Verfügbarkeit: Data mining techniques

Data mining techniques: for marketing, sales, and customer relationship management

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Bibliographische Detailangaben
1. Verfasser:	Berry, Michael J. A. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Indianapolis, Ind. Wiley 2004
Ausgabe:	2. ed.
Schriftenreihe:	Wiley technology publication
Schlagworte:	Exploration de données (Informatique) Gestion - Informatique Marketing - Informatique Datenverarbeitung Wirtschaft Business > Data processing Data mining Marketing > Data processing Data Mining Kundenmanagement Marketing Data-Warehouse-Konzept
Online-Zugang:	Table of contents Publisher description Klappentext
Beschreibung:	Includes index
Beschreibung:	XXV, 643 S. graph. Darst.
ISBN:	0471470643

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Datensatz im Suchindex

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adam_text	Contents Acknowledgments XIX About the Authors xxi Introduction xxiii Chapter 1 Why and What Is Data Mining? 1 Analytic Customer Relationship Management 2 The Role of Transaction Processing Systems 3 The Role of Data Warehousing 4 The Role of Data Mining 5 The Role of the Customer Relationship Management Strategy 6 What Is Data Mining? 7 What Tasks Can Be Performed with Data Mining? 8 Classification 8 Estimation 9 Prediction 10 Affinity Grouping or Association Rules 11 Clustering 11 Profiling 12 Why Now? 12 Data Is Being Produced 12 Data Is Being Warehoused 13 Computing Power Is Affordable 13 Interest in Customer Relationship Management Is Strong 13 Every Business Is a Service Business 14 Information Is a Product 14 Commercial Data Mining Software Products Have Become Available 15 vi Contents How Data Mining Is Being Used Today 15 A Supermarket Becomes an Information Broker 15 A Recommendation-Based Business 16 Cross-Selling 17 Holding on to Good Customers 17 Weeding out Bad Customers 18 Revolutionizing an Industry 18 And Just about Anything Else 19 Lessons Learned 19 Chapter 2 The Virtuous Cycle of Data Mining 21 A Case Study in Business Data Mining 22 Identifying the Business Challenge 23 Applying Data Mining 24 Acting on the Results 25 Measuring the Effects 25 What Is the Virtuous Cycle? 26 Identify the Business Opportunity 27 Mining Data 28 Take Action 30 Measuring Results 30 Data Mining in the Context of the Virtuous Cycle 32 A Wireless Communications Company Makes the Right Connections 34 The Opportunity 34 How Data Mining Was Applied 35 Defining the Inputs 37 Derived Inputs 37 The Actions 38 Completing the Cycle 39 Neural Networks and Decision Trees Drive SUV Sales 39 The Initial Challenge 39 How Data Mining Was Applied 40 The Data 40 Down the Mine Shaft 40 The Resulting Actions 41 Completing the Cycle 42 Lessons Learned 42 Chapter 3 Data Mining Methodology and Best Practices 43 Why Have a Methodology? 44 Learning Things That Aren t True 44 Patterns May Not Represent Any Underlying Rule 45 The Model Set May Not Reflect the Relevant Population 46 Data May Be at the Wrong Level of Detail 47 Contents vii Learning Things That Are True, but Not Useful 48 Learning Things That Are Already Known 49 Learning Things That Can t Be Used 49 Hypothesis Testing 50 Generating Hypotheses 51 Testing Hypotheses 51 Models, Profiling, and Prediction 51 Profiling 53 Prediction 54 The Methodology 54 Step One: Translate the Business Problem into a Data Mining Problem 56 What Does a Data Mining Problem Look Like? 56 How Will the Results Be Used? 57 How Will the Results Be Delivered? 58 The Role of Business Users and Information Technology 58 Step Two: Select Appropriate Data 60 What Is Available? 61 How Much Data Is Enough? 62 How Much History Is Required? 63 How Many Variables? 63 What Must the Data Contain? 64 Step Three: Get to Know the Data 64 Examine Distributions 65 Compare Values with Descriptions 66 Validate Assumptions 67 Ask Lots of Questions 67 Step Four: Create a Model Set 68 Assembling Customer Signatures 68 Creating a Balanced Sample 68 Including Multiple Timeframes 70 Creating a Model Set for Prediction 70 Partitioning the Model Set 71 Step Five: Fix Problems with the Data 72 Categorical Variables with Too Many Values 73 Numeric Variables with Skewed Distributions and Outliers 73 Missing Values 73 Values with Meanings That Change over Time 74 Inconsistent Data Encoding 74 Step Six: Transform Data to Bring Information to the Surface 74 Capture Trends 75 Create Ratios and Other Combinations of Variables 75 Convert Counts to Proportions 75 Step Seven: Build Models 77 viii Contents Step Eight: Assess Models 78 Assessing Descriptive Models 78 Assessing Directed Models 78 Assessing Classifiers and Predictors 79 Assessing Estimators 79 Comparing Models Using Lift 81 Problems with Lift 83 Step Nine: Deploy Models 84 Step Ten: Assess Results 85 Step Eleven: Begin Again 85 Lessons Learned 86 Chapter 4 Data Mining Applications in Marketing and Customer Relationship Management 87 Prospecting 87 Identifying Good Prospects 88 Choosing a Communication Channel 89 Picking Appropriate Messages 89 Data Mining to Choose the Right Place to Advertise 90 Who Fits the Profile? 90 Measuring Fitness for Groups of Readers 93 Data Mining to Improve Direct Marketing Campaigns 95 Response Modeling 96 Optimizing Response for a Fixed Budget 97 Optimizing Campaign Profitability 100 How the Model Affects Profitability 103 Reaching the People Most Influenced by the Message 106 Differential Response Analysis 107 Using Current Customers to Learn About Prospects 108 Start Tracking Customers before They Become Customers 109 Gather Information from New Customers 109 Acquisition-Time Variables Can Predict Future Outcomes 110 Data Mining for Customer Relationship Management 110 Matching Campaigns to Customers no Segmenting the Customer Base 111 Finding Behavioral Segments 111 Tying Market Research Segments to Behavioral Data 113 Reducing Exposure to Credit Risk 113 Predicting Who Will Default 113 Improving Collections 114 Determining Customer Value 114 Cross-selling, Up-selling, and Making Recommendations 115 Finding the Right Time for an Offer 115 Making Recommendations 116 Retention and Churn 116 Recognizing Churn 116 Why Chum Matters 117 Different Kinds of Churn 118 Contents ix Different Kinds of Churn Model 119 Predicting Who Will Leave 119 Predicting How Long Customers Will Stay 119 Lessons Learned 120 Chapter 5 The Lure of Statistics: Data Mining Using Familiar Tools 123 Occam s Razor 124 The Null Hypothesis 125 P-Values 126 A Look at Data 126 Looking at Discrete Values 127 Histograms 127 Time Series 128 Standardized Values 129 From Standardized Values to Probabilities 133 Cross-Tabulations 136 Looking at Continuous Variables 136 Statistical Measures for Continuous Variables 137 Variance and Standard Deviation 138 A Couple More Statistical Ideas 139 Measuring Response 139 Standard Error of a Proportion 139 Comparing Results Using Confidence Bounds 141 Comparing Results Using Difference of Proportions 143 Size of Sample 145 What the Confidence Interval Really Means 146 Size of Test and Control for an Experiment 147 Multiple Comparisons 148 The Confidence Level with Multiple Comparisons 148 Bonferroni s Correction 149 Chi-Square Test 149 Expected Values 150 Chi-Square Value 151 Comparison of Chi-Square to Difference of Proportions ; 153 An Example: Chi-Square for Regions and Starts 155 Data Mining and Statistics 158 No Measurement Error in Basic Data 159 There Is a Lot of Data 160 Time Dependency Pops Up Everywhere 160 Experimentation is Hard 160 Data Is Censored and Truncated 161 Lessons Learned 162 Chapter 6 Decision Trees 165 What Is a Decision Tree? 166 Classification 166 Scoring 169 Estimation 170 Trees Grow in Many Forms 170 Contents Chapter 7 How a Decision Tree Is Grown 171 Finding the Splits 172 Splitting on a Numeric Input Variable 173 Splitting on a Categorical Input Variable 174 Splitting in the Presence of Missing Values 174 Growing the Full Tree 175 Measuring the Effectiveness Decision Tree 176 Tests for Choosing the Best Split 176 Purity and Diversity 177 Gini or Population Diversity 178 Entropy Reduction or Information Gain 179 Information Gain Ratio 180 Chi-Square Test 180 Reduction in Variance 183 F Test 183 Pruning 184 The CART Pruning Algorithm 185 Creating the Candidate Subtrees 185 Picking the Best Subtree 189 Using the Test Set to Evaluate the Final Tree 189 The C5 Pruning Algorithm 190 Pessimistic Pruning 191 Stability-Based Pruning 191 Extracting Rules from Trees 193 Taking Cost into Account 195 Further Refinements to the Decision Tree Method 195 Using More Than One Field at a Time 195 Tilting the Hyperplane 197 Neural Trees 199 Piecewise Regression Using Trees 199 Alternate Representations for Decision Trees 199 Box Diagrams 199 Tree Ring Diagrams 201 Decision Trees in Practice 203 Decision Trees as a Data Exploration Tool 203 Applying Decision-Tree Methods to Sequential Events 205 Simulating the Future 206 Case Study: Process Control in a Coffee-Roasting Plant 206 Lessons Learned 209 Artificial Neural Networks 21! A Bit of History 212 Real Estate Appraisal 213 Neural Networks for Directed Data Mining 219 What Is a Neural Net? 220 What Is the Unit of a Neural Network? 222 Feed-Forward Neural Networks 226 Contents xi How Does a Neural Network Learn Using Back Propagation? 228 Heuristics for Using Feed-Forward, Back Propagation Networks 231 Choosing the Training Set 232 Coverage of Values for All Features 232 Number of Features 233 Size of Training Set 234 Number of Outputs 234 Preparing the Data 235 Features with Continuous Values 235 Features with Ordered, Discrete (Integer) Values 238 Features with Categorical Values 239 Other Types of Features 241 Interpreting the Results 241 Neural Networks for Time Series 244 How to Know What Is Going on Inside a Neural Network 247 Self-Organizing Maps 249 What Is a Self-Organizing Map? 249 Example: Finding Clusters 252 Lessons Learned 254 Chapter 8 Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering 257 Memory Based Reasoning 258 Example: Using MBR to Estimate Rents in Tuxedo, New York 259 Challenges of MBR 262 Choosing a Balanced Set of Historical Records 262 Representing the Training Data 263 Determining the Distance Function, Combination Function, and Number of Neighbors 265 Case Study: Classifying News Stories 265 What Are the Codes? 266 Applying MBR 267 Choosing the Training Set 267 Choosing the Distance Function 267 Choosing the Combination Function 267 Choosing the Number of Neighbors 270 The Results 270 Measuring Distance 271 What Is a Distance Function? 271 Building a Distance Function One Field at a Time 274 Distance Functions for Other Data Types 277 When a Distance Metric Already Exists 278 The Combination Function: Asking the Neighbors for the Answer 279 The Basic Approach: Democracy 279 Weighted Voting 281 xii Contents Collaborative Filtering: A Nearest Neighbor Approach to Making Recommendations 282 Building Profiles 283 Comparing Profiles 284 Making Predictions 284 Lessons Learned 285 Chapter 9 Market Basket Analysis and Association Rules 287 Defining Market Basket Analysis 289 Three Levels of Market Basket Data 289 Order Characteristics 292 Item Popularity 293 Tracking Marketing Interventions 293 Clustering Products by Usage 294 Association Rules 296 Actionable Rules 296 Trivial Rules 297 Inexplicable Rules 297 How Good Is an Association Rule? 299 Building Association Rules 302 Choosing the Right Set of Items 303 Product Hierarchies Help to Generalize Items 305 Virtual Items Go beyond the Product Hierarchy 307 Data Quality 308 Anonymous versus Identified 308 Generating Rules from All This Data 308 Calculating Confidence 309 Calculating Lift 310 The Negative Rule 311 Overcoming Practical Limits 311 The Problem of Big Data 313 Extending the Ideas 315 Using Association Rules to Compare Stores 315 Dissociation Rules 317 Sequential Analysis Using Association Rules 318 Lessons Learned 319 Chapter 10 Link Analysis 321 Basic Graph Theory 322 Seven Bridges of Königsberg 325 Traveling Salesman Problem 327 Directed Graphs 330 Detecting Cycles in a Graph 330 A Familiar Application of Link Analysis 331 The Kleinberg Algorithm 332 The Details: Finding Hubs and Authorities 333 Creating the Root Set 333 Identifying the Candidates 334 Ranking Hubs and Authorities 334 Hubs and Authorities in Practice 336 Contents xiii Case Study: Who Is Using Fax Machines from Home? 336 Why Finding Fax Machines Is Useful 336 The Data as a Graph 337 The Approach 338 Some Results 340 Case Study: Segmenting Cellular Telephone Customers 343 The Data 343 Analyses without Graph Theory 343 A Comparison of Two Customers 344 The Power of Link Analysis 345 Lessons Learned 346 Chapter 11 Automatic Cluster Detection 349 Searching for Islands of Simplicity 350 Star Light, Star Bright 351 Fitting the Troops 352 K-Means Clustering 354 Three Steps of the K-Means Algorithm 354 What К Means 356 Similarity and Distance 358 Similarity Measures and Variable Type 359 Formal Measures of Similarity 360 Geometric Distance between Two Points 360 Angle between Two Vectors 361 Manhattan Distance 363 Number of Features in Common 363 Data Preparation for Clustering 363 Scaling for Consistency 363 Use Weights to Encode Outside Information 365 Other Approaches to Cluster Detection 365 Gaussian Mixture Models 365 Agglomerative Clustering 368 An Agglomerative Clustering Algorithm 368 Distance between Clusters 368 Clusters and Trees 370 Clustering People by Age: An Example of Agglomerative Clustering 370 Divisive Clustering 371 Self-Organizing Maps 372 Evaluating Clusters 372 Inside the Cluster 373 Outside the Cluster 373 Case Study: Clustering Towns 374 Creating Town Signatures 374 The Data 375 Creating Clusters 377 Determining the Right Number of Clusters 377 Using Thematic Clusters to Adjust Zone Boundaries 380 Lessons Learned 381 xiv Contents Chapter 12 Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing 383 Customer Retention 385 Calculating Retention 385 What a Retention Curve Reveals 386 Finding the Average Tenure from a Retention Curve 387 Looking at Retention as Decay 389 Hazards 394 The Basic Idea 394 Examples of Hazard Functions 397 Constant Hazard 397 Bathtub Hazard 397 A Real-World Example 398 Censoring 399 Other Types of Censoring 402 From Hazards to Survival 404 Retention 404 Survival 405 Proportional Hazards 408 Examples of Proportional Hazards 409 Stratification; Measuring Initial Effects on Survival 410 Cox Proportional Hazards 410 Limitations of Proportional Hazards 411 Survival Analysis in Practice 412 Handling Different Types of Attrition 412 When Will a Customer Come Back? 413 Forecasting 415 Hazards Changing over Time 416 Lessons Learned 418 Chapter 13 Genetic Algorithms 421 How They Work 423 Genetics on Computers 424 Selection 429 Crossover 430 Mutation 431 Representing Data 432 Case Study: Using Genetic Algorithms for Resource Optimization 433 Schemata: Why Genetic Algorithms Work 435 More Applications of Genetic Algorithms 438 Application to Neural Networks 439 Case Study: Evolving a Solution for Response Modeling 440 Business Context 440 Data 441 The Data Mining Task: Evolving a Solution 442 Beyond the Simple Algorithm 444 Lessons Learned 446 Contents xv Chapter 14 Data Mining throughout the Customer Life Cycle 447 Levels of the Customer Relationship 448 Deep Intimacy 449 Mass Intimacy 451 In-between Relationships 453 Indirect Relationships 453 Customer Life Cycle 454 The Customer s Life Cycle: Life Stages 455 Customer Life Cycle 456 Subscription Relationships versus Event-Based Relationships 458 Event-Based Relationships 458 Subscription-Based Relationships 459 Business Processes Are Organized around the Customer Life Cycle 461 Customer Acquisition 461 Who Are the Prospects? 462 When Is a Customer Acquired? 462 What Is the Role of Data Mining? 464 Customer Activation 464 Relationship Management 466 Retention 467 Winback 470 Lessons Learned 470 Chapter 15 Data Warehousing, OLAP, and Data Mining 473 The Architecture of Data 475 Transaction Data, the Base Level 476 Operational Summary Data 477 Decision-Support Summary Data 477 Database Schema 478 Metadata 483 Business Rules 484 A General Architecture for Data Warehousing 484 Source Systems 486 Extraction, Transformation, and Load 487 Central Repository 488 Metadata Repository 491 Data Marts 491 Operational Feedback 492 End Users and Desktop Tools 492 Analysts 492 Application Developers 493 Business Users 494 Where Does OLAP Fit In? 494 What s in a Cube? 497 Three Varieties of Cubes 498 Facts 501 Dimensions and Their Hierarchies 502 Conformed Dimensions 504 xvi Contents Star Schema 505 OLAP and Data Mining 507 Where Data Mining Fits in with Data Warehousing 508 Lots of Data 509 Consistent, Clean Data 510 Hypothesis Testing and Measurement 510 Scalable Hardware and RDBMS Support 511 Lessons Learned 511 Chapter 16 Building the Data Mining Environment 513 A Customer-Centric Organization 514 An Ideal Data Mining Environment 515 The Power to Determine What Data Is Available 515 The Skills to Turn Data into Actionable Information 516 All the Necessary Tools 516 Back to Reality 516 Building a Customer-Centric Organization 516 Creating a Single Customer View 517 Defining Customer-Centric Metrics 519 Collecting the Right Data 520 From Customer Interactions to Learning Opportunities 520 Mining Customer Data 521 The Data Mining Group 521 Outsourcing Data Mining 522 Outsourcing Occasional Modeling 522 Outsourcing Ongoing Data Mining 523 bisourdng Data Mining 524 Building an Interdisciplinary Data Mining Group 524 Building a Data Mining Group in IT 524 • Building a Data Mining Group in the Business Units 525 What to Look for in Data Mining Staff 525 Data Mining Infrastructure 526 The Mining Platform 527 The Scoring Platform 527 One Example of a Production Data Mining Architecture 528 Architectural Overview 528 Customer Interaction Module 529 Analysis Module 530 Data Mining Software 532 Range of Techniques 532 Scalability 533 Support for Scoring 534 Multiple Levels of User Interfaces 535 Comprehensible Output 536 Ability to Handle Diverse Data Types 536 Documentation and Ease of Use 536 Contents xvii Availability of Training for Both Novice and Advanced Users, Consulting, and Support 537 Vendor Credibility 537 Lessons Learned 537 Chapter 17 Preparing Data for Mining 539 What Data Should Look Like 540 The Customer Signature 540 The Columns 542 Columns with One Value 544 Columns with Almost Only One Value 544 Columns with Unique Values 546 Columns Correlated with Target 547 Model Roles in Modeling 547 Variable Measures 549 Numbers 550 Dates and Times 552 Fixed-Length Character Strings 552 IDs and Keys 554 Names 555 Addresses 555 Free Text 556 Binary Data (Audio, Image, Etc.) 557 Data for Data Mining 557 Constructing the Customer Signature 558 Cataloging the Data 559 Identifying the Customer 560 First Attempt 562 Identifying the Time Frames 562 Taking a Recent Snapshot 562 Pivoting Columns 563 Calculating the Target 563 Making Progress 564 Practical Issues 564 Exploring Variables 565 Distributions Are Histograms 565 Changes over Time 566 Crosstabulations 567 Deriving Variables 568 Extracting Features from a Single Value 569 Combining Values within a Record 569 Looking Up Auxiliary Information 569 Pivoting Regular Time Series 572 Summarizing Transactional Records 574 Summarizing Fields across the Model Set 574 xviii Contents Examples of Behavior-Based Variables 575 Frequency of Purchase 575 Declining Usage 577 Revolvers, Transactors, and Convenience Users: Defining Customer Behavior 580 Data 581 Segmenting by Estimating Revenue 581 Segmentation by Potential 583 Customer Behavior by Comparison to Ideals 585 The Ideal Convenience User 587 The Dark Side of Data 590 Missing Values 590 Dirty Data 592 Inconsistent Values 593 Computational Issues 594 Source Systems 594 Extraction Tools 595 Special-Purpose Code 595 Data Mining Tools 595 Lessons Learned 596 Chapter! 8 Putting Data Mining to Work 597 Getting Started 598 What to Expect from a Proof-of-Concept Project 599 Identifying a Proof-of-Concept Project 599 Implementing the Proof-of-Concept Project 601 Act on Your Findings 602 Measure the Results of the Actions 603 Choosing a Data Mining Technique 605 Formulate the Business Goal as a Data Mining Task 605 Determine the Relevant Characteristics of the Data 606 Data Type 606 Number of Input Fields 607 Free-Form Text 607 Consider Hybrid Approaches 608 How One Company Began Data Mining 608 A Controlled Experiment in Retention 609 The Data 611 The Findings 613 The Proof of the Pudding 614 Lessons Learned 614 Index 615
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id	DE-604.BV019396445
illustrated	Illustrated
indexdate	2024-07-09T19:59:20Z
institution	BVB
isbn	0471470643
language	English
lccn	2003026693
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-012858946
oclc_num	265463169
open_access_boolean
owner	DE-739 DE-1047 DE-Aug4 DE-945 DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-522 DE-634
owner_facet	DE-739 DE-1047 DE-Aug4 DE-945 DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-522 DE-634
physical	XXV, 643 S. graph. Darst.
publishDate	2004
publishDateSearch	2004
publishDateSort	2004
publisher	Wiley
record_format	marc
series2	Wiley technology publication
spelling	Berry, Michael J. A. Verfasser aut Data mining techniques for marketing, sales, and customer relationship management Michael J.A. Berry, Gordon S. Linoff 2. ed. Indianapolis, Ind. Wiley 2004 XXV, 643 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley technology publication Includes index Exploration de données (Informatique) Gestion - Informatique Marketing - Informatique Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining (DE-588)4428654-5 gnd rswk-swf Kundenmanagement (DE-588)4236865-0 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf Data-Warehouse-Konzept (DE-588)4406462-7 gnd rswk-swf Data Mining (DE-588)4428654-5 s Data-Warehouse-Konzept (DE-588)4406462-7 s DE-604 Marketing (DE-588)4037589-4 s Kundenmanagement (DE-588)4236865-0 s 1\p DE-604 Linoff, Gordon Sonstige oth http://www.loc.gov/catdir/toc/ecip0412/2003026693.html Table of contents http://www.loc.gov/catdir/description/wiley041/2003026693.html Publisher description Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012858946&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Berry, Michael J. A. Data mining techniques for marketing, sales, and customer relationship management Exploration de données (Informatique) Gestion - Informatique Marketing - Informatique Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining (DE-588)4428654-5 gnd Kundenmanagement (DE-588)4236865-0 gnd Marketing (DE-588)4037589-4 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd
subject_GND	(DE-588)4428654-5 (DE-588)4236865-0 (DE-588)4037589-4 (DE-588)4406462-7
title	Data mining techniques for marketing, sales, and customer relationship management
title_auth	Data mining techniques for marketing, sales, and customer relationship management
title_exact_search	Data mining techniques for marketing, sales, and customer relationship management
title_full	Data mining techniques for marketing, sales, and customer relationship management Michael J.A. Berry, Gordon S. Linoff
title_fullStr	Data mining techniques for marketing, sales, and customer relationship management Michael J.A. Berry, Gordon S. Linoff
title_full_unstemmed	Data mining techniques for marketing, sales, and customer relationship management Michael J.A. Berry, Gordon S. Linoff
title_short	Data mining techniques
title_sort	data mining techniques for marketing sales and customer relationship management
title_sub	for marketing, sales, and customer relationship management
topic	Exploration de données (Informatique) Gestion - Informatique Marketing - Informatique Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining (DE-588)4428654-5 gnd Kundenmanagement (DE-588)4236865-0 gnd Marketing (DE-588)4037589-4 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd
topic_facet	Exploration de données (Informatique) Gestion - Informatique Marketing - Informatique Datenverarbeitung Wirtschaft Business Data processing Data mining Marketing Data processing Data Mining Kundenmanagement Marketing Data-Warehouse-Konzept
url	http://www.loc.gov/catdir/toc/ecip0412/2003026693.html http://www.loc.gov/catdir/description/wiley041/2003026693.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012858946&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT berrymichaelja dataminingtechniquesformarketingsalesandcustomerrelationshipmanagement AT linoffgordon dataminingtechniquesformarketingsalesandcustomerrelationshipmanagement

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