This case requires developing a customer segmentation to define a marketing strategy. The sample Dataset summarizes the usage behavior of 8950 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.
Columns (Features):
- CUST_ID : Identification of Credit Card holder (Categorical)
- BALANCE : Balance amount left in their account to make purchases
- BALANCE_FREQUENCY : 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
- ONEOFF_PURCHASES : Maximum purchase amount done in one-go
- INSTALLMENTS_PURCHASES : Amount of purchase done in installment
- CASH_ADVANCE : Cash in advance given by the user
- PURCHASES_FREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = * * * * frequently purchased, 0 = not frequently purchased)
- ONEOFF_PURCHASES_FREQUENCY : How frequently Purchases are happening in one-go (1 = frequently * purchased, 0 = not frequently purchased)
- PURCHASES_INSTALLMENTS_FREQUENCY : How frequently purchases in installments are being done (1 = * frequently done, 0 = not frequently done)
- CASH_ADVANCE_FREQUENCY : How frequently the cash in advance being paid
- CASH_ADVANCETRX : Number of Transactions made with "Cash in Advanced"
- PURCHASES_TRX : Numbe of purchase transactions made
- CREDIT_LIMIT : Limit of Credit Card for user
- PAYMENTS : Amount of Payment done by user
- MINIMUM_PAYMENTS : Minimum amount of payments made by user
- PRC_FULL_PAYMENT : Percent of full payment paid by user
- TENURE : Tenure of credit card service for user
- Target ==>>> We want to set Clusters