Customer Experience Research Articles

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

INOVASI E-COMMERCE UNTUK OPTIMALISASI PROSES KONSUMEN, PRODUKSI, DAN PEMASARAN BAGI PELAKU UMKM

E-commerce technology enables efficient online transactions, providing customers with a convenient and efficient purchasing experience. In terms of production, e-commerce information systems can be integrated with production management, enabling monitoring of the production process, optimizing production efficiency and reducing the risk of stock shortages. E-commerce technology also opens up opportunities for more sophisticated marketing strategies, as MSMEs can utilize consumer data analysis, increasing customer appeal and ordering and sales. E-commerce technology facilitates direct interaction between consumers, fostering closer relationships and improving customer experience. The development of e-commerce technology has significantly increased the effectiveness and efficiency of MSMEs specifically in the fried chicken sector, enabling them to compete in the digital business era. The purpose of this study is to develop an E-Commerce information system in the "Maju Makmur" Kalasan fried chicken industry center that manages MSMEs fried chicken. The research method is a prototype model. This model starts from data collection, system design and testing. The stages of the prototyping development model consist of: communication, rapid planning, rapid modeling, construction, and delivery. The results of this study are a web-based system to optimize consumer, production, and marketing processes in order to increase operational efficiency and market access through an online platform.

A strategic IT policy implementation model for enhancing customer satisfaction in digital markets

In the increasingly competitive landscape of digital markets, customer satisfaction is paramount for sustained business success. This paper proposes a Strategic IT Policy Implementation Model aimed at enhancing customer satisfaction by aligning IT policy development with customer-centric strategies. The model serves as a framework that integrates technological advancements with customer expectations, ensuring that IT policies effectively support business objectives and improve operational reliability. The proposed model emphasizes a holistic approach to IT policy formulation, incorporating stakeholder input, market analysis, and performance metrics. It identifies key areas where IT policies can directly influence customer experiences, including data management, service delivery, and communication channels. By focusing on these elements, organizations can develop IT policies that not only comply with regulatory standards but also enhance user engagement and satisfaction. Central to the model is the concept of continuous feedback loops, which facilitate the ongoing evaluation and refinement of IT policies based on customer insights. This adaptive mechanism enables businesses to remain responsive to changing customer needs and technological advancements, fostering a culture of innovation and customer-centricity. Moreover, the model advocates for cross-departmental collaboration, ensuring that IT policies are harmonized with marketing, sales, and customer service strategies. Implementation of the model is illustrated through case studies of leading technology firms that have successfully aligned their IT policies with customer-centric approaches. These examples highlight the tangible benefits of improved customer satisfaction, increased loyalty, and enhanced operational reliability. Furthermore, the model addresses common challenges faced during implementation, such as resistance to change and resource constraints, offering practical solutions to overcome these obstacles. In conclusion, the Strategic IT Policy Implementation Model provides a structured approach for organizations aiming to enhance customer satisfaction in digital markets. By aligning IT policies with customer-centric strategies, businesses can achieve greater operational efficiency and foster long-term relationships with their customers.

The Application of Virtual Reality and Augmented Reality Technology in Digital Marketing

This study investigates the utilization of Virtual Reality (VR) and Augmented Reality (AR) technologies in digital marketing, particularly their impacts on user engagement, brand awareness, conversion rates, and customer experience. With the progression of digital marketing, VR and AR have emerged as significant tools that create immersive and interactive experiences, aiding brands in distinguishing themselves within a competitive market. By analyzing applications such as virtual product demonstrations, interactive advertising campaigns, virtual showrooms, and AR shopping applications, this research underscores the substantial advantages these technologies confer. The findings reveal that VR and AR not only improve user engagement, brand visibility, but also boost customer satisfaction and drive sales. Despite obstacles such as high development costs and technical challenges, the outlook for VR and AR in digital marketing remains promising. This study provides strategic insights for businesses on effectively incorporating these technologies into their marketing frameworks to ensure long-term success and competitive advantage.

Application and Impact of the “Third Place” Concept — Starbucks as an Example

This essay explores the concept of the “Third Place” as epitomized by Starbucks, analyzing its significance in fostering community and enhancing the customer experience. By positioning itself as a communal hub, Starbucks transcends traditional retail paradigms, offering a space where social interactions flourish. The study found that Starbucks, through its successful global localization strategy, has effectively created a “third space” in different cultural contexts, enhancing customers’ sense of belonging and loyalty. Despite the impact of cultural differences on the brand experience, Starbucks’ space design and branding have become an important medium for customers to demonstrate their identity and interact socially. As the coffee shop market becomes increasingly crowded, Starbucks will need to continue to innovate and differentiate itself to meet customers’ evolving needs. In the future, Starbucks will need to continue to adapt to cultural differences and innovate to meet the challenges of the marketplace to maintain its global competitiveness.

The Role of Machine Learning in Enhancing Corporate Financial Planning

Aim: To examine the role of machine learning in enhancing corporate financial planning. Problem Statement: In the past, corporate financial planning relied on statistical models and expert judgment which are often constrained by predefined assumptions and historical data. By so doing, they usually struggled to handle the non-linear and dynamic nature of financial markets causing limitations in their responsiveness and predictive power. Significance of Study: There is need to incorporate machine learning in corporate financial planning to provide a transformative technique via the introduction of advanced algorithms which are capable of analyzing diverse and vast datasets to uncover sophisticated relationships and patterns. Methodology: Recent relevant published articles in the area of machine learning in enhancing corporate financial planning were consulted. Relevant articles were sourced from the internet using the Google search engine. The abstract of the consulted published articles were thoroughly examined to study their relevance to the subject matter. Discussion: This review article examines the role of machine learning in enhancing corporate financial planning. It was found that machine learning techniques have crucial roles to play in the context of corporate financial planning via decision-making improvement, risk management, operational efficiency enhancement, fraud detection and enabling personalized experiences. Also, Machine Learning technologies have a wide variety of uses in corporate financial planning which enable banks and insurance companies in delivering tailored experiences that give their customers some preferences. Common identified machine learning approaches in corporate financial planning include predictive analytics, natural language processing, computer vision, reinforcement learning and anomaly detection approaches. Major areas of machine learning application include dynamic pricing and offers; regulatory compliance; personalized customer service; predictive analytics for customer retention; personalized product recommendations and so on. Furthermore, the implementation of Machine Learning (ML) in corporate financial planning and financial services industry provides many chances for enhancing customer experiences, improving efficiency and driving innovation. However, ML poses numerous challenges which should be addressed to attain the complete potential of these technologies. Conclusion: As technology continues to advance, financial institutions will need to route the challenges of bias mitigation, transparency and regulatory compliance while the ML full potential is equally leveraged. Also, machine learning has the potential to transform corporate financial planning via the provision of more adaptive, accurate and real-time insights.

Perceptions and challenges of blockchain adoption in tourism industry: a study on trust, privacy and security

Purpose This study aims to investigate the perceptions of service providers in the travel and hospitality industry toward the adoption of blockchain technology (BCT), focusing on its impact on consumer experiences and expectations, especially in terms of trustworthiness and the management of privacy and security concerns. Design/methodology/approach The research used a quantitative methodology, collecting data from 135 industry practitioners across five tourism sectors (lodging, connected industries, entertainment, FandB, transportation) in Europe. This approach aimed to understand the diverse perspectives on the benefits and challenges of implementing BCT in their opera Findings The results indicate mixed perceptions regarding blockchain adoption. While there are positive views on BCT’s potential to enhance customer experience and service quality, there are significant concerns about its impact on trust and security. A consensus exists between customer and provider perspectives on BCT’s trustworthiness, but significant differences were observed in views on privacy and security enhancement. Originality/value This research contributes to the existing literature by providing insights into the service providers’ and customers’ perspectives on BCT within the travel and hospitality industry. It underscores the complexities of adopting new technologies and calls for more in-depth studies to address the identified concerns, thereby offering a novel viewpoint on the adoption of distributed ledger technologies in enhancing customer experience and service delivery.

Personalization at Scale: Big Data Technologies in Digital Content Delivery and Their Effect on Viewer Experience

This article examines the transformative impact of big data technologies on the digital content delivery industry, focusing on their application in enhancing customer experience through video streaming services. Through a comprehensive case study of a leading provider, we investigate the implementation of Hadoop and AWS Big Data product suites to optimize content delivery, personalize user recommendations, and enable dynamic ad insertion. Our findings demonstrate significant improvements in service reliability, viewer engagement, and advertising effectiveness. The article reveals how Big Data analytics facilitate real-time content optimization and personalized user experiences, increasing viewer retention and satisfaction. Furthermore, we explore the challenges and limitations encountered during implementation, providing insights into the practical applications of Big Data in the evolving landscape of digital media consumption. This article contributes to the growing body of literature on Big Data applications in media and entertainment, offering valuable insights for practitioners and researchers in digital content delivery.

Literature Review and Perspectives on Digital Customer Orientation in the Context of Digitization

With the advent of the digital era, the entrepreneurial environment has changed dramatically. Driven by demand and technological innovation, various types of platform enterprises have emerged and developed into a digital ecosystem dominated by core platform enterprises, with members of the industry evolving in a collaborative and mutually beneficial manner. For start-ups with "new entry deficiencies", only by further embracing the digital ecosystem can they make better use of complementary resources to achieve growth. Through a review of the classic literature on digital customer orientation at home and abroad, the concept, composition, measurement and application of digital customer orientation are systematically reviewed. The study shows that digital customer orientation is a concept different from customer orientation, a way for companies to provide real-time and customized customer experiences by embracing digital ecosystems, and that it plays a key role in the process of embracing digital ecosystems by start-ups. The shortcomings of digital customer orientation research are summarized and future research directions are proposed with the aim of promoting the further development of digital innovation research.

Digital Transformation and Innovation : Driving Business Growth

This comprehensive article explores the symbiotic relationship between digital transformation and innovation in driving business growth. It examines the key components of digital transformation, including cloud computing, big data analytics, AI, IoT, cybersecurity, and digital platforms. The article discusses how digital transformation catalyzes innovation through data-driven decision-making, fostering experimentation, enhancing collaboration, and accelerating time-to-market. It outlines the competitive advantages of digital transformation, such as improved customer experience, operational efficiency, new revenue streams, agility, and talent attraction. Case studies of successful digital transformations are presented, along with an analysis of organizations' challenges and considerations in their digital transformation journeys.

Overcoming Product Catalog Challenges in Telecom : A Technical Perspective

This comprehensive article explores the intricate challenges telecom providers face in managing complex product catalogs across multiple customer touchpoints. With the global telecom market projected to reach $2.47 trillion by 2028, efficient catalog management is crucial for maintaining competitiveness and customer satisfaction. The article delves into the multifaceted complexities of telecom product catalogs, including regional pricing variations, dynamic promotional offers, and the need for real-time synchronization across platforms. It outlines key challenges such as catalog complexity, slow data retrieval, frequent updates, third-party integration latencies, high traffic spikes, and campaign management inconsistencies. The article then presents innovative technical solutions and best practices, including the adoption of industry-leading catalog solutions, leveraging ElasticSearch for faster data retrieval, implementing differential processing for updates, preprocessing third-party integrations, utilizing dynamic UI templates, employing advanced campaign management tools, and implementing inventory prefetching. These solutions aim to enhance operational efficiency, improve customer experiences, and accelerate time-to-market in the rapidly evolving telecom industry.

Analysis of the Digital Transformation Path of Chinese E-commerce Companies

Digital technology has revolutionized China’s e-commerce sector, changing company operations and client relationships. This article analyzes the impact of digital transformation on online purchasing patterns. This article concentrated on the important of cloud computing, AI, and big data have become essential to online retail tactics. Digitalization is fueling economic development and innovation in China as its digital economy grows and impacts GDP. Digital technologies enable e-commerce and other industrial sectors to interact easily, improving operational efficiency and market reach. Moving from isolated firm models to connected platforms is a sign of a more responsive and integrated economy. This integration fosters a value model of co-creation in which stakeholders (including companies) collaborate to develop product and service offerings, improving customer experience and operational productivity. The route to digital maturity is not without challenges. E-commerce companies struggle with inefficient logistics, costly digital transformation, and a dearth of competent digital workers, as this study shows. Companies must make dramatic organizational and technological adjustments to overcome these constraints and maximize digital technologies. The analysis concludes that China’s e-commerce market will depend on enterprises’ digital adoption. E-commerce enterprises must embrace ongoing learning and development and remove digital integration barriers to compete in today’s dynamic online industry. The study illuminates digital transformation and how it will impact e-commerce.

From Experience to Loyalty: Understanding the Dynamics of Customer Trust in Indonesia’s Mobile Banking Industry

Objective: In the rapidly evolving financial services industry, Mobile Banking has emerged as a critical platform for customer engagement and service delivery. With the increasing adoption of mobile technologies, understanding the factors that drive customer loyalty in Mobile Banking has become a strategic priority for service providers. Key elements such as customer experience, satisfaction, trust, and loyalty intention play pivotal roles in shaping customer perceptions and behaviors. This study investigates the relationships between customer experience, customer satisfaction, trust, and loyalty intention among Mobile Banking customers in Indonesia. Method: The study utilizes an explanatory research methodology, following a quantitative approach within the positivist paradigm. This methodology is designed to clarify the causal relationships between variables, examining both direct and indirect effects. Using data from 477 respondents, analyzed through Structural Equation Modeling - Partial Least Squares (SEM-PLS). Results and Discussion: The findings reveal that enhancing customer experience significantly boosts trust, while customer satisfaction strongly influences both trust and loyalty intention. Although customer experience does not directly impact loyalty intention, customer satisfaction plays a crucial role in fostering customer loyalty. Trust also serves as an important mediator between customer satisfaction and loyalty intention, highlighting its role in strengthening customer relationships. Research Implications: The study's focus on Indonesian customers may limit the generalizability of the findings to other geographical contexts. Cultural differences, variations in mobile banking adoption rates, and differing regulatory environments across countries could influence customer behavior and perceptions, suggesting that the results may not be universally applicable. Originality/Value: The study’s emphasis on trust as a mediator between satisfaction and loyalty provides novel insights, particularly relevant for practitioners aiming to navigate the competitive landscape of Mobile Banking.

Cloud Computing in Insurance: Emerging Trends and Transformative Technologies

This article examines the transformative impact of emerging cloud computing trends on the insurance industry. As insurers increasingly adopt cloud-based solutions, several key technologies are poised to revolutionize operations, customer experiences, and business models. The integration of artificial intelligence and machine learning is enhancing underwriting accuracy, claims processing efficiency, and fraud detection capabilities. Edge computing is enabling real-time data analysis and decision-making, while serverless architectures are optimizing resource allocation and reducing costs. Blockchain technology is improving data security, transparency, and trust in transactions. Additionally, hybrid and multi-cloud strategies are providing insurers with greater flexibility and regulatory compliance. This comprehensive analysis explores these trends, their interconnections, and their potential to drive innovation in insurance. The article concludes by discussing the implications of these advancements for insurers, policyholders, and the broader financial services ecosystem, offering insights into future research directions and industry adaptations.

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

The effect of team-model on healthcare usage and treatment levels among people with type 2 diabetes

Abstract Background In North Karelia Finland, a team-based service model was implemented in some municipalities’ primary healthcare (PHC) centers during 2020. In this team-model, a healthcare customer contacts a nurse who initiates the service process immediately. Nurses can also consult or direct customers to other healthcare professionals and arrange physical appointments if needed. The aims of the model are improved customer and personnel experience and satisfaction, and improved quality and effectiveness of care. We assess the effect of this team-model on treatment levels, measurement activity, and health care usage among patients with type 2 diabetes (T2D). Methods Patients with T2D were identified using the ICD-10 code E11 from the regional electronic health records. The data include all patients diagnosed by 2016 who were alive and residing in North Karelia in 2023 (N = 6312). Diabetes-related contacts with nurses or doctors in outpatient PHC and specialized healthcare (SHC) were considered, along with measurement activity and levels of glycated hemoglobin (HbA1c) and low-density lipoproteins (LDL) between the years 2017 and 2022. Annual differences between areas were analyzed using logistic and linear mixed models. Results In 2017, patients’ mean age was 66 and age range 20-93. The proportion of men was 55% and 45% resided in team-model areas in 2023. The team-model increased T2D-related outpatient PHC remote contact with nurses for a couple of years. But eventually they decreased to a lower level than before. Additionally, the team-model reduced the number of other contacts. An increased difference between areas in measurement activity on HbA1c and LDL levels was also observed. But the difference between areas in proportion of those who achieved treatment targets stayed unchanged. Conclusions In the long-term, the team-model reduced the number of T2D-related contacts and increased the measurement activity of HbA1c and LDL, but it had no effect on treatment levels. Key messages • Team-based service models might potentially reduce service use in the long term. • More research and evidence are needed on the effectiveness and cost-effectiveness of team-based service models.

The Service Experience Equity on Repatronage Intention within Public Transportation Services

In a highly competitive market, service providers are often expected to enhance their profitability by delivering superior service quality. This principle is evident among many public transportation providers in Malaysia, who strive to elevate the passenger experience through refined service offerings. The objective of this study is to examine the factors influencing repatronage intentions among public transportation users in Malacca, Malaysia, using the Service Experience Equity (SEE) framework. To demonstrate the causal connections between the variables, a regression analysis has been employed. The findings reveal that service atmosphere and personnel interactions significantly impact users' willingness to continue using these services. The study emphasizes the importance for management to prioritize customer experience by investing in service environment strategies that foster user engagement and discourage customer churn. It further underscores that both the overall service experience and the quality of employee-customer interactions are critical determinants of future patronage. Consequently, it is recommended that transportation service providers leverage these elements to create a competitive edge, thereby encouraging repeat usage and building long-term customer loyalty.

Customer Satisfaction Towards Food Delivery Services in Selangor

This study explores the key factors influencing customer satisfaction with food delivery services at UiTM Puncak Alam, Selangor. Despite the rising demand for food delivery services, issues surrounding the quality and accuracy of delivered food remain significant challenges. This study seeks to address these concerns by identifying and analyzing the determinants that shape customer satisfaction with food delivery services in Malaysia, specifically in Puncak Alam. We adopted a quantitative research approach to gain comprehensive insights into these issues. We collected data using judgment sampling, a non-probability sampling technique and used the Statistical Package for Social Sciences (SPSS) to analyze the responses through descriptive statistics and correlational analysis. The study involved a structured survey of 357 respondents, investigating the impact of three key variables—food quality, app ease of use, and food packaging—on customer satisfaction. The findings provide important implications for businesses and institutions looking to enhance the quality of food delivery services and elevate customer satisfaction levels. By identifying these critical factors, the research offers valuable insights that can help food service providers, policymakers, and industry stakeholders refine their strategies. These insights will not only improve the customer experience but also encourage broader adoption of food delivery services within the Malaysian population. Ultimately, the study’s conclusions hold significance for enhancing service quality and fostering customer loyalty in the increasingly competitive food delivery industry.

Business and Customer-Based Chatbot Activities: The Role of Customer Satisfaction in Online Purchase Intention and Intention to Reuse Chatbots

In the online shopping context, brands aim to achieve a high level of profit by providing better customer satisfaction by using various artificial intelligence tools. They try creating a satisfactory customer experience by creating a system that provides never-ending customer support by using dialog-based chatbots, especially in the field of customer service. However, there is a lack of research investigating the impact of business and customer-based chatbot activities together on online purchase intention and the intention to reuse chatbots. This research considers the use of chatbots as a marketing tool from both customer and business perspectives and aims to determine the factors that affect the customers’ intention to purchase online and reuse chatbots. Accordingly, the impact on customer satisfaction with chatbot usage, which is based on chatbots’ communication quality and customers’ motivations to use chatbots, on online purchase intention and intention to reuse chatbots was examined. Through an online questionnaire with two hundred and ten participants, employing structural equation modeling, we revealed that customer satisfaction with chatbot usage has a greater impact on the intention to reuse chatbots than on online purchase intentions. In addition, chatbot communication quality has a greater impact on customer satisfaction with chatbot usage than customers’ motivation to use chatbots. To solidify these findings, confirmatory factor analysis, along with reliability and validity assessments, were implemented within the analytical framework to provide robust support for the study’s hypotheses. These findings not only provide empirical evidence and implications for companies in online shopping but also extend the understanding of AI tools in marketing, highlighting their subtle impact on consumer decision-making in the dynamic digital marketplace.

The Role of Business Analysts in Enhancing Customer Satisfaction through Advanced Business Systems

Business Systems Analysts (BSAs) play a pivotal role in today's digital-first business environment, particularly in sectors such as insurance, fraud detection, and e-commerce. This article examines the critical function of BSAs in leveraging advanced business systems to drive operational efficiency, risk mitigation, and customer satisfaction. With the global business intelligence market projected to reach $33.3 billion by 2025, the demand for skilled BSAs continues to grow. Their unique ability to bridge technical capabilities with business needs positions them as key enablers of digital transformation. Through case studies and industry data, this research illuminates how BSAs contribute to project success, cost reduction, and enhanced customer experiences. The article also explores emerging trends and technologies that are shaping the future of business systems analysis, highlighting the evolving nature of the BSA role in an increasingly data-driven business landscape. This revised abstract provides a more condensed overview of the article's content, touching on the key points without duplicating the detailed information presented in the introduction. It also emphasizes the forward-looking aspects of the study and its relevance to current business trends.