🔍 Market Segmentation Analysis for KTC Company

Author: Srivathsan
Date: August 08, 2025

Understanding your customers is the cornerstone of effective business strategy. In this project, I performed a comprehensive market segmentation analysis for KTC Company using descriptive mining and clustering techniques. The goal: to uncover meaningful customer groups based on demographic and financial attributes.

📊 Data Overview

The dataset consisted of 30 customer records and 7 key variables:

Age
Gender
Income
Marital Status
Number of Children
Loan Status
Mortgage Value

🧮 Descriptive Mining Insights

Exploratory Data Analysis revealed several patterns:

Age: Median age ~47, with most customers between 38–56 years.
Gender: 56.7% of customers are female.
Income: Most earn between ₹18,000–₹36,000, with a few high-income outliers.
Marital Status: Majority are married (80%).
Children: Most have 0–2 children; large families are rare.
Loan: Loan behavior is polarized—either minimal or substantial borrowing.
Mortgage: Similar U-shaped pattern as loans; customers either have no mortgage or a full one.

🔗 Clustering Analysis

To segment customers, I applied:

Hierarchical Clustering: Revealed natural groupings based on customer similarity.
K-Means Clustering: Divided customers into 3 distinct clusters using normalized variables.
Discriminant Analysis: Two principal components explained 48.75% of the data variability, validating the cluster separation.

💡 Strategic Implications

Customer groups show distinct financial and demographic traits—ideal for targeted marketing.
Polarized loan and mortgage behaviors suggest differentiated credit strategies.
Middle-aged, married females with moderate income form a dominant segment worth prioritizing.

🧭 Final Thoughts

This segmentation exercise equips KTC Company with actionable insights to tailor offerings, improve CRM, and enhance customer satisfaction. By understanding who their customers are—and how they differ—KTC can move from generic outreach to precision engagement.