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.