts’ Model 654\u003c\/p\u003e \u003cp\u003eA More Complex Viral Marketing Model 655\u003c\/p\u003e \u003cp\u003eSummary 660\u003c\/p\u003e \u003cp\u003eExercises 661\u003c\/p\u003e \u003cp\u003e\u003cb\u003e45 \u003c\/b\u003e\u003cb\u003eText Mining 663\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eText Mining Definitions 664\u003c\/p\u003e \u003cp\u003eGiving Structure to Unstructured Text 664\u003c\/p\u003e \u003cp\u003eApplying Text Mining in Real Life Scenarios 668\u003c\/p\u003e \u003cp\u003eSummary 671\u003c\/p\u003e \u003cp\u003eExercises 671\u003c\/p\u003e \u003cp\u003eIndex 673\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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