Business Analytics Using
SAS Enterprise Guide and
SAS Enterprise Miner
®

®

®

™

A Beginner’s Guide

Olivia Parr-RudFrom Business Analytics Using SAS® Enterprise
Guide® and SAS® Enterprise Miner™.
Full book available for purchase here.

Contents

About This Book ...................................................................................... vii
About the Author ...................................................................................... xi
Chapter 1: Defining the Business Objective ............................................... 1
Introduction .................................................................................................................................... 1
Setting Goals .................................................................................................................................. 1
Descriptive Analyses ...................................................................................................................... 3
Customer Profile ...................................................................................................................... 3
Customer Loyalty ..................................................................................................................... 4
Market Penetration or Wallet Share ...................................................................................... 4
Predictive Analyses ........................................................................................................................ 4
Marketing Models .................................................................................................................... 5
Risk and Approval Models ...................................................................................................... 6
Predictive Modeling Opportunities by Industry .................................................................... 9
Notes from the Field .................................................................................................................... 13

Chapter 2: Data Types, Categories, and Sources ..................................... 15
Introduction .................................................................................................................................. 15
The Evolution of Data................................................................................................................... 16
Types of Data ................................................................................................................................ 17
Nominal Data .......................................................................................................................... 17
Ordinal Data ........................................................................................................................... 17
Continuous Data .................................................................................................................... 18
Categories of Data ....................................................................................................................... 18
Demographic or Firmographic Data .................................................................................... 18
Behavioral Data ...................................................................................................................... 19
Psychographic Data .............................................................................................................. 20

iv Contents

Data Category Comparison .................................................................................................. 20
Sources of Data ............................................................................................................................ 21
Internal Sources ..................................................................................................................... 21
Storage of Data ...................................................................................................................... 27
External Sources .................................................................................................................... 28
Notes from the Field .................................................................................................................... 29

Chapter 3: Overview of Descriptive and Predictive Analyses .................... 29
Introduction .................................................................................................................................. 29
Descriptive Analyses .................................................................................................................... 30
Frequency Distributions ........................................................................................................ 30
Cluster ..................................................................................................................................... 33
Decision Tree ......................................................................................................................... 33
Predictive Analyses ...................................................................................................................... 35
Linear Regression .................................................................................................................. 36
Logistic Regression ............................................................................................................... 39
Neural Networks .................................................................................................................... 41
Modeling Process ........................................................................................................................ 43
Define the Objective .............................................................................................................. 43
Develop the Model ................................................................................................................. 43
Implement the Model ............................................................................................................ 48
Maintain the Model ................................................................................................................ 49
Notes from the Field .................................................................................................................... 52

Chapter 4: Data Construction for Analysis ............................................... 53
Introduction .................................................................................................................................. 53
Data for Descriptive Analysis ...................................................................................................... 53
Data for Predictive Analysis ........................................................................................................ 54
Prospect Models .................................................................................................................... 55
Customer Models .................................................................................................................. 57
Risk Models ............................................................................................................................ 59
External Sources of Data ............................................................................................................. 61
Notes from the Field .................................................................................................................... 61

Chapter 5: Descriptive Analysis Using SAS Enterprise Guide ................... 63
Introduction .................................................................................................................................. 63
Project Overview .......................................................................................................................... 63

Contents v
Project Initiation ........................................................................................................................... 64
Exploratory Analysis .................................................................................................................... 65
Importing the Data ................................................................................................................. 65
Viewing the Data .................................................................................................................... 66
Exploring the Data ................................................................................................................. 66
Segmentation and Profile Analysis............................................................................................. 69
Correlation Analysis ..................................................................................................................... 76
Notes from the Field .................................................................................................................... 77

Chapter 6: Market Analysis Using SAS Enterprise Guide .......................... 79
Introduction .................................................................................................................................. 79
Project Overview .......................................................................................................................... 79
Market Analysis ............................................................................................................................ 80
Project Initiation..................................................................................................................... 80
Data Preparation .................................................................................................................... 80
Penetration and Share of Wallet .......................................................................................... 89
Results .................................................................................................................................... 90
Notes from the Field .................................................................................................................... 91

Chapter 7: Cluster Analysis Using SAS Enterprise Miner .......................... 93
Introduction .................................................................................................................................. 93
Project Overview .......................................................................................................................... 93
Cluster Analysis ............................................................................................................................ 94
Initiate the Project ................................................................................................................. 94
Input the Data Source and Assign Variable Roles ............................................................. 97
Transform Variables .............................................................................................................. 99
Filter Data ............................................................................................................................. 102
Build Clusters ....................................................................................................................... 104
Build Segment Profiles ........................................................................................................ 107
Analyze Clusters and Recommend Marketing or Product Development Actions ........ 109
Notes from the Field .................................................................................................................. 109

Chapter 8: Tree Analysis Using SAS Enterprise Miner ............................ 111
Introduction ................................................................................................................................ 111
Project Overview ........................................................................................................................ 111
Decision Tree Analysis .............................................................................................................. 112
Initiate the Project ............................................................................................................... 112

vi Contents

Input the Data Source ......................................................................................................... 114
Create Target Variable ........................................................................................................ 115
Partition the Data ................................................................................................................. 117
Build the Decision Tree ....................................................................................................... 118
View the Decision Tree Output ........................................................................................... 120
Interpret the Findings .......................................................................................................... 126
Alternate Uses for Tree Analysis ........................................................................................ 128
Notes from the Field .................................................................................................................. 128

Chapter 9: Predictive Analysis Using SAS Enterprise Miner ................... 129
Introduction ................................................................................................................................ 129
Select ........................................................................................................................................... 130
Initiate the Project ............................................................................................................... 130
Select the Data ..................................................................................................................... 131
Explore ........................................................................................................................................ 133
StatExplore ........................................................................................................................... 133
MultiPlot ................................................................................................................................ 136
Modify .......................................................................................................................................... 138
Replace Missing Values via Imputation............................................................................. 138
Partition Data into Subsamples ......................................................................................... 139
Manage Outliers ................................................................................................................... 140
Transform the Variables ...................................................................................................... 142
Model ........................................................................................................................................... 145
Decision Tree ....................................................................................................................... 145
Neural Network .................................................................................................................... 147
Regression ........................................................................................................................... 148
Assess ......................................................................................................................................... 151
Notes from the Field .................................................................................................................. 155

References ............................................................................................ 157

From Business Analytics Using SAS® Enterprise Guide® and SAS® Enterprise Miner™:
A Beginner's Guide, by Olivia Parr-Rud. Copyright © 2014, SAS Institute Inc., Cary, North Carolina,
USA. ALL RIGHTS RESERVED.

From Business Analytics Using SAS® Enterprise
Guide® and SAS® Enterprise Miner™.
Full book available for purchase here.

Chapter 6: Market Analysis Using SAS
Enterprise Guide
Introduction ...................................................................................................... 79
Project Overview ............................................................................................... 79
Market Analysis................................................................................................. 80
Project Initiation ................................................................................................................ 80
Data Preparation ............................................................................................................... 80
Penetration and Share of Wallet ..................................................................................... 89
Results ............................................................................................................................... 90
Notes from the Field ......................................................................................... 91

Introduction
Chapters 1 through 5 focused on general knowledge and techniques that laid the foundation formany types of data analysis. In this chapter, you will explore a more specific topic: competitive
analysis.
When your company sets its strategy, the first questions to answer are as follows:••

What are the strengths and weaknesses of our primary competitors as compared to us?
What is our market share and how can we increase it?”For most industries, data sources are available that allow companies to determine their market
share, or “share of wallet,” within certain segments of the population. This analysis is valuable forsetting marketing strategies, guiding research and development, and informing finance and budget
allocations.

Project Overview
The leadership team at DMR Publishing Company is interested in understanding the drivers ofrevenue within its business. It has gathered U.S. customer data for the past year that consists ofrevenues, numbers of publications, and three demographic variables. For this analysis, you will use
SAS Enterprise Guide.

80 Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

The project has eight steps:

1.
2.
3.
4.
5.
6.
7.
8.

Initiate the project in SAS Enterprise Guide 6.1.
Import and view market data.
Add the DMR Publishing customer SAS data set to the project.
Use the query tool to build a new age group variable.
Summarize the DMR Publishing customer data to a gender and age group level.
Merge customer and market data.
Summarize the merged data to age group level.
Perform penetration and a “share of wallet” analysis.

Market Analysis
So far in this analysis, our main interest has been to describe the characteristics of the customer
base. If possible, it can be useful to compare characteristics of the customer database with the samecharacteristics in the overall market. This comparison enables a penetration analysis and share ofwallet analysis as defined in Chapter 1.
We have purchased a data set from General List Company that shows the annual revenues andnumber of subscriptions for the entire publishing market within the United States, segmented bygender and age group. Our goal is to compare performance within gender and age group between
the DMR Publishing’s customer data and the total market data purchased from General List
Company.

Project Initiation
Similar to the process in Chapter 5, the first step is to open the project. Double click the SAS
Enterprise Guide icon. Select New Project.

Data Preparation
Your first step is to prepare the data.

Import Data
To access the data, go to File  Import and choose the Publishing Market data set. When the firstwindow opens, click Finish. Notice that the data is already summarized as shown in Output 6.1.

Chapter 6: Market Analysis Using SAS Enterprise Guide 81
Output 6.1: Publishing Market Data

To create the penetration and wallet analyses, merge the market data to the DMR Publishingcustomer data. Before you can merge the two data sets, you need to summarize the customer data.
We plan to merge the two data sets by gender and age group.
The GENDER field is ready to match, but you must create the AGE_GROUP variable in the DMR
Publishing customer data.
Because we started a new project, we need to bring the DMR Publishing customer data into theproject. In the top menu, select View  Server List. A new window will open below the Project
Tree window; it will display the servers, as shown in Figure 6.1.
Figure 6.1: Access Server List to Locate DMR Publishing Customer Data

82 Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

Click the plus sign to the left of Servers. Continue to expand until you reach the SASUSER
library as shown in Figure 6.2.
Figure 6.2: Expand Library to Locate DMR Publishing Customer Data

Right-click the DMR_CUSTOMER_BASE data and select Open. The data has now been added tothe project and is visible in the Project Tree. You are ready to create the Age Group variable in the
DMR Publishing customer data.

Build Age Group Variable
Right-click the DMR Customer data set and select Query Builder as shown in Figure 6.3. Dragfour variables from the left over to the right column: GENDER, CUSTOMER_ID,
CUSTOMER_SUBSCRIPTION_COUNT, and CUSTOMER_REVENUE.
Figure 6.3: Query to Build Age Group on DMR Publishing Customer Data

Chapter 6: Market Analysis Using SAS Enterprise Guide 83
Next, click Computed Columns  New  Advanced Expression  Next (Figure 6.4).
Figure 6.4: Expression Builder to Create Age Group Variable

The window has a space to enter an expression. Program 6.1 displays the syntax for building thevariable in the Query Builder; copy it into the box under Enter an expression.
Program 6.1: Expression Builder to Create Age Group Variable
Case when AGE le 21 then "18-21 Years Old"
when 22 le AGE le 25 then "22-25 Years Old"
when 26 le AGE le 30 then "26-30 Years Old"
when 31 le AGE le 40 then "31-40 Years Old"
when 41 le AGE le 50 then "41-50 Years Old"
when 51 le AGE then "51+ Years Old"
end

Click Next. In the next window’s field, type AGE_GROUP in the top box next to Column Name.
Click Finish  Close.

84 Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

Sum Performance Variables
The next step is to sum the performance variables CUSTOMER_ID, CUSTOMER_REVENUE,
and CUSTOMER_SUBSCRIPTION_COUNT (Figure 6.5). Within the same query, on the righthand side, put your cursor next to CUSTOMER_ID under the Summary column. An arrow willappear. Scroll down and select COUNT DISTINCT. For easier viewing, you can adjust the column
widths with your cursor. Next, right-click the space next to CUSTOMER_REVENUE and
CUSTOMER_SUBSCRIPTION_COUNT and select SUM. You’ll notice that a Summary groups
pane appears at the bottom right section of the window. The software guesses GENDER and
CALCULATED AGE_GROUP, which is correct. Click Run.
Figure 6.5: Sum Performance Variables across Gender and Age GroupIn Output 6.2, the data set appears to be summarized correctly. The new summary variables havethe prefix SUM_. We will use these variables in the penetration and wallet analyses.
Output 6.2: Summarized DMR Publishing Customer Data

Chapter 6: Market Analysis Using SAS Enterprise Guide 85

Merge Customer and Market Data
The next step is to merge the data. If you don’t see the Process Flow on the right side of yourscreen, double-click on Process Flow in the top left corner at the top of the Project Tree, and it willappear in the work area on the right as seen in Figure 6.6.
Figure 6.6: Select Data for Merge Query

Right-click the newly summarized data and select Query Builder. After the window opens, dragall the variables from t1 (QUERY_FOR_DMR_CUSTOMER_BASE) into the right-hand column.
In the upper left-hand corner, look for Join tables. A new window will open with the fields in the
new data set. Click Add Tables. Select WORK.PUBLISHING_MARKET (Figure 6.7). If you
can’t see the whole name, put your cursor over the fields. The full name will appear. Click Open.
Figure 6.7: Select Data for Join

The software will guess a match variable. In this case, it guesses GENDER. But, it is an inner join
as shown in the Venn diagram. Right-click on the circles and select Properties. You want to matchthe records in the DMR Publishing customer data that finds a match in the Publishing Market data;
therefore, from the list at the top, select All rows from the right table given the condition ( Right
Join ). Also, notice just below the circles that it asks if you want t2.GENDER to equal (=)
t1.GENDER. The default is correct.

86 Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

You are also going to match on AGE_GROUP. Right-click on AGE_GROUP in the first data set(t1). Follow the prompt to match to AGE_GROUP in the second data set (t2). Next, a window willappear, and a prompt will ask you what kind of join you want to select (Figure 6.8). As directed in
the first join using GENDER, you want to match the records in the DMR Publishing customer datathat finds a match in the Publishing Market data; therefore, select All rows from the right tablegiven the condition (Right Join ). Also, notice just below the circles that asks if you want
t2.AGE_GROUP to equal (=) t1.AGE. The default is correct. Click Close.
Figure 6.8: Merge of DMR Customer and Market Data Sets

You will be back in the main screen of the query builder. From the left column, drag the following
variables from the (t2) PUBLISHING_MARKET to the right side under Column Name:
SUBSCRIBERS, REVENUE, SUBSCRIPTIONS (Figure 6.9).
Figure 6.9: Add Variables from Publishing Market Data

Before you run a query, you can change the column names (Figure 6.10).

Chapter 6: Market Analysis Using SAS Enterprise Guide 87
Figure 6.10: Rename Three Columns to Business-Related Names

Double-click under Column Name on the right, where COUNT DISTINCT appears. When thewindow opens, type in a new name, such as CUSTOMERS. Click OK. Repeat the process for thetwo other summary columns. Suggested names are CUSTOMER_SUBS andCUSTOMER_REVENUE. Now, click Run.
The merged data shows a perfect match between the customer data and the market data. Notice that
it is at the GENDER-by-AGE_GROUP level.
Output 6.3: Merged Data at Gender and Age Group Level

For your current analysis, you want to look at penetration and share of wallet by age group. So, youwill summarize the data one more time. Return to the Process Flow and right-click the merged dataand select Query Builder.

88 Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

When the window opens, drag each variable except GENDER to the right side and drop it under
Column Name. Put your cursor in the empty space under Summary, and a dropdown menu willappear with the word NONE (Figure 6.11). Click and select SUM next to each variable except
AGE_GROUP.
Figure 6.11: Summing of Merged Data to Age Group Level

When you are finished, click Run. This step summarizes all the customer and market values to the
AGE_GROUP level. Review and close the data view.

Calculate the Share of Wallet
The next step is to calculate the share of wallet for customers, subscriptions, and revenues with useof the query tool. With your cursor on the summarized data, right-click and then click Query
Builder. Once it opens, pull all of the variables into the right-hand pane. You can pull eachvariable separately or move the entire data set at one time by dragging the icon where t1(QUERY_FOR_DMR appears). For ease of viewing, widen the Column Name heading on theright side until you can see the full column names.
Next, go to Computed Columns  New  Advanced Expression  Next. In the box under
Advanced Expression, type SUM_of_CUSTOMERS/SUM_of_SUBSCRIBERS and click Next
(Figure 6.12). Type PCT_of_SUBSCRIBERS next to Column Name. At the bottom of thewindow, next to Format, click Change and select Numeric and Percentw.d. Leave other settings
unchanged as default. Click OK  Finish. In the remaining window, click Close.

Chapter 6: Market Analysis Using SAS Enterprise Guide 89
Figure 6.12: Computed Columns to Create Percentage Variables

Next, click New and type SUM_of_CUSTOMER_SUBS/SUM_of_SUBSCRIPTIONS. Click Next
and name the fraction PCT_of_SUBSCRIPTIONS. Create the same format and click Finish.
Repeat the process with SUM_of_CUSTOMER_REVENUE/SUM_of_REVENUE in the box. Use
the same format. Click Next and name the fraction PCT_of_REVENUE. Click Next  Finish.
Click Close  Run.
NOTE: To avoid errors, variable names must be exactly as stated.
Output 6.4 displays the final percentage values.
Output 6.4: Final Percentage Variables Showing Percent of DMR Subscriber Measures as
Percent of Total Market

Penetration and Share of WalletYour data is now ready to analyze. To get a clear view by AGE_GROUP, close the data andhighlight the last created data set. Click Tasks  Describe  List Data. This process allows you totake the variables or columns that you select and print them in a report format. The window in
Figure 6.13 allows you to manage the report output.

90 Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner
Figure 6.13: Creation of a ReportDrag AGE_GROUP and the three percentage (PCT) variables to the right side. Click Run. Look
for the output created in Microsoft Word.

Results
Reporting your results is one of the most important steps in your analysis. If you do everythingright and can’t communicate the results, your efforts and insights will be wasted.
In Output 6.5, the final report displays the market penetration analysis and share of wallet by age
group. When you are evaluating the percentage of subscribers, the lowest market penetration is in
the 18 to 21-year-old age group. It is higher with 26 to 50-year-olds, at 10% to 11%. It drops
slightly at age 51 or older (9%).
Output 6.5: Final Penetration Analysis by Age GroupChapter 6: Market Analysis Using SAS Enterprise Guide 91

The percentage of subscriptions shows a different outcome. The youth, aged 18 to 21 years, have ahigher number of subscriptions per person. So, they bring in the largest percentage of the revenue.
Their share of wallet is 14%, compared with only 6% for those aged 51 or older.
Several conclusions can be drawn from this analysis. If you are looking for additional subscribersthat bring in high average revenue, the 18- to 21-year-old market is the place to focus. These
subscribers seem to be happiest with the current offerings, although there is still much room to
grow.
On the other hand, growth opportunities for the business may emerge in the other age groups if new
products are emphasized. The share of wallet is 10% or less for customers aged 22 years or older.
This result suggests that products could be developed to appeal to groups older than 21.

Notes from the Field
This case study provides some rich insights and potential opportunities for DMR Publishing. What
the company does with the information depends much more on the abilities of the organization.
Consider that there are two new opportunities identified. The first opportunity is to find additional
subscribers that look like their current subscriber base. The job of finding additional subscribers
might be handled by the marketing department. The second opportunity is to create new products
for the underserved areas of the market. This need for new products would likely be a project forthe product development side of the business. The optimal solution may be a mixture of both
departments.
Because both market growth for the current business and product development are important,management must make the decision about how to allocate resources. The best outcomes are seen
when companies have fostered a culture of big data so that departments are able to collaborate andpartner in the implementation of the analysis results, optimizing profits for the overall company.

From Business Analytics Using SAS® Enterprise Guide® and SAS® Enterprise Miner™:
A Beginner's Guide, by Olivia Parr-Rud. Copyright © 2014, SAS Institute Inc., Cary, North Carolina,
USA. ALL RIGHTS RESERVED.

From Business Analytics Using SAS® Enterprise
Guide® and SAS® Enterprise Miner™.
Full book available for purchase here.

IndexA
Account Numberin B2C customer database 22
in transaction database 24
activation model 5
Activity Amount, in transaction database 24
Activity Date, in transaction database 24
Activity Type, in transaction database 24
Actual Performance, in model log 50
Advanced Expression function 70
Age Group variable 82–83
airline industry, predictive modelingopportunities for 12
analysis
See also data
See also descriptive analysis
See also predictive analysis
categorical data 68
correlation 76–77
interval data 68
linear regression 36–39
market penetration 4
profile 69–76
Apache Hadoop 26–27
approval models 6–9
art schools, predictive modeling opportunities for
11
Assess step, in model development 130, 151–
155
attitudinal data 20
attrition 6, 59
See also retention/loyalty/attrition model
auto industry, predictive modeling opportunities
for 12
auto insurance companies, predictive modelingopportunities for 10
average performance 45

B
B2B (business to business) customer database
22–23
B2C (business to consumer) customer database
21–22
banks, predictive modeling opportunities for 9–
10
behavioral data 19"Big Data: What It Is and Why It Matters" 16–17binary variables 70, 71
biotech industry, predictive modelingopportunities for 12
business objective
about 1
descriptive analysis 3–4
predictive analyses 4–12
setting goals 1–3
business to business (B2B) customer database
22–23
business to consumer (B2C) customer database
21–22

C
C statistic 47–48
cable companies, predictive modelingopportunities for 10
casinos, predictive modeling opportunities for11
casualty insurance companies, predictivemodeling opportunities for 10
catalog companies
predictive modeling opportunities for 10
for retailers 10
categorical data analysis 68
categorical variables 17, 74–76
cellular companies, predictive modelingopportunities for 10

160 Index
chemical industry, predictive modelingopportunities for 12
churn 6, 59
claims 7class variables 17, 30–31
classification tree 34
cluster analysis (clustering) 33
See also SAS Enterprise Miner, cluster
analysis using
Cluster icon 104
clusters
analyzing 109
building 104–106
Cody, Ron
Cody's Data Cleaning Techniques Using
SAS 66
Cody's Data Cleaning Techniques Using SAS(Cody) 66
collection model, for banks 10colleges, predictive modeling opportunities for11
columns, renaming 86–87
community, predictive modeling opportunities
for 12Company ID
in B2B customer database 23
in contact database 24
Company Namein B2B customer database 23
in contact database 24
Company URL, in B2B customer database 23
complexity, of data 17
computer hardware/software, predictivemodeling opportunities for 11
contact database 23–24
Contact ID
in contact database 23
in score database 25
in transaction database 24
Contact Name, in contact database 23
continuous data 18, 68
continuous data analysis 68
continuous variables 31–32, 72–73, 76–77

correlation analysis 76–77
Count, in nodes 124
Create Data Source icon 131
Create icon 101, 115
credit card banks, predictive modelingopportunities for 9–10
cross-sell/up-sell model
about 5for banks 10
cases 57
for gaming industry 11
for hospitality and travel industry 12
for insurance companies 10
for retailers 10
for technology companies 11
for telecommunications companies 10
using life-stage segments 58–59
for utility companies 11
CR_TOP_25PCT build expression 71–72
cumulative lift 46
customer data, merging with market data 85–88
Customer ID
in B2C customer database 21–22
in marketing database 25
in score database 25
in transaction database 24
customer life cycle 8–9
customer models 57–59
Customer Name, in B2C customer database 22
customers
fraud risk for 60
insurance risk for 60–61
leveraging your best 126
loyalty of 4
profile for 3–4
targeting your worst 126

D
data

about 15, 53
attitudinal 20
behavioral 19Index 161
categories of 18–21
continuous 18, 68
customer 85–88
demographic 18–19
for descriptive analysis 53–54
evolution of 16–17
exploring 66–69
external sources of 27, 61
filtering 102–104
firmographic 18–19, 23
importing 65, 80–82
internal sources of 21–25
lifestyle 20
market 85–88
nominal 17
ordinal 17
partitioning 117–118, 139–140
partitioning into subsamples 139–140
for predictive analysis 54–61
psychographic 20
ratio 18
selecting 131–133
sources of 21–27
storage of 26–27
types of 17–18
viewing 66
data category comparison 20–21
Data Partition icon 117, 118
data source 97–99, 114–115
Data Sources icon 97
data warehouse 26
Date, in marketing database 25
decision tree 145–147
decision tree analysis 33–35, 120–126, 128
See also SAS Enterprise Miner, tree analysis
using
Decision Tree icon 118, 145
Decision Trees for Analytics Using SAS
Enterprise Miner (DeVille andNeville) 44
default models
about 7for banks 9for insurance companies 10
for telecommunications companies 10
for utility companies 11
default risk, for prospects 60
Delwiche, Lora D.
The Little SAS Book for Enterprise Guide
65
demographic data 18–19
Demographic Information, in B2C customer
database 22
dependent variables 132
descriptive analysis
about 29, 30
cluster analysis (clustering) 33
customer loyalty 4
customer profile 3–4
data for 53–54
decision tree analysis 33–35
external sources of data 61
frequency distributions 30–32
modeling process 43–51
using SAS Enterprise Guide. See SAS
Enterprise Guide, descriptive analysis
using
DeVille, Barry
Decision Trees for Analytics Using SAS
Enterprise Miner 44
diagnostics, of decision trees 123–124
discrete variables 17
distribution analysis, selecting variables for 70
DMR_CUSTOMER_DATA icon 115
DUNS Number, in B2B customer database 23

E
education, predictive modeling opportunities for
11, 12electric companies, predictive modelingopportunities for 11–12
Email Addressin B2C customer database 22
in contact database 24

162 Index
entertainment industry, predictive modelingopportunities for 10–11
Expected Loss Given Default, in score database25
Expected Performance, in model log 50
Expected Value, in score database 25
Explore step, in model development 130, 133–
138
Expression Builder, creating Age Groupvariables with 83
external sources, of data 27, 61

Ffailure modelfor entertainment and social media industry
11
for utility companies 12
Filter icon 103–104, 140, 141, 142
filtering data 102–104
firmographic data 18–19, 23
food industry, predictive modeling opportunities
for 12
former/lapsed customer, of customer life cycle
8–9
fraud 7–8
fraud model
for banks 10
for gaming industry 11
for insurance companies 10
for Public sector and nonprofit 12
for telecommunications companies 10
for utility companies 12
fraud risk, for customers 60
frequency distributions 30–32

G
gains chart 46–47
gains table 45–46
gaming industry, predictive modelingopportunities for 11

gas companies, predictive modelingopportunities for 11–12
geo usage model, for transportation and shipping
industry 12
goal setting 1–3
government, predictive modeling opportunities
for 12graph properties, of decision trees 121–122

H
Hadoop 26–27
Headquarter ID, in B2B customer database 23
health insurance companies, predictive modelingopportunities for 10
healthcare industry, predictive modelingopportunities for 12
hospitality and travel industry, predictivemodeling opportunities for 12
hotels, predictive modeling opportunities for 12
Household ID, in B2C customer database 22

I
importing data 65, 80–82
imputation, replacing missing values via 138–
139
Impute function 145
Impute icon 138, 139
indicator variables 17
industry, predictive modeling opportunities by
9–12
inputting data source 97–99, 114–115
insurance companies, predictive modelingopportunities for 10
insurance risk, for customers 60–61
Interactive setting (decision tree analysis) 119
internal sources, of data 21–25
Internet companies, predictive modelingopportunities for 10
interval data analysis 68

Index 163

K
Key Drivers, in model log 50

Llife insurance companies, predictive modelingopportunities for 10
life-stage segments, up-sell model using 58–59
lifestyle data 20
lifetime value (LTV) 8–9
lift 46
line width, of nodes 124
linear function 39
linear regression analysis 36–39
link function 41
The Little SAS Book for Enterprise Guide(Delwiche and Slaughter) 65
logistic regression 39–41
logit function 40
loss-given-default models 7, 60
loyalty
See also retention/loyalty/attrition model
about 6
cases 59
customer 4
LTV (lifetime value) 8–9

M
manufacturing industry, predictive modelingopportunities for 12
market analysis
See SAS Enterprise Guide, market analysis
usingmarket data, merging with customer data 85–88
market penetration analysis 4marketing database 25
marketing models 5–6
Maximum Branch setting (decision tree analysis)
119
Maximum Depth setting (decision tree analysis)
119
Merge Query, selecting data for 85

merging customer and market data 85–88
metadata, attaching to transform variables 116–
117
Metadata icon 116, 117, 118
Microsoft Word icon 68
missing values, replacing via imputation 138–
139
Model Comparison icon 151
Model Comparison node 151–155
Model Details, in model log 50
Model Developer, in model log 50
Model Development Campaign Data, in modellog 50Model Implementation Campaign Data, in modellog 50Model Implementation Launch Date, in modellog 50model log 50–51
Model Name, in model log 50
model of life 49
Model Scoresin B2B customer database 23
in B2C customer database 22
in contact database 24
Model step, in model development 130, 145–
150
Model Type, in model log 50
modeling data set 54
modeling process 43–51
modelsSee also specific models
developing in modeling process 43–48
implementing in modeling process 48
maintaining in modeling process 49–51
Modify step, in model development 130, 138–
144
mortgage banks, predictive modelingopportunities for 9–10
multicollinearity 45
multiple linear regression 38–39
MultiPlot icon 136
MultiPlot node 136–138

164 Index

N
Neural Network icon 147
neural networks 41–42, 147–148
Neville, Padraic
Decision Trees for Analytics Using SAS
Enterprise Miner 44
new/established customer, of customer life cycle
8–9
node characteristics, of decision trees 124
Node ID 124
node interpretation, of decision trees 125–126
nominal data 17

Oobjectives, defining in modeling process 43
Offer Detail, in marketing database 25
offline retailers 10
online gaming, predictive modelingopportunities for 11
online retailers 10
online sharing sites, predictive modelingopportunities for 10–11
ordinal data 17
outliers, managing 140–142
Overall Objective, in model log 50

PPartition icon 139
partitioning
data 117–118, 139–140
data into subsamples 139–140
penetration, share of wallet and 89–90
Percentage, in nodes 124
performance variables 84
performance window 54
pharmaceutical industry, predictive modelingopportunities for 12
Phone Numberin B2B customer database 23
in B2C customer database 22
in contact database 24

Physical Addressin B2B customer database 23
in B2C customer database 22
in contact database 24
politics, predictive modeling opportunities for
12
predictive analysis
See also SAS Enterprise Miner, predictive
analysis using
about 29, 35–36, 54–55
customer models 57–59
data for 54–61
linear regression analysis 36–39
logistic regression 39–41
marketing models 5–6
modeling process 43–51
neural networks 41–42
prospect models 55–57
risk models 59–61
Pre-Selects, in model log 50
Probability to Buy, in score database 25
Probability to Default, in score database 25
process failure modelfor hospitality and travel industry 12
for manufacturing industry 12
for technology companies 11
for telecommunications companies 10
for transportation and shipping industry 12
product failure model, for manufacturingindustry 12
Product or Service ID, in B2B customer database
23
Products or Services, in B2C customer database22
profile analysis 69–76
profiling
selecting categorical variables for 74–76
selecting continuous variables for 72–73
property insurance companies, predictivemodeling opportunities for 10
Property menu (Add-Cluster process) 104, 105
Prospect ID, in marketing database 25
prospect models 55–57

Index 165
prospect phase, of customer life cycle 8–9
prospects
buying more 126
default risk for 60
loss-given-default risk for 60
psychographic data 20
public sector and nonprofit industry, predictivemodeling opportunities for 12

Q
Query Builder
building Age Group variables with 82–83
creating binary variables with 70, 71

Rradio industry, predictive modeling opportunities
for 10–11
rail industry, predictive modeling opportunities
for 12
ratio data 18
rebuild 49
recruitment model, for sports industry 12
refresh 49
regression 148–150
Regression icon 148
religion, predictive modeling opportunities for
12
resorts, predictive modeling opportunities for 12
response models
about 5for banks 9for education 11
for entertainment and social media industry
10
for gaming industry 11
for hospitality and travel industry 12
for insurance companies 10
for manufacturing industry 12
for Public sector and nonprofit 12
for retailers 10
for technology companies 11

for telecommunications companies 10
for utility companies 11
restaurants, predictive modeling opportunities
for 12
retail banks, predictive modeling opportunities
for 9–10
retail companies, predictive modelingopportunities for 10
retailers, predictive modeling opportunities for10
retention model, for education 11
retention/loyalty/attrition model
for banks 10
for entertainment and social media industry
11
for gaming industry 11
for hospitality and travel industry 12
for insurance companies 10
for retailers 10
for technology companies 11
for telecommunications companies 10
for utility companies 11
revenue models
about 5for banks 10
for entertainment and social media industry
11
for hospitality and travel industry 12
for manufacturing industry 12
for Public sector and nonprofit 12
for retailers 10
for technology companies 11
for telecommunications companies 10
risk models 6–9, 59–61

S
SAS Enterprise Guide, descriptive analysis using
about 63
correlation analysis 76–77
exploratory analysis 65–69
project initiation 64–65
project overview 63–64

166 Index
segmentation and profile analysis 69–76
Task menu 67
SAS Enterprise Guide, market analysis using
about 79
building Age Group variable 82–83
calculating share of wallet 88–89
data preparation 80–89
importing data 80–82
market analysis 80–89
merging customer and market data 85–88
penetration and share of wallet 89–90
project initiation 80
project overview 79–80
results 90–91
summing performance variables 84
SAS Enterprise Guide icon 64, 80
SAS Enterprise Miner, cluster analysis using
about 93
analyzing clusters 109
assigning variable roles 97–99
building clusters 104–106
building segment profiles 107–108
cluster analysis 94–109
filtering data 102–104
initiating the project 94–96
inputting data source 97–99
project overview 93–94
transforming variables 99–102
SAS Enterprise Miner, predictive analysis using
about 129–130
Assess step 151–155
decision tree 145–147
Explore step 133–138
initiating the project 130–131
managing outliers 140–142
Model step 145–150
Modify step 138–144
MultiPlot node 136–138
neural network 147–148
partitioning data into subsamples 139–140
regression 148–150
replacing missing values via imputation
138–139

Select step 130–133
selecting data 131–133
StatExplore node 133–136
transforming variables 142–144
SAS Enterprise Miner, tree analysis using
about 111
building decision tree 118–119
creating target variables 115–117
decision tree analysis 112–128
initiating the project 112–114
inputting data source 114–115
interpreting findings 126–128
partitioning data 117–118
project overview 111–112
viewing decision tree output 120–126
SAS Enterprise Miner icon 94
savings and loan banks, predictive modelingopportunities for 9–10
score database 24–25
Score Distribution, in model log 50
Segment Plot 105–106
Segment Profile icon 107
segment profiles, building 107–108
Segment Size 105–106
segmentation 69–76
Select step, in model development 130–133
Selection Business Logic, in model log 50
Selection Criteria, in model log 50
selection method 44
shading, of nodes 124
share of wallet
calculating 88–89
penetration and 89–90
shipping industry, predictive modelingopportunities for 12
sigmoidal function 40, 42
Slaughter, Susan J.
The Little SAS Book for Enterprise Guide
65
social media activity, as form of behavioral data19
social media data 27

Index 167
social media industry, predictive modelingopportunities for 10–11
social network data 19
Specific Target, in model log 50
sports industry, predictive modelingopportunities for 12
StatExplore icon 133
StatExplore node 133–136
storage, of data 26–27
summing performance variables 84

T
Table icon 152
target modelfor Public sector and nonprofit 12
for sports industry 12
target variables 115–117
Task menu (SAS Enterprise Guide) 67
technical colleges, predictive modelingopportunities for 11
technology companies, predictive modelingopportunities for 11
telecommunications companies, predictivemodeling opportunities for 10
television industry, predictive modelingopportunities for 10–11
Time of Model Development, in model log 50
transaction database 24
Transform function 98
Transform Variables icon 99, 102, 115, 116,
142, 143transforming variables 99–102, 142–144
transportation and shipping industry, predictivemodeling opportunities for 12
tree analysis
See SAS Enterprise Miner, tree analysis
using
trends 19
trucking industry, predictive modelingopportunities for 12Uunique identifier 27
universities, predictive modeling opportunities
for 11
up-sell model
See cross-sell/up-sell model
usage model
about 5for hospitality and travel industry 12
for manufacturing industry 12
for Public sector and nonprofit 12
for transportation and shipping industry 12
for utility companies 11
utility companies, predictive modelingopportunities for 11–12

Vvalues, of nodes 124
variability, of data 16–17
variables
adding 86, 86f
Age Group 82–83
assigning roles for 97–99, 114–115
attaching metadata to transform 116–117
binary 70, 71
categorical 17, 74–76
class 17, 30–31
continuous 31–32, 72–73, 76–77
dependent 132
discrete 17
indicator 17
optimal number of 44
performance 84
selecting basic statistics on for profiling 73
selecting for distribution analysis 70
selecting of percentiles on for profiling 73
target 115–117
transforming 99–102, 142–144
Variables setting (decision tree analysis) 119

168 Index

W
wallet share analysis 4water companies, predictive modelingopportunities for 11–12
websites, as form of behavioral data 19
win-back model 5
wired-phone companies, predictive modelingopportunities for 10

From Business Analytics Using SAS® Enterprise Guide® and SAS® Enterprise Miner™:
A Beginner's Guide, by Olivia Parr-Rud. Copyright © 2014, SAS Institute Inc., Cary, North Carolina,
USA. ALL RIGHTS RESERVED.

About the Author
Olivia Parr-Rud, an internationally recognized expert in predictive
analytics, business intelligence, and innovative leadership, founded the
SAS Data Mining Users Group, having been a SAS user since 1991 and a
SAS instructor and conference presenter for many years. She hosts the
popular VoiceAmerica Business radio show Quantum Business Insights(http://www.voiceamerica.com/show/2240/quantum-business-insights) andis a thought leader in the integration of analytic tools and leadership
practices to optimize performance and organizational agility. Her
predictive analytics research founded her first book, Data Mining
Cookbook: Modeling for Marketing, Risk and Customer RelationshipManagement (Wiley 2000), which unveiled links between the global
economy and organizational dynamics, a topic featured in her second
book, Business Intelligence Success Factors: Tools for Aligning Your Business in a Global Economy(Wiley and SAS Institute Inc. 2009). Her current research will develop a model that tracks thealignment of business intelligence with human intelligence. Parr-Rud holds a B.A. in mathematics andan M.S. in statistics. In addition to public speaking, she offers training and consulting in bothpredictive analytics and business leadership, with clients that include numerous Fortune 500
companies. For more, visit http://oliviagroup.com.
Learn more about this author by visiting her author page at
http://support.sas.com/publishing/authors/parr-rud.html. There you can download free book excerpts,
access example code and data, read the latest reviews, get updates, and more.

Gain Greater Insight into YourSAS Software with SAS Books.
®

Discover all that you need on your journey to knowledge and empowerment.

support.sas.com/bookstore
for additional books and resources.
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names
are trademarks of their respective companies. © 2013 SAS Institute Inc. All rights reserved. S107969US.0613