bistp / segmentation-of-credit-card-customers

Performing Unsupervised Learning on a Credit Card Data to Segment Customers into different clusters using KMeans Clustering.

Segmentation-of-Credit-Card-Customers

Performing Unsupervised Learning on a Credit Card Data to Segment Customers into different clusters using KMeans Clustering.

DATA AVAILABLE:

• CC GENERAL.csv

BUSINESS CONTEXT:

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:

• Advanced data preparation: 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.
• Advanced reporting: Use the derived KPIs to gain insight on the customer profiles.
• Identification of the relationships/ affinities between services.
• Clustering: Apply a data reduction technique factor analysis for variable reduction technique and a clustering algorithm to reveal the behavioural segments of credit card holders
• Identify cluster characterisitics of the cluster using detailed profiling.
• Provide the strategic insights and implementation of strategies for given set of cluster characteristics

DATA 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