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CRM-Analytics-Finished

Importing and Cleaning the First Table: Customer Importing and Cleaning the Second Table: Products Importing and Cleaning the Third Table: Transactions Joining and Cleaning the Tables RFM scores Computation CRM Customer MDCRM Product MDCRM TransactionsCheck for Missing ValuesNo missing values No missing values Double check that there are no missing valuesRemove Unspecified countriesCheck for duplicate StockCode, no duplicatesCorrect Air Fragance to Air Fragrance.check for duplicates,no duplicatesRemove duplicate records based on Invoice, StockCode,Customer_ID,Invoice_timeRemove negative quantities and pricesAdded Total_Spend Column Remove the leading D in the Invoice_time columnSeparate into date and time columnsRename Date and TimeString to TimeString to DateJoin Transaction with Product TableConvert Customer Idto string to join tablesJoin with Customer IDRename ColumnsCreate Table to performRFM AnalysisRename ColumnsCompute Time Difference in DaysCompute RFM Scoresusing weighted RFM scorefor more balanced distributionCompute Z-score NormalisationCompute K-means,found that 3 clusters is most optimalTable with mean RFM of the 3 clustersAdd Cluster LabelsCluster-Level SummaryJoin Cluster label and RFM Scoreto clustering modelComplete-Customer-Level SummaryCSV Reader CSV Reader Excel Reader Missing Value Missing Value Missing Value Rule-basedRow Filter Row Filter DuplicateRow Filter String Manipulation DuplicateRow Filter DuplicateRow Filter Rule-basedRow Filter Math Formula String Manipulation Cell Splitter Column Renamer String to Date&Time String to Date&Time Joiner Number to String Joiner Column Renamer GroupBy Column Renamer Date&TimeDifference Math Formula Normalizer k-Means GroupBy Rule Engine Excel Writer Joiner Excel Writer Importing and Cleaning the First Table: Customer Importing and Cleaning the Second Table: Products Importing and Cleaning the Third Table: Transactions Joining and Cleaning the Tables RFM scores Computation CRM Customer MDCRM Product MDCRM TransactionsCheck for Missing ValuesNo missing values No missing values Double check that there are no missing valuesRemove Unspecified countriesCheck for duplicate StockCode, no duplicatesCorrect Air Fragance to Air Fragrance.check for duplicates,no duplicatesRemove duplicate records based on Invoice, StockCode,Customer_ID,Invoice_timeRemove negative quantities and pricesAdded Total_Spend Column Remove the leading D in the Invoice_time columnSeparate into date and time columnsRename Date and TimeString to TimeString to DateJoin Transaction with Product TableConvert Customer Idto string to join tablesJoin with Customer IDRename ColumnsCreate Table to performRFM AnalysisRename ColumnsCompute Time Difference in DaysCompute RFM Scoresusing weighted RFM scorefor more balanced distributionCompute Z-score NormalisationCompute K-means,found that 3 clusters is most optimalTable with mean RFM of the 3 clustersAdd Cluster LabelsCluster-Level SummaryJoin Cluster label and RFM Scoreto clustering modelComplete-Customer-Level SummaryCSV Reader CSV Reader Excel Reader Missing Value Missing Value Missing Value Rule-basedRow Filter Row Filter DuplicateRow Filter String Manipulation DuplicateRow Filter DuplicateRow Filter Rule-basedRow Filter Math Formula String Manipulation Cell Splitter Column Renamer String to Date&Time String to Date&Time Joiner Number to String Joiner Column Renamer GroupBy Column Renamer Date&TimeDifference Math Formula Normalizer k-Means GroupBy Rule Engine Excel Writer Joiner Excel Writer

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Created by: allam
Created at: 2024-10-30
On NodePit since: 2024-11-03
Last update: 2024-11-21
Created with KNIME version: v5.3.2

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