Tag credit card segmentation

Credit Card Segmentation

By inMachine learning projects Tag , , ,

Data Available:

  • CC GENERAL.csv

Business Context:

A Bank wants to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behaviour of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioural variables.

Business Requirements:

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, instalments)
  • 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 characteristics 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

Let’s develop a machine learning model for further analysis.