{"product_id":"marketing-analytics-isbn-9781118373439","title":"Marketing Analytics","description":"\u003cp\u003e\u003cb\u003eHelping tech-savvy marketers and data analysts solve real-world business problems with Excel\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing 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. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results.\u003c\/p\u003e \u003cp\u003ePractical exercises in each chapter help you apply and reinforce techniques as you learn.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eShows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools\u003c\/li\u003e \u003cli\u003eReveals how to target and retain profitable customers and avoid high-risk customers\u003c\/li\u003e \u003cli\u003eHelps you forecast sales and improve response rates for marketing campaigns\u003c\/li\u003e \u003cli\u003eExplores how to optimize price points for products and services, optimize store layouts, and improve online advertising\u003c\/li\u003e \u003cli\u003eCovers social media, viral marketing, and how to exploit both effectively\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eImprove your marketing results with Microsoft Excel and the invaluable techniques and ideas in \u003ci\u003eMarketing Analytics: Data-Driven Techniques with Microsoft Excel.\u003c\/i\u003e\u003c\/p\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 \u003cp\u003eWayne 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.\u003c\/p\u003e   \u003cp\u003ePowerful techniques for analyzing business data with Excel\u003c\/p\u003e \u003cp\u003eMost businesses are awash in data. To make that data work for your business, you need a simple, cost-effective tool — ideally, one you already know something about. Excel is that tool.\u003c\/p\u003e \u003cp\u003eEvery example in this book features step-by-step instructions, a downloadable Excel file containing data and solutions, and plenty of screenshots. To sharpen your marketing analytics, you just need this guide and Excel.\u003c\/p\u003e \u003cp\u003eThis book will help you master many important marketing analytic concepts, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUsing Excel charts and functions to summarize marketing data\u003c\/li\u003e \u003cli\u003eEstimating demand curves and using Solver to determine profit-maximizing pricing strategies\u003c\/li\u003e \u003cli\u003eUsing cluster analysis for market segmentation\u003c\/li\u003e \u003cli\u003eDeveloping customized forecasting models that show you how your marketing mix impacts sales\u003c\/li\u003e \u003cli\u003eMeasuring the effectiveness of your advertising program\u003c\/li\u003e \u003cli\u003eUnderstanding the analytics underlying social networks and viral marketing\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eCompanion website\u003c\/p\u003e \u003cp\u003eAt the companion website, www.wiley.com\/go\/marketinganalytics, you can download all the Excel files used in this book, find answers to all the exercises at the ends of the chapters, and be advised of any errors discovered.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989575057637,"sku":"NP9781118373439","price":54.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118373439.jpg?v=1761784659","url":"https:\/\/k12savings.com\/products\/marketing-analytics-isbn-9781118373439","provider":"K12savings","version":"1.0","type":"link"}