Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 4
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Digital Marketing Analytics and Predictive Customer Behavior

Author(s) Pallavi Sharad Raut, Dr. Prashant Ramkrishnarao Patil
Country India
Abstract The rapid growth of digital technologies has transformed the way organizations interact with customers. Digital marketing analytics enables businesses to collect, analyze, and interpret customer data generated through websites, social media, mobile applications, and e-commerce platforms. Predictive customer behavior analysis utilizes statistical models, machine learning algorithms, and artificial intelligence techniques to forecast future customer actions such as purchase intentions, customer retention, churn probability, and product preferences.
This paper examines the relationship between digital marketing analytics and predictive customer behavior, extending an initial conceptual outline into a fuller research design that includes formal hypotheses, a layered conceptual model, a structured survey instrument, and a corresponding statistical (SPSS) analysis framework. The study highlights key analytical techniques, data sources, applications, benefits, and implementation challenges, and proposes a conceptual framework demonstrating how customer data can be transformed into actionable insights that improve marketing performance and customer engagement.
Based on the reviewed literature and the proposed framework, the paper argues that predictive analytics is likely to enhance decision-making, campaign effectiveness, customer satisfaction, and business profitability, while data quality, privacy, and organizational capability remain the principal constraints on realizing these benefits. The paper concludes with a research agenda and practical recommendations for organizations seeking to operationalize predictive customer analytics responsibly.
Keywords Digital Marketing Analytics, Predictive Analytics, Customer Behavior, Machine Learning, Consumer Insights, Customer Segmentation, Artificial Intelligence
Field Business Administration
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-17
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E-ISSN 3048-7641
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