Customer Segmentation Using K-Means

Overview

Customer Segmentation is the process of dividing a customer base into distinct groups of individuals that share similar characteristics relevant to marketing, such as gender, age, interests, and spending habits. This project aims to explore the dataset, gain insights, and apply K-Means Clustering to identify and group customers based on their spending behavior.

K-Means Clustering

K-Means is an iterative algorithm that partitions the dataset into K distinct, non-overlapping subgroups (clusters). Each data point belongs to one cluster, aiming to minimize the sum of squared distances between data points and the cluster centroids. The algorithm strives to keep intra-cluster points as similar as possible and inter-cluster points as distinct as possible.

How K-Means Works

1. Initialization: Select K initial centroids randomly from the dataset.
2. Cluster Assignment: Assign each data point to the nearest centroid based on Euclidean distance.
3. Centroid Update: Calculate new centroids by averaging the assigned points.
4. Iteration: Repeat the assignment and update steps until the centroids no longer change.

Application in Customer Segmentation

K-Means Clustering helps identify distinct customer segments based on their spending behavior, age, and annual income. This allows companies to target specific user bases with tailored marketing strategies, gaining a deeper understanding of customer preferences and maximizing profit.

Benefits of Customer Segmentation

• Targeted Marketing: Address the specific needs of different customer groups.
• Improved Customer Understanding: Gain insights into customer preferences and behaviors.
• Efficient Strategy Development: Formulate marketing techniques that minimize investment risk.

Key Differentiators in Customer Segmentation

• Demographics: Age, gender, education level, etc.
• Geography: Location-based segmentation.
• Economic Status: Income levels and financial behavior.
• Behavioral Patterns: Spending habits, product preferences, etc.

Conclusion

Using K-Means Clustering for customer segmentation enables companies to understand their customer base better and tailor their marketing strategies accordingly. By dividing customers into distinct segments, businesses can enhance their marketing efficiency and customer satisfaction.