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Credit Card User Insights
E-commerce & Online Transactions
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About
This dataset provides credit card customer information, designed for customer segmentation and targeted marketing within the marketing industry. It serves as a valuable resource for identifying loyal customers and other relevant use cases.
Columns
- Sl_No: Customer Serial Identification Number, primarily for indexing values.
- Min: 1, Max: 660, Mean: 331, Std. Deviation: 191
- Customer Key: A unique identifier for each customer.
- Min: 11,300, Max: 99,800, Mean: 55,100, Std. Deviation: 25,600
- AvgCreditLimit: The average credit card limit for the customer.
- Min: 3,000, Max: 200,000, Mean: 34,600, Std. Deviation: 37,600
- TotalCreditCards: The total number of credit cards owned by the customer.
- Min: 1, Max: 10, Mean: 4.71, Std. Deviation: 2.17
- Totalvisitsbank: The total number of times the customer has visited a bank branch.
- Min: 0, Max: 5, Mean: 2.4, Std. Deviation: 1.63
- Totalvisitsonline: The total number of times the bank customer has visited online platforms.
- Min: 0, Max: 15, Mean: 2.61, Std. Deviation: 2.93
- Totalcallsmade: The total number of calls made by the customer to the bank.
- Min: 0, Max: 10, Mean: 3.58, Std. Deviation: 2.86
Distribution
The dataset is in CSV format and has a file size of 16.48 kB. It includes 7 distinct columns and contains 660 valid records across all attributes.
Usage
This dataset is ideal for various analytical and marketing applications, including:
- Performing data cleaning, pre-processing, visualisation, and feature engineering.
- Implementing clustering models such as Hierarchical Clustering and K-Means Clustering for effective customer segmentation.
- Creating an RFM (Recency, Frequency, Monetary) Matrix to pinpoint loyal customers.
Coverage
The provided source material does not specify the geographic, time range, or demographic scope of the data. It represents a general collection of credit card customer information.
License
CC0: Public Domain
Who Can Use It
- Marketing industry professionals aiming to understand customer behaviour and optimise marketing strategies.
- Data scientists and analysts for applying machine learning techniques like clustering to customer data.
- Businesses seeking to segment their customer base for more effective and targeted outreach.
Dataset Name Suggestions
- Credit Card Customer Behaviour Dataset
- Customer Segmentation for Marketing
- Loyal Customer Identification Data
- Credit Card User Insights
Attributes
Original Data Source: Credit Card User Insights
QUALITY