Elevating E-commerce Customer Experience: A Machine Learning-Driven Recommendation System

With their vast selection of products, low prices, and ease of use, e-commerce platforms have changed the game for customers and companies alike in the modern day. However, in today's day of fierce competition and rapid technological development, client pleasure has become an essential factor in determining the success and loyalty of online businesses. Examining the experience and functional components of the online purchasing journey, this study seeks to identify the critical success elements impacting consumer happiness in e-commerce platforms. In order to gather information from active e-commerce consumers of different demographics, the research uses a mixed-method approach that combines quantitative surveys with qualitative interviews. Website usability, product quality, price, shipping speed, customer service responsiveness, refund policies, security, personalisation, and overall shopping experience are key factors that are studied. In order to determine how these parameters relate to total customer satisfaction, statistical methods like regression, factor, and correlation analysis were used. The most important factors influencing satisfaction, according to the findings, are dependable delivery, user- friendly navigation, open and honest communication, and simple return procedures. Security and trust are no longer differentiating characteristics, but simply baseline expectations, and user engagement is greatly enhanced by personalisation and intuitive interfaces. Personalised advice and proactive problem resolution are becoming increasingly important post-purchase services, according to the report. The thesis further emphasises that customer satisfaction is an ever-changing concept that is influenced by shifting consumer expectations, patterns of digital behaviour, and technological standards. We offer a strategic framework for e-commerce enterprises who want to increase consumer happiness and build long-term connections based on these insights. This research has important implications for e-commerce strategists, logistics providers, marketers, and UI/UX designers who are looking to gain a competitive edge by providing an exceptional customer experience. Going beyond transactional excellence to give holistic pleasure is increasingly crucial for sustained growth in the e- commerce sector, according to the report. This requires consistent, value-driven, and emotionally aware connection with customers.

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic—we conclude the paper with a summary of the lessons learned from this experience.

PurposeThe purpose of this paper is to advance research on the relationship between customer experience and customer loyalty by exploring the serially mediating roles of brand equity and customer satisfaction and the moderating roles of age, gender, education and family income in the retail banking industry.Design/methodology/approachA total of 500 responses of retail banking customers were used to test the model using the partial least squares structural equation modeling approach. Advanced statistical techniques, such as importance-performance map analysis and a joint application of FIMIX-PLS and PLS-POS, were used to gain new insights.FindingsThe study highlighted that the relationship between customer experience and loyalty is serially mediated by brand equity and customer satisfaction. Age, gender and education were found to be significant moderators in the customer experience–loyalty relationship. Age and gender were found to be significant moderators in the brand equity–loyalty relationship.Practical implicationsThe study strongly suggests that practitioners not only focus on delivering exceptional customer experiences but also on providing leverage brand equity and satisfaction to build customer loyalty. Practitioners should focus on training their front-line employees to improve the quality of their behavior and relations with customers and thereby build customer loyalty.Originality/valueTo the authors’ knowledge, this is the first study to explore the mediating role of several variables sequentially and the moderating role of customer demographics in the customer experience–customer loyalty relationship.

Since trading has been transformed into online platforms, marketing strategies have adapted to digital systems in order to enhance the Customer Relationship Management (CRM) in the E-commerce era. E-commerce systems are the most widely used digital platforms where customer information including personal, and behavioral information, flows as a big data stream. Conducting business intelligent observation on digital big data assists to improve digital marketing policy through the customer intention prediction, decision-making to advertise based on the target group clustering, and customer assist recommendation. To discover the business intelligent, sentiment analysis technology can assist as a solution to understand the customer behavior through the opinion mining where the natural language processing, text analysis, computational linguistics, and biometrics are conducted to analysis the customer information and feedbacks, for smart digital marketing applications. This research observes the applications of sentiment analysis in E-commerce systems as a comprehensive study, and the critical role of discovering business intelligent for smart digital marketing in E-commerce platforms is pointed out according to the technical perspective. Furthermore, the concept of a business intelligent framework integrated with the modelling of decision-making, prediction, and recommendation systems using the contribution of hybrid feature selection which is based on rule-based and machine learning-based sentiment analysis, is proposed for the future innovative smart digital marketing trend.

Customer experience (CX), which affects long-term loyalty, brand impression, and purchasing decisions, has become a critical factor in determining corporate success. E-commerce platforms' capacity to establish strong emotional bonds with clients may have a big influence on their advocacy and retention. One of the most important factors influencing the success of e-commerce businesses is the customer experience. Understanding the connection between customer experience, emotional intelligence (EI), and customer loyalty is crucial for long-term success as competition heats up. By analyzing the relationships between these three traits, this study aims to provide a thorough analysis of the ways in which emotional intelligence influences customer experience, which in turn fosters loyalty. The study examines consumer perceptions, emotional responses, and behavioral patterns in online purchasing environments using both qualitative and quantitative methodologies. Surveys and in-depth interviews are used in the study to assess customer satisfaction, engagement, and retention. Hence, a non-probability sampling method has been applied in the study. The convenient sampling method for the selection of the samples is applied with 200 sample respondents. The combined impact of CX and EI on consumer loyalty in e-commerce is still poorly understood, despite the rising corpus of research on both topics. By examining the ways in which emotional intelligence and customer experience combine to promote consumer loyalty in online buying settings, this study seeks to close this gap.

Artificial intelligence (AI) is one of today’s most significant technological advances; machine-learned technologies benefit both businesses and customers. The journey that a customer takes from pre-purchase to post-purchase is referred to as customer experience. People have developed a desire for positive relationships as technology has gradually taken over our lives. Particularly now, almost any product imaginable can be ordered online. AI is returning to our DNA of one-on-one customer relationships. It can improve customer interaction; however, marketers must understand how well these advances in technology affect customer experience in this ever-changing environment. Moreover, the presence of an omnichannel e-commerce environment necessitates a consistent customer experience across all platforms and devices. Customer personalization can be improved by recommender systems, and customer engagement can be improved by conversational agents, both individually and collectively. This analysis chapter presents a framework for businesses and other researchers to understand how recommendation systems can assist businesses in improving customer experience throughout the customer journey.

This study aims to determine the role of religiosity as mediation in the relationship between online transactions and customer satisfaction and loyalty in Sharia banking in the Jakarta area. The population of this research is all sharia banking customers in Jakarta. The number of samples in this study was 175 Sharia banking customers in Jakarta by sampling purposive sampling method. The data processing method used is Structural Equation Modelling. The results showed: 1) there is no effect of failure on the experience of online transactions in Sharia banking; 2) there is an effect of service recovery on the customer experience of online transactions in Sharia banking; 3) there is an influence of customer experience on customer satisfaction in online transactions in Sharia banking; 4) there is an effect of customer satisfaction on customer loyalty using online transactions in Sharia banking; 5) customer religiosity moderates between customer experience to satisfaction using online transactions; 6) Customer religiosity moderates the satisfaction of customer loyalty using online transactions in Sharia banking.

In today’s competitive service industries, delivering exceptional customer experiences is paramount. This paper explores how AI-driven language processing technologies enhance customer service interactions. Key technologies such as Natural Language Processing (NLP), chatbots, virtual assistants, speech recognition, and machine learning are crucial in transforming customer service. These technologies enable personalized, efficient, and consistent customer interactions, significantly improving overall customer satisfaction. However, implementing these technologies has challenges, including data quality, system integration, privacy concerns, user acceptance, and financial investment. Addressing these challenges is essential for successful AI adoption. Future trends indicate further advancements in AI capabilities, integration with IoT and blockchain technologies, and evolving ethical and regulatory considerations. The long-term impact of AI on service industries promises more efficient operations, cost savings, and superior customer experiences, giving companies a competitive advantage in the market. This paper provides insights into leveraging AI to enhance customer experience, outlining the benefits and challenges while speculating on future directions and implications.

With the development of the Internet, science, and technology, the Internet has penetrated all aspects of the national economy. The amazing sales of "Double Eleven" in 2012 tell the world that in the era of e-commerce, every day is a shopping carnival. E-commerce giants rely on e-commerce platforms to launch an impact on banks, banks in the opposite direction but also started e-commerce. Research methods Single case study method was used. Taking the e-commerce platforms Shanrong Commerce and Global E-Trade Service built by China Construction Bank and Industrial and Commercial Bank of China as examples, this paper analyzes the marketing strategies of their e-commerce platforms, online and offline user transformation, customer relationship platform construction, and other marketing links. And evaluate its marketing model, and analyze its advantages and disadvantages. Through the case analysis of Shanrong commerce and Global E-Trade Service, the paper gives suggestions on the network marketing mode of the bank e-commerce platform, which has certain reference significance for the current bank e-commerce network marketing.

- The growing number of students facing mental health problems emphasizes how urgently proactive, individualized stress management strategies are needed. This study suggests a Machine Learning-Based Intervention Recommendation System (MLIRS) that predicts stress levels and suggests customized therapies by analyzing behavioral, academic, and physiological data from students. Academic records, wearable technology, and questionnaires are all integrated into the system to collect data. We use three supervised learning models to categorize stress levels: Random Forest, Support Vector Machine (SVM), and Gradient Boosting. A recommendation engine that combines collaborative and content-based filtering methods makes recommendations for relevant treatments. A real-world student dataset evaluation reveals a 92% prediction accuracy and excellent user satisfaction with the suggested interventions. Key Words: Student Stress, Machine Learning, Stress Detection, Intervention Recommendation, Mental Health, Hybrid Recommender System, Academic Performance, Predictive Analytics

In the E-commerce era, recommender system is introduced to share customer experience and comments. At the same time, there is a need for E-commerce entities to join their recommender system databases to enhance the reliability toward prospective customers and also to maximize the precision of target marketing. However, there will be a privacy disclosure hazard while joining recommender system databases. In order to preserve privacy in merging recommender system databases, we design a novel algorithm based on ElGamal scheme of homomorphic encryption.

<span lang="EN-US">In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.</span>

The exponential growth of recommender systems research has drawn the attention of the scientific community recently. These systems are very useful in reducing information overload and providing users with the items of their need. The major areas where recommender systems have contributed significantly include e-commerce, online auction, and books and conference recommendation for academia and industrialists. Book recommender systems suggest books of interest to users according to their preferences and requirements. In this article, we have surveyed machine learning techniques which have been used in book recommender systems. Moreover, evaluation metrics applied to evaluate recommendation techniques is also studied. Six categories for book recommendation techniques have been identified and discussed which would enable the scientific community to lay a foundation of research in the concerned field. We have also proposed future perspectives to improve recommender system. We hope that researchers exploring recommendation technology in general and book recommendation in particular will be finding this work highly beneficial.

Recommendation systems are commonly used for suggesting products or services. Among different existing techniques, Model-Based Collaborative Filtering (MBCF) approaches have been proven to address scalability and cold-starting problems that often arise. In this paper we investigate two MBCF algorithms: Self-Organizing Maps (SOM) for Collaborative Filtering and Item-based Fuzzy Clustering Collaborative Filtering (IFCCF). These two techniques have been selected because preliminary results have proven that when applied to the clustering of users or items the quality of the recommendation system increases with respect to the k-means. Within recommendation systems, no comparison of these two techniques exists. Therefore, our experimentation is aimed at comparing these two techniques by means of MovieLens and Jester dataset in order to provide a guideline for their implementation in the e-Commerce domain.

The U.S. retail sector, a $7 trillion economic engine, is undergoing rapid transformation fueled by digital commerce, shifting consumer expectations, and intensified competition. At the heart of this shift lies the imperative to modernize customer service infrastructure. This paper explores how Salesforce Service Cloud—a leading Customer Relationship Management (CRM) platform—can be strategically deployed to optimize service delivery, reduce churn, and support omnichannel responsiveness across the retail value chain. We begin by contextualizing the urgency of service innovation, drawing on recent data showing that U.S. companies lose over $75 billion annually due to poor customer experiences. We then examine Salesforce Service Cloud’s capabilities, including AI-driven case management, real-time analytics, automation, and integration with e-commerce platforms. Through use-case illustrations and performance metrics, we demonstrate how these features enable faster resolution times, improved customer satisfaction, and more efficient use of the retail workforce. Importantly, we highlight the platform’s role in enhancing employment efficiency for over 2.7 million Americans working in customer-facing roles, enabling frontline staff to shift from routine inquiries to high-value engagement. Finally, we position CRM modernization within the broader context of U.S. economic priorities, referencing Executive Order 14058 and other federal mandates that call for customer experience transformation. Our findings support the conclusion that Service Cloud is more than a software solution—it is critical infrastructure for ensuring that the U.S. retail sector remains competitive, resilient, and aligned with the digital economy of the future. Keywords: Customer Experience, Salesforce, Retail Sector, Digital Commerce.