IntelliTag: An Intelligent Cloud Customer Service System Based on Tag Recommendation

2021 ◽  
Author(s):  
Minghui Yang ◽  
Shaosheng Cao ◽  
Binbin Hu ◽  
Xianling Chen ◽  
Hengbin Cui ◽  
...  
Keyword(s):  
Customer Service ◽  
Service System ◽  
Tag Recommendation

scholarly journals Sistem Informasi Pelayanan Jasa pada Lucky Photo

2021 ◽  
Vol 11 (1) ◽  
pp. 81-95
Author(s):  
T Pradita ◽  
A Mubarok
Keyword(s):  
Information System ◽  
Customer Service ◽  
Service System ◽  
The Internet ◽  
Application System ◽  
Web Based ◽  
Customer Data ◽  
Service Information ◽  
Internet Media ◽  
A Company

The development of services has developed into the internet media, to make it easier for customers and employees in managing a job. In the problem of Lucky Photo, which covers services including printing, sales, stock of goods, purchases, and reports are not effective properly. The researcher aims to develop a service system entitled Service Information Systems at Lucky Photo. By building a web-based application, a waterfall method is needed to become a benchmark for the creation of a service information system, so the results will be obtained on a web-based application system to demand progress in a company, including services that become easier, easier customer service in conduct transactions, generate reports, and process customer data. So it can be concluded that with the construction of a new Service Information System it will be easier to make transactions, make it easier for customers, create reports, and process customer data that is embedded in the Mysql database which will become a well-systemized report.

scholarly journals Customer’s spontaneous facial expression recognition

2021 ◽  
Vol 22 (3) ◽  
pp. 1436
Author(s):  
Golam Morshed ◽  
Hamimah Ujir ◽  
Irwandi Hipiny
Keyword(s):  
Facial Expression ◽  
Customer Service ◽  
Positive Emotion ◽  
Service System ◽  
Negative Emotion ◽  
Sliding Window ◽  
High Accuracy ◽  
Facial Features ◽  
Expression Recognition ◽  
Specific Product

<span lang="EN-US">In the field of consumer science, customer facial expression is often categorized either as negative or positive. Customer who portrays negative emotion to a specific product mostly means they reject the product while a customer with positive emotion is more likely to purchase the product. To observe customer emotion, many researchers have studied different perspectives and methodologies to obtain high accuracy results. Conventional neural network (CNN) is used to recognize customer spontaneous facial expressions. This paper aims to recognize customer spontaneous expressions while the customer observed certain products. We have developed a customer service system using a CNN that is trained to detect three types of facial expression, i.e. happy, sad, and neutral. Facial features are extracted together with its histogram of gradient and sliding window. The results are then compared with the existing works and it shows an achievement of 82.9% success rate on average.</span>

scholarly journals Customer Service System Design Based on Big Data Machine Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012017
Author(s):  
Yuqiang Kong ◽  
Yaoping He
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Service ◽  
Service System ◽  
Rapid Development ◽  
Good Method ◽  
Service Systems ◽  
Labor Costs ◽  
High Turnover ◽  
Online Transactions

Abstract In recent years, with the rapid development of big data, traditional offline transactions have been moved to online in large numbers driven by the Internet. The virtual nature of online transactions has caused it to have problems such as difficulty in guaranteeing product quality and difficulty in user consultation. In addition, consumers are paying more and more attention to the quality of services, and the participation of customer service in the process of online transactions is very important. However, the current e-commerce market in our country is large and the number of online shopping users is extremely large. Customer service personnel are facing great work pressure. In addition, customer service has the characteristics of difficulty in recruiting, high labor costs, and high turnover rate. Such a dilemma is not conducive to our country. The sound development of e-commerce needs to be solved urgently. In order to solve these problems, it is a good method to apply related technologies to realize the automatic response of customer service. The purpose of this article is to design and research a customer service system based on big data machine learning. This article first through the understanding of the basic concepts of big data, and then extend the core technology of big data. Combining with the design ideas and concepts of contemporary customer service systems in our country, we will discuss the design and research of customer service systems based on big data machine learning. Research shows that traditional customer service in the era of big data can no longer meet people’s growing needs, and customer service systems based on big data machine learning are more efficient and convenient.

scholarly journals MARKET ORIENTATION PADA PT GUNUNG HIJAU MASARANG BERDASARKAN SKALA MARKOR

2018 ◽  
Vol 14 (2) ◽  
pp. 255 ◽  
Author(s):  
Alan R. Rombon ◽  
Leonardus R. Rengkung ◽  
Jen ., Tatuh
Keyword(s):  
Market Orientation ◽  
Customer Service ◽  
Service System ◽  
Likert Scale ◽  
Primary Data ◽  
Survey Method ◽  
Market Intelligence ◽  
Service Activities ◽  
As If

This study aims to know the Market Orientation (market intelligence, market intelligence dissemination, responsiveness of market intelligence). This research was conducted for three months from May until July 2017. The data used are primary data which is quantified by using Likert scale. The data taken by using survey method and use questioner to be distributed to 6 respondents in company. Questionnaires are used to identify market intelligence, market intelligence spread, responsiveness to market intelligence. Based on the results of research conducted at PT Gunung Hijau Masarang, the company's market orientation value was 67.29 percent. The company's market intelligence carried out customer service activities to know the consumer's response to the product. In the Market Intelligence Spread, Company companies understoodmarket integrations such as informally discussing competitor strategies, formal and informal discussions of needs. Whereas in the responsiveness of market intelligence the Company understood market orientation towards the response to market integrity. The company received complaints such as if there is a defectiveproduct, a change of packaging This is an opportunity for the company to correct the performance of the company as well as the service system like this makes it easier to encourage companies to more sharply looked at consumer needs.*jnkd*.

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