Credit Card Segmentation DATA AVAILABLE:
⮚ credit_card.csv
This case requires trainees 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. Expectations from the Trainees:
Build an ‘enriched’ customer profile by deriving “intelligent” KPIs such as: ∙ Monthly average purchase and cash advance amount ∙ Purchases by type (one-off, installments) ∙ Average amount per purchase and cash advance transaction, ∙ Limit usage (balance to credit limit ratio), ∙ Payments to minimum payments ratio etc.
DICTIONARY: CUST_ID: Credit card holder ID BALANCE: Monthly average balance (based on daily balance averages) BALANCE_FREQUENCY: Ratio of last 12 months with balance PURCHASES: Total purchase amount spent during last 12 months ONEOFF_PURCHASES: Total amount of one-off purchases INSTALLMENTS_PURCHASES: Total amount of installment purchases CASH_ADVANCE: Total cash-advance amount PURCHASES_ FREQUENCY: Frequency of purchases (Percent of months with at least one purchase) ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off-purchases PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency AVERAGE_PURCHASE_TRX: Average amount per purchase transaction CASH_ADVANCE_TRX: Average amount per cash-advance transaction PURCHASES_TRX: Average amount per purchase transaction CREDIT_LIMIT: Credit limit PAYMENTS: Total payments (due amount paid by the customer to decrease their statement balance) in the period MINIMUM_PAYMENTS: Total minimum payments due in the period. PRC_FULL_PAYMEN: Percentage of months with full payment of the due statement balance TENURE: Number of months as a customer