Decision Science in Action

It is desirable for a retailer to have information of the life cycle stage of  a customer. This helps greatly to a retailer in offer optimizations. Targeting right customer with rights offers is greatest challenge for a retailer. They run hundreds of promotions and optimizing it saves huge cost and helps in retaining customers.  In the era of super markets, large retailers issue loyalty cards and maintain customer purchase data. But most of the demographic data is not available with them as customers normally do not fill these fields and leave them blank. Although customer demographic data are not available using purchase data certain life stages of customer can be predicted using rule base techniques or predictive modeling. Life stages bring changes in purchasing pattern. 1.  A single male will mostly buy his needs and daily eatables.  Eating habits gets changed if he is married etc.  A man can occasionally buy woman's product but it will be regular if ...

PROC FASTCLUS can be used to perform a k-means clustering for observations. All the observations in the training dataset are assigned to clusters on the basis of the parametrization of the procedure and of their variable values. Scoring the observations in the validation dataset using PROC FASTCLUS seems a little bit challenging because the cluster assignment rules depend on new observations now. Scoring new observations without changing the cluster assignment rules can be achieved by using a SEED dataset in PROC FASTCLUS. /*original clustering */ %let indsn = input; *your input dataset; %let nclus = maxclus; *number of clusters to request; %let indvars = varlist; *independent variables to run proc fastclus on; %let valid = val_data; *validation dataset to score; proc fastclus data=&indsn maxclusters = &nclus outseed= clusterSeeds; var &indvars; run; /*scoring new observations using the seed dataset */ proc fastclus data=&valid out=&valid....