Customer Care Service Research Articles

Utilizing online reviews for analyzing digital healthcare consultation services: Examining perspectives of both healthcare customers and healthcare professionals

IntroductionDigital healthcare consultation services, also known as telemedicine, have seen a surge in their usage, especially after the COVID-19 pandemic. The purpose of this study is to investigate the satisfaction determinants of healthcare customers (patients) and healthcare professionals (doctors), providing digital healthcare consultation services. MethodsThe analysis involved scraping online reviews of 11 telemedicine apps meant for patients and 7 telemedicine apps meant for doctors, yielding a total of 44,440 patient reviews and 4748 doctor reviews. A structural topic modeling analysis followed by regression, dominance, correspondence, and emotion analysis was conducted to derive insights. ResultsThe study identified ten determinants of satisfaction from patients’ and eight from doctors’ perspectives. For patients, ’service variety and quality’ (β = 0.5527) was the top positive determinant, while ’payment disputes’ (β = -0.1173) and ’in-app membership’ (β = -0.031) negatively impacted satisfaction. For doctors, ’patient consultation management’ (β = 0.2009) was the leading positive determinant, with ’profile management’ (β = -0.1843), ’subscription’ (β = -0.183), and ’customer care support’ (β = -0.0908) being the negative ones. The most influential negative emotion for patients, anger, was closely associated with ’customer care service’ and ’in-app memberships,’ while joy was tied to ’service variety and quality’ and ’offers and discounts.’ For doctors, anger was associated with ’cost-effectiveness,’ and joy with ’app responsiveness.’ ConclusionThis study offers new insights by examining patient and doctor determinants at a granular level which can be used by telemedicine app developers and managers to build customer-centric services.

Management and sustainability of event facilities: perceptions of end-users in higher education institutions in Ghana

Purpose Users are key stakeholders in event facilities, and therefore facilities management (FM) services must meet their needs and expectations. The paper aims to assess users’ perceptions of FM practices and sustainability in event facilities in higher education institutions. Design/methodology/approach The study used a descriptive design approach to explore the perceptions of end-users of event facilities. Using Kwame Nkrumah University of Science and Technology as a case study, 384 users of the event facilities were contacted through the email directory of the event facilities. Questionnaires were used to collect data and analysed in descriptive and inferential statistics with the aid of the Statistical Package for Social Sciences (SPSS v22.0). Findings The study categorised users’ perceptions into customer care, security and safety and service quality dimensions. The results revealed that constituent key important indices of these three dimensions were not significant at both the composite and individual levels. Thus, the users generally perceived FM practices in the event facilities to be below standard. None of the three sustainability constituents was significant, suggesting that users perceived event FM sustainability practices as below standard. Practical implications The study presents users’ perceptions of FM practices in event facilities. It has also suggested effective ways of managing event facilities to improve user comfort, safety and satisfaction. Originality/value The study provides relevant information on users’ perceptions of FM practices. It specifically has sustainable FM as a key component, which is crucial for achieving sustainable development goals.

Factors Influencing Brand Switching in Mobile Telecommunication Industry in Bangladesh

Today telecommunications industry is a key indicator of productivity across economies and societies. In recent times, developing nations have witnessed significant transformation within this sector due to the impact it has had on their economies. Higher the degree of competiveness among the brands available to the consumers, higher would be the intensity of brand switching. This research has been conducted in order to examine the factors influencing brand switching in mobile telecommunication industry in Bangladesh. Qualitative and quantitative methods have been developed a strong base of research. Primary data for research has been collected through an interview schedule from 80 mobile subscribers in Bangladesh. Data were coded and analyzed by using SPSS and Excel. The results of research suggest that service charge is the most influential factor on brand switching in telecommunication sector. Other factors that have been found influential factor on brand switching were poor customer care service of previous operator, occupation and economic circumstances, switching cost, better service availability, wide and strong network. So the mobile operator and other related agencies should provide better service to the customers for reducing brand switching of mobile telecommunication industry in Bangladesh.

Trademark Empowerment using Optimal Neutrosophic Topological Vector Space for Maximizing Customer Attraction

Neutrosophic set is introduced as a generalization of intuitionistic fuzzy set, where any elements x ∈ X we have membership (T), non-membership (F), and indeterminacy (I)degrees. Neurosophic vague topological spaces are presented in various notations like neurosophic vague compactness and continuity. Trademarks are the essential components of intellectual property that allow owner to earn profit based on their name. In this industry, retailers typically use feedback channels like customer care service, website review complaints and suggestions boxes to gain user reviews on service satisfaction. But, there is a gap between these techniques. Customers are not fulfilled with them due to lack of trust in management, a lack of flexibility and slow responsiveness. This has prompted examination of the effect of customer feedback channels (CFCs) on client satisfaction and the necessity to develop a new CFC using artificial intelligence (AI). Thus, this study designs a Trademark Empowerment using Optimal Neutrosophic Topological Vector Space (TE-ONTVS) technique for Maximizing Customer Attraction. The intention of the TE-ONTVS technique lies in the prediction of customer behaviour and attraction. To accomplish this, the TE-ONTVS technique undergoes data scaling using Z-score normalization. In addition, the TE-ONTVS technique uses NTVS approach for the identification of customer behaviour and attraction. Lastly, whale optimization algorithm (WOA) is applied for optimal parameter tuning of the NTVS algorithm. A series of experiments were involved to demonstrate the enhanced outcomes of the TE-ONTVS algorithm. The obtained results stated that the TE-ONTVS technique reaches optimal performance over other models

Is online-to-offline customer care support essential for consumer service?

Customer satisfaction is the main pillar for convenient and profitable retailing. The retail sector continuously tried to develop new strategies to improve consumer satisfaction. It is impossible to provide each service directly to the consumer by the retailer. In this direction, the retailer establishes customer care to provide the best service to consumers. Consumer care can provide promotional or prevention services, increasing the consumer's satisfaction level. This study is developed to show the impact of retailers' customer care service for an offline-to-online retailing strategy. The demand is consumer support, advertisement, and selling price dependent. For maintaining consumer service, some free home delivery policies are offered, when consumers ordered more than a certain percentage of the amount. Finally, the total profit of this O2O retailing system is calculated by the classical optimization technique. Some special cases are discussed in the numerical section to prove the impact of customer care services. Numerical results prove that customer care support enhances the profit by 48.20%, whereas investment in the advertisement and home delivery strategy helps the retail industry to earn 44.80% and 16.74% more profit, respectively. Finally, from this study, it is clear that customer care activities are essential to increase the profit of the retailing sector.

A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality.

Customer Satisfaction with Customer Care Service Quality at Baoviet Life Corporation, Vietnam

Based on relevant theoretical bases and through conducting qualitative research to build a research model with a customer care service quality scale including 22 descriptive variables for 5 components of service quality. Customer service, that is: physical facilities, reliability, assurance, response, empathy; customer satisfaction scale for customer service. The result has 159 valid samples. The results of the linear regression analysis between the independent variable which is 5 components of customer service quality and the dependent variable which is customer satisfaction show that all 5 components have a positive relationship. Accordingly, the tangible component has the strongest impact, followed by the trust component and finally the feedback component. Based on this result, the research team has identified the shortcomings and proposed solutions such as: Building uniform policies, remuneration as well as sanctions for agents; Develop customer care regulations for agents; improve the capacity of customer service staff or improve customer support.

Customer Satisfaction with Customer Care Service Quality at Baoviet Life Corporation, Vietnam

Customer Satisfaction with Customer Care Service Quality at Baoviet Life Corporation, Vietnam

The Role and Impact of Mediating Factors in Grameenphone’s Success: A Study on Bangladesh Perspective

Grameenphone attains its leading position by continuously offering better services to its existing and potential customers. This study also explains how the mediating factors of Grameenphone like network coverage, internet service, information service, emergency balance, 4G service, voice call, voice message, my zone facilities, mobile banking facilities, SMS & MMS service, customer care service, CSR activities, advertisement & promotion, variety of package, call rate, quality of telecommunication service, video call service, responsiveness to the customer, hotline service 121, available retail store & physical product, internet service, healthcare service, GP offers, etc which influence to the success of Grameenphone. The article is devoted to the study of a quick overview of the subscribers’ demographics for Grameenphone in Bangladesh. The paper seeks to explore the role and impact of mediating factors, which in turn, lead to the success of the grameenphone. The main purpose of this study is to substantiate the position of the relationship between mediating factors and customer satisfaction that helps to divert potential customers to loyal customers and the ultimate objectives to achieve the company’s mission. A business can gain the trust and loyalty of its customers as well as long-lasting, successful business relationships if it can recognize and meet true customer expectations.

Topic Modeling for Automatic Analysis of Natural Language: A Case Study in an Italian Customer Support Center

This paper focuses on the automatic analysis of conversation transcriptions in the call center of a customer care service. The goal is to recognize topics related to problems and complaints discussed in several dialogues between customers and agents. Our study aims to implement a framework able to automatically cluster conversation transcriptions into cohesive and well-separated groups based on the content of the data. The framework can alleviate the analyst selecting proper values for the analysis and the clustering processes. To pursue this goal, we consider a probabilistic model based on the latent Dirichlet allocation, which associates transcriptions with a mixture of topics in different proportions. A case study consisting of transcriptions in the Italian natural language, and collected in a customer support center of an energy supplier, is considered in the paper. Performance comparison of different inference techniques is discussed using the case study. The experimental results demonstrate the approach’s efficacy in clustering Italian conversation transcriptions. It also results in a practical tool to simplify the analytic process and off-load the parameter tuning from the end-user. According to recent works in the literature, this paper may be valuable for introducing latent Dirichlet allocation approaches in topic modeling for the Italian natural language.

Digital Marketing and Performance of Banks in Port Harcourt

The study examined digital marketing and performance of selected banks in Port Harcourt. A descriptive survey research design was used for the study. The study adopted structure questionnaire instrument for data collection with a judgmental sampling method targeted on three money deposit banks which include, First Bank Plc, Access Bank Plc and First City Monument Banks in Port Harcourt. The sample size was 30 and 10 respondents were selected from the three banks and the unit of analysis are customers, managers, internal control managers and general managers. Data collected were analyzed with Pearson product moment correlation (PPMC) and simple regression analysis statistical tools at 0.05 percent level of significance. The result showed that there is a significant relationship between content marketing, social media marketing, etc. and customer patronage of selected banks in Port Harcourt. The study recommended among others that customer care service unit should create a friendly relationship with their customers.

Market Management System for Customer Care Service

In order to seek out the amount of effectiveness of selling management and make a good system, it becomes evident that an additional in-depth study of the management of marketing activity as a method that happens beneath the influence of a group of factors, that thought will alter to higher sight and solve existing problems, is necessary. The topic of the study is the theoretical and method foundations of management of the marketing activities of an enterprise and strategies for evaluating its effectiveness. The aim of the study is to create a system for managing marketing activities of an enterprise. In order to attain the aim of the study, the subsequent tasks are set and solved: to substantiate theoretical and method foundations of the formation of the management system of selling activity of enterprise, to specify elements of this system, and to spot styles of links between the components of the management system of marketing activity of enterprise and also the factors influencing it and its potency, to see composition of indicators of efficiency of marketing management system.

FACTORS AFFECTING INDIVIDUAL CUSTOMERS' SATISFACTION WHEN SHOPPING AT CONG QUYNH CO.OPMART SUPERMARKET, HO CHI MINH CITY

The main objective of the research is to determine the factors affecting the satisfaction of individual customers when shopping at Cong Quynh Co.opmart, Ho Chi Minh City. To achieve the research objectives proposed, the author has conducted a survey of 285 customers who are purchasing goods and services in the study area. The results of testing the hypotheses of the research model show that there are 05 independent variables that have a positive influence on customer satisfaction when shopping at Cong Quynh Co.opmart including (1) Commodities; (2) Employees; (3) Displaying goods in supermarkets; (4) Safety in the supermarket; (5) Customer care service. Based on the research results, a number of corresponding solutions have been proposed to contribute to improving the quality of goods & services in order to improve customer satisfaction when shopping at Cong Quynh Co.opmart.

A multiapproach generalized framework for automated solution suggestion of support tickets

Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end-to-end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End-to-End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end-to-end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real-world IBM ticket data set. Overall, we observed that end-to-end model outperformed similarity search-based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.

Dialog Router: Automated Dialog Transition via Multi-Task Learning

Dialog Router is a general paradigm for human-bot symbiosis dialog systems to provide friendly customer care service. It is equipped with a multi-task learning model to automatically capture the underlying correlation between multiple related tasks, i.e. dialog classification and regression, and greatly reduce human labor work for system customization, which improves the accuracy of dialog transition. In addition, for learning the multi-task model, the training data and labels are easy to collect from human-to-human historical dialog logs, and the Dialog Router can be easily integrated into the majority of existing dialog systems by calling general APIs. We conduct experiments on real-world datasets for dialog classification and regression. The results show that our model achieves improvements on both tasks, which benefits the dialog transition application. The demo illustrates our method’s effectiveness in a real customer care service.

IMPROVING OPERATIONAL EFFICIENCY OF GRIDS BY INTEGRATING GEOGRAPHIC MAPS IN SCADA SYSTEM: DaNang STUDY CASE

The continuous development of the present-day smartgrid technology is required to improve the operational efficiency of the power system. Energy management system (EMS) technology solutions, including Supervisory Control And Data Acquisition (SCADA) and Distribution Management System (DMS), must be constantly developed in terms of application range and efficiency to meet the mentioned requirements. The article proposes a technique to integrate Google Earth geographic map into the SCADA system. The efficiency of the proposed method is evaluated through PSS/ADEPT software. The results show that the proposed solution contributes to improving the reliability of the grid and improving the efficiency of customer care service as well as training of junior dispatchers.

The Influence of Customer Care Service Management on the Performance of the Shops and Clothes Businesses in Bukoba Municipality in Tanzania

This paper assessed the effects of customer care service management on business performance for the shop and clothes business owners in Bukoba municipality, Tanzania. Specifically, the paper assessed the effects of selling quality products, complaints management and positive impression on business performance. The study targeted a total population of 110 shop sellers and 850 customers. The systematic random sampling technique and sampling formula were used to select a sample size of 70 participants. The data were analyzed using descriptive statistics. The findings indicate that on average, shop sellers were selling products of the desired quality and this contributed to business performance. The findings further indicated that the positive impression was managed as required. However, the findings indicate that the impaired products replacement service was missing, the management for complaints was not sufficient, and shop sellers were busy and attended calls while serving customers. This, therefore, reduced business performance. This paper recommends that the business owners should accept replacing the impaired products, strive to make sure that all complaints are handled appropriately and timely and should refrain from being busy and attending calls when serving customers.

Emotion mining from text for actionable recommendations detailed survey

In the era of Web 2.0, people express their opinion, feelings and thoughts about topics including political and cultural events, natural disasters, products and services, through mediums such as blogs, forums, and micro-blogs, like Twitter. Also, large amount of text is generated through e-mail which contains the writer's feeling or opinion; for instance, customer care service e-mail. The texts generated through such platforms are a rich source of data which can be mined in order to gain useful information about user opinion or feeling which in turn can be utilised in specific applications such as: marketing, sale predictions, political surveys, health care, student-faculty culture, e-learning platforms, and social networks. This process of identifying and extracting information about the attitude of a speaker or writer about a topic, polarity, or emotion in a document is called sentiment analysis. There are variety of sources for extracting sentiment such as speech, music, facial expression. Due to the rich source of information available in the form of text data, this paper focuses on sentiment analysis and emotion mining from text, as well as discovering actionable patterns. The actionable patterns may suggest ways to alter the user's sentiment or emotion to a more positive or desirable state.

Emotion mining from text for actionable recommendations detailed survey

In the era of Web 2.0, people express their opinion, feelings and thoughts about topics including political and cultural events, natural disasters, products and services, through mediums such as blogs, forums, and micro-blogs, like Twitter. Also, large amount of text is generated through e-mail which contains the writer's feeling or opinion; for instance, customer care service e-mail. The texts generated through such platforms are a rich source of data which can be mined in order to gain useful information about user opinion or feeling which in turn can be utilised in specific applications such as: marketing, sale predictions, political surveys, health care, student-faculty culture, e-learning platforms, and social networks. This process of identifying and extracting information about the attitude of a speaker or writer about a topic, polarity, or emotion in a document is called sentiment analysis. There are variety of sources for extracting sentiment such as speech, music, facial expression. Due to the rich source of information available in the form of text data, this paper focuses on sentiment analysis and emotion mining from text, as well as discovering actionable patterns. The actionable patterns may suggest ways to alter the user's sentiment or emotion to a more positive or desirable state.

Real to H-Space Autoencoders for Theme Identification in Telephone Conversations

Machine learning (ML) and deep learning with deep neural networks (DNN), have drastically improved the performances of modern systems on numerous spoken language understanding (SLU) related tasks. Since most of current researches focus on new neural architectures to enhance the performances in realistic conditions, few recent works investigated the use of different algebras with neural networks (NN), to better represent the nature of the data being processed. To this extent, quaternion-valued neural networks (QNN) have shown better performances, and an important reduction of the number of neural parameters compared to traditional real-valued neural networks, when dealing with multidimensional signal. Nonetheless, the use of QNNs is strictly limited to quaternion input or output features. This article introduces a new unsupervised method based on a hybrid autoencoder (AE) called real-to-quaternion autoencoder (R2H), to extract a quaternion-valued input signal from any real-valued data, to be processed by QNNs. The experiments performed to identify the most related theme of a given telephone conversation from a customer care service (CCS), demonstrate that the R2H approach outperforms all the previously established models, either real- or quaternion-valued ones, in term of accuracy and with up to four times fewer neural parameters.