Client Segments Research Articles

Review of Literature: Business Sustainability Essentials For Business Startup

This literature review discusses research related to business model innovation and startup sustainability. The review shows that business model innovation plays an important role in increasing profits and value creation, which also helps startups survive in a dynamic business environment. Sustainable business models, concepts such as a business approach that emphasizes ethical practices and accountability in addressing social, environmental, and economic impacts, consumer awareness and technological innovation play an important role. The importance of the elements of an eco-conscious approach to business operations, particularly in light of the fierce competition and significant transformations shaping the food industry. These elements include client segments, the connections and interactions a business maintains with its customers to build loyalty and trust., key activities, key resources, partner network and cost structure. With regard access to resources and activities that involve the use of innovative, distinctive, and eco-friendly raw materials, along with certain assets, can ensure high-quality products with low environmental impact

Using machine learning to predict investors’ switching behaviour

Individual investors’ decisions to switch investments very often lead to significantly lower investment returns so having an effective predictive model of these switches would be of value to clients, advisors and investment managers. A random forest algorithm was applied to a new dataset of over 20 million observations relating to 95,685 clients on Momentum Investments’ platform between 2018 and 2024. It identified a combination of investor characteristics (number of holdings, past switching behaviour, total assets) and external features (past returns, macroeconomic variables) as the key features of investor switch behaviour. This model exceeds commercially accepted standards in respect of the AUC and Gini metrics showcasing the model’s strength in its ranking capability. It can thus provide a useful basis for client segmentation and engagement by financial advisors.

Assessing the Customer Perception of Quick Response (QR) Code Application and their Purchase Intention in Penang's Casual Dining Restaurants, Malaysia

In the era of globalization, some individuals no longer need the physical presence of a human to place an order due to extensive technological advancements that have triggered the use of contactless systems such as QR codes. QR codes represent modern technology that is easy to use by simply scanning the QR code at the guest table providing an easier way to access the restaurant menu. Hence, this study aims to assess the customer’s perception of the usefulness of QR code usage at casual dining restaurants and how the ease of use of QR code applications at casual dining restaurants affects their purchase intention. These factors play a crucial role in any technology-driven initiative's long-term adoption and success. Online questionnaires collected data from 299 valid respondents who have experience scanning QR code at particular casual dining restaurants. The findings indicate that the use of QR codes may have significantly influenced customers' purchase decisions, especially among the younger generation. One key reason cited is the ease of reviewing the menu through the QR code system. Therefore, it is recommended that the food service industry must be more accepting of this technology to gain significant advantages from its implementation. Overall, the study clarifies the potential positive aspects of using QR code ordering systems in restaurants and how they might meet the interests and demands of various client segments. Contactless technologies like QR codes are probably going to play a big part in the foodservice sector's continued evolution in terms of improving customer experiences and optimizing business processes.

Navigating challenges and opportunities: perspectives on digital service development in substance use disorder treatment

BackgroundSome people with substance use disorders (SUD) can experience multiple co-occurring social problems. Digital solutions have been developed to support effective and cost-effective social welfare and healthcare in addictions treatment. Given the varying severity of problems from alcohol and other drug use, digital service tools can save money and provide tailored care.ObjectiveIn this study we aimed to understand the perspectives of those who develop digital service tools on people with SUD and treatment encounters. As a case, we interviewed those who have been involved in the development of a digital client segmentation tool The Navigator.MethodsTen (N = 10) semi-structured interviews were conducted with professionals involved in digital client segmentation tool development and were analysed with inductive content analysis. Participants were asked about the development of the Navigator from the perspectives of their own role as developers, the clients, the effectiveness of the services, and decision-making processes.FindingsSome people with SUD may face several obstacles when using digital services. Digital divide, feared or experienced stigma and biased attitudes, complex life situations, and difficulties in committing to treatment were identified as challenges. Nevertheless, digital solutions can offer the clients alternative ways of using the services that can better meet their individual needs. The anonymity and facelessness of digital solutions can reduce the fear of immediate judgement. Implementing digital solutions in substance use work poses challenges due to chronic staff shortages. Digitalisation often results in the creation of multiple simultaneously managed channels, potentially reducing time-consumption but increasing the perceived workload. There is a call for multi-professionalism, acknowledging inequalities between various disciplines within the field.

Implementation of a client–centric model of customer service (based on the example of customer service in the field of telecommunications and communications)

Growing competitive pressure and problems of customer churn in the telecommunications industry determine the need for an intensive transition to customer–centric service. The article studies the features of the implementation of a client–centric model of customer service using the example of customer service in the field of telecommunications and communications. The key stages and principles of transition to a customer–centric model are formulated, and the maturity levels of the customer relationship management system are highlighted. Based on the analysis of scientific literature on the research topic, the features and key approaches to managing customer service, development and strategic planning in the activities of telecommunications companies are determined. Objective differences are established in building relationships with B2C and B2B client segments. Based on the results of the study, recommendations are formulated for the implementation of a client–centric model of client service in the field of telecommunications and communications, suggesting a transition to an integrated construction of a client–centric business model for the telecommunications business. It is emphasized that despite the historical tendency towards short–term planning in the telecommunications industry, a customer–centric business model is becoming increasingly in demand in inextricable connection with the ecosystem approach and maximum customer retention.

Customer Segmentation Using Machine Learning to Improve Marketing

Customer segmentation is the process of classifying customers according to characteristics they have in common. This enables organizations to effectively and accurately market to each group. In business-to-business marketing, a firm may divide its clientele based on a variety of criteria, such as location, industry, number of employees, and previous purchases made from the business. Marketers can more effectively target different audience subsets with their marketing efforts by using segmentation. These initiatives may pertain to product development as well as communications. In particular, segmentation aids in a business's ability to craft and deliver marketing communications that are specifically intended to appeal to some client segments while excluding others. Search and select the most effective communication channel. Based on the segmented clusters, this could be radio advertising, social media (Instagram, Telegram, Twitter, Youtube, etc.) email, or another strategy. We have used Gradio (an API for UI), the KNN Classification Algorithm (to classify new consumers), and the K-Means algorithm (to segment the customers) to accomplish this goal. Determine how to make goods or new opportunities for products or services better. It starts with collecting and evaluating data and concludes with taking suitable and useful action based on the information obtained. Key Words: Segmentation, targeted marketing, K-Means, KNN, Gradio.

The Impact of Financial Inclusion Dimensions on Improving the Quality of Financial Reports Published by Banks: A Field Study on a Sample of Sudanese Banks

This study aims to investigate the impact of customers’ access to financial services on improving the quality of financial reports. It also explores how customer utilization of financial services and the quality of services provided enhance the quality of published financial statements. A questionnaire was directed at a sample of employees working in Sudanese banks, with a total of 30 respondents. After conducting the field study, the researcher confirmed the hypotheses and obtained several key findings. Notably, a significant obstacle to financial inclusion is the lack of awareness regarding its importance among a large segment of clients. Additionally, the high frequency of bank account usage indicates that many Sudanese society members are under the umbrella of financial inclusion. Financial inclusion is a governmental regulatory tool that controls bank accounts and safeguards clients. Given its impact on financial inclusion, improving the quality of financial reports becomes crucial. The researcher recommends several actions, including raising awareness about financial inclusion and using it to monitor investors within banks. This encourages clients to participate in financial inclusion. Furthermore, banks should emphasize the importance of enhancing financial reporting quality, as it leads to increased investment and engagement with financial institutions. Efforts to establish financial inclusion in the Sudanese banking environment should be intensified, bolstering confidence in the banking sector. Finally, banks should provide specialized services for individuals with limited income and seek help from regional experiences.

A Comprehensive Review and Investigation of Business Models

The formulation and execution of a business model significantly influence the achievement and durability of a company. The business model serves as a strategic framework encompassing components such as value proposition, revenue streams, market segmentation, and core operations. Companies must possess a profound comprehension of the crucial components within their business models due to the constantly evolving and intricate nature of the business environment. Market dynamics, technological advancements, and shifts in customer behavior are influential elements in the evolution of company concepts. The value proposition is the primary factor in determining why customers should select a company's products or services. The analysis of business models highlights the significance of establishing distinctive characteristics and advantages that offer additional value to customers. Market analysis and client segmentation assist organizations in comprehending customer requirements and inclinations. Customizing marketing strategies and product offers for each market group is an essential step towards attaining increased relevance and attractiveness. Research on business models emphasizes the significance of expanding revenue streams and effectively handling risk. Companies should evaluate several pricing strategies and income streams in order to enhance their ability to respond to market fluctuations. Cost analysis and operational efficiency are crucial factors in defining a business's cost-competitiveness. Companies can enhance efficiency by identifying opportunities for improvement through a thorough understanding of the fundamental activities that underpin their business model. Utilizing technology and innovation is crucial for sustaining competitiveness and staying relevant. Enterprises must consistently seek opportunities to enhance operational procedures, create novel offerings, and adjust to technological advancements.

Кастомізація послуг кредитних установ

The article is devoted to the study of customization of services of credit institutions and features of their implementation. IT solutions have been proven to change the way businesses are organized and communicated with customers. Among the promising areas of such innovations in the financial sphere, financial software solutions aimed at customizing the services of credit institutions are allocated. It is argued that the trend towards customization of services is relevant for the financial industry, credit institutions are interested in the development of personalized services and a customer-oriented approach, which should replace the traditional technologies of working with clients of credit institutions. It is determined that the customization of services of credit institutions has its own specific features: making personalized decisions, using new financial technologies (artificial intelligence, big data analysis), identifying unique financial needs. The stages of customization of services are outlined, namely: conducting a detailed assessment of the client, segmentation of clients for the development of an individual strategy, individual financial planning, risk management and insurance. It is studied that strategies for customizing the services of credit institutions can be developed in the following ways: individual pricing, which means that institutions offer a service with an optimal and attractive price; personalized services, which provides the ability of credit institutions to analyze data on customer interactions and financial behavior in order to offer personalized services; individual financial consultations, which are implemented through programs with personal financial consultants. It is proved that the customization of credit institutions’ services still requires overcoming many problems, including: inefficient data management, outdated computer or software, organizational isolation, data confidentiality, etc.

AI-Enhanced IMC: Leveraging Data Analytics for Targeted Marketing Campaigns

Data analytics to improve targeted marketing efforts examines how artificial intelligence (AI) transforms Integrated Marketing Communications (IMC). The main focus is AI technology's effects on customization, real-time marketing, and client segmentation. The secondary data-based study reviews academic literature, industry publications, and case studies from Netflix, Coca-Cola, Sephora, and Starbucks. Major studies show that AI improves customization, real-time optimization, and accurate targeting, improving engagement, customer happiness, and campaign performance. However, data privacy, algorithmic prejudice, and ethics are issues. The report emphasizes the need for robust data security and fair AI rules that balance innovation and consumer protection. Responsible AI marketing requires robust legal frameworks and ethical standards to address these difficulties.

Pos Aja Customer Segmentation at the Yogyakarta Post Office Using the RFM (Recency, Frequency, Monetary) Method

Every business must continue to innovate due to the industry's rapid evolution if it hopes to avoid being disrupted by innovations in the future. The Pos Aja! app, a revolutionary new offering from courier and logistics services provider Pos Indonesia, was introduced in 2020. With Just Post!, consumers can transact from anywhere without physically visiting the post office. Pos Aja! facilitates not just transaction processing for clients but also helps businesses better understand their clientele. The technique known as customer relationship management, or CRM, works to maintain and improve ties between businesses and their clients. Client segmentation is one method of investigating client profiles. Recency, Frequency, and Monetary analysis, or RFM, is the customer segmentation method employed in this study in order to identify the traits of Pos Aja! customers. Three types of Pos Aja! customers—Gold, with 857 clients; Silver, with 566 customers; and Bronze, with 688 customers—were obtained based on the RFM data. The RFM data were then sorted to determine the category of new Pos Aja! clients. The Random Forest, Decision Tree, and Support Vector Machine (SVM) classification techniques are employed. With an accuracy level of 96.69%, the SVM model emerged as the top performer among these three models. Therefore, the SVM model can be applied to determine new client groups.

Unleashing the Power of Big Data: Designing a Robust Business Intelligence Framework for E-commerce Data Analytics

E-commerce companies are struggling to make use of the enormous amounts of data collected from diverse sources in the big data era. Designing and implementing a strong business intelligence (BI) framework that makes use of big data analytics is essential to overcoming this difficulty. The study examines how cloud computing affects mobile e-commerce, stressing its benefits for real-time data processing, scalability, and analysis. which boosts the competitiveness of Chinese e-commerce businesses. This study's goal is to deploy a comprehensive BI platform designed especially for e-commerce research to unleash the power of big data. Furthermore, the deployment of precision marketing techniques based on the RFM model and historical data analysis increases client segmentation, leading to targeted marketing efforts, greater customer happiness, and higher conversion rates. The major goal of this research is to equip e-commerce companies with the tools they need to take advantage of big data's potential and make decisions that will give them a competitive edge. Data storage, retrieval, and data mining are made possible by the integration of big data technologies, such as relational and distributed databases, along with parallelization via MapReduce. Ordinary, the findings of this newsletter spotlight the importance of embracing massive information technologies and methodologies in the e-trade sector. Leveraging cloud computing, records mining, and enterprise intelligence strategies can free up the capability of tremendous records assets, permitting enterprises to make informed choices, drive innovation, and gain a competitive facet. The paper highlights the value of security safeguards and risk assessment models in e-commerce systems, offering suggestions for spotting and reducing potential dangers and preserving the integrity of the system.

Predicting default risk bancassurance using GMDH and dce-GMDH neural network models

Purpose This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system. Design/methodology/approach This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer. Findings The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria. Originality/value This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

The value of reverse knowledge transfer: Case of Austrian Bank subsidiary in Slovakia

This research focuses on reverse knowledge transfer from Central Europe to Western Europe by analyzing the contribution of a specific Slovak subsidiary to MNC knowledge. It explores the first digital bank created in Slovakia, its origin, expectations, results, and expertise transmitted from the daughter company to the Austrian headquarters. The paper analyses the situation of Raiffeisen Bank International, headquartered in Vienna, and its digital daughter company ZUNO founded in Slovakia in 2010. It identifies the learnings transferred to the parent in digital banking, marketing, operational, HR, and strategic management. The authors also study subsidiary integration within MNC, alignment of its structure and processes with the parent and conclude that the new knowledge generation was especially valuable in digital banking, at work with new client segments, in implementing new forms of marketing, working with new technologies, creating new structures, and transferring them to the other foreign markets.

The effects of non-Big 4 mergers on audit efficiency and audit market competition☆

This study examines whether recent mergers between small and midsize accounting firms influence competition by increasing the number of firms that can compete with larger rivals in the U.S. public company audit market. I find that in-market mergers generate efficiencies that are reflected in a post-merger reduction in audit hours but not in reduced audit quality. For both in-market and out-of-market mergers, clients switching to post-merger firms are more likely to be accelerated filers and are significantly larger in terms of several proxies for size and complexity. Lastly, I find that an increase in market-level merger activity is associated with lower profitability of Big 4 firms operating in the same market but only in the smaller client segment. These findings suggest that recent mergers have actually increased competition in some segments of the audit market despite already high concentration and concerns about a lack of sufficient competition.

Customer Churn Prediction on E-Commerce Using Machine Learning

Abstract: For E-commerce businesses to produce successful marketing plans and customer retention tactics, client churn vaticination is pivotal. In order to handle the longitudinal timeframes and multiple data variables of B2Ce-commerce consumers' buying habits, the authors of this study present a loss vaticination model that integrates k- means client segmentation with support vector machine (SVM) vaticination. guests are divided into three groups according to the approach, which also defines the main customer groupings. In order to anticipate client development, the study analyses the efficacity of logistic retrogression and SVM vaticination. The findings show that client segmentation greatly increases each indicator’s capability to read values, emphasizing the significance of k- means clustering segmentation. also, it's demonstrated that SVM vaticination is more accurate than logistic retrogression vaticination. The conclusions of this study have important ramifications for client relationship operation.

LRFMV: An efficient customer segmentation model for superstores.

The Recency, Frequency, and Monetary model, also known as the RFM model, is a popular and widely used business model for determining beneficial client segments and analyzing profit. It is also recommended and frequently used in superstores to identify customer segments and increase profit margins. Later, the Length, Recency, Frequency, and Monetary model, also known as the LRFM model, was introduced as an improved version of the RFM model to identify more relevant and exact consumer groups for profit maximization. Superstores have a varying number of different products. In RFM and LRFM models, the relationship between profit and purchased quantity has never been investigated. Therefore, this paper proposed an efficient customer segmentation model, namely LRFMV (Length, Recency, Frequency, Monetary and Volume) and studied the profit-quantity relationship. A new dimension V (volume) has been added to the existing LRFM model to show a direct profit-quantity relationship in customer segmentation. The V stands for volume, which is derived by calculating the average number of products purchased by a frequent superstore client in a single day. The data obtained from feature extraction of the LRMFV model is then clustered by using conventional K-means, K-Medoids, and Mini Batch K-means methods. The results obtained from the three algorithms are compared, and the K-means algorithm is chosen for the superstore dataset of the proposed LRFMV model. All clusters created using these three algorithms are evaluated in the LRFMV model, and a close relationship between profit and volume is observed. A clear profit-quantity relationship of items has yet not been seen in any prior study on the RFM and LRFM models. Grouping customers aiming at profit maximization existed previously, but there was no clear and direct depiction of profit and quantity of sold items. This study applied unsupervised machine learning to investigate the patterns, trends, and correlations between volume and profit. The traits of all the clusters are analyzed by the Customer-Classification Matrix. The LRFMV values, larger or less than the overall average for each cluster, are identified as their traits. The performance of the proposed LRFMV model is compared with the legacy RFM and LRFM customer segmentation models. The outcome shows that the LRFMV model creates precise customer segments with the same number of customers while maintaining a greater profit.

Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming Over HTTP (DASH)

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

THE IMAGE OF COOPERATIVE BANKS IN THE RETAIL CLIENT SEGMENT

The aim of the research was to assess the image of cooperative banks in the retail client segment and to identify the factors that determine it. The subject of the research was the actual image of cooperative banks in the retail client segment. Primary information from own research was the main source of data. The survey method was used in the research (n = 400 respondents). The following methods were used to analyze the empirical material: descriptive statistics methods and one-way analysis of variance (F statistics). Based on the conducted research, it can be concluded that the actual image of the analyzed group of banks was assessed as neutral. Opinion on cooperative banks varied depending on gender, age and average monthly income per person in the respondent’s family. As the analysis shows, the assessment of the image of cooperative banks by individual clients is performed by assessing the ability to ensure security. The elements of the visual identification system were considered the least important in assessing the actual image of the bank. In the opinion of the respondents, cooperative banks are reliable institutions, meeting customer expectations, acting in a responsible manner and solving problems quickly. On the other hand, clients did not notice the innovativeness of cooperative banks, which in terms of technological innovations are not inferior to commercial banks. Therefore, bank managers should take a number of actions and initiatives aimed at changing the actual image of cooperative banks in this area.

The audit market for listed Australian companies from 2012 to 2018: A state of play

Motivated by recent regulatory scrutiny of auditing in Australia, we provide an overview of the audit market for Australian listed companies from 2012 to 2018. Using descriptive analyses, we explore audit market competition, the provision of non-audit services (NAS), and audit firm tenure. We find that the Australian audit market is highly segmented. Big 4 firms increasingly dominate the larger client segment, while Non-Big 4 firms focus on medium and smaller clients. Auditor-provided NAS fees represent a relatively small fraction of audit fees for smaller clients, but a relatively high fraction for larger clients. We further observe that the share of total revenue from NAS of Big 4 firms increases over time. Finally, a relatively small percentage of clients has long audit firm tenure, and that long tenure is more common in the larger client segments. We discuss the implications of these findings and research opportunities that emerge. JEL Classification: D40, L11, M42, L84