In the ever-evolving landscape of modern marketing, the convergence of management information systems (MIS) and predictive analytics has become a potent force. This chapter explores the transformative potential of combining MIS infrastructure with predictive analytics techniques to reshape marketing management. Predictive analytics, utilizing historical data and machine learning, empowers organizations to forecast future trends, customer behavior, and market dynamics. MIS, as the information backbone, facilitates the collection, processing, and dissemination of data for strategic decision-making. Marketing, once reliant on intuition, has transitioned into a data-driven discipline. This synergy enables businesses to not only understand past performance but also anticipate and respond to the dynamic demands of the marketplace. However, seizing this opportunity is not without challenges. Success hinges on the alignment of technical capabilities, organizational structure, and strategic vision with the digital landscape's evolution.
Top1. Introduction
Predictive analytics is the practice of using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data (Abu-Rumman, 2021). It involves analyzing past and current information to make predictions about future events or trends (Abu-Rumman and Qawasmeh, 2021). The primary goal of predictive analytics is to forecast what might happen in the future so that organizations can make informed decisions and take proactive actions to achieve desired outcomes or mitigate potential risks (Al Shraah et al., 2022).
In today’s business landscape, even small- and midsize enterprises engage with customers on an extensive scale, spanning a multitude of communication channels (Al Shraah et al., 2013). These channels encompass websites, social media platforms, mobile applications, and physical store interactions (Alayli, 2023). This proliferation in the speed, volume, and diversity of customer interactions has presented companies with a unique opportunity to foster and nurture the kind of personal relationships that were once the cornerstone of successful business practices (Al-maaitah et al., 2021).
In the rapidly evolving landscape of modern business, where data reigns supreme, and decision-makers seek to stay ahead of the competition, the convergence of Management Information Systems (MIS) and predictive analytics in marketing management has emerged as a transformative force (Al-maaitah et al., 2021). The union of these two powerful domains has ushered in a new era where data-driven insights and informed decision-making are the cornerstones of success for marketing professionals (Ameisen, 2019).
Marketing management, once reliant on intuition and experience, has now evolved into a sophisticated field that harnesses the vast troves of data generated in the digital age (Anand et al., 2023; Gupta, 2021a). MIS, on the other hand, has long been recognized as the backbone of effective organizational management, providing the infrastructure for collecting, processing, and disseminating information to support strategic and tactical decision-making (Arslan et al., 2021). The synergy between MIS and predictive analytics has reshaped the marketing landscape, enabling businesses to not only understand past performance but also anticipate future trends, customer behavior, and market dynamics (Bari et al., 2016; Gupta, 2021b).
This comprehensive exploration seeks to delve deep into the dynamic relationship between Management Information Systems and predictive analytics within the realm of marketing management (Burkov, 2019). We will uncover the pivotal role that MIS plays in facilitating predictive analytics, transforming marketing strategies, and enabling organizations to adapt to the ever-changing demands of the marketplace (Demeter et al., 2021; Gupta, 2022).
However, seizing this opportunity is far from straightforward, and numerous businesses falter in their attempts due to the absence of essential technical infrastructure, organizational capabilities, and a coherent strategic direction (Khaled Lafi Al-Naif and Ata E. M. Al Shraah, 2018). The burgeoning digitization of customer interactions, coupled with our increasing reliance on digital platforms for daily activities, has granted businesses access to an unparalleled wealth of customer data (Mert, 2022). This treasure trove of information holds the potential to significantly enhance customer service and engagement (Murphy, 2012). Yet, it’s imperative to recognize that successfully leveraging this data is contingent on a company’s ability to harness its technical capabilities, streamline its organizational structure, and align its strategy with the evolving digital landscape (Ogunmola et al., 2021).