Customer Groups Research Articles

ANALISIS PENGARUH LAYANAN I-BANKING, M-BANKING, DAN FASILITAS ATM TERHADAP KEPUASAN NASABAH (STUDI KASUS NASABAH BANK BTN KCP TULUNGAGUNG)

The pattern of human’s behavior in using electronic services to acquire various kinds of information is affected by the rapid technological advancement. This is beneficial for, particularly banking, industries, as evidenced by the fact that banks nowadays come up with cutting-edge services such as internet banking and mobile banking. The sample of this quantitative descriptive research was selected using non-probability sampling, while the data was examined using factor analysis in SPSS version 15.0 of 2023. The result of the F test (simultaneous) is 56.554, which is greater than the F table of 2.699, and therefore H0 is rejected, and H1 is accepted. As the significance value of 0.000 is smaller than 0.05, internet banking, mobile banking, and ATM facilities simultaneously have a significant effect on the satisfaction of customers at the sub-branch office of Bank BTN in Tulungagung. Despite the positive and significant effect of the three variables, mobile banking is more preferable for some customer groups. In addition to its practicality, the service is helpful when they are in a difficult condition. In the future, the bank will develop a service where facilities in the internet banking will be integrated into its mobile banking application for greater customer benefit and ease

Research on the Influencing Factors of Consumer Buying Behavior

Today's businesses depend greatly on the buying habits of its customers. Understanding consumer behavior is essential for businesses to optimize their marketing strategies and boost customer satisfaction in today's fiercely competitive industry. This study examines customer personalities and their influence on purchasing behavior by using the purchase histories and demographic information of 2,240 clients. 10 research sources from Google Scholar are used to analyze the key factors that affect consumer behavior and the effectiveness of different clustering algorithms in identifying distinct customer groups in order to improve the accuracy of the study. The study emphasizes how important lifestyle, psychographic, and demographic characteristics are for affecting customer behavior. Businesses may better understand their clients and create marketing strategies that are tailored to their requirements by utilizing these characteristics. The work also raises the possibility of using clustering techniques to improve marketing efforts and increase client loyalty. Businesses may more effectively target their marketing efforts and boost customer satisfaction by categorizing customers into several categories based on their preferences and behavior patterns. As a result, this research offers insightful information about customer purchase habits. Businesses may better understand their clients and target their marketing initiatives to fit their demands by employing demographic and behavioral data. In doing so, companies may boost client pleasure and loyalty, eventually leading to long-term success.

Dynamics of user-generated content and service failure recovery: evidence from millennials

PurposeThere is considerable interest in the value of user-generated content (UGC) and its antecedents. Despite its growing importance, existing studies have largely ignored the effects of UGC on customers’ responses to recovery efforts in the fashion industry. The aim of this paper is to examine the extent to which UGC influences customers’ responses to providers’ service failure and recovery efforts, particularly how millennials’ interactions impact recovery efforts.Design/methodology/approachThe study uses a phenomenological hermeneutics and adopts theoretical sampling to collect empirical data from three European countries (France, Italy and the UK). The authors interviewed 60 millennials who had online service failure experiences in online fashion. This methodological framework was designed to illustrate the close relationships between subject and object as well as identify that data analysis and collection are undertaken in relation to consistent iterative interpretations in an evolving process of study. Drawing on multi-theoretical lenses, using actor–network and social influence theories, this study advances understanding through the development of a new conceptual model relating to individual characteristics.FindingsUsing actor–network theory and social influence theory, this study developed a conceptual model of four customer groups’ responses to service failure based on the severity of service failure and the level of customers’ online response following service failure.Originality/valueThe authors suggest some pragmatic implications of their conceptual model and explain how awareness of different customer groups can lead to effective decision-making for marketers. This study provides a set of practical insights that brand managers can use to recover service failures.

Solution Of Capacitated Vehicle Routing Problem For A Food Delivery Company With Heuristic Methods

A food delivery company operates in Balıkesir performs to the distribution for the products of a certain brand from the central warehouse to the customers located in the central districts of Altıeylül and Karesi by using two vehicles with high capacity. The company visits customers on certain routes to meet their daily demands and is able to meet all demands at the end of the day. In this study, the distribution of the company's products was considered as a Vehicle Routing Problem, and it was aimed to reconstruct the distribution routes of the vehicles with the help of various algorithms and to provide cost savings in terms of the distance traveled. In order to solve the problem, first of all, an appropriate capacity assumption was made for the vehicles by considering the daily demand amounts of the customers. Under this assumption, first new customer groups to be visited in daily periods were created, and then new routes were obtained for the relevant customer groups. In this process, the problem was designed as a Capacity Constrained Vehicle Routing Problem, and the results obtained using Fisher and Jaikumar's Algorithm and Clarke and Wright's Savings Algorithm were evaluated. When the results obtained are compared with the current route status of the company, it has been determined that it is possible to achieve a high rate of improvement by using the routes determined by algorithms.

Shopping Mall Customers Clustering Based on LOF and K-means++

Effective customer analysis is vital for business success, and AI-based customer analysis can significantly improve its accuracy, particularly regarding customer grouping and labeling, enabling merchants to generate personalized marketing strategies. Theories on machine learning models for grouping data have been developed since the 1950s, including logistic regression, Support Vector Machine (SVM), decision tree, and random forest; however, computational limitations hindered their practical application. Recent advances in computer technology have led to the development of more accessible machine learning algorithms that generate high-value results. The K-means clustering algorithm is one such model that best fits the customer labeling requirements. As an unsupervised training model, the K-means algorithm clusters customer data into a predetermined number of clusters. In this paper, we apply the K-means algorithm to separately cluster data on male and female clients, while using the K-means++ model to keep initial cluster centers as far apart as possible. We also apply the LOF algorithm to remove any outliers and modify the dataset accordingly.

Does Thinking and Working Politically improve support for social business? Evidence from a water business in Cambodia

AbstractMotivationSocial business is business that aims primarily to achieve social ends, while at least covering its costs. Social business has been put forward as a means to achieve development aims, such as the Sustainable Development Goals (SDGs). Thinking and Working Politically (TWP) is an approach to ensure development interventions are politically feasible and not stymied by political opposition. Are the two approaches compatible?PurposeThe article asks whether social business can ally with TWP for more effective development, taking a social business for water provision in Cambodia as a case in point.Methods and approachA review of development policy literature on social business and TWP is combined with data we collected from fieldwork in Cambodia from November 2018 to February 2019. We interviewed key informants from social business, UNICEF, and government in the capital. At two water kiosks in eastern Cambodia, one successful the other less so, we interviewed 2 kiosk operators, 20 customers, and local leaders; we held four focus groups of customers; and we observed operations at the kiosks. Data was analysed using an ethnographic, or immersive, qualitative approach.FindingsIn this case, UNICEF intuitively used TWP to connect a social business to government departments. Water kiosks depended heavily on local political dynamics. We found Commune officials mindful of their standing in local society who often had the interests of the local community in their hearts, but the compatibility of TWP and social business at the local project level is variable.Policy implicationsTWP can potentially help development actors supporting social business to make political connections, in this case to appropriate government departments and local official. The key, however, is to understand context. The danger is to view social business as a turnkey intervention that can be used without adaptation to context, without consideration of local politics. Donors supporting social business need to appraise each business, case by case.Donors might also want to consider the motivations of international companies partnering with social business, particularly when different development objectives might be in conflict.

Contrasting the drivers of Islamic bank adoption in Indonesia

This study aims to determine Indonesia's Islamic Bank Adoption drivers, especially in some customer groups. PLS-SEM is an analytical method used to test hypotheses. The analysis results show that the mediation type is partial mediation, but the mediation type for East Java customers and Generation Z is complete mediation. Based on the results, Islamic Banks must maintain their excellence to attract more customers. The sense of security in using Islamic Bank products and services is the primary indicator for East Java, generation Z, and non-generation Z customers. The reliability of Islamic Bank products and services is the primary indicator for non-East Java customers. Islamic Banks must also be able to build customer intentions to use Islamic Bank products and services, especially for Generation Z East Java customers. This study finds that intention needs to be built to increase the Adoption of Islamic banking products and services.

A combination of forensic-based investigation algorithm and density peak-based fuzzy clustering for custom segmentation

Custom segmentation is a process of classifying potential customers based on their mutual features such as shopping habits, consumption trends, and demand to provide an effective marketing campaign for each customer group. Data clustering is one of the most common methods for custom segmentation. This study proposed a novel clustering method that employs density peak-based fuzzy c-means (DP-FCM) and forensic-based investigation (FBI) algorithms. The proposed method (denoted as DP-FBI-FCM) aims to provide an effective clustering technique that can exploit the global optimal solution for custom segmentation problems. Besides, the proposed DP-FBI-FCM is used to segment wholesale customer data of a supermarket. As a result, four distinct customer groups are classified. Businesses can implement different strategies in each cluster to retain and attract their customers.

Promoting customer loyalty through e-marketing communication at commercial banks

Purpose: The research aims to find out the relationship between e-marketing communication, customer satisfaction, and loyalty at Vietnamese commercial banks.
 Design/Methodology/Approach: Customers who use e-banking services in Ho Chi Minh City are surveyed to gather the data. Using Google Forms, the survey was first pretested on 10 people. There were 224 valid responses to the official survey. Following that, SmartPLS processed the data in two steps: evaluating the measurement model and the structural model.
 Findings: The results show that e-marketing communication has a positive relationship with customer satisfaction and loyalty. Besides, customer satisfaction mediates a positive relationship between e-marketing communication and customer loyalty.
 Conclusion: The outstanding advancement of information technology has facilitated digital transformation in all aspects of life, including the banking industry. As technology advanced, marketing was the industry that underwent the most change. E-marketing makes it possible to establish and maintain a close relationship with clients, which is essential for being successful in luring them. E-marketing must connect many people, particularly the target customer group, and is not just focused on the design, logo, and overall user interface.
 Research Limitations and Implications: The effectiveness of direct marketing is not combined or compared in this study. It only examines the effect of e-marketing.
 Practical Implications: The banks should identify, contact, and maintain regular contact with potential customers. They can incorporate unstructured data from social networks. Furthermore, the banks must develop methods for evaluating and measuring the efficacy of their marketing efforts. This study gives bank managers in Vietnam suggestions for an appropriate e-marketing strategy.

A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems

Empowered by the ubiquitous sensing infrastructure, predictive maintenance (PdM) has received increasing attention. However, designing predictive maintenance for multiple systems remains a challenging problem as it requires effective integration of the prediction and scheduling under uncertainty and constraints. This paper proposes a dynamic PdM approach for a fleet of multi-component systems, which integrates deep learning-based (DL-based) probabilistic remaining useful life (RUL) prognostics into maintenance planning. First, a DL-based framework is designed for obtaining predictive distributions of RULs, and a probabilistic gated recurrent unit model is proposed for time series data. The Gaussian distribution is used to model prediction uncertainties, and the negative log-likelihood loss is used for model training, which inclines to emphasize the late degradation period. Then, an integer program is established and embedded in a rolling horizon approach for maintenance optimization. The concept of wasted residual values is proposed to quantify the impact of early retirement of components on total costs, which enables customized group maintenance planning under different cost settings. The applicability of the overall approach is illustrated for a fleet of aircraft based on a practical dataset. The experimental results show that our approach can provide accurate RUL prognostics and schedule maintenance flexibly to reduce costs.

Dynamic Interaction and Visualization Design of Database Information Based on Artificial Intelligence

With the explosive growth of data, people's demand for data analysis has become more intense. Although modern technology can collect a large amount of data, the collected original data is often useless and contains little information. How to extract useful information from massive amounts of data has become an urgent problem. Driven by artificial intelligence (AI) technology and personalized consumption demand of users, this article puts forward a dynamic interactive and visualization algorithm of e-business database information based on an improved collaborative filtering (CF) algorithm to help enterprises more efficiently mine the required potential customer groups from massive customer data and log data. Experiment results prove the effectiveness of the model and algorithm. Data mining (DM) technology is applied to the user access control model in this model. First, the maximum forward reference sequence of mobile e-business groups is mined by data technology. Then a user access control model is established according to this sequence to control user access so enterprises can formulate reasonable marketing strategies based on this knowledge.

Perceptions about FinTech: New evidences from the Middle East

The study aimed to analyze the perceptions of FinTech in the Middle East region, specifically its usage, and performance. To attain this objective, a strategy employed on surveys as the primary data acquisition method was implemented. The survey was conducted between November 2021 and February 2022, which allowed the researcher to gather comprehensive data from respondents during this period. In order to ensure the descriptive nature of the study, cross-sectional research was employed in its design. This design allowed the researcher to gather data at a single point in time, which was appropriate for the scope of this study. The researcher utilized a questionnaire during the inquiry to elicit information for data collection purposes. The questionnaire was designed to include semi-structured questions, which allowed the respondents to provide detailed information on their perceptions of FinTech. This study employed a survey-based technique to analyze the perceptions of FinTech in the Middle East region. Using a cross-sectional design and semi-structured questions allowed the researcher to achieve the descriptive nature of the research. Additionally, cross-tab analysis and SPSS provided an in-depth understanding of the data collected. The findings from the analysis suggest that FinTech enhances the performance of financial institutions and that digital banking is the best feature of FinTech across all customer groups.

Sustainable investment initiatives and the performance of stakeholders involved in multinational technology companies' supply chains: linear or nonlinear effects?

PurposeThis study aims to examine how environmental, social and governance (ESG) initiatives and ISO 14001, which is an internationally agreed standard to set out the requirements for an environmental management system, affect firm performance in the context of the Industry 4.0 supply chain.Design/methodology/approachThe authors develop a new chance-constrained network data envelopment analysis (DEA) in the presence of non-positive data to estimate innovation, operational and profitability performances for three main relation groups (suppliers, partners and customers) in Microsoft's supply chain.FindingsResults of this study show the following: (1) the application of ISO 14001 will reduce profitability but increase overall performance (OP); (2) ESG implementation has a convex U-shaped influence on profitability and OP, which means that firms will benefit when ESG investment goes beyond a particular level; (3) the nonlinear U-shape is presented in the E and G components, but not in the S of the individual ESG initiatives, and (4) only specific subcomponents of S and G in the subcomponent of individual ESG initiatives are nonlinearly connected to OP. Research's results reveal that the customer group has a higher performance value than the other two groups, which suggests that this group will create competitive advantages for Microsoft.Originality/valueOverall, the authors provide an insightful viewpoint into supply chain management by examining the ESG initiatives, ISO 14001 and performances of Microsoft's supply chain.

PENERAPAN DATA MINING MENGGUNAKAN HIERARCHICAL K-MEANS BERDASARKAN MODEL RFM

E-commerce has become an inseparable part of the global retail work environment, where this has an impact by presenting new business competition so that companies are required to determine strategies to be able to gain profits. 
 The purpose of this study is to produce customer segmentation using a combination of Hierachical K-Means Clustering algorithms on online retail transaction data that is transformed into Recency, Frequency, and Monetary (RFM) forms and obtain grouping results that have a high degree of similarity by evaluating clusters that are formed using Silhouette analysis. 
 The results of the study stated that the validation test using the Silhouette Coefficient of the combination of the Hierachical K-Means Clustering algorithm was superior to the K-Means algorithm with the optimal coefficient value of the combination of the K-Means algorithm and the Ward method of Hierachical Clustering, namely 0.54027 with the number of k = 4 while the Hierachical Clustering method was K-Means is only 0.44060 with a total of k = 3. Clustering produces two groups of customers, namely Uncertain and Best Customers according to the customer value matrix.

Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model

The recency/frequency/monetary value (RFM) segmentation framework remains a mainstay in the retailing industry to quantify consumer values. However, the RFM model does not consider the added value travelers ascribe to auxiliary services. We extend the RFM framework to an RFM-P model by considering the likelihood of purchasing ancillary services during travel (P). We proposed four traveler groups based on the RFM-P model using unique sales data provided by Chinese airline. The four customer groups were compared through the lens of personal and scenario characteristics to estimate travelers’ purchasing behaviours in airlines. The results help managers make market decisions and fill gaps left by consumer value theory and the RFM model in the retailing industry.

An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>An example of a combinatorial problem is the vehicle routing problem with time windows (VRPTW), which focuses on choosing routes for a limited number of vehicles to serve a group of customers in a restricted period. Meta-heuristics algorithms are successful techniques for VRPTW, and in this study, existing modified artificial bee colony (MABC) algorithm is revised to provide an improved solution. One of the drawbacks of the MABC algorithm is its inability to execute wide exploration. A new solution that is produced randomly and being swapped with best solution when the previous solution can no longer be improved is prone to be trapped in local optima. Hence, this study proposes a perturbed MABC known as pertubated (P-MABC) that addresses the problem of local optima. P-MABC deploys five types of perturbation operators where it improvises abandoned solutions by changing customers in the solution. Experimental results show that the proposed P-MABC algorithm requires fewer number of vehicles and least amount of travelled distance compared with MABC. The P-MABC algorithm can be used to improve the search process of other population algorithms and can be applied in solving VRPTW in domain applications such as food distribution.</span></p></div></div></div>

The study of value network reconstruction and business model innovation driven by entrepreneurial orientation

The impacts of business models on the successes of companies are convincing. Business Model Canvas (BMC) provides the basic framework that describes business logic. Yet it lacks an academic understanding of the internal structures of business models. This paper uses the grounded theory method, qualitative analysis as well as entrepreneurial orientation theory to conduct an individual case study on SHEIN’s business model. The research shows that SHEIN accurately addresses the customer pain points and customer benefits of fashionable women, and gains the trust of end-users by integrating its supply chain based in China. Entrepreneurial orientation drives SHEIN to set up its value network. From the perspective of value discovery, SHEIN accurately targets segmented customer groups. The enterprise reaches end users through social networks and establishes customer relationship with high viscosity. From the perspective of value creation, SHEIN reconstructs the value network, delivering great shopping experiences with low costs. From the perspective of value acquisition, SHEIN has scalability in cost structure and expandability in revenue source. The research found that SHEIN has occupied the low-end market of fast fashion through low-end entry, and is moving to the mainstream market of fast fashion through customer group upgrading, design upgrading and supply chain upgrading, bringing competitive challenge to the fast fashion market incumbents. The innovation of this research lies in: First, the Business Model Iterative Canvas (BMIC) could interpret the business models and their logic in-depth. Second, the research reveals how the competitive advantage is created through value network reconstruction.

Innovation Pattern Heterogeneity and Crisis Resilience

We use the new taxonomy for innovative firms developed based on microlevel data from the Community Innovation Survey (CIS2018) on a sample of 6360 Norwegian firms, and information on users of COVID-19-related compensation schemes for firms with significant loss of turnover and furloughed employees to analyse how resilient different innovative firms have been to the crisis (in both the short and the long run). By using different probabilistic regression models, we study the probability of firms being affected negatively during the pandemic period between March 2020 and February 2022 covering three waves of societal restrictions in Norway. Our main assumption is that all firms were hit by a shock at an early stage due to a complete lockdown in March 2020, but that firms were more resilient if they either did not use the compensation schemes or used them for a briefer period than the less resilient firms. We find “active R&D doers” to be most resilient, while “strategic adapters” (firms with a main strategy of producing high-quality products for a specific group of customers) are found to be least resilient. These results imply that pre-existing innovation capabilities are important for meeting the crisis.

Analysis of digital transformation of furniture market in the context of big data application

The whole furniture industry customization boom creates more and more significant demand for digitalization, and the impact of the epidemic on the furniture industry has further made many furniture companies realize the importance of industry digitalization. And in order to achieve the digital transformation of furniture enterprises, the collection and application of big data is the key: through the collection and use of customer and equipment data, you can achieve optimization of the supply chain in the furniture industry to reduce the inventory of raw materials and finished products; to achieve digital design and production intelligence to improve production efficiency and material utilization; to analyze the target customer groups and improve marketing strategies to attract more customers and increase turnover. In recent years, many scholars have also provided ideas on specific approaches to digital transformation in the furniture industry. This paper uses the literature review method and summary induction method to summarize the role that big data can play in the digital transformation of the furniture industry from three aspects: furniture production supply chain, furniture design digitalization and manufacturing digitalization, and furniture marketing.

Specific legal solutions in the field of energy security in the natural gas sector addressed to protected customers of gaseous fuels. Selected issues

The state’s dependence on the supply of natural gas from external sources requires legislative measures to be taken to protect customers from the negative effects of political and economic events affecting the state’s internal security. In the natural gas sector, ensuring energy security and preventing emerging threats is particularly important. The purpose of this article is to present the legal solutions to protect certain groups of customers in the natural gas market and minimise the negative socio-economic effects in the event of an interruption to gas supply or a sudden increase in gas prices. The introduced legal regulations emphasise the unique specificity of the natural gas market, the essential feature of which is its relationship with imports.