{"product_id":"profit-driven-business-analytics-isbn-9781119286554","title":"Profit Driven Business Analytics","description":"\u003cb\u003eMaximize profit and optimize decisions with advanced business analytics\u003c\/b\u003e \u003cp\u003e\u003ci\u003eProfit-Driven Business Analytics\u003c\/i\u003e provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. \u003c\/p\u003e\u003cp\u003eDespite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eReinforce basic analytics to maximize profits\u003c\/li\u003e \u003cli\u003eAdopt the tools and techniques of successful integration\u003c\/li\u003e \u003cli\u003eImplement more advanced analytics with a value-centric approach\u003c\/li\u003e \u003cli\u003eFine-tune analytical information to optimize business decisions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eBoth data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. \u003ci\u003eProfit-Driven Business Analytics\u003c\/i\u003e provides a practical guidebook and reference for adopting \u003ci\u003ereal\u003c\/i\u003e business analytics techniques. \u003c\/p\u003e\u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 A Value-Centric Perspective Towards Analytics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eBusiness Analytics 3\u003c\/p\u003e \u003cp\u003eProfit-Driven Business Analytics 9\u003c\/p\u003e \u003cp\u003eAnalytics Process Model 14\u003c\/p\u003e \u003cp\u003eAnalytical Model Evaluation 17\u003c\/p\u003e \u003cp\u003eAnalytics Team 19\u003c\/p\u003e \u003cp\u003eProfiles 19\u003c\/p\u003e \u003cp\u003eData Scientists 20\u003c\/p\u003e \u003cp\u003eConclusion 23\u003c\/p\u003e \u003cp\u003eReview Questions 24\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 24\u003c\/p\u003e \u003cp\u003eOpen Questions 25\u003c\/p\u003e \u003cp\u003eReferences 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Analytical Techniques 28\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 28\u003c\/p\u003e \u003cp\u003eData Preprocessing 29\u003c\/p\u003e \u003cp\u003eDenormalizing Data for Analysis 29\u003c\/p\u003e \u003cp\u003eSampling 30\u003c\/p\u003e \u003cp\u003eExploratory Analysis 31\u003c\/p\u003e \u003cp\u003eMissing Values 31\u003c\/p\u003e \u003cp\u003eOutlier Detection and Handling 32\u003c\/p\u003e \u003cp\u003ePrincipal Component Analysis 33\u003c\/p\u003e \u003cp\u003eTypes of Analytics 37\u003c\/p\u003e \u003cp\u003ePredictive Analytics 37\u003c\/p\u003e \u003cp\u003eIntroduction 37\u003c\/p\u003e \u003cp\u003eLinear Regression 38\u003c\/p\u003e \u003cp\u003eLogistic Regression 39\u003c\/p\u003e \u003cp\u003eDecision Trees 45\u003c\/p\u003e \u003cp\u003eNeural Networks 52\u003c\/p\u003e \u003cp\u003eEnsemble Methods 56\u003c\/p\u003e \u003cp\u003eBagging 57\u003c\/p\u003e \u003cp\u003eBoosting 57\u003c\/p\u003e \u003cp\u003eRandom Forests 58\u003c\/p\u003e \u003cp\u003eEvaluating Ensemble Methods 59\u003c\/p\u003e \u003cp\u003eEvaluating Predictive Models 59\u003c\/p\u003e \u003cp\u003eSplitting Up the Dataset 59\u003c\/p\u003e \u003cp\u003ePerformance Measures for Classification Models 63\u003c\/p\u003e \u003cp\u003ePerformance Measures for Regression Models 67\u003c\/p\u003e \u003cp\u003eOther Performance Measures for Predictive Analytical\u003c\/p\u003e \u003cp\u003eModels 68\u003c\/p\u003e \u003cp\u003eDescriptive Analytics 69\u003c\/p\u003e \u003cp\u003eIntroduction 69\u003c\/p\u003e \u003cp\u003eAssociation Rules 69\u003c\/p\u003e \u003cp\u003eSequence Rules 72\u003c\/p\u003e \u003cp\u003eClustering 74\u003c\/p\u003e \u003cp\u003eSurvival Analysis 81\u003c\/p\u003e \u003cp\u003eIntroduction 81\u003c\/p\u003e \u003cp\u003eSurvival Analysis Measurements 83\u003c\/p\u003e \u003cp\u003eKaplan Meier Analysis 85\u003c\/p\u003e \u003cp\u003eParametric Survival Analysis 87\u003c\/p\u003e \u003cp\u003eProportional Hazards Regression 90\u003c\/p\u003e \u003cp\u003eExtensions of Survival Analysis Models 92\u003c\/p\u003e \u003cp\u003eEvaluating Survival Analysis Models 93\u003c\/p\u003e \u003cp\u003eSocial Network Analytics 93\u003c\/p\u003e \u003cp\u003eIntroduction 93\u003c\/p\u003e \u003cp\u003eSocial Network Definitions 94\u003c\/p\u003e \u003cp\u003eSocial Network Metrics 95\u003c\/p\u003e \u003cp\u003eSocial Network Learning 97\u003c\/p\u003e \u003cp\u003eRelational Neighbor Classifier 98\u003c\/p\u003e \u003cp\u003eProbabilistic Relational Neighbor Classifier 99\u003c\/p\u003e \u003cp\u003eRelational Logistic Regression 100\u003c\/p\u003e \u003cp\u003eCollective Inferencing 102\u003c\/p\u003e \u003cp\u003eConclusion 102\u003c\/p\u003e \u003cp\u003eReview Questions 103\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 103\u003c\/p\u003e \u003cp\u003eOpen Questions 108\u003c\/p\u003e \u003cp\u003eNotes 110\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Business Applications 114\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 114\u003c\/p\u003e \u003cp\u003eMarketing Analytics 114\u003c\/p\u003e \u003cp\u003eIntroduction 114\u003c\/p\u003e \u003cp\u003eRFM Analysis 115\u003c\/p\u003e \u003cp\u003eResponse Modeling 116\u003c\/p\u003e \u003cp\u003eChurn Prediction 118\u003c\/p\u003e \u003cp\u003eX-selling 120\u003c\/p\u003e \u003cp\u003eCustomer Segmentation 121\u003c\/p\u003e \u003cp\u003eCustomer Lifetime Value 123\u003c\/p\u003e \u003cp\u003eCustomer Journey 129\u003c\/p\u003e \u003cp\u003eRecommender Systems 131\u003c\/p\u003e \u003cp\u003eFraud Analytics 134\u003c\/p\u003e \u003cp\u003eCredit Risk Analytics 139\u003c\/p\u003e \u003cp\u003eHR Analytics 141\u003c\/p\u003e \u003cp\u003eConclusion 146\u003c\/p\u003e \u003cp\u003eReview Questions 146\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 146\u003c\/p\u003e \u003cp\u003eOpen Questions 150\u003c\/p\u003e \u003cp\u003eNote 151\u003c\/p\u003e \u003cp\u003eReferences 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Uplift Modeling 154\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 154\u003c\/p\u003e \u003cp\u003eThe Case for Uplift Modeling: Response Modeling 155\u003c\/p\u003e \u003cp\u003eEffects of a Treatment 158\u003c\/p\u003e \u003cp\u003eExperimental Design, Data Collection, and Data\u003c\/p\u003e \u003cp\u003ePreprocessing 161\u003c\/p\u003e \u003cp\u003eExperimental Design 161\u003c\/p\u003e \u003cp\u003eCampaign Measurement of Model Effectiveness 164\u003c\/p\u003e \u003cp\u003eUplift Modeling Methods 170\u003c\/p\u003e \u003cp\u003eTwo-Model Approach 172\u003c\/p\u003e \u003cp\u003eRegression-Based Approaches 174\u003c\/p\u003e \u003cp\u003eTree-Based Approaches 183\u003c\/p\u003e \u003cp\u003eEnsembles 193\u003c\/p\u003e \u003cp\u003eContinuous or Ordered Outcomes 198\u003c\/p\u003e \u003cp\u003eEvaluation of Uplift Models 199\u003c\/p\u003e \u003cp\u003eVisual Evaluation Approaches 200\u003c\/p\u003e \u003cp\u003ePerformance Metrics 207\u003c\/p\u003e \u003cp\u003ePractical Guidelines 210\u003c\/p\u003e \u003cp\u003eTwo-Step Approach for Developing Uplift Models 210\u003c\/p\u003e \u003cp\u003eImplementations and Software 212\u003c\/p\u003e \u003cp\u003eConclusion 213\u003c\/p\u003e \u003cp\u003eReview Questions 214\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 214\u003c\/p\u003e \u003cp\u003eOpen Questions 216\u003c\/p\u003e \u003cp\u003eNote 217\u003c\/p\u003e \u003cp\u003eReferences 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Profit-Driven Analytical Techniques 220\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 220\u003c\/p\u003e \u003cp\u003eProfit-Driven Predictive Analytics 221\u003c\/p\u003e \u003cp\u003eThe Case for Profit-Driven Predictive Analytics 221\u003c\/p\u003e \u003cp\u003eCost Matrix 222\u003c\/p\u003e \u003cp\u003eCost-Sensitive Decision Making with Cost-Insensitive\u003c\/p\u003e \u003cp\u003eClassification Models 228\u003c\/p\u003e \u003cp\u003eCost-Sensitive Classification Framework 231\u003c\/p\u003e \u003cp\u003eCost-Sensitive Classification 234\u003c\/p\u003e \u003cp\u003ePre-Training Methods 235\u003c\/p\u003e \u003cp\u003eDuring-Training Methods 247\u003c\/p\u003e \u003cp\u003ePost-Training Methods 253\u003c\/p\u003e \u003cp\u003eEvaluation of Cost-Sensitive Classification Models 255\u003c\/p\u003e \u003cp\u003eImbalanced Class Distribution 256\u003c\/p\u003e \u003cp\u003eImplementations 259\u003c\/p\u003e \u003cp\u003eCost-Sensitive Regression 259\u003c\/p\u003e \u003cp\u003eThe Case for Profit-Driven Regression 259\u003c\/p\u003e \u003cp\u003eCost-Sensitive Learning for Regression 260\u003c\/p\u003e \u003cp\u003eDuring Training Methods 260\u003c\/p\u003e \u003cp\u003ePost-Training Methods 261\u003c\/p\u003e \u003cp\u003eProfit-Driven Descriptive Analytics 267\u003c\/p\u003e \u003cp\u003eProfit-Driven Segmentation 267\u003c\/p\u003e \u003cp\u003eProfit-Driven Association Rules 280\u003c\/p\u003e \u003cp\u003eConclusion 283\u003c\/p\u003e \u003cp\u003eReview Questions 284\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 284\u003c\/p\u003e \u003cp\u003eOpen Questions 289\u003c\/p\u003e \u003cp\u003eNotes 290\u003c\/p\u003e \u003cp\u003eReferences 291\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Profit-Driven Model Evaluation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eand Implementation 296\u003c\/p\u003e \u003cp\u003eIntroduction 296\u003c\/p\u003e \u003cp\u003eProfit-Driven Evaluation of Classification Models 298\u003c\/p\u003e \u003cp\u003eAverage Misclassification Cost 298\u003c\/p\u003e \u003cp\u003eCutoff Point Tuning 303\u003c\/p\u003e \u003cp\u003eROC Curve-Based Measures 310\u003c\/p\u003e \u003cp\u003eProfit-Driven Evaluation with Observation-Dependent\u003c\/p\u003e \u003cp\u003eCosts 334\u003c\/p\u003e \u003cp\u003eProfit-Driven Evaluation of Regression Models 338\u003c\/p\u003e \u003cp\u003eLoss Functions and Error-Based Evaluation Measures 339\u003c\/p\u003e \u003cp\u003eREC Curve and Surface 341\u003c\/p\u003e \u003cp\u003eConclusion 345\u003c\/p\u003e \u003cp\u003eReview Questions 347\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 347\u003c\/p\u003e \u003cp\u003eOpen Questions 350\u003c\/p\u003e \u003cp\u003eNotes 351\u003c\/p\u003e \u003cp\u003eReferences 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Economic Impact 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 355\u003c\/p\u003e \u003cp\u003eEconomic Value of Big Data and Analytics 355\u003c\/p\u003e \u003cp\u003eTotal Cost of Ownership (TCO) 355\u003c\/p\u003e \u003cp\u003eReturn on Investment (ROI) 357\u003c\/p\u003e \u003cp\u003eProfit-Driven Business Analytics 359\u003c\/p\u003e \u003cp\u003eKey Economic Considerations 359\u003c\/p\u003e \u003cp\u003eIn-Sourcing versus Outsourcing 359\u003c\/p\u003e \u003cp\u003eOn Premise versus the Cloud 361\u003c\/p\u003e \u003cp\u003eOpen-Source versus Commercial Software 362\u003c\/p\u003e \u003cp\u003eImproving the ROI of Big Data and Analytics 364\u003c\/p\u003e \u003cp\u003eNew Sources of Data 364\u003c\/p\u003e \u003cp\u003eData Quality 367\u003c\/p\u003e \u003cp\u003eManagement Support 369\u003c\/p\u003e \u003cp\u003eOrganizational Aspects 370\u003c\/p\u003e \u003cp\u003eCross-Fertilization 371\u003c\/p\u003e \u003cp\u003eConclusion 372\u003c\/p\u003e \u003cp\u003eReview Questions 373\u003c\/p\u003e \u003cp\u003eMultiple Choice Questions 373\u003c\/p\u003e \u003cp\u003eOpen Questions 376\u003c\/p\u003e \u003cp\u003eNotes 377\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003eAbout the Authors 378\u003c\/p\u003e \u003cp\u003eIndex 381\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWOUTER VERBEKE\u003c\/b\u003e is assistant professor of Business Informatics and Data Analytics at Vrije Universiteit Brussel (Belgium). He is the coauthor of \u003ci\u003eFraud Analytics using Descriptive, Predictive, and Social Network Techniques.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBART BAESENS\u003c\/b\u003e is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He is the author of \u003ci\u003eCredit Risk Management\u003c\/i\u003e and \u003ci\u003eAnalytics in a Big Data World,\u003c\/i\u003e as well as coauthor of \u003ci\u003eFraud Analytics using Descriptive, Predictive, and Social Network Techniques.\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eCRISTIÁN BRAVO\u003c\/b\u003e is a lecturer vin business analytics in the department of Decision Analytics and Risk at the University of Southampton.  \u003c\/p\u003e\u003cp\u003eThe rapid and extensive growth of information, networks, and database technologies has fueled an equally dramatic advance in analytics for business. Corporate leaders who continue to make decisions based on outdated analytics are flying blind in comparison to competitors with the next-level perspective. \u003ci\u003eProfit Driven Business Analytics\u003c\/i\u003e is your convenient, one-stop resource for moving your thinking and skillset to the state-of-the art of analytical techniques for achieving business goals.\u003c\/p\u003e \u003cp\u003eComplex mathematical proofs and exhaustive algorithms are underpinning such analytics, but you only need to grasp the underlying scientific principles to develop the profit-driven mindset to inspire development, implementation, and operation of these innovative analytical models. If big-data insights have left you wanting more, this much-needed guide is your dependable framework for maximizing the amount of value you can add to your brand with data-driven decision making. In each chapter, illuminating case studies bring covered topics to life, review questions reinforce material, and open questions prepare you for actual practice. Gain tomorrow’s competitive edge today by: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eEasily making the leap from theory and research to hands-on execution by  exploring the cornerstone principles and mechanics of profit-driven analytics from  a practitioner’s perspective\u003c\/li\u003e \u003cli\u003eJumpstarting your understanding and expertise by accessing sample datasets,  code, and applications on a companion website \u003c\/li\u003e \u003cli\u003eSpearheading cutting-edge initiatives to produce significant value and lower operating costs by using advanced analytics to streamline business processes\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWhether you need to upgrade your current business analytical strategy or build one from scratch, \u003ci\u003eProfit Driven Business Analytics \u003c\/i\u003eis the reference, toolkit, and mentor you need at your fingertips every step of the way.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989870723301,"sku":"NP9781119286554","price":49.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119286554.jpg?v=1761785734","url":"https:\/\/k12savings.com\/products\/profit-driven-business-analytics-isbn-9781119286554","provider":"K12savings","version":"1.0","type":"link"}