Bank Marketing Campaign Dashboard
An interactive Bank Marketing Campaign dashboard developed using Python for data cleaning, SQL for modeling and extraction, and Tableau for visualization. Includes 32 SQL practice questions, a detailed data dictionary, and insights to help improve campaign effectiveness.
Industry:
This project analyzes a Bank Marketing Campaign dataset and generates key insights via an interactive dashboard. The dataset was sourced from Kaggle, cleaned and processed using Python, and later modeled with SQL. The project includes a set of 30+ SQL practice questions designed to help users enhance their SQL skills by working directly with the dataset.
📂 Data Source: Kaggle Bank Marketing Dataset
🧹 Data Cleaning & Preparation: Python (Pandas, NumPy)
🏗️ Data Modeling & Extraction: SQL queries and models
📊 Dashboard Development: Tableau for data visualization
💻 Practice SQL Questions: 30+ SQL questions to practice various queries
📥 Data Acquisition & Cleaning
Utilized Python to fetch data from Kaggle.
Performed data cleaning and transformations, preparing the dataset for SQL extraction.
📊 Data Modeling & SQL Operations
Used SQL for modeling and extracting key insights from the data.
Created 30+ SQL practice questions covering basic to advanced topics.
📈 Dashboard Development
Built an interactive dashboard using Tableau to visualize marketing campaign insights.
Includes filters and dynamic visualizations to allow users to explore the data in-depth.
📑 Documentation
Bank's Requirements Document: Outlines the specific objectives of the marketing campaign.
Data Dictionary: Describes the meaning of each column in the dataset.
Dashboard Overview: Contains insights derived from the data in an easy-to-navigate format.
🏠 Correlation between Subscription Rates and Loan Types
Housing Loans:
Higher Subscription Rates:
Larger loan amounts might lead to longer customer relationships and higher potential for cross-selling.
Homeownership often signifies financial stability, making customers more likely to subscribe to additional financial products.
Personal Loans:
Lower Subscription Rates:
Smaller loan amounts may not provide the same level of customer engagement or cross-selling opportunities.
Short-term loans limit the potential for building long-term relationships.
👥 Demographic Differences and Loan Preferences
Older Age Demographics (Housing Loans):
Homeownership Stage:
Older individuals are more likely to have already purchased homes, making them prime candidates for housing loans.Financial Planning:
Older individuals focus more on long-term financial planning, including homeownership.
Younger Age Demographics (Personal Loans):
Educational Expenses:
Younger individuals might be seeking loans to cover educational costs.Entrepreneurial Ventures:
Younger entrepreneurs may need personal loans to start or grow businesses.Short-Term Financial Needs:
Short-term needs, such as debt consolidation or unexpected expenses, drive demand for personal loans.
🌍 Regional Variations and Loan Demand
Housing Loan Demand:
Regions with higher housing loan demand could be influenced by:
Real Estate Market: Strong real estate markets drive demand for housing loans.
Economic Conditions: Favorable conditions encourage homeownership and related loans.
Personal Loan Demand:
Regions with higher personal loan demand could indicate:
Economic Downturns: Financial uncertainty increases demand for short-term personal loans.
Industry-Specific Needs: Certain industries or economic sectors have higher personal loan demands.
💡 Recommendations
Based on the combined insights, the bank can consider the following strategies:
🎯 Tailor Marketing Strategies
Focus marketing efforts on loan types that align with age demographics and regional variations.
🏦 Optimize Product Offerings
Develop offerings tailored to the specific needs of housing loan customers and personal loan applicants.
🤝 Improve Customer Retention
Focus on building long-term relationships with customers, particularly those with housing loans, to increase cross-selling opportunities.
📉 Risk Management
Assess risk across loan types and customer segments, adjusting lending policies to minimize financial exposure.
