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weight, 548

target market response versus, 38

combination function

controlled experiments, hypothesis

attrition history, 280

testing, 51

MBR (memory-response reasoning),

convenience users, behavior-based

258, 265

variables, 580, 587“589

neural networks, 222

cookies, Web servers, 109

weighted voting, 281“282

correct classification matrix, 79

commercial software products, 15

correlation ranges, statistics, 139

communication channels,

costs

prospecting, 89

cost columns, 548

companies. See businesses

decision tree considerations, 195

comparisons

countervailing errors, 81

comparing models, using lift ratio,
counts, converting to proportions,

81“82

75“76
data, 83

coverage of values, neural networks,
statistical analysis, 148“149

232“233
competing risks, hazards, 403

Cox proportional hazards, 410“411
622 Index


creative process, data mining as, 33
stages, 457

credit
strategies for, 6

credit applications
stratification, 469

classification tasks, 9
subscription-based relationships,

prediction tasks, 10
459“460

useful data sources, 60
survival analysis, 413“415

credit risks, reducing exposure to, transaction processing systems, 3“4

113“114
up-selling, 467

crossover, generic algorithms, 430
winback approach, 470

cross-selling opportunities
customer-centric businesses,
affinity grouping, 11
514“515, 516“521

customer relationships, 467
demographic profiles, 31

marketing campaigns, 111, 115“116
grouping, collaborative filtering





Y
reasons for, 17
and, 90

cross-tabulations, 136, 567“568
interactions, learning opportunities,





FL
cumulative gains, 36, 101
520“521

cumulative response
loyalty, 520

AM
concentration, 82“83
marginal, 553

results, assessing, 85
new customer information

customers
gathering, 109“110

attributes, clustering, 11
memory-based reasoning, 277

TE

behaviors of, gaining insight, 56
profiles, building, 283

customer relationships
prospective customer value, 115

bad customers, weeding out, 18
responses

building businesses around, 2
to marketing campaigns, 109

customer acquisition, 461“464
prediction, MBR, 258

customer activation, 464“466
retrospective customer value, 115

customer-centric enterprises, 3
segmentation, marketing campaigns,

data mining role in, 5“6
111“113

data warehousing, 4“5
sequential patterns, identifying, 24

deep intimacy, 449, 451
signatures

event-based relationships, 458“459
assembling, 68

good customers, holding on to,
business versus residential

17“18
customers, 561

in-between relationships, 453
columns, pivoting, 563

indirect relationships, 453“454
computational issues, 594“596

interests in, 13“14
considerations, 564

large-business relationships, 3“4
customer identification, 560“562

levels of, 448
data for, cataloging, 559“560

life stages, 455“456
discussed, 540“541

lifetime customer value, 32
model set creation, 68

mass intimacy, 451“453
snapshots, 562

retention, 467“469
time frames, identifying, 562

service business sectors, 13“14
single views, 517“518
small-business relationships, 2



Team-Fly®
Index 623


discussed, 64

sorting, by scores, 8

distributions, examining, 65

telecommunications, market based

histograms, 565“566

analysis, 288

intuition, 65

cutoff scores, 98

question asking, 67“68

cyclic graphs, 330“331

data marts, 485, 491“492
D data selection

data
contents of, outcomes of interest, 64

acquisition-time, 108“110
data locations, 61“62

as actionable information, 516
density, 62“63

availability, determining, 515“516
history of, determining, 63

binary, 557
scarce data, 61“62

business versus scientific, statistical
variable combinations, 63“64

analysis, 159
data transformation

censored, 161
capture trends, 75

by census tract, 94
counts, converting to proportions,

central repository, 484, 488, 490
75“76

columns

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