A Step-by-Step Tutorial on Customer Segmentation Using Cluster Modeling for Churn Analysis

Introduction:

Customer retention strategies for a business must include both churn research and customer segmentation. We acquire a greater understanding of distinct client categories and may design specific churn-reduction tactics by combining cluster modeling, churn research, and customer segmentation. In this course, we will look at how to perform churn analysis and customer segmentation using cluster modeling using Python and fictional data. The entire process, including data collection, preliminary processing, cluster modeling, analysis, and visualization, will be thoroughly explained and walked through step by step.

Step 1: Data Generation and Exploration

For our churn research, we first need a dataset that mimics customer data. A fake dataset comprising pertinent fields, including customer ID, age, total expenditure, and churn status, will be created. A customer’s churn status will show whether they have (1) or have not (0) left the company.

import pandas as pd
import numpy as np

# Set random seed for reproducibility
np.random.seed(123)

# Generate dummy data
num_customers = 1000

customer_ids = range(1, num_customers + 1)
ages = np.random.randint(18, 65, num_customers)
total_spends = np.random.uniform(50, 500, num_customers)
churn_status = np.random.choice([0, 1], size=num_customers, p=[0.8, 0.2])

# Create a DataFrame
df = pd.DataFrame({
    'customer_id': customer_ids,
    'age': ages,
    'total_spend': total_spends,
    'churn_status': churn_status
})

# Display the first few rows of the DataFrame
print(df.head())
        

In this example, we generate data for 1000 customers. Each customer is assigned a unique ID, and their age and total spending are randomly generated. The churn status is assigned based on a predefined probability distribution.

If you do not wish to produce random data, here is the generated data in this link

Step 2: Data Preprocessing and Feature Engineering

Before we can proceed with cluster modeling, we need to preprocess the data and engineer relevant features. In this step, we’ll handle any missing values, scale numerical features, and perform any necessary feature engineering tasks.

from sklearn.preprocessing import StandardScaler

# Drop unnecessary columns
data = df.drop(['customer_id', 'churn_status'], axis=1)

# Scale the numerical features
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)        

Here, we drop the customer ID and churn status columns from the dataset as they are not required for the clustering process. We then use the StandardScaler from Scikit-Learn to scale the numerical features, ensuring they have comparable ranges.

Step 3: Cluster?Modeling

With the preprocessed data in hand, we can now apply cluster modeling to group customers based on their characteristics. In this example, we’ll use the K-means clustering algorithm.

from sklearn.cluster import KMeans

# Set the number of clusters
num_clusters = 2

# Create a KMeans instance
kmeans = KMeans(n_clusters=num_clusters, random_state=42)

# Fit the data to the model
kmeans.fit(scaled_data)

# Add the cluster labels to the DataFrame
df['cluster'] = kmeans.labels_        

We set the number of clusters to 2 for simplicity, but you can adjust this based on your specific requirements. The K-means algorithm is then fitted to the scaled data, and the cluster labels are assigned to each customer in the data frame.

Step 4: Analysis and?Insights

Now that we have the clustered data, we can analyze the characteristics of each cluster and gain insights into customer segments.

# Calculate the average values for each cluster
cluster_analysis = df.groupby('cluster').mean()

# Print the cluster analysis
print(cluster_analysis)        

This code calculates the average values for each feature within each cluster. By examining these values, we can gain insight into the characteristics of each customer segment.

Cluster 0:

• Average age: 40.82
• Average total spend: 391.57
• Churn status: 0.18

Cluster 1:

• Average age: 42.41
• Average total spend: 158.90
• Churn status: 0.21

From these findings, we can derive the following insights:

Age:

• Cluster 0: Customers in this cluster have an average age of 40.82. They represent a relatively younger segment compared to Cluster 1.
• Cluster 1: Customers in this cluster have an average age of 42.41. They tend to be slightly older than the customers in Cluster 0.

Total Spend:

• Cluster 0: Customers in this cluster have a significantly higher average total spend of $391.57. They are likely to be higher-value customers who spend more on products or services.
• Cluster 1: Customers in this cluster have a lower average total spend of 158.90. They may be more price-sensitive or have lower purchasing power.

Churn Status:

• Cluster 0: The churn rate for customers in this cluster is relatively lower, with a churn status of 0.18. This suggests that these customers are more loyal and less likely to churn.
• Cluster 1: Customers in this cluster have a slightly higher churn status of 0.21, indicating a slightly higher likelihood of churn.

These insights can guide businesses in tailoring their retention strategies:

• Cluster 0 customers, who are relatively younger with a higher average total spend and a lower churn rate, may benefit from personalized loyalty programs, rewards, or targeted marketing campaigns to enhance their loyalty further.
• Cluster 1 customers, who are slightly older with a lower average total spend and a slightly higher churn rate, may require special attention. Businesses can focus on providing exceptional customer service, personalized offers, or discounts to increase customer loyalty and reduce churn.

Step 5: Visualization

To further understand the clusters and their characteristics, visualizations can be immensely helpful.

import matplotlib.pyplot as plt

# Plot the clusters
plt.scatter(scaled_data[:, 0], scaled_data[:, 1], c=kmeans.labels_, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], color='red', marker='x')
plt.xlabel('Age')
plt.ylabel('Total Spend')
plt.title('Customer Clusters')
plt.show()        

The scatter plot below visualizes the customer clusters based on age and total spending:

In the plot, each point represents a customer, with the color indicating their assigned cluster. The red ‘x’ markers represent the centroids of each cluster. By visualizing the clusters, we can observe the distribution and separation of customer segments based on their characteristics.

Customer Clusters

In the plot, each point represents a customer, with the color indicating their assigned cluster. The red ‘x’ markers represent the centroids of each cluster. By visualizing the clusters, we can observe the distribution and separation of customer segments based on their characteristics.

Cluster 0:

• The majority of customers in Cluster 0 have a higher age range, with several customers in their 60s and 50s.
• Total spending varies within this cluster, ranging from moderate to high values.
• The churn_status field indicates that the majority of consumers in this cluster have not churned.
• Cluster 0 seems to have a higher number of customers who have not churned compared to those who have churned.
• This cluster could represent loyal and long-term customers who are willing to spend more.

Cluster 1:

• Customers in Cluster 1 have a relatively younger age compared to Cluster 0, with a significant number in their 20s and 30s.
• Total spending in this cluster ranges from low to moderate values.
• The churn_status field indicates that some customers in Cluster 1 have churned.
• Cluster 1 appears to have a higher proportion of churned customers compared to Cluster 0.
• This cluster might represent younger and more price-sensitive customers who are more likely to churn.

Conclusion

After conducting a cluster analysis to understand customer segmentation and churn, we can draw several conclusions. Cluster analysis allows us to identify distinct groups of customers based on their characteristics and behaviors, which can provide valuable insights for managing churn effectively. Here are the main findings:

• Customer Segmentation: Using cluster analysis, we may divide consumers into groups based on shared features. We may discover diverse client categories with distinct tastes, habits, and demands by evaluating the clusters.
• Churn Patterns: Examining churn within each cluster reveals patterns and trends unique to each client category. For example, we could see that particular clusters have a greater turnover rate than others. This knowledge is essential for identifying high-risk consumers and designing focused retention tactics.
• Churn Drivers: We can uncover elements that lead to churn by evaluating the characteristics and behaviors of consumers within each cluster. For example, certain clusters may have a greater turnover rate among consumers who have made fewer transactions or have lower levels of involvement. Understanding these factors allows us to identify areas for improvement and take proactive steps to prevent churn.
• Retention Strategies: We can tailor retention strategies to different customer segments using cluster analysis. We can develop personalized approaches to customer retention by understanding the unique needs and preferences of each cluster. Customers in one cluster, for example, may benefit from loyalty programs or exclusive offers, whereas customers in another cluster may respond well to discounts or promotions.
• Customer Lifetime Value: Analyzing clusters in the context of churn allows us to estimate the potential lifetime value of each segment. By considering both the likelihood of churn and the value customers bring to the business, we can prioritize retention efforts on segments with the highest potential return.

In conclusion, integrating churn and cluster research yields important insights into customer segmentation, churn patterns, drivers, and effective retention measures. It enables businesses to make data-driven decisions and efficiently allocate resources to reduce churn and increase client lifetime value.