#Bank_Marketing_SQL_Analysis
Customer Analytics and campaign Effectiveness Analysis using SQL in Databricks
#Project Overview
This project leverages SQL-based data analysis to extract actionable insights from the Bank Marketing Dataset. The dataset contains customer demographic, financial, and marketing campaign details. By applying churn prediction, campaign effectiveness measurement, and customer segmentation, the project demonstrates how raw data can be transformed into strategic insights for business growth.
#Project structure
#Data Description
Features Include
• Age – Customer age
• Job – Occupation type
• Marital – Marital status
• Education – Education level
• Balance – Bank account balance
• Loan – Loan status
• Housing – Housing loan status
• Contact – Contact communication type
• Campaign – Number of contacts made during campaign
• Response – Campaign response (yes/no)
Size: Rows: ~5,000+ | Columns: 10
#Methodology
Data Preprocessing - Structured the dataset in databricks SQL Analysis - Queries for segmentation , campaign effectiveness, Churn. Insights Extraction - Identified high-risk, response patterns,and retention oppurtunities.
##SQL Queries Implemented
Customer segmentation - by Age,balance,job,marital Status.
Campaign Effectiveness - by Contact channel,occupation,education,wealth segments.
Churn Prediction - churn flagging, churn rate analysis by demographics and financial factors.
#Key Insights
Young , low-balance customers with loans showed the highest churn risk (68%).
Middle-aged, stabled-balance customers emerged as strong cross-selloppurtunities.
retired,high-balance customers displayed loyalty but require personalized engagement to maintain relationships.
Overall campaign success rate stood at ~12% (1,652/5,000 customers).
Campaigns with more than three contact attempts resulted in dimnishing returns.
top-Performing groups : Students (36.7%) , Technicians (36.5%),Entrepeneurs(34%).
Overall churn was 66.96% (~3,348 customers).
Low-balance + active loan customers were the most vulnerable segment.
A clear overlap was observed between non-responders and churn-prone customers,Understanding disengagement as a churn predictor.
###Business Impact
Optimized Marketing ROI - Enhanced targeting of high-potential segments.
Churn Mitigation - Proactive retention of at-risk customers.
Increased Cross-selling - data driven product recommendations aligned to customer needs.
Strategic Decision-Making - Leveraging churn and segmentation insights for sustainable frowth.
**databricks (SQL) ** - data analysis 7 querying
Microsoft Word / Docs - Detailed documentation
GitHub - Project hosting & portfolio showcase