Book Title: Advanced Studies in Multidisciplinary Research and Innovation (ASMRI)
Chief Editors: Dr. Jagdish Kumar Sahu and Dr. Krishna Ashutoshbhai Vyas
Associate Editors: Dr. N. Ramesh Chandra Srikanth and Dr. Lourdu Vesna J
Co-Editors: Dr. Aarti Sharma and Dr. Pushpa Mamoria
ISBN: 978-93-7183-010-2
Chapter: 13
DOI: https://doi.org/10.59646/745/13
Author: Manju Singh
Abstract
With the advent of digital technologies and the massive amount of big data available, the marketing landscape has dramatically changed. Predictive analytics has become a gamechanger in predicting customer behavior and making informed decisions, especially when it comes to customer engagement. Much of this progress comes from enhancements to the machine learning algorithms and cloud tech that enables marketers to analyze and understand massive amounts of data in real time. This has enabled companies to better predict future trends and consumer needs than ever before. In addition, this change has driven a more aggressive marketing strategy, that relies on predictive data, not just historical. This research paper looks at the use of predictive analytics in marketing and how big data is utilised to predict consumer preferences, buying behaviours and decision-making. The study is conducted in a mixed method design, using both quantitative and qualitative processes to analyse the collected data.The study is carried out by a method of mixed method, which uses quantitative method by taking data from the consumer, as well as taking data from industry reports and expert opinions in digital marketing. The results indicate that predictive analytics has been successfully implemented by large organizations, which have more sophisticated data infrastructure, technical capabilities and experience, whereas small and medium enterprises are hindered by less access to data, cost of implementation, and limited technical skills. While these challenges remain, predictive analytics has proven to be a powerful tool in the realm of marketing, with the ability to drive greater efficiency, personalized ads, and better customer retention. Moreover, there is a dual consumer sentiment, with aspects of personalisation and relevance being seen as extremely positive, but other issues of data privacy and algorithmic transparency are also key. The research aims to explore how predictive analytics can transform marketing strategies for increased precision, efficiency, and customer-centricity in the age of big data by discussing its impact across various business environments.
Keywords: Predictive analytics, marketing, big data, consumer behavior, machine learning, data-driven marketing, customer segmentation, personalization, marketing strategy, digital marketing.