Enterprise Customers Research Articles

Innovation in AI-driven customer service: Impact of large language models on enterprise solutions

Large Language Models (LLMs) have revolutionized enterprise customer service by transforming how organizations interact with their customers. This article examines the implementation of LLMs across diverse industry sectors, focusing on financial services and e-commerce platforms. The findings demonstrate significant improvements in response times, first-contact resolution, and customer satisfaction scores. A novel architectural framework incorporating compliance, integration, and language processing layers provides a foundation for enterprise-grade LLM deployment. The results reveal enhanced operational efficiency, reduced costs, and improved service quality. Best practices and implementation guidelines establish a roadmap for organizations adopting LLM solutions while addressing technical and operational challenges. Future directions highlight emerging capabilities in personalization, context understanding, and predictive service delivery.

The concept of metamarketing in the experience economy

The new conditions of the digital environment and the formation of the impression economy have caused the transformation of marketing, which involves the introduction of innovative strategies, methods and tools of digital marketing into the activities of enterprises to form a positive immersive experience for customers on the basis of impressions created by enterprises. The aim of the article is to substantiate the concept of metamarketing, which is based on the offer of impressions in the modern conditions of the digital environment. The hypothesis is formulated: the formation of the impression economy in the digital environment has led to the transformation of marketing, which includes digital marketing and impression marketing. The convergence of these types of marketing forms its new concept, in particular metamarketing, which involves the application of digital marketing methods and tools to create and provide impressions in physical and digital space. General scientific and special methods were used: analysis and synthesis, systematic, comparison, logical generalization. The research carried out: substantiation of the importance of impressions for producers and buyers; systematization of strategic directions for ensuring customer impressions; highlighting key elements and principles of the impression economy and formulating the authorʼs definition of the "impression economy in the digital environment"; systematization of the conditions of application, management decisions, limitations and risks of marketing concepts in evolutionary development; substantiation of the concept and formulation of the definition of metamarketing for forming impressions of enterprise customers in the digital environment

How does customer enterprise digitalization improve the green total factor productivity of state-owned suppliers: from the supply chain perspective

Environmental problems arising from global industrialization highlight the urgent need to green supply chains. However, the intensification of digitalization and global supply chain risks underscore the leading role of state-owned suppliers. From the supply chain perspective, this study analyzes the impact of the digitalization of downstream customer enterprises on the green total factor productivity (GTFP) of state-owned upstream suppliers using data on Chinese listed companies from 2007 to 2022. The benchmark regression shows that the digitalization of downstream customer enterprises significantly improves the GTFP of state-owned upstream suppliers. Mechanism testing shows that the digitalization of downstream customer enterprises promotes the GTFP of state-owned suppliers by reducing implicit costs and increasing the proportion of highly educated human resources. In improving the GTFP of state-owned suppliers, the external contractual environment, internal environmental responsibility, and market competitiveness act as “accelerators,” whereas the intensity of environmental regulation acts as a “speed bump” in the short term. In contrast to existing research, which mainly focuses on the impact of enterprise digitalization on the production efficiency of internal or upstream suppliers, this study facilitates the understanding of the pillar role of state-owned suppliers in the national economy and their stabilizing function in the supply chain.

Privacy-preserving MTS anomaly detection for network devices through federated learning

In the context of Maintenance-as-a-Service (MaaS), it is important for device vendors to develop multivariate time series (MTS) anomaly detection models that can accurately identify anomalies without compromising the privacy of customer enterprises' data. In this paper, we investigate the relationship between MTS data patterns and the parameters of unsupervised autoencoder (AE) models and show that they are highly consistent. Building on this insight, we propose a novel unsupervised federated learning (FL)-based framework called OmniFed, which cannot only address the heterogeneity of non-independent identically (non-iid) distributed data on different devices, but also achieve high-precision detection of device MTS anomalies while ensuring privacy. Specifically, OmniFed is initialized with an AE model and then trains local AE models on individual devices via federated learning. Finally, OmniFed clusters devices based on the parameters of the AE models and trains a cluster-specific MTS anomaly detection model using FL. Our experiments on two real-world datasets demonstrate that OmniFed achieves an F1-Score of 0.921, significantly higher than the best baseline method.

Optimizing production efficiency in semiconductor enterprises by an improve and optimized biogeographical optimization algorithm based on three-layer coding

Aiming at the problem of low production efficiency and inability to fully unleash production capacity in current semiconductor enterprises, a production efficiency optimization model for semiconductor enterprises has been studied and constructed. This model transforms the problem of low production efficiency into a problem of locating and solving the decoupling point of enterprise customer orders, and comprehensively considers the situation of sudden changes in enterprise production orders when locating and solving the decoupling point of customer orders. Propose to use a three-layer coding (TLC) mechanism to improve and optimize the biogeographical optimization algorithm, and use the improved biogeographical optimization (IOBO) algorithm to solve the production efficiency optimization problem of semiconductor enterprises. The results show that the proposed IBBO-TCL algorithm has a fast convergence speed and the minimum root mean square error after convergence. And this method can accurately solve the decoupling point of customer orders for semiconductor enterprises. The method proposed in the study has effectively improved the production efficiency of semiconductor enterprises and has guiding significance for optimizing enterprise structure.

Lindorm-UWC: An Ultra-Wide-Column Database for Internet of Vehicles

In the Internet of Vehicle (IoV) systems, intelligent vehicles generate huge amounts of data that supports diverse services and applications. In practice, database systems are deployed in the cloud to manage data uploaded from the vehicle side and provide real-time query capacities. However, existing database systems are ill-suited because IoV data contains a large number of metrics and is written at an extremely high throughput. To better understand IoV data and corresponding challenges to underlying database systems, we conduct the first extensive empirical study of real-world IoV workloads. According to our findings from the study, we design Lindorm-UWC as a superior database for IoV systems. It implements a distributed architecture and a cold/hot data separation mechanism to accommodate massive amounts of IoV data. In each data partition, it deploys an ultra-wide-column storage engine to efficiently handle the query and ingestion of multi-metric data. We evaluate Lindorm-UWC under different data scales and various types of query. Our experimental results show that it can always achieve higher write throughput (over 79% increase) and competitive query performance compared to various alternative solutions. Lindorm-UWC has been serving IoV enterprise customers on Alibaba Cloud since 2019, managing tens of petabytes of IoV data.

RNSC: A hierarchical deep learning model for net promoter scoring understanding by combining review and note through semantic consistency

The Net Promoter Score (NPS) is a widely-used metric for measuring customer loyalty and is an essential element in customer service analysis. To understand and analyze NPS effectively, customer-generated content, such as customer reviews, is commonly employed. Nevertheless, the information provided in customer-generated content may sometimes be inadequate. In this paper, we aim to enhance NPS understanding by incorporating a valuable employee-generated content known as employee notes, which provide insights into the internal operations of the enterprise's customer service. Considering the different standpoints of the two pieces of textual information, the semantic consistency is defined to calculate the semantic relationship between them, which helps better understand and analyze NPS. To effectively capture the crucial semantic consistency relationship and enhance the understanding of NPS, we propose a hierarchical deep learning approach, called RNSC (Review, Note and Semantic Consistency), which employs a bidirectional LSTM (BiLSTM) and a semantic consistency recognition module to encode and identify the local relationship between customer reviews and employee notes. Based on the trained model, the attention mechanism can detect and output the keywords that significantly contribute to the NPS through semantic consistency, showing its potential for supporting diagnostic analysis and further service optimization. Extensive data experiments, such as comparison with baseline methods, ablation experiments, and sparse data experiments, verify the superiority of the proposed RNSC approach.

Online social enterprise customer behaviour: influences of e-commerce, social media, and altruism on online impulsive buying

Online social enterprise customer behaviour: influences of e-commerce, social media, and altruism on online impulsive buying

Who should own the data? The impact of data value creation on data ownership

With the popularization and application of the Industrial Internet of Things, equipment manufacturers as upstream enterprises can collect in real time the operational data of their downstream enterprise customers’ devices. As the notion of “data as an asset” gains acceptance, enterprises increasingly value the data value chain. In the current market mechanism, ownership of data is not clear, which hinders the creation of data value. This study establishes a theoretical analytical model that considers the influences of the benefits and losses derived from value co-creation on claiming data ownership. Benefits obtained by upstream and downstream enterprises through data and losses incurred owing to data breaches are distinct, which impact their investments in data security and data authorization. Results reveal that investments in data security by upstream businesses are affected by benefits and losses and also correlated with data ownership. Conversely, if the downstream enterprise owns the data, its expected benefits can inversely affect the upstream's data security investment—increasing the downstream's benefits can lead to reduced security investment by the upstream, even though other factors may encourage the upstream to invest more in data security. The impact of who owns the data on the security investments of both upstream and downstream enterprises becomes less pronounced when the expected benefits or potential losses for one side grow. Lastly, the current study provides guiding principles for upstream businesses to claim data ownership, thereby facilitating the exploration and utilization of data value co-creation opportunities for upstream and downstream businesses.

Optimal integration decision of industrial internet platform for third-party components in two-sided networks

PurposeWith the development of digitalization and interconnection, there is a growing need for enterprise customers to ensure the compatibility of the third-party components they are using in the manufacturing process, thus raising the integration requirements for the Industrial Internet platform and its third-party developers. Therefore, our study investigates the optimal integration decision of the Industrial Internet platform while considering its access price, the integration cost, and the net utility derived by enterprise customers from the third-party components.Design/methodology/approachWe model a two-sided Industrial Internet platform that connects customers on the demand side to the developers on the supply side. We then explore the integration decision of the Industrial Internet platform and its important factors by solving the optimal profit function.FindingsFirst, despite the high integration cost of third-party developers, the platform still chooses to integrate when enterprise customers derive high utility from the third-party components. Second, due to the compatibility effect, charging the enterprise customers a higher price may reduce the platform profits when these customers derive low utility from the third-party components. Third, the platform profits will increase along with the integration cost of third-party developers when it is low in the case where enterprise customers derive low utility from third-party components.Originality/valueOur findings offer insightful takeaways for the Industrial Internet platform when making integration decisions.

Making Lemonade from Lemons: A Transaction Cost Economics Perspective on Third-Party Disruptions in a Multivendor Information Technology Service

In modern enterprise computing environments, multiple information technology (IT) services from first and third parties are often integrated to form coherent solutions for enterprise customers. In this study, we seek to understand how uncertainties introduced by third-party services shape enterprise customers’ use of various IT services in these multivendor service settings. Specifically, we analyze a case of disruption caused by a third party that affects the multivendor service but does not directly affect the first-party services. We find a temporary increase in the use of first-party services that serve as similar-goal substitutes during the disruption; however, there is a net decline in the total use of services in the long run. To assess what actions the first party can take during such disruptions to turn the challenge into an opportunity, we analyze the first party’s technical support log using deep learning techniques. We find that if the first party offers high-quality technical support that addresses product-related issues, it may be able to make lemonade out of lemons. Such technical support effectively boosts customers’ use of first-party services in the long run. Curiously, however, similar efforts by the first party in the predisruption period are ineffective in achieving the same effect.

Privacy-Preserving and Lightweight Verification of Deep Packet Inspection in Clouds

In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10-20 microseconds.

Factors affecting the intention to use QRIS on MSME customers

This study investigates the factors influencing the intention to use the Quick Response Code Indonesian Standard (QRIS) among Micro, Small, and Medium Enterprises (MSMEs) customers in Indonesia. In this study, a quantitative approach was taken with the survey method using an online questionnaire that was distributed to 240 MSME customers. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS) and Structural Equation Modeling (SEM) using the Analysis of Moment Structures (AMOS) 24 to test their validity and reliability. The study results show that factors such as perceived benefits, knowledge, and perceived safety significantly affect customer confidence in using QRIS. However, perceived ease of use does not significantly affect trust. In addition, perceived usefulness, knowledge, and trust significantly influence the intention to use QRIS, while perceived ease of use and perceived safety have no significant effect. Based on these findings, increasing perceptions of benefits, knowledge, and trust is critical to encouraging the use of QRIS among MSME customers. Although ease of use and security do not directly affect usage intentions, ensuring an easy user experience and providing security guarantees for MSME customers are still essential. This study contributes to the literature by increasing understanding of the factors driving QRIS adoption in SMEs. These findings can assist in developing effective strategies to increase the use of QRIS and the growth of the MSME sector, emphasizing benefits and advantages, increasing customer knowledge, and building trust in security.

R2D2: Reducing Redundancy and Duplication in Data Lakes

Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for these datasets. In this work, we focus on identifying and reducing redundancy in enterprise data lakes by addressing the problem of "dataset containment". To the best of our knowledge, this is one of the first works that addresses table-level containment at a large scale. We propose R2D2: a three-step hierarchical pipeline that efficiently identifies almost all instances of containment by progressively reducing the search space in the data lake. It first builds (i) a schema containment graph, followed by (ii) statistical min-max pruning, and finally, (iii) content level pruning. We further propose minimizing the total storage and access costs by optimally identifying redundant datasets that can be deleted (and reconstructed on demand) while respecting latency constraints. We implement our system on Azure Databricks clusters using Apache Spark for enterprise data stored in ADLS Gen2, and on AWS clusters for open-source data. In contrast to existing modified baselines that are inaccurate or take several days to run, our pipeline can process an enterprise customer data lake at the TB scale in approximately 5 hours with high accuracy. We present theoretical results as well as extensive empirical validation on both enterprise (scale of TBs) and open-source datasets (scale of MBs - GBs), which showcase the effectiveness of our pipeline.

The Role of SAMOTA Tourism Development in Enhancing the Revenue of Micro, Small, and Medium Enterprises Customers in the Region of Big Sumbawa

This research examines the growth of SAMOTA tourism and the increase in income of small and medium enterprises (MSMEs). The author of this study used a qualitative descriptive data analysis method primarily to collect information about the tourist attractions used as research locations. Qualitative research is based on interpretive philosophy or postpositivism and is used to conduct research in natural conditions, where the researcher acts as the primary tool. The data is usually qualitative because it combines observation, interviews, and triangulated documentation. Data is analyzed inductively or qualitatively, and the results are the result of research. The research results show that tourism in the Samota region is experiencing development. The area on Sumbawa Island called SAMOTA has been designated a Biosphere Reserve by the UN and UNESCO. The tourism development process increases the number of visitors to the SAMOTA tourist area. That can increase income and encourage people to build new MSMEs to meet increasing visitor demand. Developments in the SAMOTA tourist area have tremendously impacted the MSME sector. Samota's tourism development helps make more money. MSME players are happy with the many events in Sumbawa because they benefit from them

Early warning of telecom enterprise customer churn based on ensemble learning.

Analyzing customers' characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Assessment of Water Supply System Performance Indicators Based on the Customer Bill-Based Approach

Significant efforts have been undertaken over the last two decades by governmental organizations and local and international non-governmental sectors to improve water supply and sanitation structures; however, there are significant challenges to the town's growth and expansion. Chiro town's population is increasing, and the community is affected by water and excreta-borne diseases as a result of a lack of proper sanitation facilities and a water supply system. In this study, billed data from Chiro Town water supply service enterprise customers have been used to assess the performance of the water distribution network over ten years (2011-2020). The findings show that the Town's water coverage is 62.9%, the water demand is 8,121.6m3/day, and the computed annual water production is 4,222.3m3/day. Residents of the town have complained about a lack of access to improved drinking water supplies, low water consumption, and sanitation facilities, particularly in the community of those who live in the town's borders and practice open defecation. For all domestic use, 16.32% and 83.68% of respondents used unprotected and protected water sources, respectively. This study suggested that strong sectoral coordination, community involvement, and sufficient financial resources were required to identify the root causes and provide an efficient water supply system in the study area.

Extension Model of Administration and Management for Business of Community Enterprise

This research was aimed to 1) Study successful implementation of community enterprises, 2) Study factors relating to administration and management for business of community enterprises, 3) Analyze, synthesize, and develop the extension model of administration and management for business of community enterprises, and 4) Assess the extension model of administration and management for business of community enterprises. The research method was Mixed Method. The tools used were an In-Depth Interview with purposive sampling of 8 Key informants and a Survey of 520 cases with a Structured Interview. Descriptive Statistics, Comparative statistics (T-test), Factor Analysis, and Multiple Regression Analysis were used. The results found that the factors related to business administration and management, product distribution plans, and marketing were at a high level ( = 4.19, SD.= 0.908), raw material quality inspection or source selection was at the highest level ( = 4.25, SD.= 0.892), continuous communication with community enterprise customers was at a high level ( = 4.03, SD.= 0.966). All 3 components, which were Business Operation, Business administration, and Business Promotion and Support components, affected the success of business administration and management. They could explain 41.10% of the variation in management success and business management services.

Influence of Service Quality, Price Perception and Quality of the Customer Experience Customer Loyalty with Customer Satisfaction as a Variable Intervening

The impact Covid-19 epidemic made all operations have gone digital in response to the Next Normal. Increasing competition in the information and communication industry demonstrates the progress of digital transformation for the telecommunication requirements of not only individuals, but also businesses as the primary target market form of company. To maintain the longevity of the company's operation, steps must be taken to understand the demands of customer satisfaction. This study aims to analyze Service Quality, Price Perceptions and Customer Experience on Customer Loyalty of Telkom Indonesia's Enterprise Company with Consumer Satisfaction as an Intervening Variable. According to internal business data for 2022, the research population is 150 enterprise customers with SEM-PLS as analysis method. The results showed that service quality, price perception and customer experience had a significant positive effect on enterprise customer satisfaction and customer satisfaction had a significant positive effect on customer loyalty. Customer satisfaction has a significant positive effect on Service Quality and Price Perception on customer loyalty, while Customer Experience has an insignificant positive effect on customer loyalty.

The Design and Implementation of the Enterprise Customer Relationship Management System

In today's technological progress, the future market competition has developed to the service competition, and it is important to establish and develop long-term customer relationship with customers is particularly important. Based on customer relationship management, this paper mainly completes order management and customer service. The order is arranged according to the abnormal situation of the order and the closer to the delivery time. If the order is abnormal, you can directly notify customers with mobile phone SMS, or provide humanized service. The system uses Visual Studio 2019, SQL Server 2019 and MVC development platform to explore the design anddevelopment of the Web-based user relationship management system.