Name: Predicting Credit Card Customer Segmentation
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Predicting Credit Card Customer Segmentation

Exploring Key Customer Characteristics

By [source]

About this dataset

This dataset contains a wealth of customer information collected from within a consumer credit card portfolio, with the aim of helping analysts predict customer attrition. It includes comprehensive demographic details such as age, gender, marital status and income category, as well as insight into each customer’s relationship with the credit card provider such as the card type, number of months on book and inactive periods. Additionally it holds key data about customers’ spending behavior drawing closer to their churn decision such as total revolving balance, credit limit, average open to buy rate and analyzable metrics like total amount of change from quarter 4 to quarter 1, average utilization ratio and Naive Bayes classifier attrition flag (Card category is combined with contacts count in 12months period alongside dependent count plus education level & months inactive). Faced with this set of useful predicted data points across multiple variables capture up-to-date information that can determine long term account stability or an impending departure therefore offering us an equipped understanding when seeking to manage a portfolio or serve individual customers

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How to use the dataset

This dataset can be used to analyze the key factors that influence customer attrition. Analysts can use this dataset to understand customer demographics, spending patterns, and relationship with the credit card provider to better predict customer attrition.

Research Ideas

Using the customer demographics, such as gender, marital status, education level and income category to determine which customer demographic is more likely to churn.

Analyzing the customer’s spending behavior leading up to churning and using this data to better predict the likelihood of a customer of churning in the future.

Creating a classifier that can predict potential customers who are more susceptible to attrition based on their credit score, credit limit, utilization ratio and other spending behavior metrics over time; this could be used as an early warning system for predicting potential attrition before it happens

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BankChurners.csv

Column name	Description
CLIENTNUM	Unique identifier for each customer. (Integer)
Attrition_Flag	Flag indicating whether or not the customer has churned out. (Boolean)
Customer_Age	Age of customer. (Integer)
Gender	Gender of customer. (String)
Dependent_count	Number of dependents that customer has. (Integer)
Education_Level	Education level of customer. (String)
Marital_Status	Marital status of customer. (String)
Income_Category	Income category of customer. (String)
Card_Category	Type of card held by customer. (String)
Months_on_book	How long customer has been on the books. (Integer)
Total_Relationship_Count	Total number of relationships customer has with the credit card provider. (Integer)
Months_Inactive_12_mon	Number of months customer has been inactive in the last twelve months. (Integer)
Contacts_Count_12_mon	Number of contacts customer has had in the last twelve months. (Integer)
Credit_Limit	Credit limit of customer. (Integer)
Total_Revolving_Bal	Total revolving balance of customer. (Integer)
Avg_Open_To_Buy	Average open to buy ratio of customer. (Integer)
Total_Amt_Chng_Q4_Q1	Total amount changed from quarter 4 to quarter 1. (Integer)
Total_Trans_Amt	Total transaction amount. (Integer)
Total_Trans_Ct	Total transaction count. (Integer)
Total_Ct_Chng_Q4_Q1	Total count changed from quarter 4 to quarter 1. (Integer)
Avg_Utilization_Ratio	Average utilization ratio of customer. (Integer)
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1	Naive Bayes classifier for predicting whether or not someone will churn based on characteristics such

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Bankchurners

@kaggle.thedevastator_predicting_credit_card_customer_attrition_with_m.bankchurners

395.26 kB
10,127 rows
23 columns

CREATE TABLE bankchurners (
  "clientnum" BIGINT,
  "attrition_flag" VARCHAR,
  "customer_age" BIGINT,
  "gender" VARCHAR,
  "dependent_count" BIGINT,
  "education_level" VARCHAR,
  "marital_status" VARCHAR,
  "income_category" VARCHAR,
  "card_category" VARCHAR,
  "months_on_book" BIGINT,
  "total_relationship_count" BIGINT,
  "months_inactive_12_mon" BIGINT,
  "contacts_count_12_mon" BIGINT,
  "credit_limit" DOUBLE,
  "total_revolving_bal" BIGINT,
  "avg_open_to_buy" DOUBLE,
  "total_amt_chng_q4_q1" DOUBLE,
  "total_trans_amt" BIGINT,
  "total_trans_ct" BIGINT,
  "total_ct_chng_q4_q1" DOUBLE,
  "avg_utilization_ratio" DOUBLE,
  "naive_bayes_classifier_attrition_flag_card_category_co_121d9ef8" DOUBLE  -- Naive Bayes Classifier Attrition Flag Card Category Contacts Count 12 Mon Dependent Count Education Level Months Inactive 12 Mon 1,
  "naive_bayes_classifier_attrition_flag_card_category_co_eaea8887" DOUBLE  -- Naive Bayes Classifier Attrition Flag Card Category Contacts Count 12 Mon Dependent Count Education Level Months Inactive 12 Mon 2
);