Retention split
How the sample is divided between current and exited customers.
Pig E. Bank Customer Churn Analysis
Understanding customer attrition, identifying retention priorities, and translating banking data into actionable insights.
01 / Executive dashboard
Customer retention at a glance
A compact overview of the customer base and observed attrition in the provided sample.
Total customers—
Customer records
Exited customers—
Observed bank exits
Churn rate—
Exited ÷ all customers
Active members—
Membership activity flag
Customer snapshot
Average age: —
Average credit score: —
Average account balance: —
Average estimated salary: —
Retained customers: —
All dashboard figures describe this dataset; they do not represent the bank’s entire
real-world customer population.
02 / Research context
Project overview
Adapted from the original Pig E. Bank static analysis report.
Background
Customer attrition can weaken long-term relationships and increase the cost of customer acquisition. Examining customer characteristics helps identify segments that may benefit from greater retention attention.
Objective
Analyze customer satisfaction and account characteristics to identify factors associated with leaving Pig E. Bank, and inform customer-retention strategies.
Research question
Which observed customer characteristics are most strongly associated with customer churn?
03 / Interactive analysis
Factors associated with churn
Compare membership status, product count, age, estimated salary, and other customer attributes in one chart. Choose the outcome metric to distinguish group size from within-group attrition risk.
Churn rate by membership
Percentage of customers who exited within each membership group.
Key insight
Loading customer segment comparisons…
Churn rate is calculated within each category; a group’s share of exited customers is a different statistic.What the original report highlighted
The report identifies membership activity, number of banking products, age group, and estimated salary as the main factors worth examining. It proposes targeted retention services, product education, and support for specific customer groups.
How to interpret the new charts
Switch between percentages and counts to avoid confusing group size with churn risk. Categories with very few customers should be interpreted cautiously; associations cannot demonstrate causation.
04 / Customer profiles
Explore observed risk profiles
The original report included a decision-tree-style interpretation. This explorer uses the 18 observed combinations exported from Python, not a trained predictive decision tree.
Customer segment explorer
Choose membership, product count, age range, and estimated salary. The panel reports the measured churn rate for the matching customers.
—Churn rate
—
Select filters to examine an observed profile.
A small group may show an extreme churn rate by chance. This tool describes past
sample outcomes and cannot predict whether an individual customer will leave.
05 / Methodology
Analysis approach & limitations
Clear definitions are essential when turning a static report into an interactive analytics experience.
Data processing
Python and pandas aggregate the cleaned customer file into one small JSON export. Category labels and currency-formatted fields are normalized during calculation while retaining the original source file.
Primary measure
Churn rate = Exited customers ÷ Customers in group × 100. Every characteristic can also be displayed as total, exited, or retained customer counts.
Limitations
This is a 991-record observational dataset. Some fields may be missing, category sizes differ, and no controlled causal or predictive model is established in the dashboard. Small profiles require particular caution.
06 / Business takeaways
Conclusions & recommendations
Retention strategies suggested in the source report, informed by the interactive segment comparisons.
Increase customer engagement
Strengthen loyalty programs and personalized services for customers with low membership activity. Evaluate interventions against customer feedback and observed results.
Encourage product adoption
Use relevant product education and appropriate cross-selling to help customers understand useful banking services, without assuming additional products alone prevent churn.
Respond to age-related needs
Evaluate whether customers in the middle-age groups require different communications, financial planning options, or service support.
Support financially constrained customers
Consider affordable products and assistance for lower-salary segments, while validating that the observed relationship persists across other characteristics.
Research conclusion
Customer retention decisions should combine observed churn rates, the size of each segment, and further investigation—rather than relying on a single customer characteristic.