Yazar "Dogan, Onur" seçeneğine göre listele
Listeleniyor 1 - 20 / 22
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Automating Exam Assessment Through Digital Transformation and Image Processing(Springer Science and Business Media Deutschland GmbH, 2025) Dogan, OnurThe paper presents a study aimed at optimizing the reading and evaluating process of multiple-choice exam sheets (optical forms) as a smart campus application at Izmir Bakircay University, significantly reducing the time and cost involved. Traditional manual evaluation methods are time-consuming and expensive. The proposed solution leverages digital transformation and image processing technologies. The algorithm developed processes scanned images of optical forms, generates scores, and provides statistical reports without requiring specialized knowledge. The methodology is divided into three main steps: detecting sections of the optical form, cropping sections, reading marked circles based on coordinates, reporting exam results, and statistical analysis. Testing with previously used optical forms showed 100% accuracy at a resolution of 600×600 dpi. Errors caused by user handling were anticipated and the system was designed to be robust against such errors. The results of implementing this automated system include a 98.15% reduction in processing time and a 99.95% reduction in cost. The new evaluation time is around 5 min per exam, costing 347 TL. The process now requires only one person instead of four. This study exemplifies the potential of digital transformation and image processing to enhance operational efficiency and cost-effectiveness in academic settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Öğe BSC-Based Digital Transformation Strategy Selection and Sensitivity Analysis(Mdpi, 2024) Oner, Mahir; Cebeci, Ufuk; Dogan, OnurIn today's digital age, businesses are tasked with adapting to rapidly advancing technology. This transformation is far from simple, with many companies facing difficulties navigating new technological trends. This paper highlights a key segment of a comprehensive strategic model developed to address this challenge. The model integrates various planning and decision-making tools, such as the Balanced Scorecard (BSC), Objectives and Key Results (OKR), SWOT analysis, TOWS, and the Spherical Fuzzy Analytic Hierarchy Process (SFAHP). Integrating these tools in the proposed model provides businesses with a well-rounded pathway to manage digital transformation. The model considers human elements, uncertainty management, needs prioritization, and flexibility, aiming to find the optimal balance between theory and practical applications in real-world business scenarios. This particular study delves into the use of SFAHP, specifically addressing the challenge of effectively selecting the most suitable strategy among various options. This approach not only brings a new perspective to digital transformation but also highlights the importance of choosing the right strategy. This choice is crucial for the overall adaptation of businesses. It shows how carefully applying the SFAHP method is key. Combining this with a successful digital transformation strategy is essential. Together, they provide practical and efficient solutions for businesses in a fast-changing technological environment.Öğe Business Analytics in Customer Lifetime Value: An Overview Analysis(Wiley Periodicals, Inc, 2025) Dogan, Onur; Hiziroglu, Abdulkadir; Pisirgen, Ali; Seymen, Omer FarukIn customer-oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision-making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state-of-the-art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan-one of the study's significant contributions-was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.Öğe Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process Mining(MDPI, 2024) Topaloglu, Bilal; Oztaysi, Basar; Dogan, OnurUnderstanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology to extract and understand e-commerce visitor journeys using process mining. In order to obtain more structured process diagrams, we used techniques such as activity type enrichment, start and end node identification, and Levenshtein distance-based clustering in this methodology. For the evaluation of the resulting diagrams, we developed a model utilizing expert knowledge. As a result of this empirical study, we identified the most significant factors for process structuredness and their relationships. Using a real-life big dataset which has over 20 million rows, we defined activity-, behavior-, and process-level e-commerce visitor journeys. Exploitation and exploration were the most common journeys, and it was revealed that journeys with exploration behavior had significantly lower conversion rates. At the process level, we mapped the backbones of eight journeys and tested their qualities with the empirical structuredness measure. By using cart statuses at the beginning and end of these journeys, we obtained a high-level end-to-end e-commerce journey that can be used to improve recommendation performance. Additionally, we proposed new metrics to evaluate online user journeys and to benchmark e-commerce journey design success.Öğe Continuous Intuitionistic Fuzzy AHP & CODAS Methodology for Automation Degree Selection(Old City Publishing Inc, 2024) Alkan, Nursah; Otay, Irem; Gul, Alize Yaprak; Demir, Zeynep Burcu Kizilkan; Dogan, OnurThe automotive industry's evolution thrives on technological innovation, prioritizing efficiency, safety, and sustainability. Recent improvements in autonomous driving and IoT integration have revolutionized vehicle design, safety, and maintenance with different automation degrees from partial human control to full automation. Selecting these automation degrees involves complicated Multi-Criteria Decision-Making (MCDM) encompassing technical feasibility, societal impact, and regulatory compliance. Utilizing Analytic Hierarchy Process (AHP) and Combinative Distance-Based Assessment (CODAS) offers a structured framework to navigate these complexities. AHP establishes criteria importance, while CODAS handles uncertainties, enabling informed decisions balancing technology with ethical, societal, and regulatory considerations. Fuzzy extensions further refine these methodologies, empowering the industry to adeptly address subjective perceptions and ambiguous data, enhancing the decision-making framework for automotive technology evolution. This paper navigates the intricate landscape of automation degree selection within the automotive industry evolution, employing a structured approach merging fuzzy AHP and fuzzy CODAS methods by utilizing Continuous Intuitionistic Fuzzy Set (CINFUS). This approach not only brings a new perspective to autonomous vehicles but also highlights the importance of choosing the right automation degree. Moreover, a sensitivity analysis involved adjusting the weights assigned to different criteria within the Continuous Intuitionistic Fuzzy (CINFU) AHP framework. By systematically altering these weights and observing their impact on the final automation degree selection, decision-makers can understand the sensitivity of the chosen automation degree to changes in priority among criteria.Öğe Customer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkey(World Scientific Publ Co Pte Ltd, 2022) Dogan, Onur; Seymen, Omer Faruk; Hiziroglu, AbdulkadirThe vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, k-means and fuzzy c-means, based on transaction data that belong to Turkey's two major cities. Over 10,000 records of customers' data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.Öğe Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment(Gazi Univ, 2023) Seymen, Omer Faruk; Olmez, Emre; Dogan, Onur; Orhan, E. R.; Hiziroglu, AbdulkadirChurn studies have been used for many years to increase profitability as well as to make customer -company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we proposed were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The study results showed that the proposed deep learning-based churn prediction model has a better classification performance. The CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models.Öğe Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles(Emerald Group Publishing Ltd, 2024) Dogan, Onur; Yalcin, Emre; Hiziroglu, Ourania AretaPurposeReading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library's offerings, contributing to a holistic learning experience.Design/methodology/approachThis study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user's rating.FindingsThe comparative results indicated that users are quite satisfied with the recommendations generated by W\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.Originality/valueThe proposed W\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.Öğe Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control(MDPI, 2024) Dogan, Onur; Areta Hiziroglu, OuraniaThe production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC-PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the 'Quality Control' activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the 'Return from Waiting' activity to 'Packing and Shipment' is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.Öğe Enhancing E-Business Communication with a Hybrid Rule-Based and Extractive-Based Chatbot(MDPI, 2024) Dogan, Onur; Gurcan, Omer FarukE-businesses often face challenges related to customer service and communication, leading to increased dissatisfaction among customers and potential damage to the brand. To address these challenges, data-driven and AI-based approaches have emerged, including predictive analytics for optimizing customer interactions and chatbots powered by AI and NLP technologies. This study focuses on developing a hybrid rule-based and extractive-based chatbot for e-business, which can handle both routine and complex inquiries, ensuring quick and accurate responses to improve communication problems. The rule-based QA method used in the chatbot demonstrated high precision and accuracy in providing answers to user queries. The rule-based approach achieved impressive 98% accuracy and 97% precision rates among 1684 queries. The extractive-based approach received positive feedback, with 91% of users rating it as good or excellent and an average user satisfaction score of 4.38. General user satisfaction was notably high, with an average Likert score of 4.29, and 54% of participants gave the highest score of 5. Communication time was significantly improved, as the chatbot reduced average response times to 41 s, compared to the previous 20-min average for inquiries.Öğe Enhancing e-commerce product recommendations through statistical settings and product-specific insights(Inderscience Enterprises Ltd, 2024) Dogan, OnurIn the e-commerce industry, effectively guiding customers to select desired products poses a significant challenge, necessitating the utilisation of technology and data-driven solutions. To address the extensive range of product varieties and enhance product recommendations, this study improves upon the conventional association rule mining (ARM) approach by incorporating statistical settings. By examining sales transactions, the study assesses the statistical significance of correlations, taking into account specific product details such as product name, discount rates, and the number of favourites. The findings offer valuable insights with managerial implications. For instance, the study recommends that if a customer adds products with a high discount rate to their basket, the company should suggest products with a lower discount rate. Furthermore, the traditional rules are augmented by incorporating product features. Specifically, when the total number of favourites is below 7,500 and the discount rate is less than 75%, the similarity ratio of the recommended products should be below 0.50. These enhancements contribute significantly to the field, providing actionable recommendations for e-commerce companies to optimise their product recommendation strategies.Öğe Enhancing Hospital Services: Utilizing Chatbot Technology for Patient Inquiries(Springer International Publishing Ag, 2024) Dogan, Onur; Gurcan, Omer FarukThis paper investigates the implementation and impact of a chatbot system within a hospital environment to address patient inquiries. The study focuses on the development and deployment of a non-medical knowledge-based chatbot designed to respond to a spectrum of queries typically handled by hospital call centers. The chatbot system's primary objective is to provide prompt, accurate, and accessible information to patients, streamlining their interaction process and reducing waiting times. The research explores the technical architecture and functionality of the chatbot, emphasizing its intuitive interface and ability to cater to a diverse range of patient queries. Utilizing a chatbot, data collection includes quantitative analysis of patient inquiries' pre- and post-chatbot implementation, alongside qualitative insights derived from patient feedback. The developed chatbot based on embedding techniques and a pre-trained Large Language Model gives an accuracy of 89%. Preliminary findings emphasized the potential for intelligent virtual assistants to transform patient interactions, enhance information accessibility, and ultimately contribute to the overall improvement of hospital services.Öğe How can Digital Robots Help Creating a Smart Campus?(Machine Intelligence Research (MIR) Labs, 2023) Akyol, Sakine; Dogan, Onur; Er, OrhanThe development of technology has created the need to increase the quality of life of individuals all over the world. This need for development has initiated the transformation of the ’smart campus’ in university campuses called small cities. A transformation that started with digitalization has led universities to use digital technologies in their business processes and education periods within the scope of smart campuses. Robotic Process Automation (RPA), one of the digital transformation technologies, automates repetitive and labor-intensive business processes in these digitalization processes. The application of RPA in four different processes at Izmir Bakýrçay University was examined to show how RPA can help creating a smart campus. The university saved up to 92.59% of cost and 98.25% of time from these four processes. It has been observed that the studies carried out within the scope of smart campuses will continue to increase because some routine processes still need to be automated. © (2023), (MIR Labs, www.mirlabs.net/ijcisim/index.html). All Rights Reserved.Öğe Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach(MDPI, 2025) Cebeci, Ufuk; Simsir, Ugur; Dogan, OnurToday, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking.Öğe Optimization of travel requests with process simulation analysis(Pergamon-Elsevier Science Ltd, 2024) Celik, Atalay; Dogan, OnurEfficient travel request management is crucial in the modern corporate landscape, impacting decisionmaking, budget control, and overall operational efficiency. This study delves into the intricacies of the travel request management process with process mining and process simulation, aiming to assess the cost structure and efficiency. Three research questions were examined and tested statistically. Using a comprehensive dataset encompassing timestamps, personnel roles, and cost details, the research uncovers significant insights. The findings reveal substantial cost variations across different stages of the travel request process. Rolebased analysis highlights variations in costs and processing times associated with distinct personnel roles. Additionally, delays, bottlenecks, and process variability emerge as key contributors to elevated process costs. Furthermore, process mining and simulation were instrumental in quantifying the impact of process optimizations. The simulations demonstrated that a 20% reduction in response times for Unit Leaders, a 40% reduction for Project Managers, and a 30% reduction for both roles collectively yielded the most significant cost savings. On average, a 0.45% cost improvement was observed for each percentage reduction in response times, reinforcing the importance of efficiency enhancements. These numerical results not only validate the critical role of response time reduction in cost control but also provide actionable insights for organizations to streamline their travel request management processes, improve budget allocation, and enhance operational efficiency in the corporate landscape.Öğe Parallelism-Based Session Creation to Identify High-Level Activities in Event Log Abstraction(Springer International Publishing Ag, 2024) Dogan, Onur; De Leoni, MassimilianoProcess mining utilizes event data to gain insights into the execution of processes. While techniques are valuable, their effectiveness may be hindered when dealing with highly complex processes that have a vast number of variants. Additionally, because the recorded events in information systems are at a low-level, process mining techniques may not align with the higher-level concepts understood at the business level. Without abstracting event sequences to higher-level concepts, the outcomes of process mining, such as discovering a model, can become overly complex and challenging to interpret, rendering them less useful. Some research has been conducted on event abstraction, often requiring significant domain knowledge that may not be readily accessible. Alternatively, unsupervised abstraction techniques may yield less accurate results and rely on stronger assumptions. This paper introduces a technique that addresses the challenge of limited domain knowledge by utilizing a straightforward approach. The technique involves dividing traces into batch sessions, taking into account relationships between subsequent events. Each session is then abstracted as a single high-level activity execution. This abstraction process utilizes a combination of automatic clustering and visualization methods. The proposed technique was evaluated using a randomly generated process model with high variability. The results demonstrate the significant advantages of the proposed abstraction in effectively communicating accurate knowledge to stakeholders.Öğe Process mining based on patient waiting time: an application in health processes(Emerald Group Publishing Ltd, 2022) Dogan, OnurPurpose Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method. Design/methodology/approach In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor. Findings The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors. Research limitations/implications The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution. Originality/value This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.Öğe Process Selection for RPA Projects with MDCM: The Case of Izmir Bakircay University(Springer Science and Business Media Deutschland GmbH, 2024) Erdogan, Ali Mert; Dogan, OnurRobotic Process Automation (RPA) has emerged as a powerful technology for streamlining business operations by automating repetitive tasks. It is important for public universities as it helps streamline administrative processes, improve operational efficiency, and free up staff resources, allowing the institutions to focus more on delivering quality education and enhancing the overall student experience. However, selecting the right processes for RPA implementation poses a challenge due to the multitude of criteria involved. To address this issue, this paper proposes a multi-criteria decision-making (MCDM) approach for RPA process selection. The objective of this research is to develop a systematic methodology that enables decision-makers to evaluate and prioritize RPA processes based on multiple criteria, such as process complexity, ROI, and strategic importance. The proposed methodology incorporates two MCDM techniques, including the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assist decision-makers in effectively assessing and ranking alternative RPA processes. AHP helps determine the relative weights of criteria, while TOPSIS ranks alternatives based on their similarity to an ideal solution. A case study was conducted to validate the effectiveness of the proposed methodology. Empirical results showed that “Campus Event Management” is the most suitable alternative for RPA implementation, followed by “Campus Facility Management” and “Library Management”. In the study, sensitivity analysis was also performed by changing the weight values given for three different experts. The findings of this research contribute to the field of RPA process selection by providing a structured framework that facilitates the evaluation and prioritization of RPA processes. The proposed methodology empowers organizations to maximize the benefits of RPA implementation by selecting processes that align with strategic goals, enhance operational efficiency, and optimize resource utilization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe A process-centric performance management in a call center(Springer, 2023) Dogan, OnurDiscovering valuable information needs some extra focuses on business processes. Although data-centric techniques yield useful results, they are insufficient to explain the causes of the problems in the process. This study aims to reveal the relationship between customer satisfaction and other key performance indicators (KPIs) affected by the activities performed during the call process. The research applies process mining, a pragmatic analysis to obtain meaningful insights through event logs. Several statistical analyses also support the process mining to test the statistical significance. The study showed that customer satisfaction is positively affected by average handle time and first call resolution, whereas staff mistakes diminish it. Moreover, problem solving is much more important than waiting in the system. Waitlisted and Waitlisted back activities are crucial elements of a call center system. Moreover, the research presents an insight for customers who give the same score after the call. It explains not only KPIs' effects but also reasons for giving satisfaction scores based on call process. Additionally, in previous studies, the customer satisfaction indicator was mainly emphasized, but other KPIs' effects on satisfaction level were ignored. This paper evaluates the impact of the identified KPIs on satisfaction in a process-oriented manner.Öğe A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)(Mdpi, 2023) Dogan, OnurE-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher's perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company's profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners' standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses.
