Segmentation-of-Credit-Card-Customers

Table of Content

• Directory Tree
• DATA AVAILABLE
• BUSINESS CONTEXT
• EXPECTATIONS FROM THE TRAINEES
• DATA DICTIONARY
• LICENSE

Directory Tree

├── CC_GENERAL.csv
├── Credit_new_cust.xlsx
├── Profiling_output.csv
├── Segmentation of Credit Card Customers.ipynb
├── pandas_profiling.html
├── python_FA.xls
├── LICENSE
├── README.md

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:

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

LICENSE

MIT License

Copyright (c) 2020 Vicky Gupta

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.