Bank Deposit Segmentation Analysis

Predicting bank term deposit subscriptions using Gradient Boosting, feature importance analysis, and customer segmentation for targeted marketing.

Customer segmentation is a pivotal task for business analytics. Customer segmentation is the process of splitting customers into different groups with similar characteristics for potential business value proposition. Many companies find that segmenting their customers enable them to communicate, engage with their customers more effectively. Future Bank is conducting an analysis on the existing customer profiles and the marketing campaign data to identify the target customers who are mostly likely to subscribe long-term deposits. As a member of the data analytics team, I am tasked to analyse historical data and develop predictive models for marketing purposes. I have used SAS Enterprise Miner and Rstudio to perform the analysis.

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Exploratory data analysis of bank marketing campaigns using customer segmentation, campaign intensity, and macroeconomic factors to optimize term deposit subscriptions.

SQL analysis of bank marketing campaign data to optimize term deposit conversion — segmentation, campaign fatigue analysis, wealth paradox, and contact window optimization using MySQ

Python + SQL analysis of 41,188 bank marketing contacts - EDA, campaign segmentation and logistic regression model (ROC-AUC 0.801) to predict term deposit subscriptions.

Power BI project analyzing a European bank’s marketing campaign dataset to identify high-conversion customer segments, optimize outreach strategies, and forecast term-deposit uptake. Includes data profiling, customer segmentation, funnel analysis, and interactive dashboards.

This project analyzes a real-world bank marketing dataset to uncover patterns behind customer subscriptions to term deposits. Using Excel exclusively, it showcases data cleaning, segmentation, descriptive analysis, and visualization techniques to generate actionable insights for improving campaign effectiveness.