Abstract
Data science in marketing has become critical in gaining sustained competitive advantage in a rapidly changing business environment. It involves using advanced analytics and scientific principles to extract valuable information from large volumes of data gathered from multiple sources, such as social media platforms. There are multiple benefits to using data science in marketing, including proper data-based planning, enhanced customization, enhanced forecasting through predictive analytics, effective ROI measuring, and improved pricing models. The research explains how companies can turn the potential and opportunities of these advanced analytics techniques into real company performance in a competitive marketing environment. This research aims to explore how firms can use marketing analytics and big data to improve capabilities and performance. Specifically, the study argues that big data and marketing analytics can be used to extract valuable and meaningful marketing information and insights that can be integrated to improve marketing effectiveness and performance.
TopMethodological Approach
The study uses the systematic bibliometric literature review (LRSB) methodology to collect and synthesize data on the use of data science in marketing. The method was selected based on Donthu et al.'s (2021, p.285) description, which defines it as a method that can be used to “unpack the evolutionary nuances of a specific field while shedding light on the emerging areas in that field.” This description, thus, is applicable in studying the impact of emerging technologies in marketing. The literature review followed the process recommended by Xiao and Watson (2019), as shown in Figure 1.
Figure 1. Systematic literature review process
The continuous development of data technologies has significantly contributed to the evolution of marketing practice and research. Marketing professionals and departments must continuously adapt and integrate emerging technologies into their core processes and strategies, challenging their capability to keep pace and remain competitive in the current dynamic business environment.
Key Terms in this Chapter
Internet of Things (IoT): Is a concept that refers to the digital interconnection of everyday objects with the internet, connecting objects rather than people.
Machine Learning: Is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.
Data Science: Is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data.
Data-Driven: Refers to data-driven organizational processes.
Big Data: Area of knowledge that studies how to treat, analyze, and obtain information from data sets that are too large to be analyzed by traditional systems.
Unified Marketing Measurement (UMM): Is an approach to that combines the aggregate data and insights offered by attribution models into one holistic measurement.
Artificial Intelligence (AI): Is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals and humans.