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Segmentation

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by

FoongYee Hew

on 30 April 2013

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Transcript of Segmentation

Data Modeling Segmentation An art of finding groups in data.

Gathers objects into groups according to (unknown) common characteristics.

Internally homogenous and externally heterogeneous.

An iterative process of knowledge discovery. Application Competitive Advantage Profitability Business Opportunity Concentration of Force Provide customer distinct needs
Increase customer stickiness
Segment based pricing Optimize profit
Product margin trade off Focus marketing energy & cost
Few products to 1 customer Cross-sell
Product tailoring
Segment migration Data Exploration Data Profiling Maintenance Model Selection Model Objective Target Population Input
Variables Transaction Behavioral Psychographic Demographic sum Avg
Ratio Slope Period Hour played
T/Win Day Preference Lifestyle Activity
Interest Opinion Age
Gender Meaningful to model objective Characteristic of
Input Variables Low correlation between inputs Low kurtosis & skewness Predominantly interval inputs Data Preparation Interval Nominal Correlation Distribution Correlation Chi-Square
Test Descriptive
Statistic Frequency
Table data error
missing value
extreme value Data Modification Variables
Reduction Model Impute Replace Transform missing value invalid data skewed distribution to normal Variable Clustering Clustering Examine Standardization Method
Number of Cluster
Input Variables Try different Segment Size
Means Statistic
Segment Plot Iterative
Process Clustering Data Profiling Enterprise
Guide Desicion Tree Segment
Profile Use segment as Target
Check English Rules "BEST" R U L E elevant to business objective nderstandable & easy to characterize arge enough to warrant offering asy to develop unique offering Process Flow Model
Scoring Evaluating Model Stability Over Time Score node
Optimized code Avg distance in each cluster - Training vs New
If different, check
distance distribution
profile
overlap of scoring - old vs new model
Full transcript