Customer Segmentation for Targeted Marketing Campaigns

Context and Objective

In the financial sector, customer segmentation is a fundamental aspect for optimizing marketing strategies. This project aims to develop a segmentation model that allows the identification of homogeneous groups of customers based on their spending behavior and credit card usage.

The main objective is to offer a solution that enables personalized marketing campaigns for each customer cluster, improving communication effectiveness and maximizing return on investment.

Implemented Solution

Technologies and Tools Used

• Programming Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn
• Development Environment: Jupyter Notebook

Development Process

1. Loading Data:

import pandas as pd

df = pd.read_csv('credit_card_customers.csv')

2. Exploratory Data Analysis:

df.info()
df.describe()

3. Data Preprocessing:

df.drop('CUST_ID', axis=1, inplace=True)
df.dropna(inplace=True)

4. Data Standardization:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)

5. Determining the Optimal Number of Clusters:

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)
    kmeans.fit(df_scaled)
    wcss.append(kmeans.inertia_)

plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

6. Applying the K-Means Algorithm:

kmeans = KMeans(n_clusters=4, init='k-means++', random_state=42)
df['Cluster'] = kmeans.fit_predict(df_scaled)

7. Cluster Analysis:

df.groupby('Cluster').mean()

Results and Benefits

The analysis led to the identification of four distinct customer clusters, each with unique behaviors regarding credit card usage. This segmentation allows companies in the financial sector to:

• Personalize marketing campaigns: Create tailored messages for each cluster.
• Optimize retention strategies: Identify the most profitable customers and enhance their customer experience.
• Increase ROI: Improving the precision of marketing campaigns results in higher returns on advertising investments.

Challenges and How They Were Overcome

• Data Quality: Anomalous and missing values were identified and removed to ensure reliable analysis.
• Choosing the Number of Clusters: The elbow method helped determine the optimal number of segments.
• Interpreting Results: In-depth analysis of clusters enabled the definition of targeted marketing strategies.

Conclusion and Impact

Thanks to this approach, companies in the financial sector can adopt more targeted marketing strategies, improving offer personalization and increasing customer loyalty. Next steps include integrating additional data (e.g., real-time transactional data) and using more advanced models, such as hierarchical clustering or deep learning-based algorithms, to further refine segmentation.