Icon

Basic Customer Segmentation Use Case

Basic Customer Segmentation
Customer SegmentationCustomer 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 basiccustomer segmentation model, using a clustering procedure.Task Build a basic customer segmentation using a clustering procedure. For a more extensive analysis, check the whitepaper "Customer Segmentation comfortably from a Web Browser" at the following URL: https://www.knime.org/files/white-papers/customer_segmentation.pdf Pre-processing - Join contract data and behavioral data - Convert Churn values to String to be used as class in upcomingclassification - Normalize all numerical columns in [0,1] ClusteringClustering is performed with k-Means. Other Learner nodes train other models. MostLearner nodes output a PMML model (blue square output port). Input data withassigned cluster Cluster centers Data Reading 2 files: - contract data - behavioral (calls) dataBoth files are located in TheData/Customers Try this:Clustering Algorithms1) Choose your clustering technique and placeit here. k-Means here is just an example. 2) Open the view of the k-Means node to seecluster prototypes.Denormalization1) After executing the k-Means, denormalize thedata to see values in their original range. ReadingContractData.csvJoin calls dataand contract data10 clusterson all numericalinputsNormalize allnumerical columnsto fall in [0,1]Back to originaldata rangeBack to originaldata rangeconverting a numerical column to Stringexcludes it from the clustering procedureExclude area code and churn from subsequent clusteringReadingCallsData.xls File Reader Joiner k-Means Normalizer (PMML) Denormalizer (PMML) Denormalizer (PMML) Number To String Excel Reader (XLS) Customer SegmentationCustomer 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 basiccustomer segmentation model, using a clustering procedure.Task Build a basic customer segmentation using a clustering procedure. For a more extensive analysis, check the whitepaper "Customer Segmentation comfortably from a Web Browser" at the following URL: https://www.knime.org/files/white-papers/customer_segmentation.pdf Pre-processing - Join contract data and behavioral data - Convert Churn values to String to be used as class in upcomingclassification - Normalize all numerical columns in [0,1] ClusteringClustering is performed with k-Means. Other Learner nodes train other models. MostLearner nodes output a PMML model (blue square output port). Input data withassigned cluster Cluster centers Data Reading 2 files: - contract data - behavioral (calls) dataBoth files are located in TheData/Customers Try this:Clustering Algorithms1) Choose your clustering technique and placeit here. k-Means here is just an example. 2) Open the view of the k-Means node to seecluster prototypes.Denormalization1) After executing the k-Means, denormalize thedata to see values in their original range. ReadingContractData.csvJoin calls dataand contract data10 clusterson all numericalinputsNormalize allnumerical columnsto fall in [0,1]Back to originaldata rangeBack to originaldata rangeconverting a numerical column to Stringexcludes it from the clustering procedureExclude area code and churn from subsequent clusteringReadingCallsData.xls File Reader Joiner k-Means Normalizer (PMML) Denormalizer (PMML) Denormalizer (PMML) Number To String Excel Reader (XLS)

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: albrecht
Created at: 2016-03-09
On NodePit since: 2020-07-16
Last update: 2024-11-21
Created with KNIME version: v4.1.0
Tags: clustering k-Means customer segmentation

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!