Target Clients Research Articles

FGTL: Federated Graph Transfer Learning for Node Classification

Unsupervised multi-source domain transfer in federated scenario has become an emerging research direction, which can help unlabeled target domain to obtain the adapted model through source domains under privacy-preserving. However, when local data is graph, the difference of domains (or data heterogeneity) mainly originate from the difference in node attributes and subgraph structures, leading to serious model drift, which is not considered by the existing related algorithms. Currently, there are two challenges in this scenario: (1) The node representations extracted directly through conventional GNNs lack inter-domain generalized and consistent information, making it difficult to apply existing federated learning algorithms. (2) The knowledge of source domains has quality differences, which may lead to negative transfer. To address these issues, we propose a novel two-phase Federated Graph Transfer Learning (FGTL) framework. In the generalization phase, FGTL utilizes local contrastive learning and global context embedding to force node representations to capture the inter-domain generalized and consistent information, lightly alleviating model drift. In the transfer phase, FGTL utilizes consensus knowledge to force the decision bound of classifier to adapt to the target client. In addition, FGTL+ exploits model grouping to make consensus knowledge generation more efficient, further enhancing the scalability of FGTL. Extensive experiments show that FGTL significantly outperforms state-of-the-art related methods, while FGTL+ further enhances privacy protection and reduces both communication and computation overhead.

Managing Type 2 Diabetes Through the Lens of People With Serious Mental Illness: A Photovoice Study

Abstract Date Presented 03/23/24 This Photovoice study explored subjective experience of managing Type 2 diabetes mellitus (T2DM) from the perspective of people with serious mental illness (SMI), to inform the development of a T2DM lifestyle intervention targeting clients with SMI. Primary Author and Speaker: Laura N. Pavitt Additional Authors and Speakers: Chinyu Wu, Bradley S. Tragord, Kimberly Bristow

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

Service Quality, Satisfaction, and Loyalty in Homestays in Da Lat City, Vietnam

This empirical study aims to identify and evaluate the crucial factors that influence customer satisfaction and loyalty in homestays in Da Lat City. The research utilizes the SERVQUAL framework, and related research suggests seven factors: responsiveness, assurance, empathy, tangibles, satisfaction, and loyalty. A PLS-SEM structural model is used to test hypotheses by evaluating data from 250 residents of Ho Chi Minh City. The finding points out that adolescents’ loyalty to homestay services in Da Lat is stimulated directly by empathy and satisfaction and indirectly by responsiveness and assurance. Meanwhile, young people’s satisfaction with homestay service is affected by 4 out of 5 factors: responsiveness, assurance, empathy, and tangibles. This study provides insights into homestay services and the key elements influencing young customers’ satisfaction levels toward loyalty to the homestay. Homestay managers should use this model approach to restructure and improve their homestay services to meet the demands and desires of their target clients in a competitive marketplace.

A Low-Cost Energy Monitoring System with Universal Compatibility and Real-Time Visualization for Enhanced Accessibility and Power Savings

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform.

Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms

Learning-based adaptive bitrate (ABR) algorithms have revolutionized video streaming solutions. With the growing demand for data privacy and the rapid development of mobile devices, federated learning (FL) has emerged as a popular training method for neural ABR algorithms in both academia and industry. However, we have discovered that FL-based ABR models are vulnerable to model-poisoning attacks as local updates remain unseen during global aggregation. In response, we propose MAFL (Malicious ABR model based on Federated Learning) to prove that backdooring the learning-based ABR model via FL is practical. Instead of attacking the global policy, MAFL only targets a single ``target client''. Moreover, the unique challenges brought by deep reinforcement learning (DRL) make the attack even more challenging. To address these challenges, MAFL is designed with a two-stage attacking mechanism. Using two representative attack cases with real-world traces, we show that MAFL significantly degrades the model performance on the target client (i.e., increasing rebuffering penalty by 2x and 5x) with a minimal negative impact on benign clients.

Abstract 152: Exploring the impact of heat shock protein 90 on the antitumor efficacy and stemness in medulloblastoma cells

Abstract Background: Medulloblastoma (MB) is the most common children’s malignant brain tumor. Although MB can be treated with conventional chemotherapies, chemoresistance has been frequently developed and thereby hampers their therapeutic effectiveness. The preliminary reports of our research showed that some heat shock proteins have higher levels in MB cells. Among these heat shock proteins, HSP70 and HSP90 expressions are particularly high in MB CD133+ cancer stem cells (CSCs), which arouses our attention and interest. Numerous studies demonstrated that heat shock proteins are prominent in some chemotherapy-resistant cancer cells and CSCs. As known HSP90 and its client proteins are required for the maintenance of self-renewal/stemness and regulation of cell cycle, synergistic killing of cancer cells by simultaneous inhibition of HSP90 expression and activity may be a considerable good therapeutic strategy for treating MB. The purpose of this study is to verify the regulatory function of HSP90 in chemoresistance and stemness of MB. Materials and Methods: The levels of HSP90, its client proteins (P13K/pAKT, Erk, NFκB, and c-Myc), and stemness makers were detected using immunoblotting and immunoprecipitation after the treatment of HSP90 inhibitor (17-AAG) or knockdown of HSP90 by siRNA in MB and MB CSCs. The antitumor effects of HSPs inhibitors or knockdown of HSPs on these cells were evaluated by MTT assay, Annexin V/PI analysis and cell cycle analysis. Results: Our findings revealed elevated expression of HSP90 and its target client proteins in MB cells compared to normal astrocytes. Silencing or inhibiting HSP90 expression induced cell cycle arrest in MB cells. Notably, CD133+ MB CSCs exhibited higher HSP90 levels than parental MB cells. Inhibition of HSP90 led to reduced sphere formation and expression of stemness markers in CD133+ MB CSCs. Treatment with 17-AAG demonstrated antitumor activity in CD133+ MB CSCs, and a synergistic effect was observed when combining conventional chemotherapeutic agents with 17-AAG in MB cells Conclusion: These results illustrated that inhibition of HSP90 could eliminate CSCs, and thereby overcome resistance to chemotherapeutic agents in MB. Chemotherapies in combination with HSP90 blockade may be an emerging MB therapy in the future. Citation Format: Shu-Mei Chen, Hsieng-Yun Huang, Chien-Kai Wang, Yi-Yuan Yang, Sy-Jye Leu, Ying-Ying Li. Exploring the impact of heat shock protein 90 on the antitumor efficacy and stemness in medulloblastoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 152.

Personalized Federated Few-Shot Learning.

Personalized federated learning (PFL) learns a personalized model for each client in a decentralized manner, where each client owns private data that are not shared and data among clients are non-independent and identically distributed (i.i.d.) However, existing PFL solutions assume that clients have sufficient training samples to jointly induce personalized models. Thus, existing PFL solutions cannot perform well in a few-shot scenario, where most or all clients only have a handful of samples for training. Furthermore, existing few-shot learning (FSL) approaches typically need centralized training data; as such, these FSL methods are not applicable in decentralized scenarios. How to enable PFL with limited training samples per client is a practical but understudied problem. In this article, we propose a solution called personalized federated few-shot learning (pFedFSL) to tackle this problem. Specifically, pFedFSL learns a personalized and discriminative feature space for each client by identifying which models perform well on which clients, without exposing local data of clients to the server and other clients, and which clients should be selected for collaboration with the target client. In the learned feature spaces, each sample is made closer to samples of the same category and farther away from samples of different categories. Experimental results on four benchmark datasets demonstrate that pFedFSL outperforms competitive baselines across different settings.

Leveraging Customer Service Support to Enhance Brand Value and Corporate Performance: Evidence from the Fintech Industry

Objective –This study investigates the role of customer service support (CSS) in co-creating brand value and enhancing corporate performance in the fintech industry. The research focuses on analyzing the impact of CSS on the customer journey from registration to first deposit. Methodology – The study utilizes historical data analysis and A/B testing. Client data from a leading fintech company is analyzed to compare conversion rates between those who used CSS versus those who did not. An A/B test is then conducted using a specialized support team targeting clients who haven’t made a deposit and had less than 2 chats with CSS. Findings –Clients who used CSS before depositing had 5 times higher conversion rates. The A/B test found a 3.1 percentage point (9.3% relative) increase in conversion from using targeted support. Novelty –This study provides new empirical evidence on the impact of CSS as a brand value co-creator in fintech, with a novel methodology to enhance corporate performance. Type of Paper: Empirical JEL Classification: M14, M31. Keywords: Brand value; Brand co-creation; Fintech; Customer service; Performance Reference to this paper should be made as follows: Syropyatov, V.V; Arenkov, I.A; Sajid, S; Mahar, D.H. (2023). Leveraging Customer Service Support to Enhance Brand Value and Corporate Performance: Evidence from the Fintech Industry, J. Fin. Bank. Review, 8(3), 116 – 124. https://doi.org/10.35609/jfbr.2023.8.3(1)

The revitalization of endangered heritage buildings:A decision-making framework for investment and determining the highest and best use in Egypt.

Egypt's major cities have been losing heritage and historical buildings due to neglect and misuse, prompting non-governmental organizations, academic institutions, and researchers to advocate for adaptive reuse strategies to preserve the cities' heritage and identity. Adaptive reuse involves changing, modifying, or reusing a space based on community needs, business model, location, and proximity to facilities and services. Heritage buildings offer many tangible and intangible benefits that enhance financial returns, making them challenging but feasible and attractive for investors who value authenticity, uniqueness, and sustainability. This study examines how market value, acquisition opportunities, target clients, age groups, and socioeconomic status affect decision-making. A comparative analysis of three buildings in the Egyptian cities of Alexandria and Cairo is utilized to establish development guidelines and decision-making parameters that significantly impact project design and building functions to determine the highest and best use. In order to complete this study, AutoCAD by Autodesk was used for 2D drawings, SketchUp by Trimble for 3D models, Adobe Photoshop for diagram presentation, and Microsoft Office for tables and diagrams. The comparative analysis provided valuable insights into the adaptive reuse of heritage buildings in developing countries. Findings highlighted how cultural heritage preservation could foster socioeconomic development. Key success factors included stakeholder and community engagement, financial viability, and architectural compatibility. The decision-making framework provides a practical tool for evaluating heritage building reuse. The analysis illustrates successful reuse strategies and considerations. Decision-making frameworks and tools offer practical guidance for future investments and decisions. These findings affect heritage conservation and urban development policymakers, planners, and investors. Stakeholders can make informed decisions and implement strategies to preserve cultural and environmental value by realizing challenges and opportunities. This study hopes to inspire more research and help preserve and revitalize heritage buildings in developing countries, preserving their cultural and socioeconomic value.

DIGITAL MARKETING IN PRACTICE

Just as in the field of architecture every detail is important, from the foundation to the roof every element must be carefully planned and executed, it is the same with digital marketing - every detail, from the creation of the website, its promotion, social networks to advertising, must be carefully planned and performed. Modern companies in the world of race are looking for a way to stand out and attract new customers, which is also true for companies in the construction industry. Today, if you want to expand your business regardless of the size of the company, it cannot be achieved without digital marketing and an adequate marketing strategy. In the world of architecture and construction, every project begins with a detailed plan. Without a plan, regardless of the quality of the materials or the expertise of the workers, a construction project will not be successful. It’s the same with digital marketing – without a strategically designed marketing campaign and effective lease of media space, the online presence will not be able to compete with the competition. Creating quality content, publishing it and monitoring it is an indispensable part of a digital marketing strategy. In the construction industry, it has become very important to use content marketing as a way of informing and educating, gaining trust and attracting the target audience. Because today’s customers expect and look for quality content and information on the Internet. In this paper, we aim to present the importance of digital marketing, then the organization of marketing campaigns and the rental of advertising space in digital media in the field of architecture and the construction industry. Digital marketing in construction offers an incredible opportunity - direct connection with target clients or customers. With the right strategy, using various digital channels, one can reach potential clients who are really interested in the services. In Serbia, there are tens of thousands of companies operating in the field of architecture and construction industry, including: consulting companies, design firms, contractors, suppliers of materials and equipment, sellers of tools and machines and related activities. However, it is certain that less than 50% of these companies do not have their own website. Even those companies that have their own website, or a large number of existing websites, are not optimized. At the very beginning, this leads us to the conclusion that digital marketing in construction is an underdeveloped area.

Intelligent fault diagnosis via ring-based decentralized federated transfer learning

Federated transfer learning (FTL) can effectively address the data silos and domain shift that exist in data-driven rotating machinery fault diagnosis (RMFD). However, in FTL used for RMFD, the huge communication overhead, idle waiting between the source clients and the target client, and negative transfer caused by model aggregation are all pressing challenges. Therefore, a ring-based decentralized federated transfer learning (RDFTL) method for intelligent fault diagnosis is proposed. Firstly, a ring-based decentralized federated transfer learning framework is designed, which can be fully integrated with the bandwidth-optimal Ring-AllReduce algorithm, thereby greatly reducing the communication overhead. Secondly, an asynchronous domain adaptation strategy is proposed, which can effectively avoid idle waiting between the source clients and the target client in the collaborative model training, thereby improving the overall training efficiency of FTL. Thirdly, a multi-perspective distribution discrepancy aggregation (MPDDA) strategy is proposed to alleviate the negative transfer caused by model aggregation. The diagnosis performance of the local model of a source client on the target domain is evaluated from the three perspectives of statistical distance, domain adversarial, and stability, and these three evaluation metrics are jointly used to determine the aggregation weights, which can effectively improve the diagnosis performance of the global model. Finally, a series of experiments are carried out to verify the effectiveness of the proposed method. The results demonstrate that the proposed method can obtain a cross-domain fault diagnosis model with excellent performance in RMFD with data privacy at a fast training speed.

Professional and psychological characteristics of the psychologist brand

Introduction. Today, every psychologist is thinking about how to be competitive in the labor market, how to work effectively and increase the number of their clients. Purposeful formation of a professional brand may be helpful with this. Due to long military operations and the increased number of people who need psychological help, the professional brand of a psychologist will help them make their competences known to the target clients. Aim: To analyze the professional and psychological characteristics of the psychologist brand. Results: The conducted theoretical research allowed clarifying the concept of psychologist professionalism and professionally important qualities of a psychologist. The most important qualities for the formation of a psychologist's professional brand are the psychologist's affiliation and communication skills. The author proposes a communication model of a professional brand of a psychologist and describes the communication tools to form the psychologist's professional brand at micro-, meso-, and macro-level of communication. Conclusions. Psychologists' use of the professional brand communication model can improve their work efficiency and better meet their clients' needs.

Enhance membership inference attacks in federated learning

In Federated learning, models in training will unintentionally memorize detailed information about private data, and the aggregation process on the central server requires users to upload their model parameters, making the models still susceptible to membership inference attacks. However, the existing membership inference attacks in federated learning have been less effective. This paper proposes a new membership inference attack method in federated learning that utilizes data poisoning and sequence prediction confidence. By injecting toxic data, the model can remember the detailed information of specific classes in the target private dataset to the maximum extent. The privacy data detailed information of the target clients contained in the model will be represented through the output confidence vector. Afterward, we aggregate the confidence information obtained from multiple epochs in federated learning and utilize the AdaBoost classifier to learn the details from it. Finally, we use different thresholds to partition the predicted confidence scores output by the AdaBoost classifier obtaining the membership information. We conducted experiments on multiple datasets and models to validate the effectiveness of our attack method. The results showed a high attack effectiveness with minimal overall model performance degradation.

Federated transfer learning with consensus knowledge distillation for intelligent fault diagnosis under data privacy preserving

Fault diagnosis with deep learning has garnered substantial research. However, the establishment of a model is contingent upon a volume of data. Moreover, centralizing the data from each device faces the problem of privacy leakage. Federated learning can cooperate with each device to form a global model without violating data privacy. Due to the data distribution discrepancy for each device, a global model trained only by the source client with labeled data fails to match the target client without labeled data. To overcome this issue, this research suggests a federated transfer learning method. A consensus knowledge distillation is adopted to train the extended target domain model. A mutual information regularization is introduced to further learn the structure information of the target client data. The source client and the extended target models are aggregated to improve model performance. The experimental results demonstrate that our method has broad application prospects.

DIGITAL MARKETING IN THE ARCHITECTURE AND CONSTRUCTION INDUSTRY

Just as in the field of architecture every detail is important, from the foundation to the roof every element must be carefully planned and executed, it is the same with digital marketing - every detail, from the creation of the website, its promotion, social networks to advertising, must be carefully planned and performed. Modern companies in the world of race are looking for a way to stand out and attract new customers, which is also true for companies in the construction industry. Today, if you want to expand your business regardless of the size of the company, it cannot be achieved without digital marketing and an adequate marketing strategy. In the world of architecture and construction, every project begins with a detailed plan. Without a plan, regardless of the quality of the materials or the expertise of the workers, a construction project will not be successful. It's the same with digital marketing – without a strategically designed marketing campaign and effective lease of media space, the online presence will not be able to compete with the competition. Creating quality content, publishing it and monitoring it is an indispensable part of a digital marketing strategy. In the construction industry, it has become very important to use content marketing as a way of informing and educating, gaining trust and attracting the target audience. Because today's customers expect and look for quality content and information on the Internet. In this paper, we aim to present the importance of digital marketing, then the organization of marketing campaigns and the rental of advertising space in digital media in the field of architecture and the construction industry. Digital marketing in construction offers an incredible opportunity - direct connection with target clients or customers. With the right strategy, using various digital channels, one can reach potential clients who are really interested in the services. In Serbia, there are tens of thousands of companies operating in the field of architecture and construction industry, including: consulting companies, design firms, contractors, suppliers of materials and equipment, sellers of tools and machines and related activities. However, it is certain that less than 50% of these companies do not have their own website. Even those companies that have their own website, or a large number of existing websites, are not optimized. At the very beginning, this leads us to the conclusion that digital marketing in construction is an underdeveloped area.

Determinants of the Financial Performance and Sustainability of Microfinance Institutions in Chandrapur Municipality Nepal

The financial performance and Level of financial sustainability of microfinance institutions (MFIs) in Chandrapur Municipality is examined in this article. Access to financial services for the most vulnerable social groups has long been a problem that numerous projects and programs are trying to solve. Performance of Microfinance Institutions Various works suggest that the performance of an institution can be understood from two perspectives. This investigation used an exploratory research approach. The data has been collected for adaptation of the services and products to the target clients from Chandrapur Municipality by age and number of year involved in microfinance institutions.

Open-set federated adversarial domain adaptation based cross-domain fault diagnosis

Abstract Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative evolution of fault diagnosis capabilities among cross-domain data owners while protecting privacy. The OS-FADA is a general fault diagnosis framework that employs two-phase adversarial learning. First, faced with the data distribution shift caused by variable working conditions, a generative adversarial feature extractor training strategy is designed to achieve domain-invariant fault feature extraction by approximating the feature distributions of clients to a unified generated distribution. Second, considering the label distribution shift of unknown faults occurring in the target client, an adversarial learning method is proposed to establish decision boundaries between known and unknown faults. Ultimately, the co-evolution of fault diagnosis models between clients is achieved by combining two-phase adversarial learning and federated aggregation. Results from an industrial gearbox case demonstrate that our proposed method achieves over 20% diagnostic accuracy improvement and has excellent potential for cross-domain fault diagnosis tasks with unknown faults when the data silos problem cannot be ignored.

The revitalization of endangered heritage buildings in developing countries: A decision-making framework for investment and determining the highest and best use in Egypt

Background: Egypt's major cities have been losing heritage and historical buildings due to neglect and misuse, prompting non-governmental organizations, academic institutions, and researchers to advocate for adaptive reuse strategies to preserve the cities' heritage and identity. Adaptive reuse involves changing, modifying, or reusing a space based on community needs, business model, location, and proximity to facilities and services. Heritage buildings offer many tangible and intangible benefits that enhance financial returns, making them challenging but feasible and attractive for investors who value authenticity, uniqueness, and sustainability. Methods: This study examines how market value, acquisition opportunities, target clients, age groups, and socioeconomic status affect decision-making. A comparative analysis of three buildings in the Egyptian cities of Alexandria and Cairo is utilized to establish development guidelines and decision-making parameters that significantly impact project design and building functions to determine the highest and best use. In order to complete this study, AutoCAD by Autodesk was used for 2D drawings, SketchUp by Trimble for 3D models, Adobe Photoshop for diagram presentation, and Microsoft Office for tables and diagrams. Results: The comparative analysis provided valuable insights into the adaptive reuse of heritage buildings in developing countries. Findings highlighted how cultural heritage preservation could foster socioeconomic development. Key success factors included stakeholder and community engagement, financial viability, and architectural compatibility. The decision-making framework provides a practical tool for evaluating heritage building reuse. Conclusions: The analysis illustrates successful reuse strategies and considerations. Decision-making frameworks and tools offer practical guidance for future investments and decisions. These findings affect heritage conservation and urban development policymakers, planners, and investors. Stakeholders can make informed decisions and implement strategies to preserve cultural and environmental value by realizing challenges and opportunities. This study hopes to inspire more research and help preserve and revitalize heritage buildings in developing countries, preserving their cultural and socioeconomic value.

Account Manager PT Abbauf Mulia Konsultan Teknologi

This final internship report is a form of accountability to provide an explanation regarding the author's activities while undergoing an internship at PT Abbauf Mulia Konsultan Teknologi, which was organized through the Merdeka Campus Certified Internship program for work programs from the Government Ministry of Education, Culture, Research, and Technology. PT Abbauf Mulia Konsultan Teknologi is a consulting company engaged in technology. The contents of this report describe the routine activities and responsibilities as an apprentice placing the account manager position in the “Management of Marketing Strategy by Account Manager for Grouping Target Clients of GIS Products” project. The author explains the benefits and experience in the world of work, especially related to the analysis of market materials and product analysis from PT Abbauf Mulia Konsultan Teknologi.