Customer Support Research Articles

The Impact of Customer Retention on Organization Profitability in the FMCG Sector in Nigeria

In the competitive domain of fast-moving consumer goods (FMCG), retaining customers is essential for the overall organisational performance of an FMCG company. This research investigated the impact of customer retention on organisational profitability in the Fast-Moving Consumer Goods (FMCG) sector in Nigeria. The study employed a cross-sectional survey approach, with data collected from 248 employees in 10 different FMCG companies in Oyo State, South West, Nigeria. The findings revealed a strong positive correlation (r=0.783) significant at p < 0.01 between customer retention and organisational profitability, highlighting customer retention as a key driver of sustained profitability. The study further explored specific components of customer retention, including personalised marketing, loyalty programs, quality products, and customer support, all of which showed strong positive associations with organisational profitability. Survey results indicated that customer satisfaction is a leading factor contributing to customer retention, emphasising the importance of understanding and meeting customer expectations. Based on these findings, recommendations are provided for FMCG companies to prioritise customer retention strategies, such as personalised marketing and quality customer support, to enhance organisational profitability and success in the Nigerian FMCG sector.

Influence of AI on Shopping Experience of Generation Z Customers

Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines. AI is being used today across different industries from finance to healthcare. It is also reinventing the retail landscape. Computer vision is enabling frictionless checkout and enhancing loss prevention for brick-and-mortar stores. Retailers that harness AI to connect with customers and operate more efficiently will be better positioned to thrive in today’s AI powered world. The study is conducted with the sample size of 61 respondents by conducting survey through questionnaire in Palakkad district. This paper attempts to get an insight into the influence of AI technologies in the shopping experience of customers especially on generation Z. It is high time that customers become aware of different implications of AI and how it is contributing to their shopping life. This paper further talks about the trust issues of customers when it comes to AI and personal data sharing and how it can be improved by taking necessary measures by retailers like educating the consumers in a proper way about AI. Customers also agree that AI has made a positive impact on their shopping experience especially in customer support after purchase.

Mining software insights: uncovering the frequently occurring issues in low-rating software applications.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Knowledge retrieval and diagnostics in cloud services with large language models

Efficient customer support is the foundation for any service provider trying to improve customer relationships. An important measure of successful support is the mean time to resolve issues. The complexity and large scale of modern cloud environments make it unrealistic to reduce the resolution time without deploying intelligent solutions. The latest also provides an exceptional opportunity to leverage cross-customer product usage data for proactive solutions when the troubles of some users can be analyzed in advance to prevent similar issues of other users. We build a recommender system that matches customer support requests to other resolved support requests or knowledge base articles that contain valuable information for problem remediation. This system can be used by customers or support teams to quickly find problem-resolution tips or detect trending issues to warn vulnerable users. We utilize large language models, fine-tune for better performance, and discuss capabilities and possible improvements. During our research, we highlighted several evaluation metrics such as mean time to resolve issues and the accuracy of recommendations. However, estimating accuracy is challenging due to insufficient datasets with precise and comprehensive recommendations. Despite this, our support managers provided some estimates regarding the remediation durations. Typically, identifying and resolving an issue takes several days or weeks. With appropriate recommendations, this time can be significantly reduced to several hours and, in some simple cases, even lead to self-service capabilities.

Navigating the Educational Landscape: Unveiling the impact of ChatGPT in Teacher-Student Dynamics

Objective: The study aims to explore the role of ChatGPT as an Artificial Intelligence tool in education, critically evaluate its performance, discuss potential problems for students, and propose enhancements to improve teacher-student relations. Method: The research utilizes secondary data gathered from various publications, including magazines, books, journals, and websites relevant to the subject. The study focuses on unveiling the impact of ChatGPT on teacher-student dynamics. Results: ChatGPT offers several advantages in education, including customized learning, constant availability, instant feedback, homework assistance, and support for language learning. It can also enhance engagement and provide consistent quality at a lower cost. However, the study identifies significant drawbacks, such as a lack of emotional intelligence, contextual understanding, and personalization. There are concerns about misinformation, over-reliance on technology, privacy, security, and inadvertent bias reinforcement. To address these issues, the study recommends implementing effective feedback mechanisms, conducting regular security audits, promoting collaborative learning environments, and continuously updating AI models. Conclusions: While ChatGPT has transformative potential in education, its implementation must be carefully managed to avoid depersonalization and reliance on AI at the expense of human interaction. The study emphasizes the importance of balancing AI with the human touch in education, ensuring ethical guidelines, and promoting digital literacy. A collaborative effort among educators, policymakers, and technologists is crucial for responsible and ethical integration of AI in education.

Can sustainable biocommerce continue to support conservation, or will the captive breeding of offspring be its Achilles heel?

Abstract The trade in high‐value natural resources, particularly exotic plants and animals (henceforth biocommerce), can lead to both positive and negative impacts on wild populations. The exotic plant and animal hobbies are highly organized with internal cultures that are often poorly understood by non‐members; however, understanding them is of central importance for biocommerce with both direct and indirect consequences for the conservation of wild populations. Growing international demands for exotic species leaves simple questions related to the economics of biocommerce unexplored. The long‐term success of biocommerce businesses requires constant income, meaning they obtain and retain significant market proportions. Some biocommerce products can be reproduced by hobbyists and undercut business efforts based on sustainably produced exports. We analyse a test case in Neotropical poison frogs where sustainable biocommerce is burgeoning, yet (export‐based) businesses face direct market competition from the offspring produced within the countries, which receive their exports, and therefore are forced to rely on fervent customer support. Solution: We survey customers' behaviours where we find different classes of consumers, which differ in where they place higher values within the hobby. By taking into account these different classes, we gain insights and potential solutions to promote the stability of this unique type of biocommerce.

EXAMINING THE ROLE OF SERVICE QUALITY DIMENSIONS OF MOBILE PHONE SERVICE PROVIDERS; AN EMPIRICAL STUDY

Service quality has become a critical factor for the competitive advantage of mobile phone service providers in the telecommunication industry. This study explores the key factors influencing service quality in mobile phone service providers and their impact on customer satisfaction and loyalty. The analysis focuses on dimensions such as network quality, customer support, pricing, service reliability, and value-added services. The research aims to identify how these factors contribute to the overall perception of service quality and determine the most significant determinants of customer satisfaction. The non-probability convenience sampling technique is the most suitable method when it is not possible to obtain information from every member of the sample population. Additionally, the precise population frame of mobile phone service consumers in the study area could not be determined, as the number of users could not be ascertained. The interview schedule was employed to acquire the primary data from the respondents. The data that has been collected is coded and inserted into a primary table. The data are analysed in accordance with the nature of the data and the intent of the analysis (nature of the problem) using the SPSS software. For the purpose of the research, a sample size of 100 respondents is selected from an infinite population. A random sample strategy was used by the researcher in order to choose the objects that would be used for sampling. The findings highlight the need for service providers to continuously adapt and enhance their service offerings to meet evolving customer expectations and maintain competitiveness in a rapidly changing market

ENHANCING TECHNICIAN & PLUMBER HIRING STRATEGIES WITH PYTHON: A TRENDSETTING COMPUTER SCIENCE ANALYSIS

Abstract: The Online Platform for Technician and Plumber Hiring is a digital marketplace designed to streamline the process of connecting individuals and businesses with skilled technicians and plumbers for maintenance and repair services. The platform offers an intuitive interface accessible via web browsers and mobile applications, allowing users to search, compare, and hire technicians and plumbers based on their specific needs and preferences. Service providers create detailed profiles showcasing their qualifications, experience, areas of expertise, and customer reviews, allowing service seekers to evaluate the suitability of technicians and plumbers for their specific requirements. The platform also offers advanced search and filter functionality, allowing users to narrow down their options based on location, services offered, availability, pricing, and ratings. It facilitates seamless booking and scheduling of service appointments, allowing users to specify the date, time, and nature of the required services. Clear pricing and payment information are provided for each service, ensuring transparency and eliminating ambiguity for both service seekers and providers. After service completion, users can rate and provide feedback on the quality of work and customer service received from technicians and plumbers, maintaining accountability and encouraging continuous improvement within the service provider community. The platform also offers customer support services to address inquiries, resolve issues, and facilitate communication between parties if disputes arise during or after service delivery. Mediation mechanisms are in place to ensure fair resolution of conflicts. IndexTerms – HTML, CSS, AngularJs , NodeJs , Bootstrap, MySQL

Measuring ROI of AI Implementations in Customer Support: A Data-Driven Approach

Measuring ROI of AI Implementations in Customer Support: A Data-Driven Approach

Leveraging Intent Detection and Generative AI for Enhanced Customer Support

Customer support plays a pivotal role in shaping customer satisfaction and fostering loyalty within any business. This paper delves into how the integration of intent detection and generative AI (GenAI) can transform customer support systems. At the core of this transformation is the ability to understand user intent, which is essential for directing customers effectively through the support funnel to the appropriate services. By employing sophisticated natural language processing (NLP) techniques, training LLM to perform RAG and machine learning models, businesses can precisely discern customer intents. This capability allows for the delivery of tailored, immediate responses. The paper further explores the methodologies employed, the advantages gained, and the challenges faced in the adoption of these advanced technologies in customer support systems.

The Role of AI and Automation in Enhancing Customer Support: Opportunities and Challenges

The amalgamation of artificial intelligence (AI) and automation in customer service was swiftly progressing, offering substantial prospects with unique hurdles. AI-driven technologies, including chatbots and generative AI, were improving customer engagement by providing 24/7 support, decreasing response times, and facilitating customised interactions. Nevertheless, lingering worries around the possibility of AI delivering inaccurate data and anxieties about job displacement remain. The objective of this research was to evaluate the effectiveness of artificial intelligence (AI) and automation in customer care, examine customer happiness, and analyse the challenges associated with integrating AI. Employing a mixed-methods approach provided a thorough comprehension of the influence of AI on customer service. AI enhances operational efficiency and handles routine queries, but it is essential to maintain a harmonious combination of AI and human interaction to manage complicated and emotionally sensitive matters effectively. Ensuring ethical concerns, continuous training, and regular system upgrades were crucial for maximising the advantages of AI in customer service. Keywords: Artificial intelligence (AI), Automated Conversational Agents, Efficiency in Operations, Employment Disruption, Ethical Considerations.

Avatars’ impacts in retail: a study within regulatory engagement theory

PurposeThis study aims to test the impact of avatars on consumer psychological states, engagement, and purchase intention in the online retail environment with reference to the Regulatory Engagement Theory.Design/methodology/approachOne-factor between-subject experimental design was used to test research hypotheses. Two versions of an experimental merchant website have been designed for the purpose of the study: with and without the avatar. Participants were randomly assigned to experimental conditions and responded to an online questionnaire displayed during the visit. SEM analyses with AMOS 24 and SPSS Macro Conditional Process Analysis for bootstrapping were used to test the hypotheses.FindingsThe results confirm the avatar’s positive impact on perceived enjoyment and immersion, both positively affect consumer engagement and purchase intention. The mediating role of psychological states in the impact of avatars on consumer engagement is also confirmed.Originality/valueThis study advances the interactive marketing literature by focusing on avatars as an emerging interactive technology in the virtual retail context. Unlike previous studies on virtual agents, which primarily focused on their utilitarian role in online customer support, this study investigates how avatars influence consumers' psychological states, engagement, and purchase intention.

Enhancing business intelligence in e-commerce: Utilizing advanced data integration for real-time insights

This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chains, and data fabric, promise to further transform BI in e-commerce. Ultimately, advanced data integration and real-time insights are essential for e-commerce companies to stay competitive, optimize operations, and enhance customer satisfaction. In the fast-paced and competitive world of e-commerce, the ability to make quick, informed decisions is crucial for success. Business Intelligence (BI) plays a vital role by transforming vast amounts of data into actionable insights that drive strategic decisions and operational efficiency. This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chain, and data fabric, promise to further transform BI in e-commerce. Case studies from leading e-commerce companies illustrate the practical benefits of implementing these advanced techniques, showcasing significant improvements in operational efficiency and customer satisfaction. Keywords: Business Intelligence, E-commerce Analytics, Data Integration, Real-Time Insights.

The Co-Production of Service: Modeling Services in Contact Centers Using Hawkes Processes

In customer support contact centers, every service interaction involves a messaging dialogue between a customer and an agent; together, they exchange information, solve problems, and collectively co-produce the service. Because the service progression is shaped by the history of conversation thus far, we propose a bivariate marked Hawkes process cluster model of the customer-agent interaction. To evaluate our stochastic model of service, we apply it to an industry contact center data set containing nearly 5 million messages. Through both a novel residual analysis comparison and several Monte Carlo goodness-of-fit tests, we show that the Hawkes cluster model indeed captures dynamics at the heart of the service and surpasses classic models that do not incorporate the service history. Furthermore, in an entirely data-driven simulation, we demonstrate how this history-dependent model can be leveraged operationally to inform a prediction-based routing policy. We show that widely used and well-studied customer routing policies can be outperformed with simple modifications according to the Hawkes model. Through analysis of a stylized model proposed in the contact center literature, we prove that service heterogeneity can cause this underperformance and, moreover, that such heterogeneity will occur if service closures are not carefully managed. This paper was accepted by Elena Katok, operations management. Funding: The authors are grateful for the generous support of this work by the National Science Foundation Division of Graduate Education [Grant DGE-1650441] (A. Daw), the Israel Science Foundation [Grant 336/19] (G. B. Yom-Tov), and the United States-Israel Binational Science Foundation [Grant 2022095] (A. Daw, G. B. Yom-Tov). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04060 .

CHAT MARKETING'S IMPACT ON CUSTOMER SUPPORT SATISFACTION IN FINANCIAL STARTUPS

The subject of this paper is the influence of chat marketing on customer support satisfaction. The purpose of the study is to determine the relationship between the use of chat marketing and customer support satisfaction, as well as to identify the essential factors that contribute to this relationship. Furthermore, this paper aims to provide theoretical insights into the influence of chat marketing on customer support satisfaction in financial startups. Many companies struggle to achieve the most efficient chat session and are uncertain about the best strategies, factors and types of chat to implement. Therefore, these findings will help financial startups to theoretically understand the aspects of chat marketing and customer support satisfaction. The object of this article is the impact of chat marketing on customer satisfaction, and the purpose is to reveal theoretical knowledge that will help identify the factors that contribute to chat marketing and customer satisfaction in financial startups. This paper discusses the importance of customer support for financial startups and the different approaches to providing customer support through emotional means. In addition, this paper highlights the importance of customer satisfaction through trust and commitment to build a positive relationship with the customer. Furthermore, this paper demonstrated the framework behind chat marketing, which includes the use of different tools such as live chat platforms, chatbots and live agents or human representatives that help to achieve customer satisfaction through customer support. Moreover, using methods such as: observation, survey and regression analysis, this paper has demonstrated the importance of customer centricity in the financial sector to provide key essentials for employees as incentives that maintain customer satisfaction, while ensuring the flexibility of real-time communication with chatbots or live agents, with the need to have the ease of use and flexibility and various other factors to contribute to customer support satisfaction. As a result, the paper emphasises that in order to increase customer satisfaction with financial start-ups, it is crucial to maintain a low response rate by providing fast and reliable customer assistance. Developing and regularly updating chatbots to facilitate smooth transitions to live agents in complex situations is essential. Implementing an effective ticketing system to automate and monitor issue resolution is essential, with a focus on resolving issues during chat interactions whenever possible. Adopting a hybrid strategy that combines live agents and chatbots helps to meet diverse customer needs in a timely manner. Investing in comprehensive training for live agents is necessary to ensure professional and timely service. In addition, continuous monitoring and analysis of customer feedback plays a key role in improving overall business performance.

Empowering Sports Enthusiasts and Facility Owners: An Online Sports Field Booking System for Bhutan

With the country’s growing interest in games and sports, it is observed that there is a need for efficient sports field management. This project introduces an Online Sports Field Booking system, with an aim to have efficient sports management. The study identified the challenges posed by manual data recording and the absence of a centralized system. Therefore, our web-based solution which utilizes the Laravel framework, PHP, and MySQL for efficient backend operations aims to overcome these challenges. It employs an iterative development approach, the system allowing users to effortlessly check real-time field availability, search based on criteria such as field name and location, and effortlessly book sports facilities. Through iterative cycles of analysis, design, implementation, and evaluation, the project successfully overcame challenges, reducing risks and ensuring a high-quality system. This initiative not only addresses the growing demand for sports facilities in Bhutan but also aligns with the nation's technological progress, offering a user-friendly platform for both sports enthusiasts and facility owners/managers. By providing real-time updates and customer support, the Online Sports Field Booking system contributes to optimizing facility usage and enhancing overall customer satisfaction, and ultimately promoting an improved and connected sporting community in Bhutan.

Assessing Fine-Tuning Efficacy in LLMs: A Case Study with Learning Guidance Chatbots

Training and accurately evaluating task- specific chatbots is an important research area for Large Language Models (LLMs). These models can be developed for general purposes with the ability to handle multiple tasks, or fine-tuned for specific applications such as education or customer support. In this study, Mistral 7B, Llama-2 and Phi-2 models are utilized which have proven success on various benchmarks, including question answering. The models were fine-tuned using QLoRa with limited information gathered from course catalogs. The fine-tuned models were evaluated using various metrics, with the responses from GPT-4 taken as the ground truth. The experiments revealed that Phi-2 slightly outperformed Mistral 7B, achieving scores of 0.012 BLEU, 0.184 METEOR, and 0.873 BERT. Considering the evaluation metrics obtained, the strengths and weaknesses of known LLM models, the amount of data required for fine-tuning, and the effect of the fine-tuning method on model performance are discussed.

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Sentiment analysis is a cornerstone of natural language processing. However, it presents formidable challenges due to the intricacies of lexical diversity, complex linguistic structures, and the subtleties of context dependence. This study introduces a bespoke and integrated approach to analyzing customer sentiment, with a particular emphasis on a case study in the Portuguese retail market. Capitalizing on the strengths of SentiLex-PT, a sentiment lexicon curated for the Portuguese language, and an array of sophisticated machine learning algorithms, this research constructs advanced models that encapsulate both lexical features and the subtleties of linguistic composition. A meticulous comparative analysis singles out multinomial logistic regression as the pre-eminent model for its applicability and accuracy within our case study. The findings of this analysis highlight the pivotal role that sentiment data play in strategic decision-making processes such as reputation management, strategic planning, and forecasting market trends within the retail sector. To the extent of our knowledge, this work is pioneering in its provision of a holistic sentiment analysis framework tailored to the Portuguese retail context, marking an advancement for both the academic field and industry application.

Differentiation Strategy and the Performance of Medical Insurance Companies in Kenya

Against a backdrop of escalating global competition, health insurance firms grapple with the need to stand out amidst a saturated market. The study recognizes the imperative for organizations to adopt differentiation strategies, with a focus on product, personnel, distribution, and image differentiation. Acknowledging the current dearth of comprehensive research in the Kenyan context, the research problem centres on the critical need to investigate the extent to which differentiation strategies influence the performance of health insurance companies in the region. The general objective of the study was to investigate the effect of differentiation strategy on performance of Health Insurance Companies in Kenya. Drawing on established theories such as Porter's Competitive Advantage Model, Upper Echelons Theory, Resource-Based View, and Transaction Cost Economics, the study employs a multi-theoretical framework to provide a nuanced understanding of the complex relationships between differentiation strategies and organizational outcomes. Adopting the descriptive research design, the target population comprises 980 employees in the medical insurance firms in the top management and middle level management in Nairobi, drawn from the respective insurance firms’ Human Resources Departments. Employing a multi-stage sampling technique, the established sample size is 101 top and middle level management staff who were reached by stratified random sampling. A semi-structured questionnaire was the main data collection instrument and was distributed through a drop-and- pick- latter approach. Data was analyzed using both descriptive and inferential statistics. Descriptive statistics measures of mean and standard deviation was used, while for inferential statistics, regression equations were employed. The study established that product differentiation strategy, personnel differentiation strategy, product differentiation strategy and image differentiation strategy had a positive significant effect on performance of health insurance companies in Kenya. The study concludes that product differentiation is the key aspect distinguishing one company’s products or services from its competition. Personnel differentiation strategy gives an organization the ability to respond in time to the needs of their members through the skills and knowledge of employees. A differentiated distribution strategy allows insurance companies to expand their sale potential by getting their product in front of more potential customers. An effective image establishes the product's character and value proposition and a person responds differently to company and brand images. The study recommends that the insurance companies should consider opportunities for differentiation in all of its production areas: marketing, product management, engineering, sales, and customer support. Organizations looking to build personnel differentiation strategy will need to produce or design extremely unique or distinctive products or services that create increased value for the consumer. The insurance companies should devote resources to channel management preferably at least one dedicated manager whose sole responsibility is to manage those relationships and build the marketing programs to drive revenue through the channel. The insurance companies should identify their brand gaps, which are the discrepancies between their desired and actual brand performance.

IMPROVING THE USER EXPERIENCE OF FINANCIAL TECHNOLOGY IT SERVICES BASED ON UX/UI RESEARCH

In today’s dynamic fintech environment, user requirements are constantly changing, and digital services need to be developed and adapted to meet their expectations and needs. UI/UX research technologies and methods provide rich opportunities to study user preferences and identify problems and shortcomings in existing digital services. The main aspects of improving the user experience of the mobile bank were reviewed. Recommendations were made for the further development of the IT service. The purpose of this article is to examine the main aspects of improving the user experience and to formulate recommendations for the development of IT services in the banking sector. Research methodology – at the first stage, an analysis of user behavior in fintech was carried out: a study of user expectations and preferences in the field of digital financial services. Then user experience was analyzed and UI/UX studies were conducted for the ‘Notification Center’ of the bank’s mobile application. The final stage of the study was to evaluate the effectiveness of the redesign project of the Notification Centre section of the bank's mobile application and to formulate recommendations for the further development of the mobile application.The research conducted has shown the practical importance of improving the user experience of the bank’s mobile application, which consists of improving usability, increasing customer satisfaction and improving the overall impression of banking services. This leads to increased conversion, customer retention and increased loyalty to the bank.Improving the user experience of the bank’s mobile application makes it possible to increase the efficiency of the bank’s employees, reduce the burden on the customer support department and reduce the number of errors when performing routine operations or actions. This not only optimises the bank’s internal processes, but also helps to improve the overall quality of service, which has a particular impact on the bank’s reputation, competitiveness and financial performance.