This case requires to develop a customer segmentation to define marketing strategy
This case requires to develop a customer segmentation to define marketing strategy. The sample Dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Following is the Data Dictionary for Credit Card dataset :- CUSTID : Identification of Credit Card holder (Categorical) BALANCE : Balance amount left in their account to make purchases ( BALANCEFREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) PURCHASES : Amount of purchases made from account ONEOFFPURCHASES : Maximum purchase amount done in one-go INSTALLMENTSPURCHASES : Amount of purchase done in installment CASHADVANCE : Cash in advance given by the user PURCHASESFREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased) ONEOFFPURCHASESFREQUENCY : How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased) PURCHASESINSTALLMENTSFREQUENCY : How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done) CASHADVANCEFREQUENCY : How frequently the cash in advance being paid CASHADVANCETRX : Number of Transactions made with "Cash in Advanced" PURCHASESTRX : Numbe of purchase transactions made CREDITLIMIT : Limit of Credit Card for user PAYMENTS : Amount of Payment done by user MINIMUM_PAYMENTS : Minimum amount of payments made by user PRCFULLPAYMENT : Percent of full payment paid by user TENURE : Tenure of credit card service for user
https://www.kaggle.com/arjunbhasin2013/ccdata
The customer segmentation process was carried out using KMeans. The number of features were reduced using PCA from 18 to 2 features to enhance the performance of our clustering algorithm. The metrics used to determine the accuacy for this clustering analysis was Elbow method which gave a result of 3 to 4 clusters. The silhoutte method was used to confirm the optimum number of clusters to use and this yielded two. Customers therefore belong to either one of two clusters (0,1)