Segmenting Credit Card Users: The Power of Clustering

Business Understanding

Understanding Customer Segmentation in Credit Card Usage

In today’s highly competitive financial market, understanding customer behavior is crucial for credit card companies. By segmenting customers based on their credit card usage patterns, businesses can offer more personalized services, design effective marketing strategies, and enhance customer satisfaction.

Why is Customer Segmentation Important?

• Targeted Marketing: Segmentation allows companies to tailor their marketing messages to specific groups, making campaigns more effective.
• Product Development: By understanding different customer needs, companies can design products or offers that cater to specific segments.
• Risk Management: Identifying segments that are more prone to defaulting can help in risk mitigation.
• Enhanced Customer Experience: Personalized services can be provided based on the segment a customer belongs to, leading to increased loyalty.

Objective of Our Analysis: Our main goal is to develop a customer segmentation model based on the credit card usage data of about 9,000 active credit card holders over the last six months. By the end of this analysis, we aim to answer the following questions:

1. What are the distinct segments of credit card users?
2. What defines each segment?
3. How can businesses leverage these insights?

Data Understanding

Diving Deep into Credit Card Data

Understanding the dataset is the foundational step in any data analysis or modeling task. Before applying any algorithms, we need to ensure that we know what we’re working with. Here’s how we approached this task:

1. Initial Data Inspection

The dataset contains information on approximately 9,000 credit card holders with 18 behavioral variables. Each variable provides insight into different aspects of a customer’s credit card usage.

Key Findings:

• Data Shape: 9,000 rows and 18 columns.
• Features: The dataset includes features such as ‘BALANCE’, ‘PURCHASES’, ‘CREDIT_LIMIT’, and more.

2. Handling Missing Values

Like many real-world datasets, ours was not free from missing values. Addressing these is crucial as they can significantly impact our analysis and modeling results.

Key Findings:

• Features such as MINIMUM_PAYMENTS and CREDIT_LIMIT had missing values.
• We addressed these by filling them with the median of the respective columns.

3. Statistical Summary

A statistical overview gives us a sense of the data’s distribution, central tendencies, and spread. This information is crucial when deciding on further data processing steps or choosing appropriate modeling techniques.

Key Insights:

• The average balance (BALANCE) across all customers is approximately $1,564.47.
• The maximum credit limit (CREDIT_LIMIT) observed in the dataset is $30,000.
• The average purchase amount (PURCHASES) is approximately $1,003.20.

4. Data Visualization

Visualizing data can often reveal patterns, trends, or anomalies that might not be immediately apparent from numerical summaries.

Observations:

• The distribution of balance (BALANCE) shows a significant distribution with many customers having lower balances, but a few customers have very high balances.

Data Preparation

Preparing the Credit Card Data for Insights

Data preparation is often dubbed as the unsung hero of data science. It’s in this phase that the quality of the data is enhanced, making subsequent analysis more reliable and insightful. Here’s how we approached this crucial step:

1. Feature Engineering: For our initial analysis, we’ve decided to work with the provided features, ensuring they are ready for modeling without adding new ones.

2. Data Transformation: To ensure every feature contributes equally to our analysis, we scaled all of them to have a mean of 0 and a standard deviation of 1. This is especially crucial for clustering algorithms, which are sensitive to the scale of the data.

3. Handling Outliers: Outliers, or extreme values in the data, can often skew the results. Using the Interquartile Range (IQR) method, we identified and managed such values to ensure they don’t disproportionately influence our clusters.

4. Data Reduction: While dimensionality reduction can be beneficial, especially with a large number of features, it’s essential to ensure that we don’t lose significant information in the process. We applied Principal Component Analysis (PCA) to understand the variance explained by different components.

Key Findings:

• The first principal component explains approximately 27.3% of the variance in the data.
• The second principal component accounts for about 20.3%.
• Subsequent components explain lesser variance, with the last few components contributing negligibly.

Modeling:

After preparing our data, we venture into the world of clustering, a technique that allows us to group similar customers based on their credit card usage patterns. For our analysis, we employed the K-means algorithm, a popular choice for clustering tasks.

1. Determining the Number of Clusters: One of the pivotal decisions when using K-means is choosing the number of clusters. We used the Elbow method, a technique that plots the number of clusters against the within-cluster sum of squares. The point where the rate of decrease sharply changes (forming an “elbow”) suggests an optimal number for clusters.

Observations:

• Our Elbow curve suggested an optimal cluster count.

2. Applying K-means Clustering: Using the optimal cluster number, we applied the K-means clustering algorithm to segment our customers.

3. Characteristics of Each Cluster:

• Cluster 0:
• Average Balance: $799.75
• Average Purchases: $505.53
• Credit Limit: $3,271.51
• Purchase Frequency: 46.5%
• Cluster 1:
• Average Balance: $3,989.14
• Average Purchases: $384.53
• Credit Limit: $6,675.44
• Cash Advance Frequency: 44.7%
• Cluster 2:
• Average Balance: $2,220.00
• Average Purchases: $4,268.52
• Credit Limit: $7,733.97
• Purchase Frequency: 94.9%

Each cluster represents a distinct group of credit card users with unique behavior patterns. Understanding these segments can be a game-changer for businesses looking to tailor their strategies and offers to different customer groups.

Evaluation:

1. Evaluating Cluster Quality:

In our quest for meaningful clusters, we initially employed the K-means algorithm and achieved a Silhouette Score of 0.25060.2506, indicating some distinction between clusters. However, in the spirit of iterative improvement, we turned to the DBSCAN clustering algorithm.

Findings:

• Using DBSCAN, our clusters achieved a much-improved Silhouette Score of 0.44590.4459. This score suggests that our data now has better-defined clusters, capturing more intricate patterns.

2. Business Implications:

With the refined customer segments, a plethora of business opportunities emerges:

• Cluster Characterizations: The characteristics of each cluster (which can be derived similarly as done before) will give more nuanced insights. For example, a cluster of high-balance customers making infrequent but large purchases could be targeted with premium offers.
• Engaging the Noise: DBSCAN labels some data points as ‘noise’ which don’t belong to any specific cluster. Engaging these customers might require a different strategy, possibly personalized offers or surveys to better understand their needs.

In Conclusion:

By leveraging the power of DBSCAN clustering, we’ve enhanced our understanding of our diverse customer base. With these improved segments, businesses can craft even more tailored strategies, ensuring customer satisfaction and driving revenue growth.

Deployment

Deploying insights from clustering involves integrating them into the business processes, decision-making, and strategy to drive tangible outcomes. Here’s how this can be approached:

1. Customer Communication:

• Personalized Marketing: Tailor marketing messages to each cluster’s unique behavior. For instance, high-spenders might appreciate information on premium services, while more conservative spenders might be more responsive to cashback offers or loyalty programs.
• Feedback Collection: Engage specific segments to gather feedback. For example, if a cluster frequently avails cash advances, they might have insights into how that process can be improved.

2. Product Development:

• Tailored Offerings: If a cluster frequently makes installment purchases, it might be worth considering zero-interest installment plans or related promotions for them.
• Service Enhancements: For segments that maintain high balances but spend less, perhaps there are opportunities to offer better savings plans or investment options linked to their credit card.

3. Operational Efficiency:

• Resource Allocation: Direct resources (like customer service or marketing budgets) more efficiently based on cluster sizes and their potential revenue impact.
• Risk Management: Clusters with frequent cash advances or those nearing their credit limit might be flagged for closer monitoring to mitigate potential risks.

4. Feedback Loop:

• Continuous Learning: As new data comes in and customers’ behaviors evolve, the clustering can be re-run periodically. This ensures that the segments remain relevant and the strategies effective.
• Performance Metrics: Track metrics like customer engagement, spending patterns, or churn rate after deploying strategies for each cluster. This will help in refining strategies over time.

Implementation:

• Integrate with CRM: The cluster labels and insights can be integrated into Customer Relationship Management (CRM) systems. This way, when a customer contacts the business or vice versa, their segment and associated strategy is immediately known.
• Collaboration: Ensure that different departments (marketing, sales, customer service) are aware of these customer segments and the strategies associated with them. Regular training or workshops can ensure a uniform approach.
• Testing: Before rolling out strategies on a large scale, consider A/B testing to gauge their effectiveness. For instance, two different offers can be tested on a segment to see which one drives better engagement.

Conclusion:

Deploying insights from data science projects like clustering is not a one-time task but an ongoing process. The key is to remain agile, keeping the customer at the center of all strategies, and being ready to pivot based on feedback and new data.