Big Data In Banking: A Bird’s Eye View

GIS Business ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 1129-1139
Author(s):  
C. RADHA PRIYA ◽  
KANNIGA PRASHANTH
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Banking Industry ◽  
Big Data Analytics ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Risk Modeling ◽  
Customer Data ◽  
Customer Lifetime ◽  
Big Data Application

Banking industry is the backbone of any economy. It plays a very significant role in leading the country towards the growth path by improving the gross capital formation, which consecutively improves the GDP. Success of the banking industry depends on its ability to serve its customers efficiently and expeditiously. The functionality of the CRM (Customer Relationship Management) can be effectuated by felicitous use of customer data. Banks have voluminous data about their customers, which most of the banks failed to utilize in a well-timed manner. Banks can fortuitously satisfy their customers by offering much personalized and focused services by pursuing big data analytics and other hi-tech tools or applications. Big data analytics can be actuated in key areas like customer segmentation, offering customer lifetime value, fraud detection, risk modeling, etc. Preeminent banks in the industry are utilizing big data to leverage the accumulated customer data for improvising their services. Big data offers a promising scope of ventures to banks which consider it strategically. This article is attempts to present an overview of the big data application in the banking industry.

An Effective Model for Consumer Need Prediction Using Big Data Analytics

2021 ◽  
Author(s):  
Yihao Tian
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Data Set ◽  
Customer Data ◽  
Demand Prediction ◽  
Management Ratio ◽  
The Veil

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).

scholarly journals BIG DATA BUSINESS ANALYTICS AS A STRATEGIC ASSET FOR HEALTH CARE INDUSTRY

2019 ◽  
Vol 01 (02) ◽  
pp. 12-20 ◽  
Author(s):  
Smys S ◽  
Vijesh joe C
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Customer Relationship ◽  
Business Strategies ◽  
Business Analytics ◽  
Care Industry ◽  
Strategic Assets

The big data includes the enormous flow of data from variety of applications that does not fit into the traditional data base. They deal with the storing, managing and manipulating of the data acquired from various sources at an alarming rate to gather valuable insights from it. The big data analytics is used provide with the new and better ideas that pave way to the improvising of the business strategies with its broader, deeper insights and frictionless actions that leads to an accurate and reliable systems. The paper proposes the big data analytics for the improving the strategic assets in the health care industry by providing with the better services for the patients, gaining the satisfaction of the patients and enhancing the customer relationship.

Big Data Analytics for Smart Airport Management

2021 ◽  
pp. 209-231
Author(s):  
Desmond Narongou ◽  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Relationship ◽  
Operational Effectiveness ◽  
Airport Management ◽  
International Airport ◽  
Computing Paradigm ◽  
The Impact ◽  
Smart Airport

Smart airport management has drawn increasing attention worldwide for improving airport operational efficiency. Big data analytics is an emerging computing paradigm and enabler for smart airport management in the age of big data, analytics, and artificial intelligence (AI). This chapter will explore big data analytics for smart airport management from a perspective of PNG Jackson's International Airport. More specifically, this chapter first provides an overview of big data analytics and smart airport management and then looks at the impact of big data analytics on smart airport management. The chapter discusses how to apply big data analytics and smart airport management to upgrade PNG Jackson's International Airport in terms of safety and security, optimizing operational effectiveness, service enhancements, and customer experience. The approach proposed in this chapter might facilitate research and development of intelligent big data analytics, smart airport management, and customer relationship management.

scholarly journals Investigating the Impact of Big Data Analytics on Perceived Sales Performance: The Mediating Role of Customer Relationship Management Capabilities

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Muhammad Shahbaz ◽  
Changyuan Gao ◽  
Lili Zhai ◽  
Fakhar Shahzad ◽  
Adnan Abbas ◽  
...  
Keyword(s):  
Big Data ◽  
Customer Relationship Management ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
Relationship Management ◽  
Sales Performance ◽  
Customer Relationship ◽  
Research Model ◽  
The Impact

A persistent question for information technology researchers and practitioners is how big data analytics (BDA) can improve sales performance. Therefore, this study proposed a research model to investigate the impact of BDA on perceived sales performance in accordance with the resource-based view (RBV) and dynamic capability theory. The 416 valid responses collected from the employees of pharmaceutical organizations were analyzed using structural equation modelling to test the proposed research model. Results indicated that the BDA and customer relationship management (CRM) capabilities shared a strong positive impact on perceived sales performance. BDA, as organizational resources, creates organizational dynamic capabilities, such as CRM capabilities. BDA and CRM capabilities can influence perceived sales performance. Furthermore, CRM capabilities have a significant mediating impact on the relationships between BDA and perceived sales performance. This study also highlighted the practical and theoretical implications of the proposed model, the research limitations, and the future research directions.

scholarly journals Impact of big data analytics on sales performance in pharmaceutical organizations: The role of customer relationship management capabilities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250229
Author(s):  
Muhammad Shahbaz ◽  
Changyuan Gao ◽  
Lili Zhai ◽  
Fakhar Shahzad ◽  
Adeel Luqman ◽  
...  
Keyword(s):  
Big Data ◽  
Customer Relationship Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Relationship Management ◽  
Sales Performance ◽  
Customer Relationship ◽  
Equation Modeling ◽  
Automation System ◽  
The Impact

In this era of technology development, every business wants to equip its salesforce with a sustainable salesforce automation system to improve sales performance and customer relationship management (CRM) capabilities. This study investigates the impact of big data analytics (BDA) on CRM capabilities and the sales performance of pharmaceutical organizations. A research model was tested based on 416 valid responses collected from pharmaceutical companies through a structured questionnaire. Structural equation modeling (SEM) was employed using Smart-PLS3 to confirm the contribution of BDA to improving CRM capabilities and sales performance. The study finds that individual characteristics such as self-efficacy, playfulness, and social norms, along with organizational characteristics such as voluntariness, user involvement, user participation, and management support, are positive predictors of salesforce perception of BDA. This positive perception of BDA increased the person-technology fit in the salesforce, which ultimately increased the CRM capabilities and sales performance.

Big data breaches and customer compensation strategies

2017 ◽  
Vol 37 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Thomas Kude ◽  
Hartmut Hoehle ◽  
Tracy Ann Sykes
Keyword(s):  
Big Data ◽  
Personality Traits ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Content Type ◽  
Customer Data ◽  
Data Breaches ◽  
Large Scale Data ◽  
Scale Data

Purpose Big Data Analytics provides a multitude of opportunities for organizations to improve service operations, but it also increases the threat of external parties gaining unauthorized access to sensitive customer data. With data breaches now a common occurrence, it is becoming increasingly plain that while modern organizations need to put into place measures to try to prevent breaches, they must also put into place processes to deal with a breach once it occurs. Prior research on information technology security and services failures suggests that customer compensation can potentially restore customer sentiment after such data breaches. The paper aims to discuss these issues. Design/methodology/approach In this study, the authors draw on the literature on personality traits and social influence to better understand the antecedents of perceived compensation and the effectiveness of compensation strategies. The authors studied the propositions using data collected in the context of Target’s large-scale data breach that occurred in December 2013 and affected the personal data of more than 70 million customers. In total, the authors collected data from 212 breached customers. Findings The results show that customers’ personality traits and their social environment significantly influences their perceptions of compensation. The authors also found that perceived compensation positively influences service recovery and customer experience. Originality/value The results add to the emerging literature on Big Data Analytics and will help organizations to more effectively manage compensation strategies in large-scale data breaches.

Exploring Big Data Opportunities for Online Customer Segmentation

2014 ◽  
Vol 5 (3) ◽  
pp. 58-75 ◽  
Author(s):  
Georgia Fotaki ◽  
Marco Spruit ◽  
Sjaak Brinkkemper ◽  
Dion Meijer
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Business Environment ◽  
Customer Segmentation ◽  
Online Marketing ◽  
Basic Step ◽  
Data Set ◽  
Customer Data ◽  
Customer Information ◽  
Online Sources

In today's competitive business environment, more and more organizations move or extent their business online. Thus, there is an increasing need for organizations to build concrete online marketing strategies in order to engage with their customers. One basic step towards achieving the objectives related to online marketing is the segmentation of online customers, based on the customer data gathered online. Since there is an onslaught of customer information collected from online sources, new techniques are required for managing and analyzing the huge amount of data, and this is where the concept of Big Data can play an essential role. This research sheds light on three fields: Online Marketing, Customer Segmentation, and Big Data Analytics. The three domains are integrated into the Online Customer Segmentation (OCS) framework, which attempts to show how online marketing objectives can be supported by techniques and tools applicable to extremely large datasets. For the creation of the OCS framework a set of main online marketing objectives is defined. Moreover, the differences among customer attributes gathered from offline and online channels are discussed and OCS categories are identified. Finally, the concept of Big Data is introduced and relevant techniques and tools suitable for analyzing customer segmentation categories and segmenting customers effectively are described. This work demonstrates the OCS framework by applying it on a hypothetical business scenario using an online customer data set.

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