The CRISP Data Mining
Process
The Data Mining Process
Business
understanding
Data
evaluation
Data
preparation
Deployment
Data
Modeling
Evaluation
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Data Mining
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Business Understanding
Project
objectives
Project
requirements
DM Problem
Formulation
Preliminary
Plan
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Case Study
Data mining project done for a large
insurance company
Consider the use of data mining to
improve understanding of customer
databases
Led by the data warehousing team,
which wanted to also improve their
expertise
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Business Objectives
Understand what coverage packages are of
interest to a customer group


Targeting of new customers
Cross-selling opportunities to existing customers
Understand why a customer group terminates
coverage


Know in advance what groups are likely to
terminate
Understand what factors influence termination
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What are the Goals?
The business goals
Improve customer retention
 Increase cross-selling

Success criteria
Customer turnover rate
 Amount of cross-selling

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Data Mining Problems
Classify new and existing customers as
either interested or not interested in a
particular coverage
Classify existing customers as either
likely or unlikely to terminate coverage
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The Data Mining Process
Business
objectives
Data
evaluation
Data
preparation
Deployment
Data
Modeling
Evaluation
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Data Evaluation
Initial data collections
Data quality
Data warehousing team
Initial insights
Interesting subsets
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Case Study: Data Evaluation
Data was extracted from select
customer databases by company
personnel
Coverage programs with few customers
selected for pilot project
Five separate files extracted for five
coverage programs
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The Data Mining Process
Business
objectives
Data
evaluation
Data
preparation
Deployment
Data
Modeling
Evaluation
August 28, 2004
Data Mining
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Data Preparation
Finished
Data Set
Raw Data
Technical tasks:
Data selection
Attribute selection
Data cleaning
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Case Study: Data Preparation
Some initial formatting of data in MS
Excel
Cleaning of data file
 Combine headers/instances
 Add a new attribute: interest (yes/no)
 Must create the no interest cases

End up with a CSV formatted file
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Weka Data Mining Software
Data in CSV format loaded into Weka:
Data preprocessing
 Attribute selection
 Modeling

Classification
 Clustering
 Association rule mining


Visualization
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Data Preprocessing in Weka
Initial data inspection
Missing values
 Useless attributes
 Numeric attributes as nominal

Some helpful Weka filters
RemoveUseless
 ReplaceMissingValues

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Data Preprocessing in Weka
Data reduction:

Instance dimension


RemovePercentage, and Resample filters
Attribute dimension
Remove redundant attributes
 Remove irrelevant attributes
 Identify most important attributes

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Attribute Selection Methods
Three main methods used:

InfoGain

ChiSquared

Relief
Combined results from complimentary
methods
Final pruning of attribute list to twenty
attributes
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Selected Attributes
Location
Tax State
 Contract State
 State Code
 Zip Code

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Selected Attributes
Size

Case Size Range
Industry
Industry Classification
 Industry Classification Name
 SIC Code

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Selected Attributes
Timing
New Sale Flag
 Decision Maker Effective Month
 Decision Maker Effective Year
 Next Renewal Month
 Next Renewal Year

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Selected Attributes
Internal
Agency Number
 Office Name
 Pricing Category Code
 Product Line Name
 Small Group Flag

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Relevance of Attribute
Selection
Improved modeling
Faster model induction
 Higher accuracy
 Easier to interpret models

Structural knowledge gained from the
selection of attributes
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Most Important Attributes
What attributes effect the purchasing decision
of a customer group?
E.g., the five most important factor that
determine if a customer group purchases a
particular insurance coverage





Agency Number
Small Group Flag
Zip Code
Decision Maker Effective Year
Next Renewal Month
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Customer Segmentation
Unique groups of customers
Similar characteristics
 Similar behavior in terms of interest in
coverage

For example, separate predictive
models for customer segments for a
particular type of insurance
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Customer Segments Used for
Modeling
Results
Three segments for one database
 Two segments for two databases
 One segment for two databases

Continue modeling for each segment
independently
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The Data Mining Process
Business
objectives
Data
evaluation
Data
preparation
Deployment
Data
Modeling
Evaluation
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Modeling
Select modeling technique(s)
Calibrate modeling techniques
Make adjustments to data
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Modeling
Mathematical models for predicting if a
customer is interested in a coverage
Understand why a customer is
interested
For example:
If a customer’s state is Indiana and the office
is Indianapolis_Office1 then the customer is
interested in Coverage_3
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Modeling Techniques
Three modeling techniques tried for
predicting customer interest:



Decision trees
Artificial neural networks (ANN)
Support vector machines (SVM)
Decision trees have the advantage of
transparency
ANN and SVM did not have significantly
better prediction accuracy
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Insurance Coverage Interest
(Type 6)
Small Group Flag
Y
No
N
Product
Line Name
Group_1
Yes
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Data Mining
Group_2
No
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Insurance Coverage Interest
(Type 7)
Pricing Category
Code
Others
A4
Transportation_and
Public_Utilities
A2
Next
Renewal
Year
Industry
Classification
Name
Legal_Services
Branches
omitted
> 2002
<= 2002
Group_1
Group_2
Next Renewal Year
<= 2000
Yes
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Agency Number
<= 430
> 2000
No
Yes
No
Data Mining
Yes
Yes
No
> 430
No
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Accuracy of Predicting
Customer Interest
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Coverage
Accuracy
Type 1
Type 2
Type 3
Type 4
Type 5
Type 6
Type 7
Type 8
Type 9
84.0%
97.2%
98.3%
99.5%
88.4%
100%
76.3%
85.0%
94.8%
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Modeling
Mathematical models for predicting if a
customer will terminate coverage
Why do customers terminate a specific
type of coverage?
What are the important factors in a
customers decision to terminate
coverage?
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Who Terminates Type 3
Coverage?
Correct for 95%
of customers
Customer
Effective Year
 2000
Coverage
Effective Year
 1999
 2000
2000
Terminated
7
Terminated
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Active
Next Renewal
Month
 2000
Coverage
Effective Year
2001
Terminated
2002
Active
7
Active
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Who Terminates Type 1
Coverage?
Decision tree based on:
Distribution number
 Underwriting department number
 Price category
 Rate type
 Rate Plan Year

Predicts 96.3% of terminations correctly
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Accuracy of Predicting
Termination
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Model
Accuracy
Type 1
96.3%
Type 2
96.5%
Type 3
95.3%
Type 4
88.9%
Type 5
88.3%
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The Data Mining Process
Business
objectives
Data
evaluation
Data
preparation
Deployment
Data
Modeling
Evaluation
August 28, 2004
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Evaluation
Data analysis results in a good model
Are business objectives being achieved?
Is there an important business issue that has
not been considered?
Should the results be used?
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The Data Mining Process
Business
objectives
Data
evaluation
Data
preparation
Deployment
Data
Modeling
Evaluation
August 28, 2004
Data Mining
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Deployment
Incorporate the results in the
organization’s decision making process
Report
 Decision support system
 Personalization of web pages
 Repeatable data mining process

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