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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:
Brian Current Bill
55 Account Plan
Silver Account Age
72 Complaint Code
Billing problem Churn
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.
- Summer '10