The Impact of Big Data on Customer Satisfaction Through Marketing: A Bibliometric Analysis

Introduction

The popularization of the internet technology and the revolution brought by its many uses have changed how people communicate, creating enormous networks of users sharing and creating information, usually with close friends but also with the whole world (Alkilani et al., 2012). This has exponentially increased the amount of information companies can access and the sources creating said information. Although this may seem a huge advantage to understanding better consumer behavior, this data must not only be adequately managed to be helpful but has also transformed the already complex consumer behavior into something even more complex (Erevelles et al., 2016).

Until now, companies have relied too much on past experiences to manage their businesses, but now these obsolete models may hinder the necessary development from adapting to the rapid market changes happening (Zhou & Li, 2010). Hence, it is necessary to analyze the most recent trends and innovations that may risk the said business models. These changes may imply profound modifications in how companies manage their clients and their relationships with them (Raguseo & Vitari, 2018), having to implement Big Data (BD) to keep up with the market trends. This technology, which has turned clients into incessant data creators (Erevelles et al., 2016), has been said to be the driver of business success among many industries (McAfee et al., 2012), and the necessary tool to manage the ever-growing data volume.

Although the market for software, hardware, and services for analyzing and storing BD has developed at the same rhythm as this model has become popular, this system is not a panacea (Akter et al., 2016). It is necessary to analyze how companies can use BD to support their decision-making (Matthias et al., 2017). Businesses could find their worst enemy in BD if, after all the investment, management teams cannot discern inconsistent and unreliable data from good one and develop new perspectives (Akter et al., 2016; Matthias et al., 2017; Raguseo & Vitari, 2018). However, it can also allow developing an in-depth knowledge base about their clients, increasing the success rate of their decisions, saving money, and developing marketing campaigns based on real-time data to foster loyalty and attract new clients at the same time (Rejeb et al., 2020).

BD can assist companies in understanding environmental information, making informed decisions, and reducing uncertainty, at the same time as it assists in achieving a more agile, responsive, and flexible response to business needs and sudden opportunities (Rust & Huang, 2014; Zeng & Glaister, 2018). This can result in the development of personalized location-based marketing services and, therefore, higher customer interaction and satisfaction (Sanders, 2016). However, for the time being, and given the quantity of current research, these are simply hypotheses, and other authors have commented on the necessity for additional development of the research field (Rejeb et al., 2020; Waller & Fawcett, 2013). Academic research has expanded quickly as a result of the growing interest in these technologies as potential means of boosting company competitiveness in global marketplaces and their impact in customer satisfaction and marketing campaign, so a rigorous review of the existing literature is required in light of the recent growth of the field.

In order to fill this gap, the main goal of this article is to provide a review on the research on the relevance of BD in customer satisfaction through the mediation of marketing strategies. Therefore, the specific goals are to: (1) evaluate the effectiveness, relevance, and relative influence of the research issue; (2) provide the intellectual framework; (3) determine the thematic arrangement; and (4) determine the conceptual structure. We can also answer the following research questions in light of these objectives and previous bibliometric evaluations: