Social Media Data Analytics to Improve the Customer Services – The Case of Fast – Food Companies

Rose, S; Sreejith, R; Senthil, S

doi:10.35940/ijrte.b2205.078219

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Another practice that has been adopted is to include in the survey the option of free comments from the responders, allowing for the application of advanced sentiment analytics and including machine learning techniques [16]. However, in this case, only a portion of the responders provide comments.…”

Section: Work On Net Promoter Score-npsmentioning

confidence: 99%

A Machine Learning Based Classification Method for Customer Experience Survey Analysis

et al. 2020

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Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics.

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Section: Work On Net Promoter Score-npsmentioning

confidence: 99%

A Machine Learning Based Classification Method for Customer Experience Survey Analysis

et al. 2020

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“…The rapid development of social media directly and indirectly has an effect on the growth and development of the next generation (Bellamy et al, 2022;Damico & Krutka, 2018;Rajagiri Business School, Kochi, India. et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Concept of Islamic Education Today in the Challenges of the Social Media Era

Mufron,

Fata,

Akib

et al. 2024

Al-Hijr: J. of Adulearn World

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Nowadays, Islamic religious education is often underestimated in educational institutions. As researchers can see, with the development of the times social media is very influential on Islamic religious education. Because social media is often used in everyday life. Good against teachers, students or other communities. The purpose of researchers conducting research is to find out the concept of Islamic education in the challenges of the social media era. In this study, researchers used quantitative methods with servey models and in-depth interviews. The results of the study can be concluded that in today's era Islamic religious education is often trivialized by society. But actually Islamic education is very influential on future generations. Therefore, researchers can conclude that Islamic education is very important, because religious education is the main key to the morals and attitudes of students. Meanwhile, with the existence of Islamic religious education in schools, the morals and manners of students are problematic. Imagine if Islamic religious education is abolished or underestimated how the moral development of the nation's successors. The limitation of this study is that researchers have not been able to find ways or solutions so that religious education is not underestimated or trivialized by educational institutions or society. The researcher's hope for future researchers is that future researchers can find ways or solutions so that Islamic education is not underestimated.

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Analyzing the startup ecosystem of India: a Twitter analytics perspective

Singh

Chauhan

Dhir

2019

JAMR

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Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

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Social Media Data Analytics to Improve the Customer Services – The Case of Fast – Food Companies

Cited by 5 publications

References 10 publications

A Machine Learning Based Classification Method for Customer Experience Survey Analysis

A Machine Learning Based Classification Method for Customer Experience Survey Analysis

The Concept of Islamic Education Today in the Challenges of the Social Media Era

Analyzing the startup ecosystem of India: a Twitter analytics perspective

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