A Comprehensive Review on Advancements in Marketing Predictive Analytics and Reporting

Priyal Borole

Abstract: In an era dominated by data - driven decision - making, the application of predictive analytics in marketing has emerged as a pivotal force shaping strategies and outcomes. This scholarly review article is offering a comprehensive exploration of its evolution, methodologies, applications, and future trends. The abstract commences by elucidating the foundational concepts of traditional marketing analytics, delineating key metrics, and methodologies employed in marketing reporting. A meticulous examination of key concepts such as machine learning, data mining, and artificial intelligence ensues, providing a nuanced understanding of their roles in the realm of marketing analytics. The review delves into prominent predictive models, elucidating their applications in diverse marketing domains, including customer segmentation, lead scoring, and churn prediction. Real - world case studies underscore the efficacy of predictive analytics in informing decision - making and enhancing marketing outcomes. Methodological nuances are explicated, offering insights into the tools, data sources, and validation techniques employed in contemporary studies on marketing predictive analytics. The benefits and challenges associated with implementing predictive analytics in marketing are critically analyzed, with a focus on how these insights seamlessly integrate with broader marketing strategies. A forward - looking discussion unfolds, exploring emerging trends such as explainable AI and prescriptive analytics, providing a glimpse into the future trajectory of marketing predictive analytics.

Keywords: Predictive Analytics, Machine Learning, Data Mining, Artificial Intelligence, Marketing Analytics, Customer Segmentation, Lead Scoring, Churn Prediction, Prescriptive Analytics

How to Cite?: Priyal Borole, "A Comprehensive Review on Advancements in Marketing Predictive Analytics and Reporting", Volume 9 Issue 4, April 2020, International Journal of Science and Research (IJSR), Pages: 1796-1800, https://www.ijsr.net/getabstract.php?paperid=SR24301010649, DOI: https://dx.doi.org/10.21275/SR24301010649

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