Enhance Customer Satisfaction Research Articles

Influence of Business Process Re-Engineering on Customer Satisfaction in Corporate Organisations: A Case of Sterling Bank PLC.

Business Process Re-Engineering (BPR) has emerged as a crucial strategic tool for organisations aiming to enhance performance and competitiveness in today’s dynamic market. This study examines the influence of BPR on customer satisfaction within corporate organisations, with a particular focus on Sterling Bank Plc. A survey research design was adopted, utilising structured questionnaires to collect quantitative and qualitative data from 100 bank employees. The data were analysed using multiple linear regression to determine the influence of BPR practices and customer satisfaction in corporate organisations. The results reveal a strong positive correlation, with an adjusted R-squared value of 56.8% and a standardised coefficient of 0.775, indicating that effective BPR implementation significantly enhances customer satisfaction (p-value = 0.000). The findings demonstrate that BPR is essential for improving customer loyalty, service quality, and overall organisational success. It underscores the role of BPR in fostering a customer-centric approach, which is critical in a highly competitive business environment. To maximise these benefits, the study recommends that organisations continuously review and refine their BPR strategies, invest in employee training, and leverage advanced technologies to optimise business processes. Furthermore, fostering a culture of innovation and responsiveness and implementing regular feedback mechanisms is vital for ensuring that BPR efforts align with customer expectations and drive long-term improvements in organisational performance.

The Impact of Restaurant’s Online Food Delivery (OFD) Service Quality on Customers’ Repurchase Intention: A Conceptual Framework

The outbreak of the COVID-19 pandemic in 2020 had a profound impact on the food service industry, forcing many restaurants and eateries to pivot towards online food delivery (OFD) services to survive. This shift, driven by restrictions on dining-in, has persisted even as dine-in services resumed, positioning OFD as an integral part of restaurant operations. This paper explores the impact of service quality in OFD, proposing a conceptual framework that links service quality dimensions—such as delivery speed, order accuracy, customer service, and platform usability—to the customer’s repurchase intention of food from the restaurant via its OFD services. By addressing these dimensions, restaurants can enhance customer satisfaction, leading to improved financial outcomes. This study aims to provide actionable insights for restaurant operators and contribute to the existing literature on service quality within the OFD sector.

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

SERVICE QUALITY AND CONSUMER PATRONAGE IN ROBAN STORES, AWKA ANAMBRA STATE-NIGERIA: CONTENT ANALYSIS

<p>This study explores the relationship between service quality and consumer patronage at Roban Stores, Awka, Anambra State, Nigeria, employing a qualitative research approach through content analysis. Service quality is conceptualized as the provider's ability to meet customer expectations and enhance business performance through dimensions such as reliability, responsiveness, assurance, empathy, and tangibles. Consumer patronage is defined as the consistent preference of consumers to repeatedly choose a particular firm's products or services over competitors', influenced by factors including product quality, price competitiveness, customer service, and overall satisfaction. The study investigates these dynamics at Roban Stores, high lighting factors such as quality products, competitive pricing, excellent customer service, strategic location, diverse product offerings, promotional activities, store ambiance, word-of-mouth, loyalty programs, and social media presence as crucial in shaping consumer perceptions and fostering patronage. Findings underscore the significance of managing service quality to enhance customer satisfaction and loyalty, recommending strategies for continuous improvement to sustain competitive advantage in the retail market.</p>

Customer-Centric digital transformation framework: Enhancing service delivery in SMES for underserved populations

This paper introduces a customer-centric digital transformation framework designed to enhance service delivery in Small and Medium Enterprises (SMEs) catering to underserved populations. As digital technology becomes increasingly essential in today’s economy, SMEs face the challenge of integrating these tools effectively to serve marginalized communities. This review explores how a customer-centric approach can enable SMEs to create inclusive digital solutions that meet the specific needs of underserved populations, ultimately driving economic empowerment and social equity. The proposed framework encompasses three core components: accessibility, personalization, and engagement. Accessibility ensures that digital platforms are designed with user-friendly interfaces that accommodate individuals with varying levels of digital literacy. By prioritizing mobile optimization and providing resources in local languages, SMEs can eliminate barriers to entry and facilitate greater participation among underserved customers. Personalization is a key element that allows SMEs to tailor their offerings based on customer insights and preferences. Utilizing data analytics and feedback loops, businesses can gain a deeper understanding of their customers' needs, enabling them to deliver customized products and services that resonate with underserved populations. This tailored approach not only enhances customer satisfaction but also fosters loyalty and trust within these communities. Engagement emphasizes the importance of fostering meaningful relationships between SMEs and underserved populations. By leveraging digital channels such as social media, mobile applications, and community forums, SMEs can create platforms for two-way communication and collaboration. This engagement empowers communities, allowing them to voice their needs and influence the development of products and services. In conclusion, the customer-centric digital transformation framework presented in this paper serves as a strategic guide for SMEs seeking to enhance service delivery for underserved populations. By focusing on accessibility, personalization, and engagement, SMEs can drive economic inclusion, promote social equity, and contribute to sustainable community development. This approach not only benefits the businesses involved but also creates lasting positive impacts within underserved communities.. Keywords: Customer-Centric, Digital Transformation, Smes, Underserved Populations, Accessibility, Personalization, Engagement, Economic Empowerment, Social Equity.

Integrating Activity-based Costing with Continuous Enhancement: A Strategic Approach to Operational Efficiency and Cost Management

The main objective of this research is to show how integrating activity-based costing (ABC) with continuous improvement processes can enhance cost management and operational efficiency. The development of automated firms and global firms has necessitated a shift in principles and measurement accuracy. The principles and concepts have been modified based on economic theory, moving from stable continuous improvement to continuous enhancement, or Kaizen enhancement. The accuracy of the proposed costing system has improved by using modified activity-based costing. The current research has focused on multi-variation in explanatory variables, including the costing system and the degree of enhancement. The suggested costing system should include the modified version of activity-based costing and the new form of continuous enhancement system. The concept of an intelligent supply chain has led to the idea of a cooperative game between all parties in the firm/chain. Intelligent automated systems are closed-loop systems where all members are partners, aiming to maximize global benefits. Resilient considerations indicate different attempts to measure and estimate the multi-effects of externalities and other external effects. This work has closed-to-open excess quantitative models, achieving the cooperative state between different partners. Findings show that ABC helps manufacturing and service sectors identify non-value-adding activities, optimize resource allocation, and reduce costs by providing detailed insights into the true cost of operations. Its integration with continuous improvement strategies amplifies these benefits, enabling businesses to streamline processes, improve efficiency, and enhance profitability. ABC is a valuable tool for identifying non-value-adding activities and optimizing resource allocation. By providing a more accurate understanding of how resources are consumed, ABC can help organizations improve their efficiency, reduce costs, and enhance customer satisfaction.

The influence of supply chain agility on FMCG SME marketing flexibility and customer satisfaction

In the fast-paced Fast-Moving Consumer Goods (FMCG) sector, small and medium-sized enterprises (SMEs) must adapt swiftly to changing market conditions and consumer demands to stay competitive. This review explores the influence of supply chain agility on marketing flexibility and customer satisfaction in FMCG SMEs. Supply chain agility, characterized by responsiveness, adaptability, and real-time decision-making, enables SMEs to respond quickly to market shifts, optimize operations, and deliver products efficiently. As a result, agile supply chains directly impact the marketing flexibility of SMEs by enabling faster product launches, customization, and rapid adjustments to promotional strategies based on market feedback. This also highlights how marketing flexibility, supported by agile supply chains, enhances customer satisfaction by meeting expectations related to product availability, quality, and service delivery. Agile supply chains allow FMCG SMEs to reduce stockouts, improve order fulfillment times, and adapt to customer preferences, creating a seamless experience that fosters brand loyalty. Furthermore, this review presents case studies of FMCG SMEs that have successfully implemented agile supply chains to boost marketing responsiveness and customer satisfaction, demonstrating real-world applications of these strategies. By examining the interrelationship between supply chain agility, marketing flexibility, and customer satisfaction, this review emphasizes the importance of an integrated approach to operations and marketing in FMCG SMEs. The findings suggest that investing in supply chain agility is critical for SMEs seeking to enhance their market adaptability and customer-centricity. The study concludes by offering recommendations for FMCG SMEs to develop agile supply chains that enable them to thrive in a competitive and ever-evolving marketplace, where customer satisfaction and rapid market response are key drivers of success. Keywords: Supply Chain, FMCG SME, Marketing Flexibility, Review.

Machine Learning Applications in Salesforce CPQ Transforming Sales

This paper aims to understand the innovative disposals of machine learning applications on Salesforce Configure, Price, Quote (CPQ) applications more precisely how these innovations are revolutionizing software development. Salesforce CPQ is an ideal solution for organizations that want a modern tool to help them with their sales processes, and adding machine learning elements significantly improves the system. For example, the study analyses different cases where and how machine learning algorithm is utilised in the application of the following areas: pricing strategy, product configuration and sales forecasting. Upon collection of historical data, machine learning models are able to make computations of patterns that enables businesses to make quick analysis of the best strategies to use in an offer of various prices to the customers in an effort to enhance customer satisfaction. Furthermore, the application of I/A in the context of CPQ decreases the time engaged in manual configurations, liberating software development teams to work on other priorities essentially. These difficulties and strategies are enumerated in the paper: data quality issues and the impossibility of the Machine learning successful implementation without cooperation with other departments, specifically with Salesforce CPQ teams. In addition, it shares its vision on the tendencies in application of machine learning in CPQ systems with reference to software development paradigms. Finally, the scope of this research is as follows: The study will examine how the utilization of machine learning applications in Salesforce CPQ has changed traditional software development practices in today’s dynamic market environment by increasing efficiency and accuracy effectively, improving competitiveness within the market.

Account for the Role of Digital Literacy and Behavior Intention on Electronic Payment and Its Impact on Customer Satisfaction

Digital literacy and behavioral intentions have emerged as critical elements within the Micro, Small, and Medium Enterprises (MSMEs) ecosystem, particularly due to their importance in improving electronic payment systems and ensuring customer satisfaction. This study aims to examine how digital literacy and behavioral intentions influence electronic payment systems and assess their impact on customer satisfaction. A survey methodology was employed, gathering responses from 260 participants. Data were analyzed using Structural Equation Modelling (SEM) through Smart-PLS (Partial Least Square) software. The findings suggest that both digital literacy and behavioral intentions have a positive effect on electronic payments, which in turn significantly enhances customer satisfaction. The study provides valuable insights for MSME operators on improving e-payment processes and increasing customer satisfaction with their offerings.

Predicting Consumer Behavior in E-Commerce Using Recommendation Systems

As e-commerce platforms continue to expand, understanding consumer behavior has become crucial for enhancing customer satisfaction and driving business success. Recommendation systems play a pivotal role in predicting consumer preferences and delivering personalized product suggestions. This paper presents an extensive literature review on recommendation techniques, including collaborative filtering, content-based approaches, and hybrid models. Notable advancements, such as the use of deep learning, trust-based filtering, and context-aware models, are highlighted. Building on these foundations, we propose a novel model that integrates advanced machine learning algorithms with consumer behavior analysis to predict preferences more accurately. The expected results suggest that this model will improve the precision of recommendations, effectively addressing challenges like data sparsity and evolving user preferences and enhancing overall customer engagement in e- commerce environments.

Enhancing customer satisfaction through IIoT-Enabled coopetition: Strategic insights and impacts

This research investigates the significant role of Industrial Internet of Things (IIoT) to enable Coopetition Network Practices (CNPs) in enhancing the performance of Small and Medium Enterprises (SMEs) within the context of global digital supply chains. Employing a quantitative approach, our study reveals that CNPs contribute to a noTable 51.0 % improvement in factors determining customer satisfaction. This underscores the strategic importance of blending competition with collaboration to refine production processes and align with consumer expectations. Additionally, the research presents a remarkable 69.1 % boost in operational consistency and reports substantial progress in manufacturing flexibility and the value-to-weight ratio, witnessing increases of 125.8 % and 33.2 %, respectively. These improvements are pivotal in optimizing production resources, which in turn, have led to a 29.3 % decrease in customer complaints and a 15.6 % rise in on-time delivery rates. Conversely, a slight decline in the consistency of the value-to-weight ratio was observed, pointing to potential areas for future research. The findings decisively show that CNPs offer concrete advantages by enhancing customer satisfaction determinants and operational efficiency in SMEs. The paper advocates for future studies to directly measure customer satisfaction and to formulate actionable guidelines for the effective implementation of coopetition strategies. This proposed research direction aims to provide solutions to the manufacturing sector's emerging challenges, thereby promoting competitive advantage and growth in the digital era.

Evaluating the Role of Customer Relationship Management in Enhancing Customer Satisfaction: A Study of Arusha Urban Water Supply and Sanitation

This study explores the impact of Customer Relationship Management (CRM) on customer satisfaction at the Arusha Urban Water Supply and Sanitation Authority (AUWSA). Employing a case study research design, the research targeted AUWSA customers and staff, using convenience and purposive sampling techniques to select a sample size of 100 individuals. Both primary and secondary data were utilized, with primary data gathered through questionnaires and interviews, and secondary data obtained from various documentary sources. Data analysis involved both quantitative and qualitative methods, with descriptive and inferential statistics applied to the quantitative data using SPSS. Qualitative data was analyzed through content analysis. The findings reveal that Information Technology (IT) significantly enhances timely information processing, facilitates effective customer communication, and promotes efficient service delivery, thereby reducing customer complaints and administrative errors. The study concludes that IT is crucial for improving customer satisfaction and recommends further research to explore CRM's impact on customer satisfaction in other service industries in Tanzania.

The Impact of digital payments on small coffee business in India

India's coffee industry has experienced notable growth, fuelled by rising demand for specialty coffee and a thriving start-up culture. However, small coffee businesses continue to face challenges such as fierce competition, limited resources, and reliance on a cash-driven economy. This study examines the impact of digital payments on these small enterprises, focusing on key aspects like convenience, security, speed, data-driven insights, and reduced costs associated with cash handling. Utilizing a mixed-methods approach, the research integrates both quantitative and qualitative analyses. Primary data is gathered through structured surveys from 200-300 small coffee business owners and customers, complemented by in-depth interviews with 10-15 coffee shop owners. Additionally, secondary data from industry reports and case studies enriches the analysis. Statistical tools, including descriptive and inferential statistics, correlation analysis, and factor analysis, are employed for quantitative data, while thematic analysis interprets qualitative insights. The study reveals that digital payments significantly improve operational efficiency, enhance customer satisfaction, and boost revenue for small coffee businesses. Challenges such as digital literacy gaps, cyber security concerns, and infrastructural limitations remain. The findings recommend that small coffee businesses can optimize the benefits of digital payments by investing in digital literacy, addressing security issues, and leveraging data insights for informed decision-making.

Comparative Analysis of K-Means and K-Medoids Algorithms for Product Sales Clustering and Customer

In today's rapidly evolving business landscape, effective product management is crucial for maintaining a company's competitive advantage. Comprehensive analysis is essential for providing insights that inform strategic business development decisions. This study examines the sales data of PT XYZ from July 2020 to May 2024 using the K-Medoids algorithm, with dimensionality reduction applied through Principal Component Analysis (PCA). The clustering results identified three customer segments: Cluster 1 with 46 customers, Cluster 2 with 76 customers, and Cluster 3 with 62 customers. For product segmentation, four clusters were identified: Cluster 1 with 52 products, Cluster 2 with 12 products, Cluster 3 with 20 products, and Cluster 4 with 53 products. The K-Medoids algorithm demonstrated superior performance compared to K-Means in terms of cluster separation and interpretability, with visualizations that enhance the understanding of customer and product distributions. This research aids the company in enhancing customer satisfaction, optimizing inventory, and increasing profitability.

The Effect Of Product Quality And Price On Coal Purchase Interest Through Sales Based On Non-Kai Logistics At Pt Bukit Asam, Tbk

This study aims to analyze the effect of product quality and price on coal purchase interest through non-KAI-based logistics sales at PT Bukit Asam, Tbk. The background of this research is driven by the challenges faced by the company in increasing purchase interest through more flexible distribution strategies and reducing reliance on railway transportation. The research method used is quantitative, with data collected through surveys of 150 respondents, consisting of customers and brokers. Data analysis was conducted using multiple linear regression. The results show that product quality and price positively and significantly impact coal purchase interest. High product quality has been proven to enhance customer satisfaction, while competitive pricing is crucial in attracting consumer interest. In conclusion, PT Bukit Asam, Tbk, should continue to improve service quality and offer more competitive prices to expand its market and increase sales through the non-KAI logistics system.

The Impact of Robotic Gastronomic Experiences on Customer Value, Delight and Loyalty in Service-Robot Restaurants

This study examines the impact of robotic gastronomic experiences (RGE) on customer emotional and functional value, delight, and loyalty in service robot restaurants. Using validated scales from prior research, a 5-point Likert scale survey was conducted with 302 participants via Amazon Mechanical Turk, targeting individuals with experience in such establishments. The analysis, performed with SmartPLS 4.0, utilized partial least squares-path modelling (PLS-PM) to test the hypothesized relationships. Results show that RGE, characterized by competence and coolness, positively affects emotional and functional values, which significantly influence delight and loyalty. Methodological measures were implemented to address common method variance (CMV), confirming the robustness of the findings. This research underscores the potential of service robots to enhance customer satisfaction and loyalty through efficient and engaging service delivery, providing valuable insights for integrating advanced robotics in the hospitality industry.

Advanced Analytics for Retail Inventory and Demand Forecasting

Achieving operational efficiency and enhancing customer satisfaction levels in the retail sector is directly dependent on efficient inventory management and accurate demand forecasting. The following study employs advanced analytics techniques, such as time series forecasting and machine learning, to bolster these essential functions. By leveraging on historical sales data from 45 retail stores sourced by Kaggle, this paper has constructed predictive models with the aim to optimize inventory levels and forecast demand with precision. The Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model employed in this study adequately captures linear dependencies and seasonal patterns, while Long Short-Term Memory (LSTM) networks are responsible for the management of intricate, non-linear dependencies. The findings from this study depict the significant seasonal trends, the impact of economic factors, the impact of economic variables and the effectiveness of hybrid models in improving forecast accuracy. The integration of such advanced methodologies clearly highlights their massive potential in improving aspects such as inventory management and operational efficiency in the retail domain.

WHAT ARE BEST TOOLS FOR DIGITAL TRANSFORMATION IN TODAY WORLD HELPING PEOPLE FROM HEALTH AND BANKING SECTORS

In recent days we looks into the best digital transformation strategies for modernizing the banking and healthcare sectors in the rapidly evolving technological environment of today. It examines key technologies for the banking sector, including big data analytics, mobile banking applications, robotic process automation (RPA), and electronic health records (EHR) and artificial intelligence in healthcare diagnostics. The analysis examines how these solutions solve issues unique to the industry, boost productivity, and enhance customer satisfaction. By examining how both industries are approaching digital transformation, this report sheds light on emerging trends and best practices from across industries.[17] The results highlight the need for further innovation and cybersecurity in order to fully exploit the potential of digital transformation in improving public health outcomes. Key words: Digital transformation , Health Care, Cybersecurity, Technology.

An observational study of female body shape characteristics in multiracial Malaysia

Abstract The current study aims to explore the representative and typical female body shapes through in-depth observation and analysis of the diversity in Malaysian female body shape characteristics across different ethnic and age groups, which assists in facilitating the establishment of accurate apparel size standards. This study was conducted in Selangor, which is the most populous and developed Malaysian state. The data were collected via a highly systematic approach. The K-mean clustering method was applied to synthesise and analyse the collected data, which classified the body types into five major clusters. The results not only revealed significant differences in the distribution of body shapes across ethnic and age groups but also highlighted the limitations of the various sizing systems currently employed by the Malaysian apparel industry to fulfil the requirements of the diverse Malaysian market. The findings provided practical implications for apparel manufacturers and retailers with concrete evidence to support improved product design and enhance customer satisfaction. A deeper understanding and categorisation of Malaysian females’ body types could also assist the apparel industry in developing more tailored sizing systems. Resultantly, an improved system minimises return rates, optimises inventory management, and supports environmental and sustainability goals by reducing overproduction.

E-wom and territorial analyses: the use of opinion mining in tourism

In the digital age, social media and electronic word of mouth (eWOM) have fundamentally transformed several industries, including tourism. This paper explores how opinion mining (also known as sentiment analysis) can be a strategic tool for tourism stakeholders to extract and analyse data from social media reviews and posts. The aim is to provide actionable insights to enhance customer satisfaction and destination management by assessing the sentiment expressed in online content. From a methodological viewpoint, the implemented approach integrates qualitative and quantitative data with textual data to delve into opinions embedded in eWOM. The analyses help identify patterns and trends in tourists’ feedback, offering a valuable framework for understanding their behaviour and leveraging this information for the stakeholders’ specific purposes. A case study on Naples (Italy) highlights the potential of sentiment analysis in tourism, suggesting future research directions. Practical applications range from improving customer satisfaction and destination reputation to guiding marketing strategies and supporting sustainable tourism development.