opportunities by industry, 6 profile creation of, 273, 274–275 Auditing and scoring outside, 155 – 161 B Back-end validation, 176–177 Backward regression, 103, 105, 109–110, 112, 221, 222, 245, 268 Bad data, 175 Balance bombs, 201 Bayes' Theorem, 121 Behavioral data, 26, 27 Bootstrapping, 138 , 140 – 146, 147 , 148 adjusting, 146 analysis, 229 churn modeling and, 270, 271, 272–273 formula for, 140 jackknifing versus, 138 validation using, 138, 140–146, 147, 148, 224, 225, 227–230, 249, 251, 270 Branding on the Web, 316–317 Business builders customers, 201 Business intelligence infrastructure, 33 C Categorical variables, 69–70, 80–85, 218–220 churn modeling and, 265, 266–268 linear predictors development and, 95–97 Champion versus challenger, 166 – 167, 175 Chi-square statistic, 78, 247 backward regression and, 105 categorical variables and, 80, 82, 83, 85, 218–219 continuous variables and, 210 – 211 forward selection method and, 104 score selection method and, 105 Page 360 (continued) Chi-square statistic stepwise regression and, 105 variable reduction and, 77 , 78 , 79 Churn, 10. See also Modeling churn (retaining profitable customers) case example of, 42 definition of, 11 Classification trees, 19 – 20 example of, 19 goal of, 19 interaction detection using, 98 linear regression versus, 19 purpose of, 19 software for building, 98 Classifying data, 54–55 qualitative, 54 quantitative, 54 Cleaning the data, 60–70, 188 categorical variables, 69–70 continuous variables, 60–69 missing values, 64 – 69 outliers and data errors, 60, 62–64 Web and, 309 Cluster analysis, 184 example, 205 – 206 performing to discover customer segments, 203–204, 205–206 Collaborative filtering on the Web, 313–316 ad targeting, 315 applications in near future, 315 call centers, 315 e-commerce, 315 of information, 313 knowledge management and, 314 managing personal resources, 316 marketing campaigns, 315 trend in evolution of, 314 workings of, 9–10 Combining data from multiple offers, 47–48 Computers logistic models and, 4 Constructing modeling data set, 44–48 combining data from multiple offers, 47–48 developing models from modeled data, 47 sample size, 44–45 sampling methods, 45–47 Consummate consumers, 201 Continuous data, 54–55 Continuous variables, 76 – 79, 157 , 210 – 218, 236 churn modeling and, 263–265, 266 means analysis of, 212 transformation of, 215–216, 267 Cookie, 308 Cooking demonstration, 49–180 implementing and maintaining model, 151–180 preparing data for modeling, 51–70 processing and evaluating model, 101 – 124 selecting and transforming variables, 71–99 validating model, 125–150 Creating modeling data set, 57–59 sampling, 58–59 Credit bureaus, 3 Credit scoring and risk modeling, 232–233 Cross-sell model, 10 case example of, 41 opportunities for, 6, 41 Customer, understanding your, 183 – 206 cluster analysis performing to discover customer segments, 203–204, 205–206 developing customer value matrix for credit card company, 198–203 importance of, 184–189 market segmentation keys, 186 – 189 profiling and penetration analysis of catalog company's customers, 190–198 summary of, 204 types of profiling and segmentation, 184–186 value analysis, 109 – 203 Customer acquisition modeling examples, 37–40 Page 361 Customer database components, 28–29 Customer focus versus product focus, 22–23 Customer insight gaining in real time, 317–318 Customer loyalty, 258 Customer models, data for, 40–42 Customer profitability optimizing, 276–278 Customer relationship management (CRM), 284, 285, 286 Customer value matrix development, 198–203 D Data types of, 26–27 validation, 152–155 Data errors and outliers, 60 , 62 – 64, 69 Data marts, 31 Data mining software classification trees, 98 Data preparation for modeling. See Preparing data for modeling Data requirements review and evaluation, 187 – 188 Data sources selecting, 25–48 constructing modeling data set, 44–48 for modeling, 36–44 sources of data, 27–36 summary of, 48 types of data, 26–27 Data warehouse, 31–35 definition of, 31 meta data role, 34 mistakes and best practices, 34–35 typical, 32, 45 Dates, 75–76 Decile analysis, 101 , 247 , 297 bootstrapping and, 140–141 calculating, 239 TEAMFLY Team-Fly ® creating, 113 , 116 – 117, 122 – 123 example, 249 file cut-off determination and, 166 gains table and, 149 on key variables, 146–150 of scored file, 164 using validation data, 118, 120, 121, 123 Decision tree. See Classification trees Demographic data, 26 characteristics of, 27 Demographics and profile analysis, 7–8, 185–186 Descriptive models, 4, 5 Developing models from modeled data, 47 Duration and lifetime value modeling, 284 , 285 – 286 E E-mail inquiry on response data, 308 Evaluating and processing the model. See Processing and evaluating the model Exploratory data analysis, 175 External validity of model, 174 F Factor analysis, 184 File cut-off determination, 166 Financials, calculating, 161 – 165 Focus on product versus customer, 22–23 Fraud, 253, 254–255 Frequency type of profiling, 185 G Gains tables and charts, 125–129 creating, 126–127, 247 examples, 126, 129, 133, 138, 139, 145, 147, 149 lifetime value model and, 301, 302, 303, 304 NPV model, 165 for score comparison, 250 two model method and, 127–129 validation examples, 128 , 129 , 226 – 227, 271 , 272 Genetic algorithms, 17–18 example, 18 Goal defining, 4–12 activation, 10 Page 362 (continued) Goal defining attrition, 10 – 11 cross-sell and up-sell, 10 lifetime value, 11–12 net present value, 11 profile analysis, 7 – 8 response, 8 – 9 risk, 9–10 segmentation, 8 steps in, 5 H High-risk customers, avoiding, 231–255. See also Modeling risk Hiring and teamwork, 21–22 I Implementing and maintaining the model, 151 – 180, 230 back-end validation, 176–177 calculating the financials, 161–165 champion versus challenger, 166–167 checking, 172 churn and, 273 – 278 determining file cut-off, 166 high-risk customer avoidance and, 251–253 maintenance, 177–179 scoring a new file, 151 – 161 summary of, 179–180 tracking, 170–177 two-model matrix, 167–170 Intelligence architecture of business, 33 Interactions detection, 98–99 Internal validity of model, 174 Interval data, 54 J Jackknifing, 134 – 138, 139 L Lifecycle of model, 175, 177–178 benchmarking, 177 rebuild or refresh, 177–178 Life stage as type of profiling, 186 Lifetime value model, 4, 6, 11–12, 281–304. See also Modeling lifetime value Lift measurement, 127, 136, 137, 141, 143, 224, 225, 226 Linear predictors development, 85–97 categorical variables, 95 – 97 continuous variables, 85–95 Linear regression analysis, 12–14, 208, 209, 295 examples, 13, 14 logistic regression versus, 15 , 16 net revenues and, 292 neural networks versus, 16 List compilers, 36, 41 List fatigue, 173–174 List sellers, 36 , 41 Logistic regression, 3–4, 12, 15, 16, 295, 296 categorical variables and, 95, 218 continuous variables and, 85, 86, 93 example, 15 , 223 formula for, 16 jackknifing and, 135 linear regression versus, 15, 16, 85 processing the model and, 102, 221, 222, 245, 246 variable selection using, 240 – 241 LTV. See Lifetime value model M Mail tracking, 171 Maintaining and implementing the model, 151–180. See also Implementing and maintaining the model Maintenance of model, 177 – 179 model life, 177–178 model log, 178–179 Market or population changes, 152, 153–154, 155 MC. See Multicollinearity Meta data, 31 role of, 34 types of, 34 [...]... occasion, 181– 322 avoiding high -risk customers, 231– 255 modeling churn, 257– 279 modeling lifetime value, 281– 304 modeling response, 207– 230 modeling risk, 231– 255 modeling on the Web, 305– 322 profiling and segmentation, 183– 206 retaining profitable customers, 257– 279 Page 365 targeting new prospects, 207– 230 targeting profitable customers, 281– 304 understanding your customer, 183– 206 Referrer... 221– 224, 225, 226– 227, 244– 248, 249, 268– 270 Product focus versus customer focus, 22 – 23 AM FL Y Product profitability, 72 , 73 Profile analysis, 7– 8 Profiling definition, 184 TE types of, 184– 186 Profiling and segmentation of customer See Customer, understanding your Profitable customers, retaining See Modeling churn Profitable customers, targeting See Modeling lifetime value Propensity model,... 208, 230 9, Retaining customers proactively, 278 Retaining profitable customers (modeling churn), 27 – 279 Retention modeling See also Modeling churn advantage of, 258 case example, 42 RFM (recency, frequency, monetary) value analysis, 23, 185, 190– 193, 194 Risk index, 72, 73, 162 Risk matrix example, 73 Risk model(s), 9– 231– 10, 255 See also Modeling risk banking industry and, 9 case examples of,... using bootstrapping, 224, 225, 227– 230 Targeting profitable customers, 281– 304 See also Modeling lifetime value Telephone checking, 171 Transaction database, 29 Transforming and selecting variables See Selecting and transforming variables Troubleshooting tips, 171– 175 Two-model matrix, 167– 170 U Understanding your customer See Customer, understanding your Up-sell models, 10 case example for, 41– 42... 42 fraud and, 10 insurance industry and, 9 selecting data for, 42 – 44 Risk score, scaling, 252– 254 Risky revenue customers, 201 Rotate your lists, 173– 174 R-square, 13 genetic algorithms using, 18 S Sample size, 44– 45 Sampling methods, 45 – 58 – 188 47, 59, Scoring alternate data sets, 130– 134, 221 Scoring a new file, 151– 161 data validation, 152– 155 in-house, 152– 155 outside scoring and auditing,... 184– 186 Segmentation and profiling of customer, 7– See also Customer, understanding your 8 Selecting and transforming variables, 71– 99 categorical variables, 80– 85 defining objective function, 71– 74 deriving variables, 74– 76 developing linear predictors, 85 – 97 interactions detection, 98– 99 summary of, 99 variable reduction, 76 – 79 Selecting data for modeling, 36 – 44 for customer models, 40... calculation for single product of, 162 components of, 71– 161– 74, 162 definition of, 161 file cut-off determination and, 166 gains table, 165 goal defining and, 11 lifetime value modeling and, 281, 290, 291 opportunities by industry and, 6 predicting, 71 product profitability and, 163, 165 risk component of, 72, 73 Neural networks, 16 – 17 linear regression versus, 16, 17 sample diagram of, 17 Nominal... (targeting new prospects), 207– 230 See also Targeting new prospects Modeling risk (avoiding high -risk customers), 231– 255 bootstrapping, 249, 251 credit scoring and, 232– 233 fraud, 253, 254– 255 implementing the model, 251– 253, 254 objective defining, 234– 235 preparing variables, 235– 244 processing the model, 244– 248, 249 scaling the risk score, 252– 253, 254 summary of, 255 validating the model, 248,... development and, 95 – 97 Variables, continuous, 76– 157, 210– 79, 218, 236 churn modeling and, 263– 265, 266 cleaning, 60– 69 linear predictors development and, 85 – 95 preparing, 210– 220 segmentation, 88– 90– 89, 92 transformations, 89 , 93 – 95 Variables, deriving, 74– 76 dates, 75– 76 preparing, 235– 244 ratios, 75 summarization, 74– 75 Variables, selecting and transforming See Selecting and transforming... profitable customers), 281– 304 applications of, 286– 289 business-to-business marketing, 286– 288 calculating for renewable product or service, 290 calculation, 298, 299– 303, 304 case studies, 286– 289 case study calculation, 290– 303 components of, 284 definition of, 282– 286 duration assigning, 284– 286 duration designing and, 284– 286 insurance marketing opportunities and, 291 marketing decisions and, . method, 108 114 , 115 119 preliminary evaluation, 117 – 119 score selection method, 103, 105, 110 , 111 , 112 splitting the data, 103–104, 105, 108 stepwise regression, 103, 105, 109, 110 , 111 summary. defining attrition, 10 – 11 cross-sell and up-sell, 10 lifetime value, 11 12 net present value, 11 profile analysis, 7 – 8 response, 8 – 9 risk, 9–10 segmentation, 8 steps in, 5 H High -risk customers, avoiding,. determination and, 166 gains table, 165 goal defining and, 11 lifetime value modeling and, 281, 290, 291 opportunities by industry and, 6 predicting, 71 product profitability and, 163, 165 risk