Enhance Customer Satisfaction Research Articles

Analyzing Factors Influencing Customer Satisfaction with Organic Coffee in E-Commerce

In recent years, there has been a notable increase in interest in sustainable products, particularly organic goods, driven by the push for sustainable societies. However, Japan’s organic market lags behind the global trend, representing just 0.3% of total farmland in 2021. To tackle this issue, the Ministry of Agriculture, Forestry and Fisheries introduced the “Green Strategy,” with the goal of raising the organic share to 25% by 2050. The COVID-19 pandemic has brought about significant lifestyle changes, such as increased remote work and reduced outdoor activities, leading to a surge in the E-commerce market and an increased need for analyzing consumer satisfaction. To enhance Japan’s organic market, it is crucial to familiarize consumers with organic products like coffee, tea, and snacks. This study proposes a model using E-commerce customer reviews to predict consumer satisfaction. The aim is to improve services and develop new strategies based on customer preferences, promoting sustainable growth and enhancing customer satisfaction. This study delves into the impact of factors derived from latent Dirichlet allocation (LDA) on consumer satisfaction with organic coffee, utilizing three years of data from Rakuten Marketplace customer reviews (2017–2019). A total of 1444 review texts were analyzed, including 154 low-rated reviews (ratings 1–3) and 1290 high-rated reviews (ratings 4–5). To build a satisfaction prediction model using regression analysis, a survey was conducted using question items explored through factor analysis to assess the reliability of items in conveying the consistent meanings of high frequency words extracted by LDA: “taste,” “aroma,” “gift,” “sales/discounts,” “freshness,” and “decaffeinated.” The results of the regression analysis revealed that “gift,” “sales/discounts,” and “decaffeinated” significantly impact consumer satisfaction. Insights derived from these factors illuminate marketing strategies that focus on delivering happiness through products, adding value through pricing strategies, and introducing benefits and consumption methods that effectively meet customer needs. These findings underscore the potential of customer reviews to enhance consumer satisfaction and inform strategic marketing decisions, thereby contributing to the growth and success of the organic coffee market.

The Impact of Service Quality and Product Attributes on Customer Satisfaction in Islamic Finance

This study investigates the impact of service quality and product attributes on customer satisfaction within the context of Islamic finance. Utilizing a quantitative approach, data were collected from 500 customers across various Islamic banking institutions. The findings reveal that both service quality and product attributes significantly influence customer satisfaction, with service quality demonstrating a stronger correlation. Notably, aspects such as employee friendliness, responsiveness, and product diversity emerged as critical factors. This research contributes to the existing literature by providing empirical evidence on the importance of service quality in enhancing customer satisfaction specifically in the Islamic banking sector, an area that has received limited attention. The implications of this study are twofold: first, it offers valuable insights for Islamic banking institutions to refine their service delivery and product offerings, thereby fostering greater customer loyalty. Second, it highlights the need for continuous assessment of customer expectations and satisfaction metrics to ensure sustainable growth in the competitive financial landscape. This study lays the groundwork for future research on the interplay between service quality, product attributes, and customer satisfaction in Islamic finance.

Exploring Inventory Management System with Priority Sequencing

Effective inventory management is crucial for businesses to optimise their resources and meet customer demand. This abstract introduces an Inventory Management System (IMS) that utilises priority sequencing to enhance the control and organisation of inventory items. The system employs various prioritisation criteria, such as item demand, shelf life, or profitability, to determine the sequence in which items are received, stored, and dispatched. Prioritisation Criteria: The IMS allows businesses to set specific rules and criteria for prioritising inventory items. This includes factors like customer demand, product shelf life, or item profitability. Real-time Monitoring: The system provides real-time visibility into inventory levels, allowing businesses to make informed decisions on restocking, order processing, and inventory allocation. Automated Reordering: IMS can automatically generate reordering recommendations based on preset priorities, ensuring that essential items are always in stock. Cost Reduction: By efficiently managing inventory based on priority sequencing, businesses can minimise holding costs, reduce wastage, and optimise working capital. Enhanced Customer Satisfaction: Prioritising items based on customer demand ensures that popular products are readily available, leading to improved customer satisfaction and retention. Forecasting Capabilities: The system can use historical data to predict future demand trends and adjust prioritisation accordingly. Reporting and Analytics: IMS provides detailed reports and analytics to help businesses fine-tune their inventory management strategies.

A Study on Consumer Satisfaction Regarding Online Vs Offline Shopping of Clothes Among Genzs in Ahemdabad City

This study investigates consumer satisfaction regarding online versus offline shopping for clothes among Generation Z in Ahmedabad, aiming to understand their preferences, behaviors, and concerns. Using a survey-based approach, the research analyzes key factors influencing their shopping experiences, such as trust, convenience, security, product quality, variety, fit, and customer service. The findings reveal that while Generation Z appreciates the convenience, discounts, and variety offered by online shopping, they still express concerns about security and confidence in product quality and fit. Offline shopping, on the other hand, is preferred for its reliability in assessing product quality, fit, and personalized customer service. The study also highlights the hybrid nature of Generation Z's shopping behavior, with many consumers using both online and offline channels based on the advantages each provides. These insights suggest that retailers should adopt an omnichannel approach, integrating online and offline experiences to enhance customer satisfaction. The practical implications for retailers include improving online security, navigation, and return processes, while maintaining high-quality in-store experiences. Recommendations for future research include expanding the study to other regions, investigating the role of emerging technologies, and exploring the impact of sustainability and ethics on shopping preferences

The Impact of Components in Marketing - Mix Strategy on Customer Satisfaction and Loyalty: Case Study at Codegym Vietnam Joint Stock Company, Hue Branch

This study aims to analyze the impact of 7 components on the marketing - mix strategy on the satisfaction and loyalty of programming course students at the branch of CodeGym Vietnam Joint Stock Company in Hue. The study combined exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) to measure the impacts of each component into the success of marketing - mix strategy then applied linear structural modeling (SEM) to test the related hypothesis of driving parameters to the customers’ satisfaction and loyalty. The finding indicated that there are 3 major factors having positively impact on customer satisfaction and loyalty, including: product, distribution and process. On the contrary, price, promotion, people and facilities factors have a negative impact on student satisfaction. The study also suggests several important implications to enhancing the efficiency of marketing mix strategy - with a need to focusing on developing marketing mix programs for centers to enhance customer satisfaction and loyalty.

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.

Customer journey mapping framework for SMES: Enhancing customer satisfaction and business growth

Customer journey mapping (CJM) is an essential tool for small and medium-sized enterprises (SMEs) to understand and optimize their interactions with customers, thereby driving satisfaction and business growth. This review outlines a customer journey mapping framework designed specifically for SMEs, aiming to enhance customer experiences through a structured approach to identifying key touchpoints, pain points, and opportunities for improvement across the customer lifecycle. The proposed framework involves systematically mapping out every stage of the customer journey, from initial awareness to post-purchase interactions, highlighting moments that significantly impact customer satisfaction. By leveraging customer data, feedback, and analytics, SMEs can gain valuable insights into customer behavior, motivations, and preferences. This framework emphasizes the importance of personalization, using data to tailor the customer experience and foster deeper connections. Additionally, the framework integrates feedback loops, allowing SMEs to continuously gather and analyze real-time customer data. This iterative process helps businesses to proactively identify emerging trends, address customer concerns, and adapt to evolving market demands. By aligning customer needs with business objectives, SMEs can create more targeted marketing campaigns, improve product or service offerings, and enhance customer retention strategies. The implementation of this customer journey mapping framework can lead to several key benefits for SMEs, including improved customer satisfaction, increased brand loyalty, and greater operational efficiency. It also enables SMEs to compete more effectively in their markets by delivering personalized and seamless customer experiences. The framework supports scalability, allowing SMEs to refine their customer engagement strategies as their businesses grow. In conclusion, the customer journey mapping framework serves as a critical tool for SMEs looking to enhance customer satisfaction and drive sustainable business growth. By focusing on data-driven insights and continuous improvement, SMEs can deliver more value to customers, strengthen brand loyalty, and achieve long-term success.

Personalized customer journeys for underserved communities: Tailoring solutions to address unique needs

In today's diverse marketplace, creating personalized customer journeys for underserved communities is essential for fostering inclusion and promoting equitable access to services. This review explores the significance of tailoring solutions to address the unique needs of these communities, emphasizing the role of personalized experiences in enhancing customer satisfaction and loyalty. Underserved communities often face systemic barriers, including limited access to resources, information, and services. Traditional customer journey models may overlook these specific challenges, leading to ineffective engagement and unmet needs. By adopting a personalized approach, businesses can better understand the distinctive characteristics, preferences, and pain points of these communities. This understanding allows for the development of targeted strategies that resonate with individuals' cultural, social, and economic contexts. The framework for personalized customer journeys involves several key components. First, comprehensive data collection is crucial for identifying the unique demographics and psychographics of underserved communities. Utilizing qualitative and quantitative research methods, businesses can gather insights on customer behaviors, preferences, and challenges. Next, leveraging advanced analytics and artificial intelligence can enhance the personalization of interactions, ensuring that offerings align with the specific needs and aspirations of these communities. Additionally, fostering collaboration with community organizations and leaders is vital. Engaging with local stakeholders helps businesses build trust and credibility, facilitating the co-creation of solutions that genuinely address the community's needs. This collaborative approach can lead to the development of tailored products and services, targeted marketing strategies, and supportive customer service frameworks. Ultimately, personalized customer journeys for underserved communities not only improve customer satisfaction but also drive business growth and social impact. By prioritizing equity and inclusion, businesses can enhance their brand reputation, foster customer loyalty, and contribute to the economic empowerment of these communities. In conclusion, a commitment to understanding and addressing the unique needs of underserved communities through personalized customer journeys is essential for fostering inclusive growth and creating lasting positive change.

Unveiling the Power of AI-Driven Personalization: Transforming Consumer Behavior in the Age of Digital Marketing

Artificial intelligence (AI) is revolutionizing digital marketing, with AI-driven personalization emerging as a powerful tool for shaping consumer experiences and driving engagement. This study explores how AI-powered technologies, such as recommendation engines, dynamic content delivery, and targeted advertisements, are transforming consumer behavior in the digital landscape. By leveraging big data, machine learning, and predictive analytics, marketers are now able to deliver hyper-personalized experiences that cater to individual preferences, leading to enhanced customer satisfaction and increased conversion rates. This research examines the impact of AI-driven personalization on key aspects of consumer behavior, including decision-making processes, brand loyalty, and purchase intentions. Additionally, it addresses the ethical challenges that arise from the use of AI in marketing, particularly around data privacy, algorithmic bias, and consumer trust. Through case studies from various industries, this study provides a comprehensive analysis of how personalization influences consumer psychology and purchasing patterns. Ultimately, the research offers valuable insights into the benefits and risks of integrating AI into marketing strategies, helping businesses understand how to harness the power of AI while maintaining ethical standards and safeguarding customer relationships in an increasingly digital world. Moreover, the study delves into the nuances of AI-driven personalization by exploring how consumers respond differently based on demographic factors such as age, gender, and cultural background. By analyzing customer data in real-time, AI allows for more accurate segmentation, ensuring that marketing messages are highly relevant and resonate with individual consumers. This level of precision not only increases customer engagement but also fosters a sense of exclusivity, making consumers feel more valued and understood by the brand. The research also investigates the role of AI in predicting future consumer behavior, using historical data to forecast trends and preferences, thereby enabling brands to stay ahead of the competition. As AI algorithms continuously learn and adapt, they can refine their recommendations, becoming more effective over time. However, there are concerns about over-reliance on AI, as excessive personalization may lead to "filter bubbles," where consumers are only exposed to a limited range of products or content, potentially stifling creativity and innovation.

Interconnectivity and customer satisfaction

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Firms operating in a business-to-business-to-customer (B2B2C) context can utilize relevant Internet-of-Things (IoT) capabilities to facilitate connectivity between different stakeholders and to build a portfolio of complementary unique resources. Through emphasis on capabilities with most capacity to enhance customer satisfaction, such companies can hope for a positive impact on this performance metric. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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.

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.

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.

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.