Dynamic Customer Segmentation: Using Machine Learning To Identify and Address Diverse Customer Needs In Real-Time

• Author(s): Vamsi Katragadda
• Paper ID: 1703349
• Page: 278-286
• Published Date: 10-07-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 10 April-2022

Abstract

In contemporary marketing, effective customer segmentation is crucial for tailoring products and services to diverse customer needs. Traditional segmentation methods often struggle to keep pace with the dynamic nature of customer behavior and preferences, necessitating new approaches that can adapt in real-time. This study explores the application of machine learning techniques to achieve dynamic customer segmentation that can promptly identify and address evolving customer needs. The primary objectives of this research are twofold: firstly, to evaluate the efficacy of various machine learning algorithms in dynamically segmenting customers based on their behavior and preferences; and secondly, to implement these algorithms in a real-time setting to enable agile and personalized marketing strategies. Methodologically, the study utilizes a rich dataset sourced from [describe source], encompassing [describe scope of data]. Machine learning algorithms, including clustering (e.g., k-means, hierarchical clustering) and classification models (e.g., decision trees, neural networks), are employed for segmentation. Data preprocessing techniques such as feature scaling and dimensionality reduction are applied to enhance model accuracy and efficiency. Key findings indicate that machine learning-based segmentation models significantly outperform traditional methods in terms of accuracy and responsiveness to changes in customer behavior. The models demonstrate robust capability in adapting to real-time data inputs, thereby enabling timely adjustments in marketing strategies and personalized customer interactions. The implications of this research are profound for businesses aiming to enhance customer satisfaction and maximize marketing effectiveness. By leveraging machine learning for dynamic customer segmentation, companies can achieve greater precision in targeting, leading to improved customer retention and increased profitability. Moreover, the ability to respond swiftly to shifts in consumer behavior enhances competitive advantage in today's fast-paced market environment. In conclusion, this study underscores the transformative potential of machine learning in revolutionizing customer segmentation practices, offering a pathway towards more adaptive and customer-centric marketing strategies. Future research could explore additional machine learning techniques, evaluate longitudinal effects of dynamic segmentation on customer loyalty, and investigate ethical considerations in data-driven marketing practices.

Keywords

Customer Segmentation, Machine Learning, Dynamic Segmentation, Real-Time Marketing, Personalization

Citations

IRE Journals:
Vamsi Katragadda "Dynamic Customer Segmentation: Using Machine Learning To Identify and Address Diverse Customer Needs In Real-Time" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 278-286

IEEE:
Vamsi Katragadda "Dynamic Customer Segmentation: Using Machine Learning To Identify and Address Diverse Customer Needs In Real-Time" Iconic Research And Engineering Journals, 5(10)