Analysis of Improvisation of Customer Satisfaction in Banking Sector Using Big Data Analytics

2020 ◽  
pp. 371-380
Author(s):  
M. Aasritha ◽  
D. Hemavathi ◽  
G. Sujatha
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Data Analytics ◽  
Banking Sector ◽  
Big Data Analytics

scholarly journals Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks

2015 ◽  
Vol 50 ◽  
pp. 643-652 ◽  
Author(s):  
Utkarsh Srivastava ◽  
Santosh Gopalkrishnan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Banking Sector ◽  
Big Data Analytics ◽  
Indian Banks

scholarly journals Big Data Analytics on Banking Sector

2020 ◽  
Vol 05 (05) ◽  
pp. 113-116
Author(s):  
Yuvraj Chhikara ◽  
Chetan Agarwal ◽  
Sandeep Esap ◽  
Dr. H.S Guruprasad
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Banking Sector ◽  
Big Data Analytics

scholarly journals Big Data Analytics: A Spotify Case Study

2021 ◽  
Vol 9 (10) ◽  
pp. 1823-1829
Author(s):  
Suraj Ingle
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Exploratory Data ◽  
Insight Into

Abstract: By developing products that are in line with consumer needs, anticipating their profitability and manufacturing them, Big Data has opened up a lot of possibilities for building customer loyalty and commercial business by proactively engaging and comprehensively streamlining offers across all customer touch points. The use of big data to determine the best, most efficient ways to engage and interact with their customers will be discussed in this paper. An insight into how Spotify intends to provide music lovers additional ways to find their favourite songs, interact with artists, and improve Spotify recommendations has been provided. Keywords: Big Data, Data Analytics, Customer Satisfaction, Exploratory Data Analysis

Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

2021 ◽  
pp. 885-924
Author(s):  
Steven Chan Siang Hui ◽  
Omkar Dastane ◽  
Zainudin Johari ◽  
Mardeni Roslee
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Information Search ◽  
Dynamic Pricing ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics ◽  
System Dynamic ◽  
Mediation Effect ◽  
Repurchase Intention

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers' repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

2021 ◽  
pp. 395-434
Author(s):  
Steven Chan Siang Hui ◽  
Omkar Dastane ◽  
Zainudin Johari ◽  
Mardeni Roslee
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Information Search ◽  
Dynamic Pricing ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics ◽  
System Dynamic ◽  
Mediation Effect ◽  
Repurchase Intention

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers' repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

Big Data and Analytics

2018 ◽  
pp. 55-66 ◽  
Author(s):  
Misbahul Haque ◽  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Data Analytics ◽  
Care Delivery ◽  
Big Data Analytics ◽  
Cost Savings ◽  
Heterogeneous Data ◽  
Future Perspective ◽  
Clear Understanding ◽  
Health Organizations

In today's world, humungous and heterogeneous data are being generated from every action of researchers, health organizations, etc. This fast, voluminous, and heterogeneous generation leads to the evolution of the term big data. Big data can be computationally analyzed to uncover hidden trends and patterns that help in finding solutions to the problems arising in various fields. Analysis of big data for manufacturing operational acquaintance at an unparalleled specificity and scale is called big data analytics. Proper utilization of analytics can assist in making effective decisions, improved care delivery, and achieving cost savings. Recognizing hidden trends and useful patterns can lead us to have a clear understanding of the valuable information that these data holds. This chapter presents a quality overview of big data and analytics with its application in the field of healthcare industries as these industries requires their stream of data to be stored and analyzed efficiently in order to improve their future perspective and customer satisfaction.

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