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Basic Customer Segmentation Use Case

This workflow implements a basic customer segmentation through a clustering procedure. No input is required from business analyst.

This workflow implements a basic customer segmentation through a clustering procedure. No input is required from business analyst.

Customer Segmentation

Customer segmentation is the sub-division of a market into discrete different groups of customers, where each group shares similar characteristics. This workflow illustrates how to build a basic customer segmentation model, using a clustering procedure.

Pre-processing

  • Join contract data and behavioral data

  • Convert Churn values to String to be used as class in upcoming classification

  • Normalize all numerical columns in [0,1]

Data Reading

2 files:

  • contract data

  • behavioral (calls) data

Both files are located in TheData/Customers

Clustering

Clustering is performed with k-Means. Other Learner nodes train other models. Most Learner nodes output a PMML model (blue square output port).

Task

Build a basic customer segmentation using a clustering procedure.

Input data with assigned cluster

Cluster centers

Back to originaldata range
Denormalizer (PMML)
Join calls data and contract data
Joiner
Back to originaldata range
Denormalizer (PMML)
Exclude columns "Area code" and "Churn" from subsequent clustering: converting a numerical column to Stringexcludes it from the clustering procedure
Number to String
Reading ContractData.csv
CSV Reader
Normalize all numerical columns to fall in [0,1]
Normalizer (PMML)
10 clusters on all numerical inputs
k-Means
Reading CallsData.xls
Excel Reader

Nodes

Extensions

Links