A Study on Customer Segmentation Using Machine Learning at TVS Sai Hemanth Motors, Srikalahasthi

Abstract

In today’s competitive market, understanding customer diversity is essential for tailoring marketing strategies and improving sales. ThisstudyfocusesoncustomersegmentationatTVS Sai Hemanth Motors, Srikalahasthi, using K-Means clustering, a machine learning technique.The aim is to classify customers based on demographic and behavioralfactorsandidentifymeaningfulpatternsin purchasing and non-purchasing behavior. Data was collected from both buyers and non-buyers, and the ElbowMethod wasappliedtodeterminetheoptimal numberofsegments. The analysisresultedinclearly defined clusters that revealed specific customer characteristics and preferences. This enables the dealer to make informed decisions regarding marketing strategies and customer relationship management. The study not only enhances understanding of customer profiles but also demonstratesthepracticalapplicationofdata-driven segmentation in a real-world context

Introduction

The study focuses on customer segmentation using K-Means clustering to improve marketing strategies in the competitive two-wheeler market, specifically at TVS Sai Hemanth Motors. Customer segmentation involves grouping customers based on shared characteristics such as demographics, behavior, and preferences to enable targeted marketing and improve customer acquisition and retention.

The literature review highlights previous research applying machine learning techniques, especially clustering, in the automotive sector to enhance customer targeting and sales strategies. The study aims to classify customers by demographics (age, income, profession), analyze purchasing behavior, and apply machine learning for automated segmentation.

Data were collected from 100 customers (50 buyers and 50 non-buyers) using surveys and dealership records. Python and visualization tools were used to perform clustering and determine the optimal number of customer segments.

Three distinct buyer clusters emerged:

• Cluster 0: Young, price-conscious buyers (18-25 years, low income), focused on mileage and EMI options.

• Cluster 1: Mid-income balanced buyers (26-35 years), with moderate interest in price, style, and speed.

• Cluster 2: Affluent, feature-seeking buyers (36-45+ years), focused on premium features and style with low EMI dependence.

Non-buyer clusters showed similar but varied engagement levels, indicating different marketing needs. The study emphasizes the importance of understanding diverse customer segments for better resource allocation and targeted marketing in the two-wheeler industry.

Limitations include a small sample size, focus on one dealership, and reliance on demographic data without deeper behavioral insights.

Conclusion

K-Means clustering algorithm is successfully appliedto segment bothbuyers and non-buyers based on demographic and behavioral variables. The analysis revealed clear patterns in customer preferences and highlighted key differencesbetween similar customer groups who did and did not make a purchase. These insights help the dealers understand their customers better and craft targeted strategies for each segment. By bridging the gap between buyers and non-buyers through focused offers and engagement, the dealership can increase conversions and optimize its marketing efforts. Overall, this study demonstrates the practical value of machine learning in solving real- world business problems

References

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Copyright

Copyright © 2025 B. Akash , Dr C. Nadhamuni Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.