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Data-Driven Customer Segmentation for Strategic Marketing Insights

  • Writer: Ekramul Haque Towsif
    Ekramul Haque Towsif
  • Jan 13
  • 3 min read

Updated: Oct 17








The Business Problem

Imagine being part of a marketing team responsible for creating targeted campaigns for a diverse customer base. Without clear insights into customer behavior, campaigns risk being generic, leading to wasted resources and missed revenue opportunities.

In this project, I aimed to solve this challenge for a bank by segmenting customers into actionable groups based on their purchasing behavior, credit usage, and payment patterns. The goal was to provide the bank with insights to design personalized marketing strategies, optimizing both engagement and revenue.


Step 1: Understanding the Data

The journey began with exploring the dataset, which included customer transaction records such as balances, purchases, and credit limits. Key questions guided my exploration:

  • How do customers differ in their credit usage and purchasing behavior?

  • Are there patterns that can guide segmentation?


Initial Observations:

  • Average customer balance: $1,564.

  • Customers with high purchases tend to make fewer full payments.

  • Credit limits vary widely, indicating diverse customer profiles


A fractional overview of the dataset
A fractional overview of the dataset

Step 2: Data Cleaning and Preprocessing

To ensure accurate analysis, I addressed data quality issues:

  • Handling Missing Values: Filled missing Minimum Payments and Credit Limit values with the mean to maintain dataset integrity.

  • Feature Scaling: Standardized numerical features like balance and purchases to prepare for clustering.

  • Irrelevant Features: Removed non-actionable attributes, such as CPlease provide the text you would like me to review for grammatical mistakes.UST_ID, to focus on meaningful data.



Step 3: Exploring Behavioral Patterns

I used exploratory data analysis (EDA) to uncover trends and relationships:

  • Distribution Analysis: Examined the spread of purchases, payments, and balances using Kernel Density Estimate. It demonstrates the probability density at feature values in a continuous variable.


KDE plots showing the probability density of different features
KDE plots showing the probability density of different features

  • Correlation Heatmap: Revealed strong correlations between credit limits, purchases, and payments.

Correlation Heatmap among different features
Correlation Heatmap among different features

  • Customer Segmentation Trends: Early visualizations hinted at distinct groups, such as frequent purchasers and high-balance customers.



Step 4: Clustering Customers

With a solid understanding of the data, I moved to clustering to identify actionable customer groups.

1. Choosing the Right Number of Clusters

Using the Elbow Method, I determined that 4 to 8 clusters offered the best balance between interpretability and granularity.

Elbow Method
Elbow Method

2. Implementing K-Means Clustering

I applied K-Means clustering to segment customers into distinct groups:

  • VIP Customers: High credit limits and frequent full payments.

  • Revolvers: High balances and frequent cash advances.

  • Transactors: Low balances and cash advances, but consistent payments.

  • Low Tenure Customers: Recently joined with minimal activity.

PCA scatter plot showing clusters in 2D
PCA scatter plot showing clusters in 2D

Step 5: Enhancing Clustering with Dimensionality Reduction

To improve clustering performance and visualization:

  • Principal Component Analysis (PCA): Reduced dimensions to two for clear cluster separation.

  • Autoencoders: Used neural networks to compress high-dimensional data into meaningful representations.

Comparison of K values with PCA and Autoencoders
Comparison of K values with PCA and Autoencoders

Step 6: Insights and Business Impact

Key Insights:

  • VIP Customers: Ideal targets for exclusive rewards and premium offers.

  • Revolvers: Opportunities to promote cash-based products and incentives.

  • Low Tenure Customers: Require engagement campaigns to encourage more frequent transactions.

Quantitative Impact:

  • Enabled personalized marketing strategies, reducing campaign costs by focusing on high-value segments.

  • Improved customer retention through targeted engagement efforts.



Step 7: Visualization of Results

Visual storytelling was key to communicating the findings:

  • Cluster Analysis: Displayed distinct customer groups and their key characteristics.

  • Behavioral Trends: Highlighted spending patterns and payment behaviors across clusters.


Conclusion and Next Steps

This project demonstrated the power of data science in solving real-world business problems. By understanding customer behavior, I provided actionable insights for strategic decision-making, optimizing both engagement and ROI.

Future Work:

  • Integrate demographic data for more granular segmentation.

  • Use time-series analysis to capture customer behavior over time.

  • Build predictive models for customer lifetime value and churn.


Conclusion and Next Steps

This project demonstrated the power of data science in solving real-world business problems. By understanding customer behavior, I provided actionable insights for strategic decision-making, optimizing both engagement and ROI.


Future Work:

  • Integrate demographic data for more granular segmentation.

  • Use time-series analysis to capture customer behavior over time.

  • Build predictive models for customer lifetime value and churn.


Reflection

This project not only honed my technical skills in clustering and dimensionality reduction but also taught me the importance of translating technical insights into business value.



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