- CC GENERAL.csv
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.
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.
- 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.