On a Certain Research Gap in Big Data Mining for Customer Insights

Maria Mach-Król,Bartłomiej Hadasik

doi:10.3390/app11156993

Abstract

The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big data analyzes in scientific literature sources. This article adopts two research methods. The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. This method has been conducted in four steps: search, selection, analysis, and synthesis. The second research method is the bibliographic verification of the obtained results. The verification consisted of querying the Scopus database with previously identified key phrases and then performing trend analysis on the revealed Scopus results. The main contributions of this study are: (1) to organize knowledge on the role of advanced big data analytics (BDA), mainly big data mining in understanding customer behavior; (2) to indicate the importance of the temporal dimension of customer behavior; and (3) to identify an interesting research gap: mining of temporal big data for a complete picture of customers.

Highlights

We identify the current state of big data analytics for customer insights, and the challenges to address the time dimension of customer behavior while mining big data
“artificial intelligence (AI) approach” to big data analytics and big data mining is not obvious; the trend analysis performed in this paper has proven that our observation about such a temporal research gap in BDM is confirmed
The usability of advanced big data analytics including big data mining for gaining valuable customer insights has already been investigated by many researchers [160,213,216,223,224,225]

Summary

Introduction

The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. In a modern fast-changing economy, the Velocity and Variability dimensions become important—the features that cause the most problems in the analysis process are the speed of data inflow and their variability and the temporal dimension of big data. This dimension is interconnected with the fundamental assumption that time is an inseparable element influencing the phenomenon of big data and the process of analyzing this data.

Objectives

Findings

Discussion

Conclusion