Consumer Big Data Analytics: A Treasure for Businesses in the Socio-Digital Era

Introduction

Data is generated from a variety of sources in the digital world, and the rapid adoption of digital technology has resulted in the creation of big data. The accumulation of massive datasets enables evolutionary breakthroughs in a variety of domains. It refers to a collection of vast, complicated datasets that are difficult to process with typical database administration tools or data processing apps.

In the last two decades, Consumer Marketing has been at the center of a social and economic revolution, and the forces driving this shift, particularly the wealth of data and pervasive digital technology, continue to exert major effect. The capacity of an organization's analytical talents, especially the ability to assess consumers' wants and desires, is critical to gaining a lasting competitive edge. According to research, companies that rely on modern data analysis outperform their competitors in terms of both financial and operational performance. Organizations have been receiving a new stream of data for analysis, known as big data, for several years. Advances in business technology have dramatically improved organizations’ ability to capture information on consumer behavior and demand, resulting in new strategic decision-making options. Data-driven tactics advocate the use of a wide range of consumer data to get a comprehensive understanding of customer behavior, motives, and expectations. Businesses now have more options for tailoring product, service, and experience delivery to fit the needs of different (individual) customers. To examine a significant number of data created by various business organizations, big data and business analytics methodologies have recently been developed and used. As a result, every company need more visibility into ever-increasing volumes of transactional data. Real-time data analysis allows businesses to peer into the past and predict the future.

“Data-driven insight is the essential underpinning of many firms, and consumer marketing is becoming increasingly analytics-driven.” Consumer Behavior and Analytics is a short, innovative, unique, and approachable literature that introduces new ideas, concepts, and structures to meet the current realities of analytics-driven marketing. This chapter is a ground-breaking and informative volume that connects new possibilities and techniques with existing academic consumer research.

In complex corporate scenarios, data analytics aims to deliver operational insights. The concept of big data has been around for years, and most firms now realize that if they capture all of the data that flows into their operations, they can use analytics to extract tremendous value. Businesses were employing basic analytics (just figures on a spreadsheet that were manually inspected) to identify insights and trends as early as the 1950s, decades before the term “big data” were coined. The new advantages of big data analytics, on the other hand, are speed and efficiency. Whereas a corporation would have gathered data, ran analytics, and unearthed knowledge for future decisions a few years ago, today's organization can identify insights for current decisions. Data analytics has grown at such a rapid rate around the world that the Big Data market revenue is predicted to increase by 50% in the near future. Travel and transportation, financial analysis, retail, research, energy management, and healthcare are all affected.

Figure 1 illustrates how, in this era of Big Data, data on customer behavior and habits may be a valuable source for businesses to develop appropriate marketing strategies. Consequently, data might be considered a buried treasure in today's commercial world.

Figure 1.

Consumer behavior analysis: input to business strategy (Zhao et al., 2021)

The main goal of this chapter is to review big data analytics techniques and frameworks which are available to track consumer behavior. This chapter organized outlined as follows: (1) Literature Review, (2) An integrative 7v- framework for big data, (3) Big data analytics: challenges and issues, (4) Tools and Framework of Big Data, (5) Big Data Application in Companies, (6) Findings and Conclusions, and finally (7) Future research Implications.