E-commerce Customer Segmentation

 E-commerce Customer Segmentation 

1. Introduction


Objective: Perform customer segmentation for an e-commerce business using cluster analysis.
Purpose: Identify different customer segments to improve marketing strategies, product recommendations, and customer service.

2. Project Workflow


1. Problem Definition:
   - Understand the key characteristics of e-commerce customers.
   - Group customers based on their purchasing behavior and demographic attributes.
   - Key questions:
     - How can we cluster customers effectively?
     - What are the defining characteristics of each cluster?
2. Data Collection:
   - Source: Customer transaction data from e-commerce platforms.
   - Example fields: Customer ID, Purchase Amount, Frequency, Recency, Age, Gender.
3. Data Preprocessing:
   - Handle missing values, outliers, and standardize data.
4. Cluster Analysis:
   - Use the K-Means clustering algorithm.
5. Insights and Visualization:
   - Visualize clusters and their key attributes.

3. Technical Requirements


- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Data Preprocessing: Scikit-learn
  - Visualization: Matplotlib, Seaborn
  - Clustering: Scikit-learn (K-Means)

4. Implementation Steps

Step 1: Setup Environment


Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn
```

Step 2: Load and Explore Data


Load the customer data:
```
import pandas as pd

data = pd.read_csv("customer_data.csv")
print(data.head())
```
Explore key statistics:
```
print(data.describe())
print(data.info())
```

Step 3: Preprocess Data


Handle missing values and outliers:
```
data = data.dropna()  # Remove rows with missing values
data = data[(data['PurchaseAmount'] > 0)]  # Filter out invalid purchases
```
Standardize the data:
```
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
data_scaled = scaler.fit_transform(data[['PurchaseAmount', 'Frequency', 'Recency']])
```

Step 4: Perform K-Means Clustering


Determine the optimal number of clusters using the elbow method:
```
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(data_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()
```
Apply K-Means with the optimal number of clusters:
```
kmeans = KMeans(n_clusters=3, init='k-means++', random_state=42)
data['Cluster'] = kmeans.fit_predict(data_scaled)
```

Step 5: Visualize Clusters


Visualize the clusters using pair plots:
```
import seaborn as sns

sns.pairplot(data, hue='Cluster', vars=['PurchaseAmount', 'Frequency', 'Recency'])
plt.show()
```
Analyze cluster centroids:
```
centroids = kmeans.cluster_centers_
print("Cluster Centroids:", centroids)
```

5. Expected Outcomes


1. Identification of customer clusters based on purchasing behavior.
2. Visualization of clusters for insights into customer segmentation.
3. Recommendations for targeted marketing and promotions.

6. Additional Suggestions


- Advanced Features:
  - Use additional clustering algorithms like DBSCAN or Agglomerative Clustering for comparison.
  - Incorporate demographic data such as age, gender, and location for enriched analysis.
- Deployment:
  - Develop a web-based tool to dynamically segment customers using clustering models.
- Continuous Learning:
  - Update clusters regularly using new customer data.