SAS GF paper on RPM

Agents to take proactive steps to retain targeted

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Unformatted text preview: e proactive steps to retain targeted profitable customers before churn occurs. This churn analysis is presented through these steps: • importing and summarizing the data • developing models • reviewing the model results • saving the model to a SAS Enterprise Miner project • registering and scoring the model IMPORTING AND SUMMARIZING THE DATA Building a model requires data that represent historical events. You need input data that represent characteristics that can be used for prediction and target data that represent the event or value that you want to predict. Often, the input data are derived from one time period and the target data are derived from a later time period. To make sure that your model will be effective in production, you should have a large number of observations stored as rows of data. For example, many retail customer models use input data with tens of thousands of observations. The data used for SAS Rapid Predictive Modeler should be organized into rows that represent observations and columns that represent values. One of the columns should represent a target (independent) variable. Consider the following example table: Name Dominique Susan Brian Current Bill Amount 79 120 55 Account Plan Type Sliver Gold Silver Account Age 28 14 72 Complaint Code Call quality Billing problem Churn Y N N Table 1. Sample Model Training Table Name is an ID column and is not used by SAS Rapid Predictive Modeler; Current Bill Amount, Account Plan Type, Account Age, and Complaint Code are input columns and are used. Complaint Code has a missing value for the second customer, which is automatically handled through binning or missing value replacement. You do not need to select input columns. SAS Rapid Predictive Modeler automatically detects which columns should be used as input columns. Churn is a target column and is used. You must select one target column to use, and all other columns are treated automatically. You can also select columns that will be excluded from the analysis, 2 SAS Global Forum 2010 Customer Intelligence You can select a frequency column that represents the relative weight that should be assigned to that row; for example, in some data sets a single row can represent more than one observation. The data used for scoring should have all input columns. The target column is optional. When the model is used for making predictions on new data, the target column is missing. When the model is used for monitoring effectiveness, the target column is present. Scoring data usually contain the ID column. Churn propensity scores are derived from analysis of the historical behavior of customers who churn. Figure 1 shows a partial listing of the sample training data which includes 4,708 customers along with columns that represent the customer id, the churn flag (1=churner, 0=non-churner), and thirteen candidate predictors that cover factors such as phone usage patterns, account billing and status information, technical support complaints, and...
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This note was uploaded on 09/30/2013 for the course FINANCE 4013 taught by Professor Jamescameron during the Summer '10 term at Ohio State.

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