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Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management [Pehme köide]

  • Formaat: Paperback / softback, 416 pages, kõrgus x laius x paksus: 236x187x22 mm, kaal: 592 g
  • Ilmumisaeg: 27-Nov-2000
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471385646
  • ISBN-13: 9780471385646
Teised raamatud teemal:
  • Formaat: Paperback / softback, 416 pages, kõrgus x laius x paksus: 236x187x22 mm, kaal: 592 g
  • Ilmumisaeg: 27-Nov-2000
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471385646
  • ISBN-13: 9780471385646
Teised raamatud teemal:
Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions

In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.

Arvustused

ACA"ACA...the descriptions are clear, concise, unambiguousACA...she has clearly succeededACA...ACA"(The Institute of Direct Marketing -theidm.com

Acknowledgments xv
Foreword xvii
Introduction xix
About the Author xxiii
About the Contributors xxv
Part One: Planning the Menu 1(48)
Setting the Objective
3(22)
Defining the Goal
4(8)
Profile Analysis
7(1)
Segmentation
8(1)
Response
8(1)
Risk
9(1)
Activation
10(1)
Cross-Sell and Up-Sell
10(1)
Attrition
10(1)
Net Present Value
11(1)
Lifetime Value
11(1)
Choosing the Modeling Methodology
12(8)
Linear Regression
12(3)
Logistic Regression
15(1)
Neural Networks
16(1)
Genetic Algorithms
17(2)
Classification Trees
19(1)
The Adaptive Company
20(3)
Hiring and Teamwork
21(1)
Product Focus versus Customer Focus
22(1)
Summary
23(2)
Selecting the Data Sources
25(24)
Types of Data
26(1)
Sources of Data
27(9)
Internal Sources
27(9)
External Sources
36(1)
Selecting Data for Modeling
36(8)
Data for Prospecting
37(3)
Data for Customer Models
40(2)
Data for Risk Models
42(2)
Constructing the Modeling Data Set
44(4)
How Big Should My Sample Be?
44(1)
Sampling Methods
45(2)
Developing Models from Modeled Data
47(1)
Combining Data from Multiple Offers
47(1)
Summary
48(1)
Part Two: The Cooking Demonstration 49(132)
Preparing the Data for Modeling
51(20)
Accessing the Data
51(6)
Classifying Data
54(1)
Reading Raw Data
55(2)
Creating the Modeling Data Set
57(3)
Sampling
58(2)
Cleaning the Data
60(10)
Continuous Variables
60(9)
Categorical Variables
69(1)
Summary
70(1)
Selecting and Transforming the Variables
71(30)
Defining the Objective Function
71(3)
Probability of Activation
72(1)
Risk Index
73(1)
Product Profitability
73(1)
Marketing Expense
74(1)
Deriving Variables
74(2)
Summarization
74(1)
Ratios
75(1)
Dates
75(1)
Variable Reduction
76(4)
Continuous Variables
76(4)
Categorical Variables
80(5)
Developing Linear Predictors
85(13)
Continuous Variables
85(10)
Categorical Variables
95(3)
Interactions Detection
98(1)
Summary
99(2)
Processing and Evaluating the Model
101(24)
Processing the Model
102(22)
Splitting the Data
103(5)
One Model
108(11)
Two Models---Response
119(1)
Two Models---Activation
119(2)
Comparing Method 1 and Method 2
121(3)
Summary
124(1)
Validating the Model
125(26)
Gains Tables and Charts
125(5)
One Model
126(1)
Two Models
127(3)
Scoring Alternate Data Sets
130(4)
Resampling
134(12)
Jackknifing
134(4)
Bootstrapping
138(8)
Decile Analysis on Key Variables
146(4)
Summary
150(1)
Implementing and Maintaining the Model
151(30)
Scoring a New File
151(10)
Scoring In-house
152(3)
Outside Scoring and Auditing
155(6)
Implementing the Model
161(9)
Calculating the Financials
161(5)
Determining the File Cut-off
166(1)
Champion versus Challenger
166(1)
The Two-Model Matrix
167(3)
Model Tracking
170(7)
Back-end Validation
176(1)
Model Maintenance
177(2)
Model Life
177(1)
Model Log
178(1)
Summary
179(2)
Part Three: Recipes for Every Occasion 181(142)
Understanding Your Customer: Profiling and Segmentation
183(24)
What Is the Importance of Understanding Your Customer?
184(6)
Types of Profiling and Segmentation
184(6)
Profiling and Penetration Analysis of a Catalog Company's Customers
190(8)
RFM Analysis
190(3)
Penetration Analysis
193(5)
Developing a Customer Value Matrix for a Credit Card Company
198(5)
Customer Value Analysis
198(5)
Performing Cluster Analysis to Discover Customer Segments
203(1)
Summary
204(3)
Targeting New Prospects: Modeling Response
207(24)
Defining the Objective
207(3)
All Responders Are Not Created Equal
208(2)
Preparing the Variables
210(11)
Continuous Variables
210(8)
Categorical Variables
218(3)
Processing the Model
221(3)
Validation Using Boostrapping
224(6)
Implementing the Model
230(1)
Summary
230(1)
Avoiding High-Risk Customers: Modeling Risk
231(26)
Credit Scoring and Risk Modeling
232(2)
Defining the Objective
234(1)
Preparing the Variables
235(9)
Processing the Model
244(4)
Validating the Model
248(3)
Bootstrapping
249(2)
Implementing the Model
251(2)
Scaling the Risk Score
252(1)
A Different Kind of Risk: Fraud
253(2)
Summary
255(2)
Retaining Profitable Customers: Modeling Churn
257(24)
Customer Loyalty
258(1)
Defining the Objective
258(5)
Preparing the Variables
263(5)
Continuous Variables
263(2)
Categorical Variables
265(3)
Processing the Model
268(2)
Validating the Model
270(3)
Bootstrapping
271(2)
Implementing the Model
273(5)
Creating Attrition Profiles
273(3)
Optimizing Customer Profitability
276(2)
Retaining Customers Proactively
278(1)
Summary
278(3)
Targeting Profitable Customers: Modeling Lifetime Value
281(24)
What Is Lifetime Value?
282(4)
Uses of Lifetime Value
282(2)
Components of Lifetime Value
284(2)
Applications of Lifetime Value
286(4)
Lifetime Value Case Studies
286(4)
Calculating Lifetime Value for a Renewable Product or Service
290(1)
Calculating Lifetime Value: A Case Study
290(13)
Case Study: Year One Net Revenues
291(7)
Lifetime Value Calculation
298(5)
Summary
303(2)
Fast Food: Modeling on the Web
305(18)
Web Mining and Modeling
306(10)
Defining the Objective
306(1)
Sources of Web Data
307(2)
Preparing Web Data
309(1)
Selecting the Methodology
310(6)
Branding on the Web
316(1)
Gaining Customer Insight in Real Time
317(1)
Web Usage Mining---A Case Study
318(4)
Summary
322(1)
Appendix A: Univariate Analysis for Continuous Variables 323(24)
Appendix B: Univariate Analysis of Categorical Variables 347(8)
Recommended Reading 355(2)
What's on the CD-ROM? 357(2)
Index 359


OLIVIA PARR RUD (Olivia@datasquare.com) is Executive Vice President of Data Square, LLC, a leading database marketing consulting firm. She has over 22 years' experience in data mining, predictive modeling, and segmentation for a variety of industries, including credit card, insurance, high tech, telecommunications, and catalog industries. She provides analysis and solutions for her clients in the areas of acquisition, retention, risk, and overall profitability for direct mail, telemarketing, broadcast marketing, and the Internet.