Citation:

Chetankumar Patel, 2023. "A Survey of Data-Driven Customer Segmentation Methods for Targeted Marketing Campaigns " ESP Journal of Engineering & Technology Advancements 3(3): 154-162.

Abstract:

Businesses are quickly moving to online platforms in the present digital environment, so personalized and data-driven marketing has become indispensable for customer acquisition and retention. Traditional marketing methods that treat all customers the same have lost their effectiveness, as they do not take into account the preferences and behaviors of individuals. As a result, customer segmentation has become the most important measure for companies to separate a diverse customer base into smaller groups of people sharing the same characteristics with the help of demographic (DEM), geographic (GEO), psychographic (PSY), and behavioral (BEH) factors. The development of big data (BD), machine learning (ML), and artificial intelligence (AI) has changed the segmentation process from a manual, rule-based operation to an automated, data-driven one that can identify the unobserved patterns and forecast customer behavior. This paper is an overview of both conventional and modern data-driven customer segmentation methods and their significance for targeted marketing campaign design. It covers the main ML methods, such as clustering algorithms (CA), predictive modeling (PM), and dimensionality reduction (DR), which facilitate segmentation accuracy and campaign efficiency. Moreover, the article recognizes obstacles like poor data quality, scalability, privacy issues, and high implementation costs. Ultimately, it points out the upcoming research topics that involve the creation of smart, adaptable, and real-time segmentation systems, helping to implement more personalized and efficient marketing strategies in the competitive digital economy.

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Keywords:

Customer Segmentation, Data-Driven Marketing, Machine Learning, Predictive Modeling, Targeted Marketing.