Detecting Customer Spending Patterns and Preferences Using AI

Authors: Balaji Soundararajan

DOI: https://doi.org/10.5281/zenodo.15054615

Short DOI: https://doi.org/g88375

Country: USA

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Abstract: In the era of AI-driven consumer insights, businesses leverage advanced machine learning (ML) and data mining techniques to transform raw customer data into actionable intelligence. We will explore the integration of AI in customer analytics, emphasizing data collection from diverse sources (transactional, clickstream, social media, and open data), preprocessing strategies, and the application of ML models such as clustering (k-means, hierarchical, DBSCAN) and classification (decision trees, SVM, logistic regression). Case studies across industries highlight the efficacy of recommendation systems, personalized marketing campaigns, and ethical considerations in data usage. By synthesizing real-time data analysis with predictive modeling, organizations can enhance customer engagement, optimize revenue, and deliver hyper-personalized experiences while addressing challenges related to algorithmic fairness and privacy.

Keywords: Customer Analytics, Machine Learning, Data Mining, Clustering Algorithms, Classification Algorithms, Personalized Marketing, Ethical AI, Recommendation Systems, Data Preprocessing

Paper Id: 232260

Published On: 2023-07-05

Published In: Volume 11, Issue 4, July-August 2023

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