An Introduction to Customer Segmentation with K-Means
Customer segmentation is a core concept in marketing and data analysis that involves dividing customers into groups (segments) based on shared characteristics. This allows businesses to target the right audience, personalize marketing, and improve customer satisfaction.
One of the most popular techniques for customer segmentation is K-Means Clustering — an unsupervised machine learning algorithm.
🧠 What is Customer Segmentation?
Customer segmentation involves grouping customers based on:
Demographics (age, gender, income)
Behavior (purchase frequency, product preferences)
Geography (location)
Engagement (website/app usage)
Goal: Understand different types of customers to make better business decisions.
📌 What is K-Means Clustering?
K-Means is an algorithm that groups data into K distinct clusters based on similarity.
🔁 How It Works:
Choose K: Decide the number of clusters (segments).
Initialize Centroids: Randomly select K initial cluster centers.
Assign Points: Assign each customer to the nearest centroid.
Update Centroids: Recalculate the centroids of the clusters.
Repeat: Continue until cluster assignments stabilize.
🛠️ Steps to Perform Customer Segmentation with K-Means
1. Collect Customer Data
Data can include:
Age
Income
Spending score
Purchase history
Website behavior
Example:
CustomerID | Age | Income | SpendingScore
-----------------------------------------
1 | 25 | 40k | 60
2 | 45 | 100k | 30
3 | 35 | 70k | 80
2. Preprocess the Data
Handle missing values
Normalize or scale numerical features (important for K-Means)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)
3. Choose the Right K (Number of Clusters)
Use the Elbow Method:
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
inertia = []
for k in range(1, 11):
km = KMeans(n_clusters=k)
km.fit(scaled_data)
inertia.append(km.inertia_)
plt.plot(range(1, 11), inertia)
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method')
plt.show()
Look for the "elbow point" where the inertia (within-cluster sum of squares) stops decreasing sharply.
4. Apply K-Means Clustering
kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(scaled_data)
data['Cluster'] = kmeans.labels_
Now, each customer is assigned to a cluster (segment).
5. Analyze and Interpret Clusters
Group customers by their cluster and analyze characteristics:
data.groupby('Cluster').mean()
You might find:
Cluster 0: Young, low income, high spending
Cluster 1: Older, high income, moderate spending
Cluster 2: Middle-aged, low income, low spending
Cluster 3: High income, high spending (target VIPs)
✅ Benefits of Customer Segmentation with K-Means
Benefit Description
🎯 Better Targeting Personalized marketing and product recommendations
📈 Increased ROI Focus resources on high-value customers
🧍♂️ Customer Retention Tailor experiences to different segments
🧪 Strategy Testing Run A/B tests by customer group
🚫 Limitations of K-Means
Assumes spherical clusters
Sensitive to initial centroids
Requires pre-defining the value of K
Doesn’t work well with categorical variables (consider K-Modes or Gower distance)
🔁 Alternatives to K-Means
DBSCAN – For irregular-shaped clusters
Hierarchical Clustering – Doesn’t need predefined K
Gaussian Mixture Models (GMM) – Probabilistic clustering
K-Prototypes – Mixed data (numerical + categorical)
📌 Summary
Aspect Detail
Technique K-Means Clustering
Use Case Segmenting customers by behavior or demographics
Tools Python (scikit-learn, pandas, matplotlib)
Key Steps Preprocess → Choose K → Cluster → Analyze
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