Data For Marketing Risk And Customer Relationship Management_8 pot

Page 202 [...]... natural form, psy_sq and pamsy13 (pamsy < 13) These two forms will be candidates for the final model This process is repeated for the remaining 32 variables The winning transformations for each continuous variable are combined into a new data set called acqmod.down_mod; Figure 9.5 Variable transformation selection Table 9.2 is a summary of the continuous variables and the transformations that best... code creates the buckets and produces the frequency for the variable for median education, pamsy: proc univariate data= ch09.downing noprint; weight boostwgt; var pamsy; output out=psydata pctlpts= 10 20 30 40 50 60 70 80 90 100 pctlpre=psy; run; data freqs; setch09.downing; if (_n_ eq 1) then set psydata; retain psy10 psy20 psy30 psy40 psy50 psy60 psy70 psy80 psy90 psy100; run; data freqs; set freqs;... used to assist in solicitation design and other marketing decisions The validation gains table created in proc tabulate can be seen in Figure 9.9 These values are expanded into a spreadsheet that can be seen in Figure 9.10 This table calculates cumulative values for response, 12-month sale, and lift Notice how the lift is much greater for 12months sales than it is for lift Again, this is a direct result... prospects by response and sales The deciles are all monotonically decreasing with a strong decline, especially in 12-month sales In Figure 9.10 the cumulative values and lift measures show the true power of this model Notice how the lift for sales is so much better than the lift for response This is a result of the weighting Figure 9.10 also provides a wealth of information for making marketing decisions... that is not the emphasis of this chapter, I will just use mean substitution For each variable, I also create a new identify which records have missing values The following code replaces the missing values for pid80c4 and ppbluec and creates four n variables , pid80crn, pc4_miss, ppbluecn, and pec_miss (Note: I'm using the first and last two letters to create a three-character referenc variable.) Figure... Page 220 having the values of 0 and 1 If they are both equal to 0, then the value = 2 or Branch I run frequencies for all categorical variables and analyze the results I create indicator variables for each categorical variable to capture the difference in weighted response The following code creates indicator (0/1) variables for all categorical variables in the final model: data ch09.down_mod; set ch09.down_mod;... code creates boostwght: proc univariate data= ch09.downing noprint; var sale12mo; output out=wgtdata mean=s12mmean; run; data ch09.downing; set ch09.downing; if (_n_ = 1) then set wgtdata; if respond = 1 then boostwgt = sale12mo/s12mmean; else boostwgt = 1; run; I will use the weight boostwgt to assist in transforming the variables This will help me find the form of the variable that fits a model to... groupings in Figure 9.3 will work for our segmentation OBS PSY10 PSY100 1 12 PSY20 12 PSY30 12 PSY40 12 PSY50 12 PSY60 13 PSY70 13 PSY80 14 PSY90 16 18 Based on the response rates for the groups in Figure 9.3, I will create two binary variables by segmenting pamsy at the values for PSY60 and PSY90 When you look at the column percent for respond = 1, you'll notice that as the values for pamsy increase, the response... WHITECO2 output out=ch09.scored(where=(splitwgt=.)) p=pred; run; proc sort data= ch09.scored; Figure 9.7 Logistic regression using score selection by descending pred; run; proc univariate data= ch09.scored noprint; var records; output out=preddata sum=totrec; run; data ch09.scored; set ch09.scored; if (_n_ eq 1) then set preddata; retain totrec; number+1; if number < 1*totrec then val_dec = 0; else if... else else else else else proc freq data= freqs; weight boostwgt; table respond*psygrp10; run; In Figure 9.4, I see that the output gave us only 4 groups I was expecting 10 groups This is probably due to the limited number of values for pamsy In the next step, I print out the values for the different deciles of pamsy to see why I have only 4 groups: proc print data= psydata; run; Page 214 The following . h1" alt="" Figure 8. 8 Customer profiles by segment. Page 203 Figure 8. 9 Customer profiles by segment, simplified. Performing Cluster Analysis to Discover Customer Segments Cluster. finding and creating profitable customers is determining what drives profitability. This leads to better prospecting and more successful customer relationship management. You can segment and profile. powerful uses for profiling, segmentation, and scoring on demand. Page 205 Figure 8. 10 Cluster analysis on age and income. Page 206 Figure 8. 11 Plot of clusters on age and income.