E-commerce Retailers Research Articles

Local Search Heuristic for the Two-Echelon Capacitated Vehicle Routing Problem in Educational Decision Support Systems

This study focuses on developing a heuristic for Decision Support Systems (DSS) in e-commerce logistics education, specifically addressing the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). The 2E-CVRP involves using Urban Transshipment Points (UTPs) to optimize deliveries. To tackle the complexity of the 2E-CVRP, DSS can employ fast and effective techniques for visual problem-solving. Therefore, the objective of this work is to develop a local search heuristic to solve the 2E-CVRP quickly and efficiently for implementation in DSS. The efficiency of the heuristic is assessed through benchmarks from the literature and applied to real-world problems from a Brazilian e-commerce retailer, contributing to advancements in the 2E-CVRP approach and promoting operational efficiency in e-commerce logistics education. The heuristic yielded promising results, solving problems almost instantly, for instances in the literature on average in 1.06 s, with average gaps of 6.3% in relation to the best-known solutions and, for real problems with hundreds of customers, in 1.4 s, with gaps of 8.3%, demonstrating its effectiveness in achieving the study’s objectives.

The Effect of Retail E-Commerce Sales and Domestic Direct Investment on the RCA Value of the Indonesian Medium High Technology Sector: RLS and FMOLS Approach

The Effect of Retail E-Commerce Sales and Domestic Direct Investment on the RCA Value of the Indonesian Medium High Technology Sector: RLS and FMOLS Approach

Scheduling of e-commerce packaging machines: blocking machines and their impact on the performance–waste tradeoff

AbstractTo streamline their fulfillment processes, many e-commerce retailers today use automated packaging machines for their outbound parcels. An important performance–waste tradeoff is associated with these machines: To reduce packaging waste when handling different sized goods, packaging machines should be able to handle different carton sizes. However, more carton sizes lead to a more involved scheduling process, so that the throughput performance deteriorates (and vice versa). To investigate this tradeoff, this paper develops scheduling procedures for a specific type of packaging machine, called blocking machines. These packaging machines provide multiple back-to-back packaging devices, each continuously processing a dedicated carton size, but blocking each other whenever incoming goods are not properly ordered according to carton sizes on the infeed conveyor. To reduce the resulting throughput loss, we derive various scheduling problems for optimizing the inflow of goods, provide a thorough analysis of the computational complexity, and derive an exact dynamic programming approach that is polynomial in the number of orders to be packed. This allows us to solve even large real-world instances to proven optimality with which we can analyze the performance–waste tradeoff of blocking machines.

Sustainable practices in last-mile logistics of small Brazilian e-retailers: an analysis using an MCDM approach

ObjetivosEl objetivo es contribuir al desarrollo de estrategias mediante la identificación de prácticas sostenibles. Por lo tanto, la investigación tiene un doble objetivo: revelar las prácticas de sostenibilidad integradas en las actividades logísticas de última milla de minoristas de comercio electrónico, analizar las interrelaciones entre estas prácticas y categorizarlas según su importancia para los pequeños minoristas de comercio electrónico en Brasil.MetodologíaSe llevó a cabo un mapeo de la literatura para identificar prácticas de sostenibilidad en las actividades de última milla adoptadas por minoristas en línea, lo que llevó a la propuesta de un metamodelo. Un total de 67 artículos forman parte de la muestra final. Posteriormente, el estudio aplicó la técnica de toma de decisiones multicriterio Decision-Making Trial and Evaluation Laboratory (DEMATEL) para estructurar el problema de investigación mediante análisis de causa y efecto entre las prácticas, con el objetivo de priorizar las prácticas críticas.ResultadosEl resultado indica una fuerte dependencia de prácticas económicas para implementar la sostenibilidad en la logística de última milla. En la dimensión económica de la sostenibilidad, los costes operacionales y la calidad de los servicios son variables de efecto y presentan una fuerte correlación.OriginalidadLa originalidad de esta investigación radica en identificar prácticas de sostenibilidad económica, social y ambiental adoptadas por minoristas en línea en los procesos logísticos de última milla. Además, el estudio se centra en el segmento minorista de comercio electrónico a pequeña escala, un área poco explorada en el contexto brasileño. Por último, el metamodelo propuesto contribuye a abordar prácticas desde la perspectiva de la Teoría de los Stakeholders y el enfoque de Sostenibilidad.Limitaciones/implicaciones para la investigaciónEl artículo proporciona contribuciones teóricas al abordar dos áreas complementarias de conocimiento: la Teoría de los Stakeholders y la Sostenibilidad, alineándose también con las directrices de la Iniciativa de Reporte Global (GRI). En términos prácticos, destaca al ofrecer ideas valiosas sobre la implementación de estrategias sostenibles al identificar prácticas económicas, ambientales y sociales en la logística de última milla, particularmente en el contexto de las pequeñas empresas en el segmento minorista en línea.Implicación prácticaLos resultados ayudan a los minoristas electrónicos a reevaluar los procesos logísticos de última milla desde una perspectiva de sostenibilidad. Este estudio pretende contribuir el avance del conocimiento de la literatura actual sobre la adopción de prácticas sostenibles en pequeñas empresas de comercio electrónico.Implicación socialTeniendo en cuenta que uno de los pilares de la sostenibilidad es el compromiso con el desarrollo social, este estudio contribuye a comprender la visión de los pequeños minoristas electrónicos sobre los trabajadores independientes encargados de la entrega de mercancías.

Scattered storage for retail e-commerce fulfillment warehouses with consideration for product turnover

Storage assignment policy of a warehouse controls how the stocks are assigned to various storage locations. Traditionally used storage policies, such as dedicated or random storage, do not address the specific needs of retail e-commerce fulfillment warehouses that store a wide assortment of products and service a large number of single-unit orders under strict timelines. This has led to the development of scattered storage assignment policy, which deliberately spreads the stocks of every product across the warehouse. However, the available approaches towards scattering have largely ignored product turnover, which is a significant determinant of order picking travel. To fill this research gap, we propose a new, turnover-incorporated scattered storage policy. More specifically, we develop a new measure of scattering of stocks and a mathematical model that optimizes the measure by scattering the stocks across the warehouse, while considering product turnover. Certain important properties of this measure are proved and eventually exploited in developing a heuristic approach to solve the proposed mathematical model for large problem instances. An upper bound for the objective function of the mathematical model is used as a benchmark to evaluate the performance and computation time of the proposed heuristic, as well as solutions obtained through genetic algorithms and surrogate optimization. We conduct a simulation study using the proposed heuristic to analyze the impact of the scattering of stocks on order picking performance in a single block, low level, picker-to-parts warehouse. We also present the impact of warehouse operating parameters on the performance of the proposed scattered storage policy.

Agency or reselling: optimal return policy in E-commerce retail systems under cap-and-trade regulation

In recent years, the government has issued regulations on environmental protection, carbon emissions, and trading. Enterprises’ environmental awareness has increased, and the operational strategies of manufacturers and platforms have also been affected by environmental regulations. This study considers the interactive effects of agency and reselling contracts between manufacturers and platforms and the cap-and-trade regulation on return policy. Our findings reveal that government-imposed caps on total carbon emissions consistently influence optimal pricing and profits under cap-and-trade regulation, regardless of the presence of a return policy. Additionally, the emission intensity of manufacturers plays a pivotal role in determining the return policy. High manufacturer emission intensity leads decision-makers to provide return services only under specific conditions. Moreover, manufacturers opt for agency contracts when faced with low commission rates, and this threshold is intricately linked to carbon emission intensity, the cap, and the return rate. The government’s ability to adjust the cap enables control over changes in manufacturers’ profits and output, thereby influencing industrial structure and carbon emissions. Manufacturers strategically choose return policies, considering factors such as carbon intensity, return rate, and commission rate. Remarkably, even under cap-and-trade regulations, manufacturers remain inclined to offer returns under optimal conditions.

Leadership in Platform Engineering: Best Practices for High-Traffic E-Commerce Retail Applications

High-traffic retail applications need great platform engineering leadership to assure operational excellence and scalability in the continually changing e-commerce industry. This study examines how leadership matters in platform engineering for high-traffic e-commerce retail systems. It covers best practices, methods, and frameworks for managing and optimizing platforms with high traffic and flawless user experiences. The report defines platform engineering executives' key duties as connecting technology strategy with business objectives. Platform engineering leaders must be technical, innovative, manage cross-functional teams, and respond to market changes. Leaders must promote continual development and adaptability to maintain high performance in dynamic e-commerce settings. it focuses on the architectural design concepts that support high-traffic e-commerce systems. Leaders must ensure platform design is durable, scalable, and can handle peak traffic without performance deterioration. The article explores microservices architectures, containerization, and cloud-native solutions for scalability and flexibility. Effective data management and caching are also investigated to improve platform performance and user experience. Security is another important paper topic. E-commerce sites are obvious targets for cyberattacks, therefore executives must employ robust security measures. Proactive security, strict access limits, and vulnerability monitoring are best practices. The article emphasizes the need of data protection rules and industry standards to secure consumer data and build confidence.

Artificial intelligence and marketing innovation: The mediating role of organizational culture

The rapid advancement of artificial intelligence (AI) is transforming the e-commerce landscape, prompting businesses to adopt innovative marketing strategies. This study investigates the relationship between AI applications and marketing innovation in Egyptian e-commerce retailers, with a focus on the mediating role of organizational culture. The research employed a quantitative approach, utilizing a survey to gather data from 260 Egyptian e-retail store owners, managers, and marketers. The findings reveal a significant positive correlation between AI applications and marketing innovation, with organizational culture playing a crucial mediating role. The correlation coefficient (R) between AI and organizational culture was found to be 0.76, indicating that AI explains 57% of the variance in organizational culture. Similarly, the correlation coefficient (R) between AI and marketing innovation was 0.70, suggesting that AI explains 49% of the variance in marketing innovation. Path analysis further demonstrated a significant indirect effect of AI on marketing innovation through organizational culture. The study concludes that the integration of AI into marketing strategies can substantially enhance innovation, particularly when complemented by a supportive organizational culture. It underscores the importance for e-commerce retailers to invest in AI technologies and cultivate a culture that embraces technological advancements to drive marketing innovation and achieve sustainable competitive advantage. AcknowledgmentThe authors are thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.

Modeling e-commerce retailer demand using Stacking Ensemble and K-means clustering patterns.

Demand forecasting plays a crucial role in e-commerce. By accurately forecasting product demand, e-commerce companies can better manage inventory levels and decrease excess or out-of-stock situations, thereby reducing inventory costs and increasing customer satisfaction. To address the issue of the traditionally low accuracy in market demand forecasting by practitioners who relied on manual experience, this paper proposed a demand forecasting model for e-commerce retailers based on a Stacking ensemble model. The paper successfully modeled and analyzed past user behavior data from JD.com platform, and the model achieved automated forecasting of future demand based on historical data. Upon testing, the proposed Stacking ensemble model demonstrated RMSE (Root Mean Square Error) is 256.34 and 1-WMAPE is 0.916 close to 1, indicating that the model performed well and was capable of accurately predicting the demand for these products. Additionally, this paper also employed a K-means clustering model based on a cost function to classify products into five distinct categories according to their demand characteristic indicators and product attribute indicators.

The Optimal Logistics Distribution Service Strategy of the E-commerce Closed-Loop Supply Chain Network under Blockchain Technology and the Government Blockchain Subsidy

The booming development of e-commerce has promoted the diversified development of logistics distribution services (LDS). For LDS, e-commerce retailers (e-retailers) often choose either the outsourced logistics distribution services strategy (OLDSS) or the self-built logistics distribution services strategy (SBLDSS). Although there are problems such as products getting lost and damaged during the logistics distribution process, the high transparency and traceability characteristics of blockchain technology (BT) can help solve the problem of products being lost and damaged in the logistics distribution process. However, due to the high cost of BT, e-retailers may encounter reduced sales, which causes the supply chain corporate profits to decrease. To encourage the BT investment enthusiasm of the e-retailers and regulate corporate profits, the government implements subsidies for e-retailers’ BT, namely, the government blockchain subsidy (GBS). In addition, in recent years, environmental degradation has become increasingly severe, causing negative impacts on people’s lives. To promote sustainable development, we use variational inequality to establish an e-commerce closed-loop supply chain (E-CLSC) network equilibrium model in which the network equilibrium decisions of e-retailers choosing the OLDSS and those choosing the SBLDSS are obtained. Then, we analyze the impact of the BT input cost and the GBS quota on equilibrium decisions by studying their properties and verifying the theoretical results by performing numerical examples. Finally, we analyze the profits of the e-retailers to obtain the impact of the BT input cost and the GBS quota on e-retailers’ choice of the optimal LDS strategy; in this way, we provide a scientific basis for e-retailers to choose the optimal LDS strategy. The results show that increasing the BT input costs reduces e-retailers’ product sales under the two LDS strategies, which decreases the production rate and the recovery rate of the products. When the BT input cost is low, SBLDSS is the best choice for e-retailers. When the BT input cost is high, OLDSS is the best choice for e-retailers. Moreover, there is a positive correlation between GBS and e-retailers’ product sales; thus, GBS is conducive to expanding market demand, regulating the profits of manufacturers, increasing the e-retailers’ profits, improving the enthusiasm of the e-retailers for BT investment, and promoting the overall development of supply chain enterprises. For e-retailers, choosing the OLDSS can lead to a better development of the E-CLSC.

Does cross-border e-commerce improve China’s imported product quality? – A quasi-natural experiment based on the establishment of cross-border e-commerce comprehensive pilot zones

We take the establishment of cross-border e-commerce comprehensive pilot zones as the quasi-natural experiment, based on China’s General Administration of Customs Database, China Customs Online Inquiry Platform, ‘Cross-Border E-Commerce Retail Import Commodity List’ and other data. By employing the staggered DID method, this paper systematically investigates the potential of cross-border e-commerce to enhance the quality of imported products. The research findings reveal the following: Since the introduction of the cross-border e-commerce comprehensive pilot zones, there has been a significant improvement in the quality of imported products in China. These effects, however, vary across different product types and regions, suggesting heterogeneous impacts. The cross-border e-commerce comprehensive pilot zones have facilitated the development of import trade in China through three main avenues: reducing search costs, resolving financing challenges, and lowering tariffs. These measures have collectively contributed to the reduction of trade costs.

Three-scenario forecast of retail e-commerce market volume changes in Kazakhstan until 2026

The new stage of global development is characterized by the movement of data across national borders, which is changing the nature and patterns of commerce. Despite the existence of e-commerce for many years, the current scale of these transactions is not commensurate with the previous ones. In addition, the emergence of marketplaces has fundamentally changed the rules of the game, causing a transformation of commerce processes. The purpose of the research is to compile a forecast model of changes in the volume of Kazakhstan's retail e-commerce market in the optimistic, pessimistic and baseline scenarios. Forecast models were formed as a result of correlation and multiple regression analysis. Scenario forecasts were built taking into account changes in the volume of retail trade in the country based on current conditions of the economy functioning. The results of the study allowed to identify factors that positively and negatively affect the development of the retail e-commerce market in Kazakhstan. The first group includes the growth of the total volume of retail trade, the share of Internet users aged 16–74, the share of non-cash payments in the total structure of payments, and the second group includes the growth of cash payments. The scientific significance of the obtained results lies in the justification of the influence of a set of factors on the change in the volume of the retail e-commerce market. The obtained results can be used by authorized bodies to regulate the market under study by influencing individual factors of e-commerce development.

Enhancing Financial Performance: The Crucial Role of Working Capital Management in E- Commerce Retail Sector

The rapid expansion of ecommerce stores has changed the competitive landscape, requiring new ways to maintain financial health. This study examines the critical role of working capital manag ement (WCM) in financial development in the ecommerce retail industry. Implementing WCM i nvolves monitoring existing assets and liabilities to ensure adequate capacity and efficiency. By analyzing key components such as inventory management, accounts receivable, and payroll, this study demonstrates how effectively WCM can increase revenue, reduce operating costs, and incr ease revenue. Case studies of ecommerce companies revealed best practices and the impact of te chnology solutions on WCM. Findings show that companies that maintain effective WCM are ab le to withstand business fluctuations and sustain longterm growth. This article highlights the imp ortance of integrating financial strategies and strategies into business management to improve fin ancial performance in e-commerce.

E-Commerce Demand Forecasting Using SARIMA Model and K-means Clustering Analysis

E-commerce has become a significant driver in the retail industry within the digital era, with escalating online transaction volumes. Accurate demand forecasting and effective inventory management are crucial for retailers to minimize costs related to overstock and stockouts while maximizing customer satisfaction and revenue. This study explores the application of advanced time series models for demand forecasting by e-commerce retailers, with a particular emphasis on the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The SARIMA model, an extension of the ARIMA model, incorporates autoregression, moving averages, and accounts for seasonal variations, making it suitable for analyzing data with regular periodic patterns, such as seasonal fluctuations in consumer purchasing behavior. The study methodology includes data preprocessing, establishment of the ARIMA model, construction of a Linear Regression (LR) model, development of the SARIMA model, demand forecasting, and clustering analysis. The SARIMA model demonstrated higher fit and predictive accuracy compared to traditional LR and ARIMA models, as substantiated by the 1-mWAPE and RMSE metrics. The application of the K-means clustering algorithm enhanced the homogeneity of demand forecasting and improved the model's adaptability to new sequence data. Cluster analysis categorized products into four classes, with more similar demand characteristics within each class, aiding the model in more accurately capturing potential demand changes. In conclusion, this research confirms the effectiveness and practicality of combining the SARIMA model with the K-means clustering algorithm for e-commerce demand forecasting.

A single-blinded, randomized, parallel intervention to evaluate genetics and omics-based personalized nutrition in general population via an e-commerce tool: The PREVENTOMICS e-commerce study

BackgroundPersonalized nutrition (PN) has been proposed as a strategy to increase the effectiveness of dietary recommendations and ultimately improve health status. ObjectivesWe aimed to assess whether including omics-based PN in an e-commerce tool improves dietary behavior and metabolic profile in general population. MethodsA 21-wk parallel, single-blinded, randomized intervention involved 193 adults assigned to a control group following Mediterranean diet recommendations (n = 57, completers = 36), PN (n = 70, completers = 45), or personalized plan (PP, n = 68, completers = 53) integrating a behavioral change program with PN recommendations. The intervention used metabolomics, proteomics, and genetic data to assist participants in creating personalized shopping lists in a simulated e-commerce retailer portal. The primary outcome was the Mediterranean diet adherence screener (MEDAS) score; secondary outcomes included biometric and metabolic markers and dietary habits. ResultsVolunteers were categorized with a scoring system based on biomarkers of lipid, carbohydrate metabolism, inflammation, oxidative stress, and microbiota, and dietary recommendations delivered accordingly in the PN and PP groups. The intervention significantly increased MEDAS scores in all volunteers (control—3 points; 95% confidence interval [CI]: 2.2, 3.8; PN—2.7 points; 95% CI: 2.0, 3.3; and PP—2.8 points; 95% CI: 2.1, 3.4; q < 0.001). No significant differences were observed in dietary habits or health parameters between PN and control groups after adjustment for multiple comparisons. Nevertheless, personalized recommendations significantly (false discovery rate < 0.05) and selectively enhanced the scores calculated with biomarkers of carbohydrate metabolism (β: −0.37; 95% CI: −0.56, −0.18), oxidative stress (β: −0.37; 95% CI: −0.60, −0.15), microbiota (β: −0.38; 95% CI: −0.63, −0.15), and inflammation (β: −0.78; 95% CI: −1.24, −0.31) compared with control diet. ConclusionsIntegration of personalized strategies within an e-commerce–like tool did not enhance adherence to Mediterranean diet or improved health markers compared with general recommendations. The metabotyping approach showed promising results and more research is guaranteed to further promote its application in PN.This trial was registered at clinicaltrials.gov as NCT04641559 (https://clinicaltrials.gov/study/NCT04641559?cond=NCT04641559&rank=1).

Study on Supply chain and Logistics in India’s E-Commerce Retail

India is witnessing a seismic shift in its approach towards e-commerce retail logistics, marked by a burgeoning interest and curiosity. The realm of e-commerce retail is burgeoning at an unprecedented rate, fueled by a myriad of supply and demand catalysts. Factors such as the ubiquitous presence of the internet and smartphones, the swelling number of urban households, seamless payment options, and tantalizing promotional offers synergize with the convenience, accessibility, and assortment afforded by online shopping, propelling market growth. In this dynamic landscape, e-commerce retailers are incessantly innovating, leveraging novel business paradigms and cutting-edge technology to stay ahead. Embracing customer-centric policies, expeditious deliveries, and hassle-free return mechanisms, these companies entice customers towards online platforms. Novel concepts like exclusive app-only sales, invite-only discounts, and cash-back schemes, coupled with special online events, further galvanize the online shopping experience. The symbiotic relationship between retail e-commerce and the logistics sector is reshaping the industrial panorama, fostering significant investments and value creation. This metamorphosis has birthed a new breed of logistics operators, attuned to the evolving needs of the e-commerce realm. Recognizing logistics as a linchpin for customer satisfaction and market reach, e-commerce giants are investing heavily in fortifying their logistical infrastructure and capabilities. The logistical landscape is evolving at breakneck speed, with an emphasis on elevated service standards, deeper penetration into tier-II and tier-III cities, robust Cash on Delivery services, expansive geographic coverage, and fortified supply chain security measures. This paradigm shift underscores the imperative for adaptability and agility in catering to the ever-changing exigencies of the e-commerce ecosystem.

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The article is devoted to the study of the activities of foreign companies in the context of digitalization. The role of digitalization and its impact on the economic development of companies are highlighted. The authors carried out a SWOT analysis of such foreign companies as Amazone and Google, which made it possible to identify the strengths and weaknesses, opportunities and threats of economic development in times of strong competition. An analysis of the dynamics of retail e-commerce development in the United States from 2017 to 2022 and the revenue dynamics of Amazon and Google compared to research and development costs. A comparison of the strategies of these companies in the context of global digitalization is carried out. The key features and advantages of enterprise management in the context of digitalization are identified. Therefore, digitalization requires enterprises to actively develop flexibility, innovation, and innovative thinking in order to always be competitive in the market

Research on E-Commerce Retail Demand Forecasting Based on SARIMA Model and K-means Clustering Algorithm

With the rapid development of e-commerce, precise demand forecasting and efficient inventory management have become essential for the success and profitability of retail businesses. This study focuses on demand forecasting for e-commerce retailers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the K-means clustering algorithm. The research utilizes a dataset containing 1996 time series of sales data from various products, merchants, and warehouses, aiming to predict demand changes for the next 15 days. The study initially evaluates three models—Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), and SARIMA—by fitting them to historical sales data to forecast future demand. The SARIMA model is identified as the most effective through rigorous evaluation using 1-mWAPE (mean weighted absolute percentage error) and RMSE (root mean square error) metrics. To enhance homogeneity within demand categories, the K-means clustering method is applied to divide products into four distinct groups, further refining the forecasting process.The paper also addresses the challenge of integrating new sequences into the dataset by leveraging clustering results to classify sequences and using cosine similarity to identify analogous historical time series. These matched sequences serve as the basis for demand prediction using the established SARIMA model. The findings highlight the robustness of the SARIMA model in capturing trends and seasonality, providing a reliable framework for e-commerce demand forecasting that can significantly impact inventory strategies and operational efficiency.

Predicting e-commerce CLV with neural networks: The role of NPS, ATV, and CES

Accurately predicting Customer Lifetime Value (CLV) is paramount in optimizing customer relationship management. This study introduces a novel deep learning approach, employing a neural network model to forecast CLV for a leading international e-commerce retailer. This investigation delves into the interplay of key performance indicators—Net Promoter Score (NPS), Average Transaction Value (ATV), and Customer Effort Score (CES)—and their collective impact on CLV. The research utilized a dataset of 15,000 customer profiles and crafted a sequential neural network with dense layers optimized through hyperparameter tuning and regularization to thwart overfitting. The model's efficacy was assessed on a 10 % test set, revealing its adeptness at capturing intricate, nonlinear predictor-customer lifetime value (CLV) relationships. Consistency in training and test performance metrics underscored the model's generalizability, while high R-squared and explained variance scores confirmed the predictive strength of the chosen factors. This research seeks to provide a solution for building an artificial intelligence model with an artificial neural network algorithm to predict customer lifetime value. By incorporating this principle, the study aims to leverage the power of neural networks to forecast CLV, enabling retailers to make informed decisions and optimize customer relationships accurately. This study finds that Net Promoter Score (NPS) and Customer Effort Score (CES) have a powerful impact on the neural network model's ability to predict Customer Lifetime Value (CLV) accurately. On the other hand, while Average Transaction Value (ATV) exhibits the least impact, it still significantly contributes to the accuracy of Customer Lifetime Value (CLV) predictions. These results underscore the importance of incorporating customer feedback metrics like NPS and CES when building predictive models for CLV estimation. Integrating critical customer metrics into neural networks gives retailers enhanced insights, enabling precise customer segmentation, resource allocation, and strategic growth. The study paves the way for future research to refine further CLV prediction, including dataset expansion, customer profile enrichment, and prescriptive marketing optimization.

A Qualitative Approach to Reverse Logistics in the Retail E-commerce Sector in Barcelona, Spain

Purpose: To explore the challenges and opportunities in the reverse logistics practices of the retail e-commerce sector in Barcelona, Spain. To capture diverse perspectives and insights, the study focused on various stakeholders, such as retailers, logistics providers, and policymakers. Design/Method/Approach: The study is Qualitative research design in the case study by inductive research is a bottom-up approach that starts with specific observations of logistics practices of the retail e-commerce sector in Barcelona and then draws general conclusions or theories, making it exploratory in nature. Interpretivism is a research philosophy that asserts that reality is subjectively observed by the researcher. The time horizon used is cross-sectional for secondary data collection in this study. The data analysis is Thematic Analysis by looking at recurring patterns of meaning in a data set by similarities in views and opinions. Findings: Customer satisfaction and loyalty are the key drivers for designing and implementing such strategies. Collaborative efforts among stakeholders are essential for addressing environmental concerns, regulatory compliance, and sustainability, contributing to the overall resilience of the retail e-commerce sector in Barcelona. Theoretical Implications: The study provides practical insights for businesses, policymakers, and academics to incorporate into their strategic planning for enhanced innovation, sustainability, and adaptability in the reverse logistics landscape. The study began in February 2023 and concluded in March 2024, primarily focusing on 26 references related to logistics studies and segments, which could help develop logistics and sustainability. Practical Implications: This study provides practical recommendations and guidelines for retailers in Barcelona's e-commerce sector to enhance their reverse logistics practices, specifically focusing on sustainability and cost efficiency. Originality/Value: This research gives practical and actionable recommendations for businesses, policymakers, and academics to improve reverse logistics in Barcelona's growing e-commerce systems. When these inputs are included in strategic planning, market players can become flexible to the industry's new realities and continue to compete on the same level as their competitors in the retail sector. Research Limitations/Future Research: The unavailability and inaccessibility of data on reverse logistics in Barcelona's retail e-commerce sector can pose a challenge in gaining valuable insights into the effectiveness of current practices. Paper Type: Empirical JEL Classification: M11, L81, R40, O18