Improve Customer Service Research Articles

Peran Customer Service dalam Meningkatkan Kualitas Pelayanan Nasabah PT. Bank Rakyat Indonesia (Persero) TBK Kantor Cabang Madiun

Achieving customer happiness necessitates the adoption of enhanced cutomer service. The present study examines how cutomer service might enhance cutomer servi satisfaction at the Madiun Branch Office of PT Bank Rakyat Indoensia (Persero)Tbk. For the intention of understanding how customer service contributes to the PT Bank Rakyat Indoensia (Persero)Tbk Madiun Branch Office’s improved customer service. Qualitative research with data gathering methods from libraries is the sort of study that’s is being done. The study’s findings show that PT Bank Rakyat Indoensia (Persero)Tbk Madiun Branch Officeis customer service is crusial to the provision of services to clients

Exploring the Application of Machine Learning for Automatic Inbound Email Classification in CRM System at XYZ Company

Customer service has become increasingly crucial in today's business landscape, necessitating companies to provide fast, responsive, and personalized assistance to their clientele. However, amidst the challenges posed by surges in email volume, manual categorization and response strategies often lead to performance declines. To address this, we propose a system leveraging Machine Learning techniques for automated email classification. Our evaluation reveals promising results, with SVM achieving the highest accuracy of 96.59%, followed by XGB (96.02%) and RF (95.27%). These models exhibit commendable precision, recall, F1 scores, and Matthews Correlation Coefficient (MCC), showcasing their effectiveness in improving customer service efficiency and responsiveness. This integration of technology not only enhances operational efficiency but also fosters harmonious customer relationships, ultimately leading to increased loyalty and profitability for companies.

The Impact of AI on International Trade: Opportunities and Challenges

This study aims to explore the transformative potential of Artificial Intelligence (AI) in international trade, focusing on its key roles in optimizing trade operations, enhancing trade finance, and expanding market access. In trade optimization, AI leverages advanced machine learning and predictive analytics to enhance demand forecasting, route optimization, and customs procedures, leading to more efficient logistics and inventory management. In trade finance, AI can automate document processing and risk assessment, increasing access to finance and enhancing transactional transparency, particularly through integration with blockchain technology. In terms of market access, AI-driven analytics can identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. Due to the lack of quantitative data, this study employed qualitative research methods, specifically a multiple-case-study approach. The case studies of leading companies such as Alibaba, DHL, and Maersk showcase how they leverage AI to optimize their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI’s practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include issues around data quality, ethical concerns, technological complexity, and public perception. Policy recommendations highlight the need for a robust data infrastructure, establishing ethical AI guidelines, and fostering international cooperation to align data protection regulations.

Service center in electricity distribution sector with variable peak electricity time: A two-stage queuing model approach with nested simulation

Customer service is essential for any business, significantly impacting customer satisfaction, brand reputation, and reducing associated costs. In the electricity industry, this importance is heightened due to the critical nature of electricity in daily life and growing usage rate of new technologies. Effective customer service in electric distribution companies involves handling a wide range of inquiries, from billing issues to technical support, requiring both remote and on-site assistance. The variability in customer calls, influenced by factors such as peak electricity hours, and different service and customer types, presents unique challenges in designing efficient service centers. This research develops a tailored model for the service center operations of a power distribution company, focusing on optimal staffing levels within budget constraints. A two-stage queuing model is proposed to address the needs of residential and non-residential customers. The study employs nested simulation techniques to account for variability in customer arrival times and peak hours. Key performance indicators (KPIs) are used to evaluate the effectiveness of staffing policies, considering both quantitative and qualitative costs. The study integrates stochastic optimization and ranking and selection methods to identify the best staffing configurations. The findings offer actionable insights for improving customer service efficiency and operational effectiveness in electricity customer service centers.

Frekuensi Gangguan dan Energi Tak Tersalurkan Sebagai Indikator keandalan Sistem Jaringan Tegangan Menengah

The increasing need for electrical energy must be balanced with improved customer service. Customers will feel comfortable if they can enjoy an uninterrupted electricity supply in the form of a disconnection of the electricity supply for a certain period of time or what is commonly called the ENS (Energy Not Supplied) index and the AENS (Average Energy Not Supplied) index. The reliability of the electric power system is one of the things that is very important in order to maintain the performance of the three main pillars of PT. PLN (Persero). A reliable distribution network can be seen from the number of disruptions that often occur in the distribution network. Disruptions that arise in the distribution network arise due to internal factors such as less reliable distribution network components. In addition, it can also be caused by external factors such as natural disasters or the influence of trees and animals. Based on the results of the research that has been conducted, it is known that the occurrence of disruptions and the length of the duration of the disruption cause the Energy Not Supplied (ENS) value to increase as in April and July, the high duration of the blackout also makes the ENS higher, namely 792.857804 kW and 789.046811 kW. However, the distribution network at the Wagom feeder is still considered reliable in distributing electrical energy based on the SAIDI and SAIFI indices. From the research results, the SAIDI index at the Wagom feeder reached 8.96 hours/customer/year. While the SAIFI index at the Wagom feeder reached 2.08 times/customer/year.

Optimalisasi Operasional Layanan dan Integrasi Sistem pada Arsitektur Perusahaan PT X melalui Penerapan TOGAF ADM

This research discusses the application of the TOGAF ADM method in modelling X Ltd,which operates in the field of printing and book publishing services. X Ltd facesoperational challenges that are still manual and not fully integrated with the system sothat the service operational process becomes ineffective in terms of time. This researchaims to design an integrated architecture, including business process architecture,application and data architecture, and technology architecture. The research method usesa qualitative approach by conducting interviews with two sources from X Ltd to analyzecurrent operations. The results show that TOGAF ADM can help X Ltd Optimizes itsoperational system, accelerates data management, publishing books, and improvingcustomer service, and facilitating adaptation to changes through new system architecturemodelling.

Enhancing service quality in the insurance industry with AI-powered humanoid chatbots

PurposeThis study aims to explore the potential of artificial intelligence with AI-powered humanoid Chatbots (AIPHC) as transformative tools to improve customer service quality in the insurance sector. The usability and efficiency of integrating advanced AI chatbots that can replicate human-like interactions in insurance services will be examined by taking into consideration customers’ technological readiness and chatbots’ anthropomorphism.Design/methodology/approachThis empirical study analysed 688 customer responses collected through purposive sampling using structural equation modelling. With the help of SmartPLS 4.0, the study determines whether anthropomorphism, that is AIPHC system-specific and customer personality-specific dimensions, can influence the acceptance of AIPHC in the insurance sector.FindingsThe results show that the chatbot’s anthropomorphism positively influenced customers’ optimism and innovativeness but negatively impacted discomfort and security. Further optimism and innovativeness favourably impact AIPHC adoption. Insecurity had a significant negative impact, while discomfort was insignificant for AIPHC adoption.Research limitations/implicationsThe study determines how people will react to AI-powered information systems. The results could help us better understand how the technological readiness of customers can be used in emphasizing the significance of system-specific theories like anthropomorphism in sectors like insurance, where customer interactions and delivery of quality services are important.Practical implicationsThe results highlight chatbots’ potential to raise the quality of service, simplify processes and enhance customers’ overall experiences in the insurance sector. This study contributes to the continuing discussion on using AI technologies in customer service by considering the interplay between technology readiness and anthropomorphism. It also provides insightful information for insurance professionals and technology developers.Social implicationsAnthropomorphic humanoid chatbots can increase the availability, affordability and accessibility of essential services. They have the potential to increase users’ competence, autonomy and—possibly counterintuitively social relatedness.Originality/valueThis empirical research explores the link between anthropomorphism and technology readiness to enhance service quality provided by AI powered chatbots in the insurance sector.

Implementing Long Short Term Memory (LSTM) in Chatbots for Multi Usaha Raya

The furniture industry is an important sector in Indonesia that supports the economy and provides quality furniture. An in-depth understanding of the furniture business is essential for industry players to improve operational efficiency and customer satisfaction. This research aims to develop a chatbot for Multi Usaha Raya furniture company to improve customer service and operational efficiency. In its development, the Machine Learning Model Development Life Cycle (MDLC) and deep learning approach using the Flask platform are employed. LSTM, a type of recurrent neural network (RNN) architecture capable of handling long-term dependencies, is utilized in this chatbot model. The model training results show an accuracy of 99%, validation accuracy of 96%, loss of 0.1%, and validation loss of 0.2% after 200 epochs, demonstrating the effectiveness of the LSTM algorithm for developing a chatbot in this company.

Application of machine learning techniques for churn prediction in the telecom business

The telecom business generates a significant amount of data on a daily basis due to its massive client base. Acquiring a fresh client base is more expensive than retaining existing customers, whereas churn refers to customers transitioning from one company to another within a specified timeframe. Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. In this work employs categorization methodologies to determine the instances of leave subscriptions and gathers the rationales behind client leave subscriptions in the telecommunications sector. The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. This work aims to provide retention strategies and plans to address churn. This work utilizes machine learning (ML) technique such as random forests (RF) to collect and classify client data for leave subscriptions. These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. This allows business management to take timely action to prevent churn and minimize profit loss. In this work obtains an accuracy of 98.1 % by utilizing the random forest classifier for churn prediction. The classifier matrix has obtained a precision of 92.8 % and a recall factor of 92.7 %, resulting in an overall accuracy of 95.6 %. Similarly, our research endeavors enhance churn prediction, encompass additional business domains, and furnish prediction models to retain their current consumers, improve customer service, and efficiently prevent churn.

Implementation of Chatbot Artificial Intelligence in a Company Website to Improve Customer Service Automatically Using the TF-IDF Method

In today's digital age, improving customer service is one of the keys to business success. Successfully retaining and attracting new customers is a challenge that must be faced. The application of chatbot technology powered by artificial intelligence (AI) on business websites has been proven to provide more efficient and responsive customer service. This research aims to develop and implement an AI chatbot that uses the TF-IDF (Term Frequency-Inverse Document Frequency) method to automatically understand and answer customer queries. The TF-IDF method is used to extract key features from the text of customer questions and match them with the most relevant answers in the database. The results of implementing this AI chatbot showed a significant improvement in the speed and quality of customer service responses, thus helping to improve customer satisfaction and company performance. This research provides valuable insights for businesses looking to integrate AI technology into their customer service strategy.

НЕОБХІДНІСТЬ ВИКОРИСТАННЯ МЕТОДІВ КРЕАТИВНОГО МИСЛЕННЯ ДЛЯ РОЗВИТКУ ПІДПРИЄМСТВА В СУЧАСНОМУ КОНКУРЕНТНОМУ СЕРЕДОВИЩІ

The purpose of the study was to determine the main methods of developing the creative thinking of enterprise employees under the conditions of increasing competition. The article states that creativity is a competitive advantage of any enterprise and argues that companies should solve their problems in an innovative way with the help of ideas that allow them to constantly adapt to the external environment. An analysis of research was carried out, which made it possible to highlight creativity as one of the most important leadership qualities for CEOs, which surpasses even decency and global thinking. The benefits of developing creativity have been highlighted, including increased revenue, market share, increased employee productivity, improved customer service, fostering innovation, and ultimately financial success. Analyzed and formulated methods of development of creative thinking: brainstorming of ideas, which provides an opportunity to open up and share thoughts and ideas freely, without restrictions and prejudices; mind maps, as a graphical tool that facilitates idea generation and allows you to dive deeper into a concept or topic, narrowing it down to the most specific topics as ideas are broken down into pieces in the process; changing the way of thinking, which means changing the point of view on the issue and opportunities to approach it differently, this will allow the team to open new perspectives on the issue; role playing as the method most useful for coming up with ideas for a product and predicting how it will perform in the market; study of various psychological theories of creativity; learning to ask the right questions around issues related to product features, customer feedback, problem nature, etc. or using the six thinking hats strategy. The importance of building a program for developing the creativity of the company's employees is proven, which is based on the principle of phasing, when the next stage is logically connected to the previous one and is aimed at consolidating what has been achieved and its further development at the next stage.

Exploring the Future of Salesforce with Einstein GPT

This paper discusses how far the impact of Einstein GPT can go into changing Salesforce, talking from its architecture, applications, and future possibilities. Einstein GPT helps in the integration of advanced generative pre-trained transformers in Salesforce, enabling it to allow natural language interactions and enhanced insights of data. The essay will research through its architecture, including the Data Layer, AI/ML Layer, and Interaction Layer, bringing into focus key applications such as sales automation, improvement in customer service, optimization in marketing, and predictive analytics. This paper considers the moral dimensions of using AI, too. That is, by considering these dimensions, the essay points out how Einstein GPT is bound to set a new course of CRM capabilities and future innovations within Salesforce. Keywords—Salesforce, GPT, Einstein, AI, Integration

Market Penetration Strategy by Increasing Firm Competitiveness

To achieve successful market penetration, businesses must navigate complexities related to culture, legal frameworks, economic conditions, and competition. Businesses must decide whether to standardize their marketing strategies across different markets or adapt them to suit unique market characteristics. Competition is the ability to perform better, and faster. In facing competition, companies need to determine the right strategy, because in general companies are trying to survive, grow and develop. The purpose of the Market Penetration Strategy research by Increasing Company Competitiveness can include several aspects, namely, Assessing the company's strengths and weaknesses. Conducting market analysis to understand existing opportunities and challenges, including consumer preferences and behavior, and market trends and Developping an effective Market Penetration Strategy to increase market penetration, such as products, improving customer service, or competitive pricing strategies. This study uses a descriptive qualitative method to find out how PT. AJS Bumiputera markets its products, using market penetration to increase competitiveness, or to find out what the biggest challenges are in its market penetration strategy. This study uses a qualitative method as one of the methodologies in research that does not yet have a standard definition and is generally agreed upon for use. From the results of the study it is known that PT. Bumiputera Sharia Life Insurance, using market penetration through digital platforms such as social media, company websites, and online advertising campaigns to reach a wider audience. Organizing events, seminars, or workshops, related to finance or health. Providing education about the benefits of insurance products to the public, conducting direct promotions through telemarketing, or face-to-face marketing to introduce products directly.

Top-up Frequency Forecasting for Lao Telecommunication Company Using Time-Series Analysis

The study aimed to forecast the top-up frequency for Lao Telecommunication Company (LTC) customers using time-series analysis. Accurate forecasts help the company optimize resource allocation, improve customer service, and boost revenue by anticipating customer demand. The time-series analysis techniques are employed, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average + exogenous variables (SARIMAX) and exponential smoothing (ETS) models, to analyze historical top-up data. The data is pre-processed, identified patterns, and trained the model to forecast future top-up frequencies. The model's predictions closely aligned with the actual data, indicating its effectiveness. The ARIMA model produced reliable forecasts for short-term top-up frequencies, showing the best performance overall with the lowest RMSE and MAE, and a positive R-squared indicating some ability to explain the data. SARIMAX and EST both perform poorly, with very high RMSE and MAE, and negative R-squared values. The results demonstrated that time-series analysis could enhance decision-making in the telecommunications industry by enabling better forecasting of customer behavior. This helps in optimizing marketing strategies, inventory management, and service availability.

Impacts of industry 4.0 on supply chain management of logistics providers: A methodical review

The arrival of Industry 4.0 transformed and reshaped manufacturing, and the logistics industry is not excluded. As such, there is a need to explore the influence of this latest technological innovation on industries with a focus on logistics providers; this research examines the impact of Industry 4.0 on logistics providers' supply chain management. Using qualitative analysis of secondary sources, this research identifies the key relevance of Industry 4.0 technologies to logistics providers while assessing their adoption and exploring their impact on supply chain performance. Through the series of examination of secondary sources, it was revealed that Industry 4.0 components such as IoT enable real-time tracking, reducing lead times and improving customer service. AI, on the other hand, helps optimize routing and scheduling while also enhancing efficiency. Nevertheless, the industry 4.0 implementation into the logistics provider supply chain is not without challenges; the challenges evidenced through the research include data security, interoperability, and workforce skills. This study recommends investments in technology and skills, data security measures, collaborative organizational structures, agility, change management, and benchmarking to navigate the transformative landscape of Industry 4.0 in logistics supply chain management.

The Role of AI in Improving Customer Service, Fraud Detection, and Risk Management in Banking

The Role of AI in Improving Customer Service, Fraud Detection, and Risk Management in Banking

The Effect of Inventory Management Practices on Service Delivery: A Study of Sawula General Hospital, Southern Ethiopia

The purpose of this study was to assess the effect of inventory management practices on service delivery of Sawula General Hospital. The study employed both descriptive and explanatory research designs to conduct this research. It contained a qualitative and quantitative component that enables the researcher to assess the study easily. The total population of this study was 459 and the researcher takes 293 as sample size from staff’s members, Health department experts and patients. The sampling technique of the study was stratified sampling technique. Primary data were mainly collected using self- administered questionnaires consisting of closed ended questions with 5-point Likert Scale. Data were also collected through questionnaires and interview. The collected data were quantitatively analyzed using SPSS to produce descriptive and inferential statistics and regression analysis. The key findings from the study revealed that there is gap on Sawula General Hospital was relation to applying poor inventory management practice which caused dissatisfaction of the patient/client with the service delivery. Moreover, the finding shows that the inventory management practice was also challenged by shortage of budget and lack of training on inventory management, bureaucratic process and lack of computerized system to manage the inventory. Based on the major findings, the study recommends that applying the scientific concepts of inventory management practices and implementing awareness creation mechanisms to develop patients’ perception of service delivery to increase proper inventory management which realize improved customer service and inventory management by responsible bodies. Moreover, it was also recommended that, the hospital should create ways in solving the critical challenges by allocating enough budgets, avoiding bureaucratic process in procurement, filling knowledge gaps through different trainings.

EFFICIENT CUSTOMER SERVICE AND OPERATION MAINTENANCE BY INVENTORY MANAGEMENT

Effective customer service and operational excellence are critical components of corporate success, particularly in today's changing business world. This paper examines the crucial role that inventory management plays in controlling and improving customer service and operational efficiency in businesses. Companies that efficiently manage inventory levels may ensure the timely fulfillment of client orders, minimize stockpiles, and increase efficiency. Furthermore, efficient inventory management contributes to boosting operating efficiency, lowering logistical costs, and increasing profitability. This study extensively reviews the literature and case studies to explore the best strategies utilized in inventory management to attain these objectives. It also investigates the influence of inventory management on performance and offers useful insights for companies looking to use inventory management as a strategic strategy to gain a sustained competitive advantage.

Assuring Service Quality through SERVQUAL at the Land Bank of the Philippines North NCR Branches

This study aims to assess and assure service quality through SERVQUAL in the Land Bank of the Philippines North NCR Branches. Quantitative methods were employed, with 100 clients/customers, 216 front-liners, and 66 administrators from 82 LANDBANK North NCR Branches participating as respondents. A structured questionnaire served as the primary data collection tool. Statistical analyses included weighted mean, frequency and percentage distribution, standard deviation, Slovin’s formula, and Cronbach alpha reliability test. This study assesses the service quality perceptions among administrators, front-liners, and clients at LANDBANK. Results revealed a majority of female presence across all respondent groups. Administrators, aged 51 years and above, are mainly single and highly educated with postgraduate degrees. In contrast, majority of the front-liners and clients are aged 26-30 years and are married and hold mostly college degrees. Assessment across five dimensions of LANDBANK's service quality – tangibility, reliability, responsiveness, assurance, and empathy – revealed different perceptions. Tangibility was rated as commonly observed, with concerns about outdated equipment and system functionality. Reliability was rated higher by administrators than the front-liners and clients, issues encountered include long waiting times, system maintenance, ATM failures, and client-reported poor customer service. Administrators rated responsiveness higher than frontliners and clients, who highlighted delays and system failures. Assurance received higher ratings from administrators and front-liners but there are concerns encountered regarding hidden charges and security from clients. Empathy was consistently rated higher by administrators and front-liners compared to clients, citing instances of unsatisfactory customer service. Administrators rated LANDBANK’s service quality higher than the front-liners and clients, suggesting opportunities for enhancing alignment with customer expectations. Key issues identified include system reliability, long waiting times, and ATM failures. Recommendations focus on facility upgrades, IT enhancements, staffing increases, professionalism development, and improved customer service training to elevate service quality, customer satisfaction, and competitiveness at LANDBANK North NCR Branches.

Trends and Transformations in Home Loan Processing: A Case Study of HDFC Hyderabad

The housing finance sector in India is growing, driven by rising home ownership demand and supportive policies. This study, "Trends and Transformations in Home Loan Processing: A Case Study of HDFC Hyderabad," explores HDFC Bank's home loan offerings, focusing on types like purchase, construction, and renovation loans. It examines recent trends, customer satisfaction, and challenges, using surveys and interviews for data collection. The findings highlight evolving borrower profiles, technological advancements, and government initiatives like PMAY, offering insights for improving customer service and operational efficiency.