Summit Bank Customer Retention Analysis
Project Overview
In this solo project, I stepped into the role of a sales data analyst at Summit Bank to identify the key factors that drive customer churn. Using historical client data and Excel-based data mining techniques, the goal was to support the bank’s customer retention strategy.
Objective:
Uncover risk indicators that predict whether a customer will leave the bank, and model them in a decision tree.
Tools & Techniques
- Microsoft Excel (formulas, pivot tables)
- Data quality checks & cleaning
- Descriptive statistics
- Customer segmentation
- Decision tree modeling
Methodology
-
Data Quality Review:
Assessed 991 client records for missing or inconsistent values. Cleaned fields including credit score, tenure, and account balance. - Segmentation:
Split data into two groups:- Exited Customers: (
ExitedFromBank = 1) - Active Customers: (
ExitedFromBank = 0)
- Exited Customers: (
-
Descriptive Statistics:
Analyzed patterns by country, gender, credit score band, product count, and estimated salary. - Top Risk Factors Identified:
- Inactive customer status (70% of churned clients)
- Short tenure (under 2 years)
- High account balance with low product count
- Poor credit score (under 580)
- Decision Tree Model:
Designed a simple decision tree prioritizing the most predictive features, withIsActiveMember = 0at the root node.
Key Insights
- Customer activity was the strongest predictor of churn: inactive users had double the churn rate.
- Customers with a high balance but few products are more likely to leave.
- Credit score and tenure also showed strong churn correlations.
- Retention strategies should focus on re-engaging inactive, high-value customers early.
File Access
You can view the full workbook here:
Open Summit Bank Analysis (Google Sheets)
Skills Demonstrated
- Exploratory data analysis (EDA)
- Data cleaning and integrity checks
- Churn segmentation logic
- Decision tree modeling and prioritization
- Visual storytelling through pivot tables