Customer Groups Research Articles

Blinded by the ‘green-halo’? Equity in financing climate adaptation of urban sanitation

Adapting urban sanitation systems to changing climate conditions will require substantial investments. However, there is a gap in understanding the funding strategies for such adaptation measures, especially amid concerns that resilience measures might reinforce existing urban sanitation inequalities. Through cross-case document analysis and complementary key informant interviews, we examined the sanitation adaptation investments in eight cities, focusing on their funding arrangements and social and intergenerational equity implications. Debt financing of sanitation adaptation often relies on repayment through customer bills with only opaque considerations of the affordability for different socio-economic customer groups. The lack of appropriate accounting for the lifecycle costs of resilient infrastructure threatens to mortgage future generations. There is no convincing evidence that ‘greening’ of adaptation financing either shifts or redistributes the financial risk more equitably nor does it make the repayment of the investments substantially cheaper for customers. We conclude that a public sector funding approach is most appropriate to ensure social and intergenerational equity within climate-resilient sanitation systems.

Profiling Peminjam Berdasarkan Demografi dan Karakteristik Kredit Menggunakan Metode Clustering: Studi pada Koperasi Serba Usaha KSU Bintang Muda 88

This research aims to ensure that the credit provided is truly safe, so as to expedite the process of granting credit, and prevent bad credit. The data collection procedure in this research is through documentation and literature. Based on data analysis carried out by researchers using the clustering method, it can be concluded that the customer groups analyzed show different characteristics and require adapted strategies from cooperatives.

Identifying Points of Interest (POIs) as sentinels for infectious disease surveillance: a COVID-19 study

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.

Game Analysis of Customers' Participation in New Product Conceptions Selection Based on Separation Sequential Equilibrium

ABSTRACTCustomer participation in new product development (NPD) has various advantages and disadvantages, research has proven that its beneficial factors are more in the early stages. The key in the initial phase of NPD is to understand the real needs of customers. In recent years, research in this field has been done mainly through questionnaires or data mining. However, most of the research have ignored the fact that customer preferences are private information and the influence of different customer groups on their willingness to participate. Because the selection of new product ideas takes place in the early stages of NPD and the degree of advance of customers is also an important parameter that indicates their willingness to cooperate in NPD activities, this paper will study the mechanism of private transfer of customers. Preferences will be considered in the selection phase of NPD concepts and their top grade. Results showed that the leading customers have reasonable motivation to participate in new product development, and advanced customers are more willing to express their product viewpoints.

Impact of minimum viable product on software ecosystem failure

ContextCompanies are interested in building successful value-producing ecosystems together to offer end users a broader digital service offering and better meet customer needs. However, most ecosystems fail in the early years. ObjectiveWe investigated one small software ecosystem from the planning phase to the operative phase, where the participating companies left one by one because the software ecosystem was unsuccessful, and the software ecosystem ended after four operative years. The software ecosystem provided a digital service offering based on the defined MVP (Minimum Viable Product). That is why we were interested in understanding the MVP's impact on the ecosystem's failure. MethodWe conducted a case study, the results of which are based on the semi-structured interviews of eight representatives of the software ecosystem. ResultsThis study showed that the actors prioritized out functionalities from the MVP, and the MVP was no longer based on the defined value proposition, target customer groups, and customer paths. It was then difficult for the actors to achieve their objectives. The companies’ commitment depended on the set objectives, and when the objectives were not achieved, the actors left the ecosystem, and the software ecosystem failed. ConclusionThe results show that the MVP can significantly affect the failure of the small software ecosystem, where all actors have a keystone role. The MVP largely defines what kind of digital service offering the software ecosystem provides and whether the actors can achieve the objectives, especially their sales goals. Thus, prioritizing the functionalities of the MVP is a critical activity.

Customer Value Classification Model Based on Improved TOPSIS Method and BP-Adaboost Algorithm

Identifying customer groups with different consumption values can help enterprises understand and analyze customers' purchasing behaviors and preferences, so as to effectively improve their operating profits. In this paper, the customer value evaluation index system is constructed on the basis of existing research on customer value classification using entropy weight method and improved TOPSIS method, and different grades are classified according to the comprehensive score of customers. Subsequently, the customer comprehensive score is used as the output layer, and the BP-Adaboost algorithm is used to construct the customer value classification model. The empirical results show that the improved TOPSIS method is more advantageous in terms of error control and classification accuracy, and the BP-Adaboost algorithm outperforms the traditional BP neural network in the customer value classification model, which improves the classification precision and accuracy. This study provides a powerful tool for dealing with the customer value classification problem, which helps enterprises make more accurate decisions in customer relationship management and resource optimization allocation.

Research on Jelly Cat's Marketing Strategy Based on SWOT Analysis

Abstract: In recent years, against the backdrop of the rapid development of the times, people's lives and mental pressure have become increasingly high. People increasingly need items that can express emotions, so some plush toys with calming effects have emerged. Based on this background, the SWOT model will be used to analyze and study the marketing strategy of Jelly Cat, a plush toy. Based on this analysis, it can be understood that Jelly Cat's marketing advantage is its superior quality. Moreover, its strong innovation ability continues to attract customer groups. Its disadvantages are higher prices and fewer consumption channels. In the current environment where people urgently need emotional support, Bringing it development opportunities. It is also important to note that the emergence of highly imitated products may impact genuine products, which is also a threat that needs to be considered. People can not only comprehend and acquire knowledge of Jelly Cat's marketing approach, but also offer valuable insights for other comparable businesses, foster the advancement of plush toys.

A Review on Sustainable Marketing Strategy for Crocs

Crocs is an American shoe brand. This study talking about how brand marketing and brand decisions have made Crocs' success, decline, and return to the masses a popular trend again. Nowadays the social economy rapid develop. various products advent more faster, the new things with creativity could always attract peoples attention. However, the society trend changed quickly, then all brands need to think about how they could expand their customer base in addition to consolidating their existing customers same as Crocs. To make a sustainable marketing strategy crocs needs to combine their brand history, the market requirement, their brand positioning, and their consumer buying behavior. Therefore, the purpose of this study is to provide a sustainable development plan for Crocs from the author's point of view through the several items of information above. The sustainable development suggestions in this paper include, but are not limited to, clarifying brand positioning and target customer groups, developing e-commerce sales channels, etc.

Factors affecting the growth of small and medium Enterprises in Luangnamtha Province

The purpose of this study is to study the operating conditions and factors affecting the growth of small and medium enterprises in LuangNamtha Province. which uses descriptive study methods, surveys, and questionnaires to collect data from small and medium-sized enterprise operators who have registered enterprises at the Department of Industry and Trade in LuangNamtha Province according to the law in the number of 233 business units using basic statistics such as average value and standard deviation and analyzed with an ordinary least squares regression model. The results of the study found that most of the business operators are males aged between 37-43 years old, have a high degree of specialization, and have previous experience in the service sector, which a business is owned by a single Lao person. The registered capital of the business is less 100,000,000 kip, it is a source of funds for the family, there are between 5-20 people, the duration of the business operation is less 5 years, the customer group is in the province, the business is mainly in the commercial sector, but the performance is not good compared to last year. Such as, revenue and net profit decreased. The importance of factors affecting the growth of small and medium-sized enterprises in LuangNamtha province is very important. The factors affecting the growth of enterprises include four factors that have a relationship in the same direction, such as leadership factors affecting the growth of enterprises with a statistical significance level of 0.1, strategic decision-making factors affecting the growth of enterprises with a statistical significance level of 0.05, financial factors affecting the growth and stability of enterprises with a statistical significance level of 0.01 and 0.1, respectively, and marketing factors affecting the stability of the enterprise with a statistical significance level of 0.1.

Improvement of Productivity by Using Means of Lean Manufacturing, Just in Time, and Production Technology in Thai SMEs Manufacturing Industry

This study investigated the current situations, opportunities, and obstacles in manufacturing industry, and to create a model for improving the efficiency while reducing the levels of inventory. This study gathered information from in-depth interview using snowball sampling from 20 manufacturing companies in Thailand. The results revealed that the organization has implemented lean production principles as a strategic initiative to mitigate losses. The implementation of comprehensive work manuals, offered in diverse languages such as Thai, Burmese, Japanese, and English, serves as an additional mechanism to mitigate losses by ensuring clarity and understanding among the workforce. Furthermore, the integration of modern technologies, encompassing machine technology, robotics and artificial intelligence, is actively pursued to streamline production processes and consequently reduce losses. The results presented that companies refrain from adopting Just-In-Time production systems due to operational challenges associated with fulfilling customer orders. Specifically, frequent machine shutdowns for adjustments are necessitated, leading to elevated unit costs. Manufacturing companies in Thailand continue to harbor potential for growth, despite the inevitable rise in employee wages leading to increased operational costs. To navigate this challenging landscape, companies must remain adaptive, responding not only to shifts in customer preferences but also to advancements in technology, proactive examination of customer needs prior to initiating the production process, a paradigm shift from low-cost production to the premium-grade products addresses the demands of customers seeking superior product quality, and the process of broadening customer bases both overseas and globally, and the analysis of distinct customer groups in identifying new target demographics.

Monetary discounts, metaphoric communication and mass prestige based branding: a comprehensive assessment

Purpose This study aims to examine the role of price discounts and how to communite such discounts for masstige brands. While a price discount might encourage potential (first time) customers seeking higher status to buy a masstige brand, it might deter existing (repeat) customers from purchasing the brand due to a decline in perceived status. Such paradoxical effect of price discounts on masstige brand’s purchase requires a detail investigation into whether masstige brands should offer price discounts and if so, how to communicate such discounts. Current research investigates this phenomenon. Design/methodology/approach Four experimental studies were executed. Study 1 investigated the impact of monetary discount (absent vs. present) on the purchase intention of masstige brands for different customer types (potential vs. existing). Study 2 investigated the mediating role of perceived status. Study 3 examined the effectiveness of metaphoric communication of monetary discounts (absent vs present) on masstige brand’s purchase. Study 4 tested the moderating effect of customers’ need for cognition. Findings Overall, monetary discounts positively affect purchase intention of masstige brand; however, the effect is negative for existing customers and positive for potential customers owing to differences in perceived status these customer groups experience (positive for potential and negative for existing customers). Metaphoric communications of monetary discounts restrict the declining purchase intention and status perception of existing customers besides keeping the potential customers’ purchase intention intact. Research limitations/implications This research is confined to a particular country limiting the gneralisability of the study’s findings. Furthermore, this research is cross-sectional in nature. Practical implications The findings of this research provide valuable insights and actionable recommendations for masstige brands to effectively leverage price discounts, especially in the emerging markets. Originality/value To date, a question of whether or not masstige brands should offer price discounts and if so, how to communicate such discounts remain opaque. This is the pioneering study exploring this phenomenon.

A 6-Month Follow-Up Comparative Study of Single-Step Transepithelial Photorefractive Keratectomy (Trans-PRK) Using the StreamLight Software with and without Epithelial Thickness Customization.

To compare corneal aberrometry, densitometry, and refractive outcomes of single-step Transepithelial Photorefractive Keratectomy (Trans-PRK) with and without epithelial thickness customization. This was a prospective, interventional, randomized controlled study. Patients undergoing Trans-PRK using the WaveLight EX500 laser with StreamLight software (Alcon Laboratories, Forth Worth, TX, USA) were randomly assigned to control (55µm standard epithelial thickness) or customized (thinnest point of epithelial thickness for each patient) groups. MS-39 (CSO, Italy) anterior segment optical coherence tomography was used to measure the epithelial thickness. Inclusion criteria were spherical equivalent <6 diopters (D), astigmatism <4D, and CDVA 20/25 or better. The assessments were at baseline and 6 months post-op: visual acuity, refraction, aberrometry, and corneal densitometry. 108 eyes were enrolled, [control group (n=56) and customized group (n=52)]. Mean epithelial ablation thickness in the customized group was 54.81±3.56µm (p=0.470 vs control group). Both groups experienced significant postoperative increases in higher-order aberrations (HOA) and spherical aberrations, with no significant intergroup differences. Mean HOA RMS (µm) of the frontal cornea and total cornea increased by 0.27, and 0.29, respectively, in the control group, and 0.26 and 0.28, respectively, in the customized group (p<0.001 for all). Mean change in spherical aberrations in the frontal cornea and total cornea was 0.23µm (p<0.001) and 0.25µm (p<0.001), in the control group, and 0.19µm (p<0.001) and 0.20µm (p<0.001), in the customized group. Mean corneal densitometry in anterior cornea decreased by 0.63GSU (p=0.021) and 1.18GSU (p<0.001) in the control and customized groups. In the posterior cornea, it increased by 1.67GSU (p=0.004) and 0.38GSU (p=0.006). No significant differences in refractive and aberrometry outcomes between control and customized Trans-PRK groups, with corneal densitometry changes not affecting visual acuity.

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

ON THE APPLICATION OF THE CONVERSION METHOD FOR WEBSITE EVALUATION OF POLISH BANKS IN 2022

This article aims to examine the application of the new conversion method for evaluating electronic banking services. The conversion method consists of aggregating the average ratings of the respondents. Its intuitiveness results from the data collection process; the results collected from the respondents are converted using the algorithm presented in this article into the final result. The data was collected in 2022 using the CAWI method. The results of the calculations are compared with the results obtained by the scoring method, and the differences are interpreted. The conversion method reduces the subjectivity of the test results. The results indicate that economic factors have a large impact on users’ opinions about the quality of banking services. This is additionally confirmed by research conducted among this group of customers. The main implication for researchers is to demonstrate the effectiveness of the conversion method in the analysis of electronic banking services.

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.

Pricing strategies in the Silicon Valley housing market: an update on TOM and recent events

Purpose This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market. Design/methodology/approach The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity. Findings The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest. Practical implications Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market. Originality/value The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants

BackgroundThe variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.ResultsTo address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.ConclusionsIn summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC.

Discussion of Consumers’ Preference for Food Product Traceability Information: Beijing Traceable Tomato Case Study

The paper exemplifies a practical application of combining MNL, RPL and LCM econometric models to study consumer preference heterogeneity in the multi-attributive setting, by analyzing a case study of information traceability preferences of Beijing consumers who buy fresh tomatoes in the post-COVID period. Methodologically, such application of different models (MNL, RPL, LCM) has initially allowed to identify general patterns in Chinese consumers’ preference in the tomato traceability information, then to identify and categorize distinct groups of customers and finally to provide details to their ‘marketing’ profiles towards their willingness to pay. As a result, consumer groups in this study were classified around three key attributes of tomato traceability information which reflect their priorities: consumers from “Price sensitivity” group demonstrated a higher willingness to pay for information on how products were produced (production condition) and products’ certification; “Testing Information Preference” group was willing to pay for the information about tomato’s product quality detection, and “Official Authority Approval Preference” group has developed priority for information on production condition. Such methodological approach provides rather precise characteristics about three different consumer groups, and thus fills in the existing lacunae in the literature and can serve a guiding tool for designing a regional food safety policy. The suggested methodology is transferrable for analyzing consumers’ choices for traceability information about other food products and beyond China.

Ứng dụng công nghệ trong quy trình cho vay của các ngân hàng thương mại Việt Nam

Adapting to the digital transformation trend, Vietnamese commercial banks in recent years have quickly caught up and implemented various software and applications to support the lending process. However, there are not many studies that focus on analyzing the detailed activities and the level of digitization in the lending process at Vietnamese commercial banks. Therefore, this article looks at the current situation of technology application at each stage of the lending process for three groups of commercial banks (large scale, medium sized, and small sized) and for two different customer groups (individuals and businesses). The research utilizes a survey method to collect data from employees currently working at commercial banks. The descriptive analysis results show that the level of technology application in improving the lending process is uneven across stages (primarily focusing on the document creation and decision making stage). Moreover, digital transformation in the lending process is implemented more rapidly in medium sized banks and primarily focuses on individual customers.

Behavioral analysis of electricity consumption characteristics for customer groups using the k-means algorithm

In the fierce competition of the electricity market, how to consolidate and develop customers is particularly important. Aiming to analyze the electricity consumption characteristics of customer groups, this paper used a k-means algorithm and optimized it. The number of clusters was determined by the Davies-Bouldin index (DBI). An improved Harris Hawks optimization (IHHO) algorithm was designed to realize the initial cluster center selection. Based on data such as electricity purchase and average electricity price, electricity customer groups were clustered using the IHHO-k-means algorithm. The IHHO-k-means algorithm achieved the best clustering effect on Iris, Wine, and Glass datasets compared with the traditional k-means and PSO-k-means algorithms. Taking Iris as an example, the optimal value of the IHHO-k-means algorithm was 96.538, with an accuracy rate of 0.932, precision and recall rates of 0.941 and 0.793, respectively, an F-measure of 0.861, and an area under the curve (AUC) value of 0.851. In the customer dataset, the number of clusters determined by DBI was 4. The power customers were divided into four groups with different characteristics of electricity consumption, and their electricity consumption behaviors were analyzed. The results prove the reliability of the IHHO-k-means algorithm in analyzing electricity consumption characteristics of customer groups, and it can be applied in practice.