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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition

Name: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition
ISBN: 9780470650936

by Gordon S. Linoff, Michael J. A. Berry

March 2011

Beginner to intermediate

888 pages

26h

English

Wiley

Audiobook available

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Cover
Title Page
Copyright
Dedication
About the Authors
Credits
Acknowledgments
Introduction
Chapter 1: What Is Data Mining and Why Do It?
What Is Data Mining?Why Now?Skills for the Data MinerThe Virtuous Cycle of Data MiningA Case Study in Business Data MiningSteps of the Virtuous CycleData Mining in the Context of the Virtuous CycleLessons Learned
Chapter 2: Data Mining Applications in Marketing and Customer Relationship Management
Two Customer LifecyclesOrganize Business Processes Around the Customer LifecycleData Mining Applications for Customer AcquisitionA Data Mining Example: Choosing the Right Place to AdvertiseData Mining to Improve Direct Marketing CampaignsUsing Current Customers to Learn About ProspectsData Mining Applications for Customer Relationship ManagementRetentionBeyond the Customer LifecycleLessons Learned

Chapter 3: The Data Mining Process
What Can Go Wrong?Data Mining StylesGoals, Tasks, and TechniquesFormulating Data Mining Problems: From Goals to Tasks to TechniquesWhat Techniques for Which Tasks?Lessons Learned
Chapter 4: What You Should Know About Data
Occam's RazorLooking At and Measuring DataMeasuring ResponseMultiple ComparisonsChi-Square TestAn Example: Chi-Square for Regions and StartsCase Study: Comparing Two Recommendation Systems with an A/B TestData Mining and StatisticsLessons Learned
Chapter 5: Descriptions and Prediction: Profiling and Predictive Modeling
Directed Data Mining ModelsDirected Data Mining MethodologyStep 1: Translate the Business Problem into a Data Mining ProblemStep 2: Select Appropriate DataStep 3: Get to Know the DataStep 4: Create a Model SetStep 5: Fix Problems with the DataStep 6: Transform Data to Bring Information to the SurfaceStep 7: Build ModelsStep 8: Assess ModelsStep 9: Deploy ModelsStep 10: Assess ResultsStep 11: Begin AgainLessons Learned
Chapter 6: Data Mining Using Classic Statistical Techniques
Similarity ModelsTable Lookup ModelsRFM: A Widely Used Lookup ModelNaïve Bayesian ModelsLinear RegressionMultiple RegressionLogistic RegressionFixed Effects and Hierarchical EffectsLessons Learned
Chapter 7: Decision Trees
What Is a Decision Tree and How Is It Used?Decision Trees Are Local ModelsGrowing Decision TreesFinding the Best SplitPruningExtracting Rules from TreesDecision Tree VariationsAssessing the Quality of a Decision TreeWhen Are Decision Trees Appropriate?Case Study: Process Control in a Coffee Roasting PlantLessons Learned
Chapter 8: Artificial Neural Networks
A Bit of HistoryThe Biological ModelArtificial Neural NetworksA Sample Application: Real Estate AppraisalTraining Neural NetworksRadial Basis Function NetworksNeural Networks in PracticeChoosing the Training SetPreparing the DataInterpreting the Output from a Neural NetworkNeural Networks for Time SeriesCan Neural Network Models Be Explained?Lessons Learned
Chapter 9: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering
Memory-Based ReasoningChallenges of MBRCase Study: Using MBR for Classifying Anomalies in MammogramsMeasuring Distance and SimilarityThe Combination Function: Asking the Neighbors for AdviceCase Study: Shazam — Finding Nearest Neighbors for Audio FilesCollaborative Filtering: A Nearest-Neighbor Approach to Making RecommendationsLessons Learned
Chapter 10: Knowing When to Worry: Using Survival Analysis to Understand Customers
Customer SurvivalHazard ProbabilitiesFrom Hazards to SurvivalProportional HazardsSurvival Analysis in PracticeLessons Learned
Chapter 11: Genetic Algorithms and Swarm Intelligence
OptimizationGenetic AlgorithmsThe Traveling Salesman ProblemCase Study: Using Genetic Algorithms for Resource OptimizationCase Study: Evolving a Solution for Classifying ComplaintsLessons Learned
Chapter 12: Tell Me Something New: Pattern Discovery and Data Mining
Undirected Techniques, Undirected Data MiningWhat is Undirected Data Mining?Methodology for Undirected Data MiningLessons Learned
Chapter 13: Finding Islands of Similarity: Automatic Cluster Detection
Searching for Islands of SimplicityCustomer Segmentation and ClusteringThe K-Means Clustering AlgorithmInterpreting ClustersEvaluating ClustersCase Study: Clustering TownsVariations on K-MeansData Preparation for ClusteringLessons Learned
Chapter 14: Alternative Approaches to Cluster Detection
Shortcomings of K-MeansGaussian Mixture ModelsDivisive ClusteringAgglomerative (Hierarchical) ClusteringSelf-Organizing MapsThe Search Continues for Islands of SimplicityLessons Learned
Chapter 15: Market Basket Analysis and Association Rules
Defining Market Basket AnalysisCase Study: Spanish or EnglishAssociation AnalysisBuilding Association RulesExtending the IdeasAssociation Rules and Cross-SellingSequential Pattern AnalysisLessons Learned
Chapter 16: Link Analysis
Basic Graph TheorySocial Network AnalysisMining Call GraphsCase Study: Tracking Down the Leader of the PackCase Study: Who Is Using Fax Machines from Home?How Google Came to Rule the WorldLessons Learned
Chapter 17: Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining
The Architecture of DataA General Architecture for Data WarehousingAnalytic SandboxesWhere Does OLAP Fit In?Where Data Mining Fits in with Data WarehousingLessons Learned
Chapter 18: Building Customer Signatures
Finding Customers in DataDesigning SignaturesWhat a Signature Looks LikeProcess for Creating SignaturesDealing with Missing ValuesLessons Learned
Chapter 19: Derived Variables: Making the Data Mean More
Handset Churn Rate as a Predictor of ChurnSingle-Variable TransformationsCombining VariablesExtracting Features from Time SeriesExtracting Features from GeographyUsing Model Scores as InputsHandling Sparse DataCapturing Customer Behavior from TransactionsLessons Learned
Chapter 20: Too Much of a Good Thing? Techniques for Reducing the Number of Variables
Problems with Too Many VariablesThe Sparse Data ProblemFlavors of Variable Reduction TechniquesSequential Selection of FeaturesOther Directed Variable Selection MethodsPrincipal ComponentsVariable ClusteringLessons Learned
Chapter 21: Listen Carefully to What Your Customers Say: Text Mining
What Is Text Mining?Working with Text DataCase Study: Ad Hoc Text MiningClassifying News Stories Using MBRFrom Text to NumbersText Mining and Naïve Bayesian ModelsDIRECTV: A Case Study in Customer ServiceLessons Learned
Index

Content preview from Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition

To Stephanie, Sasha, and Nathaniel. Without your patience and understanding, this book would not have been possible.

—Michael

To Puccio.

Grazie per essere paziente con me.

Ti amo.

—Gordon

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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition

by Gordon S. Linoff, Michael J. A. Berry

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.