Credit Card and Customer Data Analysis

Introduction

This project provides an in-depth analysis of credit card usage and customer behavior to derive actionable insights that drive revenue growth and customer engagement. Using detailed transaction data, customer demographics, and financial metrics, we examine key performance indicators (KPIs), revenue patterns, and the influence of various attributes on business outcomes. The analysis equips businesses with the insights needed to optimize marketing strategies, customize product offerings, and enhance customer experiences.

Dataset link

• cc_add

• credit_card

• cust_add

• customer

• Power BI Dashboard CC-Det Cust

Dataset

The analysis utilizes four CSV files:

1. cc_add.csv: Contains additional credit card details, including associated revenue and expenditure metrics.
2. credit_card.csv: Records transactions, card usage by type, and swipe methods, detailing revenue and interest generation.
3. cust_add.csv: Provides supplementary customer attributes, such as occupation and spending categories.
4. customer.csv: Includes customer demographic data, such as education level, income, age group, and marital status.

Dataset link

• cc_add
• credit_card
• cust_add
• customer

Power BI Dashboard CC-Det Cust

These datasets combine financial performance metrics and customer profiles to deliver a holistic view of business performance.

Power BI Analysis and Objectives

1. Credit Card Details Analysis

• Key Performance Indicators (KPI): Revenue $57m, Total Interest $8m, Amount $8m, and Count 667k.
• Bar charts illustrate:
  • Revenue breakdown by chip swipe ($36m as the most used method).
  • Expenditure types (bills $14m, entertainment $10m).
  • Education levels (graduate $23m, high school $11m).
  • Customer job categories (businessmen $47m, white collar $10m).
  • Card category breakdown (blue $47m, silver $6m).
• A chart showing card categories with total revenue, transaction amounts, and interest earned.
• Filters applied for week date, cards, income, and gender.

2. Customer Details Analysis

• Key Performance Indicators (KPI): Revenue $57m, Total Interest $8m, Income $588m, and CSS 3.19.

• A line chart showing revenue trends by week.

• Bar charts for:

  • Top 5 states (Texas, New York).
  • Revenue by marital status (married $27m).
  • Revenue by income, dependents, education (graduate, high school), and age group.

• Filters applied for gender (male, female), transaction methods (swipe, online, chip), and income levels (low, medium, high).

• Power BI Dashboard CC-Det Cust

Recommendations

Based on the analysis, the following recommendations are proposed:

1. Optimize Transaction Channels

   • Focus marketing efforts on chip swipe transactions, as they generate the highest revenue ($36m).
   • Encourage increased usage of underperforming transaction methods through targeted promotions.

2. Tailor Product Offerings by Customer Profile

   • Expand premium offerings for businessmen and blue cardholders, who contribute the largest revenue ($47m).
   • Create customized financial products for high-income and married customers, identified as key revenue segments.

3. Enhance Regional Strategies

   • Develop region-specific marketing campaigns for top-performing states, including Texas and New York.

4. Improve Customer Segmentation

   • Leverage insights on revenue trends by income and age groups to refine customer segmentation strategies.
   • Use education-based data (graduate, high school) to design tailored loyalty programs and offers.

5. Monitor Key Performance Indicators (KPIs)

   • Track KPIs such as revenue, total interest, and transaction amounts to measure the effectiveness of strategies and identify growth areas.

By implementing these recommendations, businesses can harness data-driven insights to enhance customer satisfaction, maximize profitability, and secure a competitive edge in the market.

Dataset link

• cc_add

• credit_card

• cust_add

• customer

• Power BI Dashboard CC-Det Cust