Customer Profiles Research Articles

Exploring Consumer Satisfaction and Service Quality at Bhatbhateni Supermarket

Background: Customer satisfaction and service quality are pivotal to the success of businesses, particularly in the competitive retail landscape of Kathmandu. Bhatbhateni, as one of the leading retail chains in Nepal, plays a crucial role in offering a wide array of products and services. Understanding customer perceptions regarding service quality and satisfaction is essential for enhancing operational strategies and maintaining a competitive edge in this dynamic market. Objectives: This study aims to analyze the demographic profile of Bhatbhateni's customers, evaluate their perceptions of service quality and satisfaction, identify key service needs and expectations, and assess the likelihood of repurchase and recommendations among customers. Methods: A quantitative research approach was employed, utilizing structured questionnaires distributed to a sample of 120 customers of Bhatbhateni, selected through convenience sampling to ensure a diverse representation of gender and age. The questionnaire encompassed demographic inquiries and Likert scale items measuring customer satisfaction, service quality, and overall service experience. Data analysis involved descriptive statistics to summarize findings and identify trends in customer perceptions. Findings: The results indicated that the sample comprised 53.3% male and 46.7% female customers, with 61.7% of respondents aged between 15-25 years. A substantial 70.0% of respondents felt that Bhatbhateni understood their service needs effectively. In terms of satisfaction levels, 43.3% of customers reported being somewhat satisfied, while 12.5% expressed being very satisfied with the products. Moreover, 45.8% of respondents rated Bhatbhateni's quality management as good, and 41.7% positively rated staff responsiveness. Additionally, 48.3% indicated a high likelihood of repurchasing products, and 61.7% stated they would likely recommend Bhatbhateni to others. Conclusion: The study reveals a generally positive perception of service quality among Bhatbhateni's customers, highlighting key areas of strength and potential for improvement. Customer satisfaction is significantly influenced by the understanding of service needs and the quality of management. These findings suggest that Bhatbhateni is well-positioned for sustained growth through enhanced customer engagement and service delivery. Novelty: This research contributes valuable insights into the relationship between service quality and customer satisfaction within the specific context of Bhatbhateni, a prominent retail chain in Nepal. By employing a structured questionnaire and statistical analysis, the study adds to the limited existing literature on customer service perceptions in Nepal's retail sector. The findings can guide Bhatbhateni and similar businesses in developing targeted strategies to improve customer satisfaction and loyalty, ultimately enhancing business service management practices in the region.

SMART SHOPPING ASSISTANTS: HOW GUIDE ROBOTS IMPROVE CUSTOMER EXPERIENCE IN LARGE MALLS

The Shopping Guide Robot System is a cutting-edge solution designed to enhance the customer experience in large shopping malls and commercial spaces. This system provides real-time route guidance to help customers quickly locate their desired stores by calculating the shortest path. It offers personalized recommendations based on customer profiles, adjusts dynamically to environmental changes, and supports multilingual communication to cater to diverse clientele. The robot not only guides users to their destinations but also answers inquiries about mall events, discounts, restaurants, and other services. Through advanced autonomous navigation technology, the system ensures a safe and efficient shopping experience, making it an invaluable tool in modern retail environments.

Perancangan Value Proposition Canvas untuk Produk Indibiz PT Telkom

This Indibiz is a digital service aimed at micro, small, and medium enterprises (MSMEs) in Indonesia. This service provides consistent and high-quality internet connectivity to support various MSME businesses. Indibiz is designed to offer affordable internet, helping to reduce operational costs and boost MSME productivity in the digital era. However, over the past year, Indibiz has not met its sales targets. The main cause is that Indibiz has yet to fully meet the desired customer profile. To address this, the author designed a Value Proposition Canvas by collecting data on Indibiz's services, customer profiles, and competitor products through observation, literature review, and interviews with 10 MSMEs. These data were used to create a customer profile and value map for Indibiz's products. A strategy canvas analysis and ERRC framework were applied to develop a new Value Proposition Canvas. The proposed solution includes adding an all-in-one MSME digitalization package, bundling digital marketing services, and offering a sales guarantee for each digitalization service purchase.

Evaluasi dan Perancangan Model Bisnis UMKM Miss Bluder dengan Business Model Canvas

Miss Bluder is an MSME that sells bluder bread in the city of Bandung. In recent times, Miss Bluder has experienced a significant decline in sales. This is due to limitations in human resource management, suboptimal partnerships, and lack of effectiveness in the sales media used. This research aims to evaluate and design a new business model for Miss Bluder using the Business Model Canvas method. The data used included current business models, customer profiles from interviews, as well as business environment data from literature studies and online research. A SWOT analysis was carried out to formulate an improvement strategy, followed by the design of a Value Proposition Canvas, Channels, and Customer Relationships. After the business model design is completed, verification, financial simulation, and validation processes are carried out through evaluations and interviews with business owners. With the implementation of the new business model, it is hoped that Miss Bluder can overcome the decline in sales, improve business performance, and strengthen its position in the market.

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

The Patterns of Digital Deception

Current consumer-protection debates focus on the powerful new data-analysis techniques that have disrupted the balance of power between companies and their customers. Online tracking enables sellers to amass troves of historical data, apply machine-learning tools to construct detailed customer profiles, and target those customers with tailored offers that best suit their interests. It is often a win-win. Sellers avoid pumping dud products and consumers see ads for things they actually want to buy. But the same tools are also used for ill—to target vulnerable members of the population with scams specially tailored to prey on their weaknesses. The result has been a dramatic rise in online fraud that disproportionately impacts those least able to bear the loss. The law’s response has been technology centric. Lawmakers race to identify those technologies that drive consumer deception and target them for regulatory restrictions. But that approach comes at a major cost. General-purpose data analysis and communications tools have both desirable and undesirable uses, and uniform restrictions on their use impede the good along with the bad. A superior approach would focus not on the technological tools of deception but on what this Article identifies as the legal patterns of digital deception: those aspects of digital technology that have outflanked the law’s existing mechanisms for redressing consumer harm. This Article reorients the discussion from the power of new technologies to the shortcomings in existing regulatory structures that have allowed for their abuse. Focus on these patterns of deception will allow regulators to reallocate resources to offset those shortcomings and thereby enhance efforts to combat online fraud without impeding technological innovation.

Profiling Standards to Improve Practical Interoperability

Abstract. Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.

Inference and design of antibody specificity: From experiments to models and back.

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

The effect of eating alone on behavioral intention: A case of Rize

In the modernizing and developing world conditions, eating and drinking habits and behaviors of individuals also change. With the effect of this change, consumers' collective eating habits have started to be replaced by individual eating. It is possible to state many reasons for this. The concept of eating alone, which has come to the fore in recent years, is very important for food and beverage businesses. It is because determining the needs of this changing customer profile and knowing the behaviors of this customer profile means determining the steps to be taken for these businesses. Therefore, in this study, the effect of eating alone on behavioral intention was investigated. There is no study examining the relationship between these variables. In addition, bungalow accommodation facilities, which have been under construction in Rize in recent years, are also businesses that will attract the attention of tourists with a eating alone profile. Therefore, it is also important to investigate these variables in Rize. A questionnaire form was created to measure this effect, and a total of 406 usable data were obtained. These data were subjected to various analyses using SPSS 24 and AMOS programs. As a result of these analyses, it was found that eating alone had no significant effect on behavioral intention. This result could not be compared since there is no finding in the literature addressing these variables. Therefore, testing the relationship between eating alone and behavioral intention as a whole through the model created for this research fills the gap in the literature. As a result of this result, suggestions for future studies and tourism policy makers are presented.

A Psychological Approach to Credit Risk Management: A Qualitative Dyadic Survey of Moroccan SMEs

The banking environment has become both unstable and vulnerable due to various fluctuations in the monetary sphere. Faced with these various disruptions, banks are increasingly threatened by risks affecting their activity and position on the financial market, notably credit risk. Risk management is the best defense shield for ensuring a healthy financial equilibrium in the long term. Nevertheless, it remains an advanced practice, which Moroccan banks are unable to support, due to the lack of human and financial resources. This article intends to highlight the importance of customer and banker behavior in the credit granting process. It analyses the customer's profile, which can have an impact on the credit granting decision, using differential psychology and in particular the theory of personality traits -BIG5-, and the banker's behavior, using the theory of judgement and decision-making - JDM-.

Customer Segmentation by using Annual E-Invoice Data

As businesses navigate the digital landscape, the proliferation of electronic transactions has led to an abundance of valuable data that can be harnessed for strategic decision-making. This study explores the application of CRM and RFM analysis for customer profiling and segmentation, utilizing e-invoice data as a rich source of information. By leveraging these advanced statistical techniques, the research aims to uncover hidden patterns within electronic transaction records, allowing for the identification of distinct customer segments based on their purchasing behavior. The methodology involved collecting and pre-processing one year of e-invoice data from Fit IT Company, followed by applying statistical models to uncover underlying structures and relationships. Furthermore, the research examines the implications of customer segmentation on marketing strategies, customer relationship management, and personalized service offerings. CRM and RFM analyses were performed on the annual sales data obtained as a result of e-invoice usage service to customers. When the results of the analysis were analyzed, the number of transactions belonging to the sender, recipient, and parties in the top 10 every month were extracted. It has been demonstrated that customer segmentation can be conducted more comprehensively by using CRM and RFM analyses together. While CRM analysis focuses on transaction volume and customer relationships, RFM analysis provides a more detailed perspective on customer behavior by evaluating purchase frequency, recency, and monetary value. In the study, by analyzing e-invoice data through these two methods, the most valuable customer groups were identified, and how strategic marketing approaches can be developed for these groups was illustrated. The combined use of CRM and RFM analyses allows for more accurate customer segmentation based on both transaction volume and spending habits. This approach concludes that strategies can be developed to increase customer loyalty, optimize marketing strategies, and improve business performance.

Perancangan Model Bisnis Kaki Bukit Coffee Menggunakan Metode Business Model Canvas

Kaki Bukit Coffee is a coffee shop located in West Bandung Regency, precisely on Jl. Maribaya No.16, Kayuambon, Lembang District, which was founded on December 16 2018. This coffee shop offers various types of drinks, both hot and cold, such as Basic Coffee, Flavored Coffee, Freshener, and Tea. However, Kaki Bukit Coffee faces several challenges, including not achieving its revenue target. This challenge is caused by several factors such as lack of employee capability, limited space capacity, lack of product innovation, many competitors, the influence of rainy weather, less than optimal promotional strategies, and limited use of promotional media only via Instagram. This research aims to identify and overcome the problems faced by Kaki Bukit Coffee through evaluating and designing a new business model using the Business Model Canvas (BMC) method. The improvement process begins with collecting data regarding the current condition of the business model, including customer profiles obtained through questionnaires and analysis of the business environment through literature studies. Next, a SWOT analysis is carried out to understand the strengths, weaknesses, opportunities and threats from internal and external aspects, as well as identifying the value proposition canvas to formulate the right strategy. Several proposals were put forward to solve problems such as in the Value Proposition block, proposals included increasing product variations, offering variations in glass sizes and toppings, as well as providing bundling packages. In the Customer Segments block, the focus is on increasing the number of Lembang customers. For Channels, proposals include increasing the use of online food delivery applications such as GoFood, increasing promotions via social media such as Instagram, adding the ShopeeFood and TikTok platforms, as well as displaying menu lists and linktr.ee on Instagram profiles. In the Customer Relationship block, proposals include offering discounts and promos, providing special promotions for local residents, as well as providing WiFi connections. In the Key Activities block, increasing sales and marketing activities, adding training and HR development programs, as well as improving indoor and outdoor facilities are priorities. In the Key Partners block, namely increasing cooperation with raw material suppliers. Meanwhile, in the Cost Structure block, the proposal includes additional costs for improving indoor and outdoor facilities, training and human resource development costs, as well as costs for WiFi connections. Lastly, in the Revenue Streams block, the focus is on increasing product sales offline and online.

Consumer in the Buy Now, Pay Later Market – Opportunities and Threats

The purpose of the article. The purpose of the article is to identify opportunities and threats for consumers related to BNPL payments. Methodology. The article uses the analysis of existing data (in particular the current reports on the BNPL sector) and the results of a questionnaire survey. The CAWI questionnaire survey was conducted on a group of 100 people (50 women and 50 men) in January–February 2024. The survey was addressed to the group of people aged 25–34 due to the fact that, in the light of literature research, people of this age most often use deferred payments. The survey was sent to graduates of the Faculty of Economics and Sociology of the University of Lodz who fit into the deferred payment customer profile. Results of the research. The article identified opportunities and threats for customers of the BNPL sector, highlighted a profile of a customer using deferred payments, and presented the legal and psychological aspects related to deferred payments. Additionally, the article compared BNPL payments with credit cards, bank loans and non-bank loans, and related the results to the potential risk of over-indebtedness.

Consumer perspectives on interventions to combat fraudulent product returns in omnichannel fashion retail

PurposeThe expansion of online shopping aligned with challenging economic conditions has contributed to increasing fraudulent retail product returns. Retailers employ numerous interventions typically determined by embedded perspectives within the company (supply side) rather than consumer-based assessments of their effectiveness (demand side). This study aims to understand how customers evaluate counter-fraud measures on opportunistic returns fraud in the UK. Based on the fraud triangle and the theory of planned behaviour, we develop an empirically informed framework to assist retail practice.Design/methodology/approachWe collected 485 valid survey responses about consumer attitudes regarding which interventions are effective against different types of returns fraud. First, a principal component section evaluates the policies' effectiveness to identify any policy grouping that could help prioritise specific sets of policies. Second, cluster analysis follows a two-stage approach, where cluster size is determined, and then survey respondents are partitioned into subgroups based on how similar their beliefs are regarding the effectiveness of anti-fraud policies.FindingsWe identify policies relating to perceived effectiveness of interventions and create customer profiles to assist retailers in conceptualising potential opportunistic fraudsters. Our product returns fraud framework adopts a consumer perspective to capture the perceived behavioural control of potential fraudsters. Results suggest effectiveness of different types of interventions vary between different types of consumers, which leads to the development of propositions to combat the fraud.Originality/valueThis study is unique in assessing the perceived effectiveness of a range of interventions based on data collection and advanced analytics to combat fraudulent product returns in omnichannel retail.

Research on the use of communication big data and AI artificial intelligence technology to construct telecom fraud prevention behavior portrait

A solid foundation for behavior portrait construction in the fight against telecom fraud is the goal of this research. The study explores the integration of communication AI and Big Data technologies, focusing on the perspective of artificial intelligence. By using insights obtained from a telecom fraud detection model that relies on users’ behavior variations expressed through time-varying signatures, the goal of this study is to enhance fraud prevention strategies in the telecom industry. Through the examination of call detail records and customer profile information, the TeleGuard AI Fraud Prevention Framework (TGAI-FPF) aims to recognize suspicious trends and variations that are potentially suggestive of fraudulent actions. The purpose of the model is to generate behavior portraits that are capable of capturing the distinctive aspects of fraudulent conduct in telecom networks. This will be accomplished through the utilization of advanced analytics and machine learning algorithms. The study highlights the significance of leveraging big data analytics and artificial intelligence technologies to efficiently detect and thwart fraudulent activity in the telecom industry. The results of this study should fortify the defenses of telecom networks against growing fraudulent schemes and help in the development of preventative measures to combat fraud. This is the anticipated manner in which the results will add.

Cognitive customer profiling at the edge: Challenges and research directions

The proliferation of automated services and applications has generated large amounts of data that must be handled with artificial intelligence and machine learning techniques, impacting businesses in different fields. Cognitive customer profiling services allow a better user experience enabling the tailoring of the customer experience to specific consumer patterns and preferences. Cognitive customer profiling services require heavy processing power in order to get accurate results from the artificial intelligence/machine learning models, which has led to services taking advantage of the cloud-to-edge continuum. This trending approach brings a set of challenges that have to be addressed. The more recent edge intelligence paradigm introduces artificial intelligence capacities to edge nodes, enabling a new set of solutions. However, artificial intelligence techniques must be adapted to deal with the specific characteristics of these scenarios. This paper identifies the challenges of running cognitive customer profiling in centralized scenarios, such as the cloud, to later introduce other approaches more suited for scenarios like the edge, identifying challenges and future research directions in the area.

The Emerging Challenges of Customer Relationship Management with Customer Data Platforms (CDP): Constraints and Perspectives

Customers today are increasingly demanding a personalized shopping experience, coupled with stringent data security measures and prompt responses. As the landscape of online privacy evolves, particularly with the anticipated sunset of third-party cookies by 2024, businesses face a critical imperative to revamp their customer data management strategies. The emergence of Customer Data Platforms (CDPs) stands out as a pivotal solution in this evolving ecosystem. This article undertakes a detailed critique of CDPs, starting with insights gathered from both users and industry experts, and subsequently examining consumer perceptions and behaviors in the digital realm. At the heart of contemporary consumer expectations lies the desire for tailor-made interactions that reflect their preferences and behaviors. This demand necessitates robust data management tools capable of aggregating, analyzing, and activating customer data across multiple touchpoints. CDPs have emerged as a central tool in this endeavor, offering businesses a unified platform to integrate disparate data sources and derive actionable insights in real-time. By leveraging comprehensive customer profiles built from first-party data, CDPs enable businesses to deliver personalized experiences that foster customer loyalty and drive revenue growth. Moreover, the impending demise of third-party cookies, compounded by stringent data protection regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), underscores the urgency for businesses to adopt privacy-centric data practices. CDPs play a crucial role in this paradigm shift by empowering businesses to ethically collect and utilize customer data, thereby enhancing transparency and trust. However, challenges persist, including the complexity of integrating CDPs into existing infrastructures and ensuring compliance with evolving regulatory frameworks. From a consumer standpoint, attitudes towards data privacy are evolving, with an increasing emphasis on transparency and control over personal information. Consumers are more inclined to engage with brands that demonstrate a commitment to data security and ethical data usage practices. This shift necessitates a nuanced approach from businesses using CDPs, emphasizing not only personalization but also data protection and consent management. While CDPs offer substantial benefits in navigating the complexities of modern data management and meeting consumer expectations for personalized experiences, their successful implementation hinges on addressing technical, regulatory, and ethical considerations. By doing so, businesses can not only adapt to the evolving landscape of digital privacy but also foster enduring customer relationships grounded in trust and mutual value.

Impact of atmospheric vertical profile on hyperspectral simulations over bright desert pseudo-invariant calibration site

ABSTRACT This paper investigates how atmospheric vertical profiles of pressure, temperature, and concentration affect molecular absorption calculation. This sensitivity analysis is performed in preparation to the upcoming TRUTHS mission, anticipated to provide hyperspectral TOA BRF records with a radiometric accuracy better than 1%. Two methods for characterizing the atmospheric vertical profile are compared: rescaling the H2O and O3 concentrations of an AFGL U.S. Standard vertical profile, and using customized profiles based on CAMS data. The study investigates the effects of those methods on multi-spectral observations in key spectral regions affected, respectively, by water vapour, ozone and methane, as well as on hyperspectral observations covering the visible to SWIR region. Results show that when molecular transmittance exceeds 97%, the choice of method has minimal impact, with less than 1% uncertainty. When the molecular transmittance decreases from 97% to 75%, the corresponding uncertainty on the TOA BRF simulation increases from 1% up to 5%. For transmittance below 75%, using CAMS data for vertical profile characterization is recommended. The study also highlights how pressure and temperature profiles influence Rayleigh optical thickness estimation, particularly affecting TOA BRF in the blue spectral region.

Economic aspects of Industry 4.0 marketing technologies implementation in the agricultural sector of Ukraine

The study aimed to assess the impact of Industry 4.0 marketing technologies on the efficiency of business processes in the agro-industrial complex of Ukraine in the current market conditions. The study established that Industry 4.0, as a new stage of technological development, radically changes traditional methods of production, business and management by integrating digital technologies with physical processes. With the use of big data, artificial intelligence, and machine learning, agricultural businesses can create accurate customer profiles, develop personalised marketing campaigns, and automate routine tasks such as audience segmentation and forecasting market changes. The study also addressed the impact of the Internet of Things, social media and digital platforms on customer engagement and supply chain management. Innovations in e-commerce and the use of augmented and virtual reality also substantially improve the marketing efficiency of agricultural enterprises. The impact of the war in Ukraine on the introduction of digital technologies in the farming sector was emphasised. While investment in digitalisation declined during the conflict, the war also stimulated the search for innovative solutions, such as the use of drones to monitor fields and technology to improve cybersecurity. The impact of the war on consumer habits and priorities has also necessitated rapid adaptation of marketing strategies. Based on the study, which included a survey of 727 enterprises, the level of adoption of Industry 4.0 technologies among large, medium and small enterprises, as well as their plans for further implementation, was determined. The results showed that large enterprises are leading the way in adopting new technologies such as data analytics and artificial intelligence, while small and medium enterprises are just beginning to implement these solutions

Café Management System

The Café Management System (CMS) project is designed to revolutionize the traditional café operations by integrating digital solutions to streamline management processes and enhance customer experiences. This thesis explores the development and implementation of a comprehensive CMS, encompassing an Admin Panel, Staff Panel, and Customer Panel. The Admin Panel serves as the central hub for managing the entire system. It includes functionalities such as Staff Management, Product Management, and Customer Management. Admins can efficiently oversee staff schedules, update menu items, and manage customer profiles, ensuring smooth operations. The Staff Panel empowers café employees with tools to improve workflow efficiency. Staff members can take orders, process payments, and generate order reports seamlessly. This panel facilitates better communication and coordination among staff members, resulting in improved service quality. The Customer Panel offers patrons a user- friendly interface to interact with the café digitally. Customers can place orders, make payments, and view order history conveniently. Additionally, they can provide feedback and suggestions, fostering a collaborative relationship between the café and its clientele. Key features of the CMS include order management functionalities such as Place Food Orders and Cancel Orders, facilitating a hassle-free ordering experience for customers. The system also supports secure payment processing and generates detailed receipts for transactions, ensuring transparency and accountability. Furthermore,the CMS provides robust reporting capabilities, allowing administrators to access Order and Payment Reports, as well as view Total Sales data. These insights enable informed decision-making and strategic planning to optimize café performance. Key Words: Payment report, update menu items, food order, cancle order, user friendly interface, Total sales data,Admin panel Services,managing the entire system, staff panel, customer panel, Admin panel,