Segmentation Scheme Research Articles

Customer Segmentation Through RFM Analysis and K-Means Clustering: Leveraging Data-Driven Insights for Effective Marketing Strategy

Consumer segmentation is a very effective methodology that could enable organizations to gain a deeper comprehension of their consumer base and customize their tactics accordingly in order to cater to their unique requirements. Through the process of categorizing clients according to common attributes, organizations can acquire valuable knowledge regarding their requirements, inclinations, and purchasing behaviors. This comprehension empowers firms to develop focused marketing strategies and provide customized experiences that foster customer loyalty and enhance revenue generation. The prevalent categories of criteria in consumer segmentation encompass demographic, psychographic, geographic, and behavioral factors, whilst the prevailing methodologies for constructing customer segments involve rule-based and cluster-based techniques. Rule-based segmentation involves the utilization of pre-established rules to allocate clients into distinct segments. Conversely, cluster-based segmentation employs statistical techniques to identify inherent clusters or groups within the customer population. This research investigates the application of the K-Means clustering technique for the purpose of segmenting customer behavioral data into several categories, namely Platinum, Gold, Silver, Bronze, or Bad. The clustering approach employed demonstrated a notable degree of accuracy and precision. Through the implementation of an appropriate strategy for customer segmentation, organizations have the potential to strengthen their product offers, concentrate their marketing communications, and augment client loyalty.

Profiling consumers’ online shopping and following social media influencers behaviors

In order to develop effective digital marketing strategies, this study explores how demographic factors may affect consumers’ online shopping and following social media influencers behaviors. Market segmentation theory and consumer demographics theory provide the theoretical foundation for this study. By analyzing survey data collected from 6,034 U.S. adults with decision tree analysis, there are a number of significant findings. First, educational level, income, and age category are the important predictors for consumers’ buying things online using a desktop or laptop computer behavior. Second, age category, geographic location (urban, suburban, rural) are the predictors for consumers’ buying things online using a smartphone behavior. Third, educational level, age, and geographic location are the predictors for consumers’ preferring online shopping over in-store shopping behavior. Fourth, age category and race-ethnicity are the predictors for following social media influencers behavior. Fifth, gender and age category are the predictors for purchasing something after seeing an influencer’s posts behavior. Finally, age category and gender are the predictors for purchase decisions getting impacted by influencers. The results provide valuable insights about consumer behaviors online, market segmentation, and influencer marketing strategies.

How Differences in Herbal Beverage Consumer Decisions Based on Demographic Factors?

For a long time, people have been consuming homemade herbal drinks, which are processed traditionally. However, micro, small, medium, and large-scale businesses are now emerging in the production and marketing of ready-to-consume packaged herbal drinks. Research is needed on whether there are differences in consumer motivation in using homemade and industrial-made herbal beverages in terms of the factors of age, gender, education, occupation, and marital status of consumers, as well as how the simultaneous influence of these five variables on the motivation to consume herbal drinks. This study used a sample of 327 people whose data was collected through a digital questionnaire. Data were analyzed using Mann Whitney, Kruskal Wallis, and multiple linear regression. The results showed differences in consumer decisions based on age, education, occupation, and marital status in the choice of herbal drinks, but not gender differences. Most consumers chose the non-packaged type. Simultaneous testing, age, and occupation factors significantly affected consumer decisions, while gender, education, and marital status were not necessary. The results have implications for the segmentation strategy, targeting, and market positioning of the herbal beverage processing industry and the herbal beverage raw material supply industry, better market understanding, more targeted product development, more effective promotional interventions, increased public awareness of the benefits of herbal drinks, and government policy support for the herbal beverage industry.

CBAM-CRLSGAN: A novel fault diagnosis method for planetary transmission systems under small samples scenarios

In various and complex industrial scenarios, the fault data acquisition of planetary transmission system is expensive and unavailable, thus intelligent fault diagnosis under small fault samples scenarios is a challenging task. Therefore, we propose a Convolutional Block Attention Mechanism Conditional Regularized Least Squares Generative Adversarial Network (CBAM-CRLSGAN) method for intelligent diagnosis of planetary transmission systems. First, the diversity of original samples is increased by an overlapping segmentation strategy. Then, a novel data augmentation method is proposed via incorporating the CBAM module and conditional regularized least squares loss function into least squares generative adversarial network, which enables the proposed method to extract data features efficiently and improve training stability. Finally, the real and obtained fake samples are input into the classifier to realize fault diagnosis. The experimental results on the planetary transmission system test rig show that the proposed CBAM-CRLSGAN can obtain superior diagnostic performance with a diagnosis accuracy of 99.35%.

THE STRATEGY OF ONLINE MARKETING AT Mc. DONALD'S RESTAURANT TO INCREASING SALES IN THE DIGITAL ERA

One of the essential foundations in the digital business era is online marketing, which uses internet platforms such as websites, social media, and online advertising to reach target audiences effectively, expand brand reach, and increase sales volumes in a market that is becoming increasingly connected and competitive. Within the context of the digital era, this research aims to investigate the online marketing strategies McDonald's Restaurant has implemented to boost sales. In the context of online marketing strategy, the primary focus of the research is on the segmentation, targeting, product positioning, and mixing strategies associated with online marketing. The purpose of this research is to investigate the difficulties encountered when implementing and promoting sales strategies and online marketing strategies using a qualitative approach. Collecting primary data through interviews and secondary data from documents and newspapers are both methods utilized in research. According to the research findings, successful online marketing has the potential to increase sales volume significantly. This research provides valuable insight into practical methods to improve sales performance by implementing integrated online marketing strategies, strengthening brand positioning, identifying market opportunities, and overcoming challenges in a dynamic and changing business environment. This research analyzed strengths, weaknesses, opportunities, and threats (SWOT)

Efficient brain tumour detection system by Cascaded Fully Convolutional Improved DenseNet with Attention-based Adaptive Swin Unet-derived segmentation strategy

Efficient brain tumour detection system by Cascaded Fully Convolutional Improved DenseNet with Attention-based Adaptive Swin Unet-derived segmentation strategy

A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology

Simultaneous segmentation and classification of nuclei in digital histology remains challenging. The highest achieved Panoptic Quality (PQ) remains low due to overlapping nuclei, higher staining and tissue variability, and rough clinical conditions. The generic deep-learning methods usually rely on end-to-end models, which fail to address these problems associated explicitly with digital histology. We resolve these issues using a dual attention-based model combined with post-processing in a bottom-up fashion. We use three attention decoder heads, which produce semantic segmentation, edge proposals, and classification maps. We use these outputs to apply post-processing, including controlled watershed and pixel grouping, to produce instance segmentation and classification. Our multi-stage approach utilizes edge proposals and semantic segmentations compared to direct segmentation and classification strategies followed by most generic state-of-the-art methods. Due to this, we demonstrate a significant performance improvement in producing high-quality instance segmentation and nuclei classification. We have achieved a 0.841 Dice score for semantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633 mPQ for nuclei classification. Furthermore, the framework is less complex compared to the state-of-the-art.

Heterogeneous PSMA ligand uptake inside parotid glands

The purpose of this investigation is to quantify the spatial heterogeneity of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) uptake within parotid glands. We aim to quantify patterns in well-defined regions to facilitate further investigations. Furthermore, we investigate whether uptake is correlated with computed tomography (CT) texture features. Methods: Parotid glands from [18F]DCFPyL PSMA PET/CT images of 30 prostate cancer patients were analyzed. Uptake patterns were assessed with various segmentation schemes. Spearman’s rank correlation coefficient was calculated between PSMA PET uptake and feature values of a Grey Level Run Length Matrix using a long and short run length emphasis (GLRLML and GLRLMS) in subregions of the parotid gland. Results: PSMA PET uptake was significantly higher (p < 0.001) in lateral/posterior regions of the glands than anterior/medial regions. Maximum uptake was found in the lateral half of parotid glands in 50 out of 60 glands. The difference in SUVmean between parotid halves is greatest when parotids are divided by a plane separating the anterior/medial and posterior/lateral halves symmetrically (out of 120 bisections tested). PSMA PET uptake was significantly correlated with CT GLRLML (p < 0.001), and anti-correlated with CT GLRLMS (p < 0.001). Conclusion: Uptake of PSMA PET is heterogeneous within parotid glands, with uptake biased towards lateral/posterior regions. Uptake within parotid glands was strongly correlated with CT texture feature maps.

Evaluation of Segmentation, Rotation, and Geographic Delivery Approaches for Deployment of Multiple First-Line Treatment (MFT) to Respond to Antimalarial Drug Resistance in Africa: A Qualitative Study in Seven Sub-Sahara Countries.

Several studies recently confirmed the emergence of resistance to antimalarial drugs in sub-Saharan Africa. Multiple first-line treatment (MFT) is one of the measures envisaged to respond to the emergence and spread of this resistance. The aim of this study was to identify the perceived advantages and disadvantages of several MFT deployment strategies and to better understand potential implementation drivers and barriers. A qualitative survey was conducted in seven sub-Saharan countries amongst key opinion leaders, national decision makers, and end users. A total of 200 individual interviews were conducted and findings were analyzed following a thematic inductive approach. From a policy perspective, the new MFT intervention would require endorsement at the global, national, and regional levels to ensure its inclusion in guidelines. Funding of the MFT intervention could be a bottleneck due to costs associated with additional training of healthcare workers, adaptation of drug delivery mechanisms, and higher costs of drugs. Concerning the MFT deployment strategies, a slight preference for the segmentation strategy was expressed over the rotation and geographic approaches, due to the perception that a segmentation approach is already in place at country level. The findings highlighted the need for a collective approach to MFT deployment through the engagement of stakeholders at all levels of malaria management.

An image segmentation and point cloud registration combined scheme for sensing of obscured tree branches

Automated robots are emerging as a solution for labor-intensive fruit orchard management. Three-dimensional (3D) reconstruction of tree branches is a fundamental requirement for robots to perform tasks like pruning and fruit harvesting. Current branch sensing methods often rely on planar segmentation with limited 3D information or computationally expensive point cloud segmentation, which may not be suitable for natural orchards with obscured tree branches. This study proposes a novel scheme that reconstructs occluded branches from RGB-D (Red-Green-Blue-Depth) images by integrating the point clouds converted from planar segmentation masks and depth images. The proposed approach extends the existing 2D branch sensing techniques to 3D, leveraging multi-view information. The deep learning models DeeplabV3+ and Pix2pix are employed to generate the segmentation masks, separately. And the Fast Global Registration (FGR) is used to register the multi-view point clouds. The results demonstrate that the output point clouds have at least a 24 % increase in the number of corresponding points after FGR. Furthermore, the time cost per hundred corresponding points is reduced by 85 % and 69 % when using the DeepLabV3 + and Pix2pix-based schemes, respectively, compared to the PointNet++ approach. These findings indicate that the proposed scheme significantly improves the sensing of occluded branches in terms of output richness and computational efficiency, making it applicable to natural orchard working spaces.

A reliable knowledge processing framework for combustion science using foundation models

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

How does green consumers' self‐concept promote willingness to pay more? A sequential mediation effect of green product virtue and green perceived value

AbstractThis study investigates the consumers' processing route of WTPM for green products from consumers' self‐concepts. Employing the norm activation model and Lazarus appraisal theory, this study proposes the direct and indirect routes of self‐concepts (i.e., green and product self‐concepts) that influence consumers' WTPM. The causal chain of green product virtue and green perceived value is explored. Online survey data for 313 Indian green consumers inform the findings. The findings reveal that consumers' product self‐concept and green self‐concept influence their WTPM through green product virtue and green perceived value. Such findings support the sequential mediation effect. This study provides an alternative and novel psychological mechanism for how consumers' self‐concepts can influence WTPM through green product virtue and green perceived value. The findings inform green marketers to use green product virtue and green perceived value to induce green consumers' WTPM while designing effective segmentation and communication strategies.

Incremental convolutional transformer for baggage threat detection

Detecting cluttered and overlapping contraband items from baggage scans is one of the most challenging tasks, even for human experts. Recently, considerable literature has grown up around the theme of deep learning-based X-ray screening for localizing contraband data. However, the existing threat detection systems are still vulnerable to high occlusion, clutter, and concealment. Furthermore, they require exhaustive training routines on large-scale and well-annotated data in order to produce accurate results. To overcome the above-mentioned limitations, this paper presents a novel convolutional transformer system that recognizes different overlapping instances of prohibited objects in complex baggage X-ray scans via a distillation-driven incremental instance segmentation scheme. Furthermore, unlike its competitors, the proposed framework allows an incremental integration of new item instances while avoiding costly training routines. In addition to this, the proposed framework also outperforms state-of-the-art approaches by achieving a mean average precision score of 0.7896, 0.5974, and 0.7569 on publicly available GDXray, SIXray, and OPIXray datasets for detecting concealed and cluttered baggage threats.

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.

Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).

Marketing Strategy of NOVA Employment

This paper targets identifying challenges faced by the Australian non-profit organization NOVA in providing more suitable job opportunities for disabilities, focusing on communication barriers between NOVA and decision-makers in Small and Medium Enterprises (SMEs). It discusses how NOVA improve its competitiveness by providing unique services through market segmentation, targeting, differentiation, and positioning (STDP) strategies. This paper also emphasizes the current situation for disabilities of finding jobs and explores leveraging LinkedIn to achieve SMART goals, including showcasing services, expanding networks, and using LinkedIn tools to attract target audience.

Beyond STP Marketing Strategy for Online Service Businesses

Purpose: This research presents the new marketing strategy concept with the customization strategy as the specific consumer satisfied to be in line with the online service business. Theoretical Framework: The researcher studied the elements of marketing strategies for online service businesses. The strategy development process is divided into 4 components as segmentation, targeting, positioning and customization. Design/methodology/approach: In this research, the researcher uses mixed-methods as qualitative research that analyzed the consensus with fuzzy logic techniques, and quantitative research. Findings: The research result brings about a new marketing strategy that is going to be a guideline for online service business entrepreneurs, which is comprised of the following market strategy: “segmentation, targeting, positioning, and customization strategy as the specific consumer satisfied”. Practical implications: The objective of the study is to find consensus among experts using fuzzy logic and develop market strategies through segmentation, targeting, positioning, and customization strategies to the specific consumer satisfied. In this research, mixed-methods research, among other things, qualitative research was used with 19 experts with in-depth interviews, which analyzed the consensus with fuzzy logic techniques, and quantitative research was used with 983 online service business entrepreneurs. Originality/value: The research result brings about a new marketing strategy that is going to be a guideline for online service business entrepreneurs, which is comprised of the following market strategy as “segmentation, targeting, positioning, and customization strategy as the specific consumer satisfied”. The obvious thing is that marketing should apply these strategies in decreasing order importance, segmentation, and customization strategies involve the specific consumer needs that are inferior to their importance, plus combining with the targeting and positioning, respectively.

Religiosity-based behavioral study, Islamic finance users’ approach and selection standard: empirical evidence from Pakistan

Purpose Considering the speedy growth of Islamic finance and limited research work on Muslim behavior regarding Islamic Banking, this study aims to investigate to comprehend the stimulus of religiosity on customer’s behavior. Design/methodology/approach A conceptual model is developed on existing literature. The key dimensions of religiosity in the model include practice, knowledge, experience and consequences to capture the whole religiosity of customers. Model of the study investigates the impact of customer’s religiosity on their behavior in decision-making about selection of Islamic bank. Analysis of the study is based on the sample of 370 customers of Islamic banks from District Nowshera Khyber Pakhtunkhwa, Pakistan. The data for the study collected through random sampling by a comprehensive survey questionnaire. Binary logistic model is used to test the data for statistical analysis. Findings The key findings of the study suggest that religiosity influence customer behavior positively in decision-making regarding Islamic finance. Service standards of Islamic banking has also significant impact on customer perception, while the financial education of the customers has insignificant impact on customer behavior. Research limitations/implications This study mainly focused on the curiosity of the customer religious commitment, so religiosity is a vast phenomenon; there are deep sections in each dimension of religiosity, so further study is suggested for the comprehensive capture of each dimension of religiosity. Practical implications The results of the study have a great importance for the managers of Islamic finance industry to identify and detect the potential customers and divide the target market of banking industry on the base of religiosity. Furthermore, the study may bring significant managerial suggestions for marketing planners and can help them in market segmentation strategies. Originality/value The study examined the association between Muslim religiosity and Islamic banking customer’s selection behavior. This study spread the understanding of religiosity and its impact on Islamic banking customer’s behavior. Furthermore, the study is valuable to discover the level to which religiosity determines the inclinations of customers. This study helps marketing practitioners and researchers to grow their knowledge about customer’s motives in terms of religious commitment.

Social media and politics as usual? Exploring the role of social media in the 2022 Kenyan presidential election

ABSTRACT How is social media used in African elections and what is its impact? This article builds on existing research on the role of social media in African elections to argue that its impact is linked to its interaction with traditional media and pre-existing political features of a country. Drawing on original survey data, Afrobarometer data, and content analysis of social media pages of the two main presidential candidates – William Ruto and Raila Odinga – and a selection of allied political influencers’ accounts, we explore the use of social media in Kenya’s 2022 presidential election campaigns. This article highlights how a complex media and electoral landscape incentivised candidates to adopt a segmentation strategy online. Through this strategy, the dissemination of negative information was delegated to social media influencers to avoid alienating undecided voters, while candidate’s main pages were curated to publicise policy and cultivate a positive public image. In this regard, African politicians’ innovative use of social media was shaped by both normative and contextual political factors. We outline how social media affords new avenues for electioneering that may mask old habits, and we encourage researchers to look beyond official campaign accounts when assessing the impact of social media on elections.

LCFNets: Compensation Strategy for Real-Time Semantic Segmentation of Autonomous Driving

LCFNets: Compensation Strategy for Real-Time Semantic Segmentation of Autonomous Driving

Quantitative assessment of cement bridges and voids in cement-stabilized permeable base materials using a mask R-CNN-based CT image segmentation strategy

Accurate extraction of aggregates, cement bridges, and voids in permeable cement-stabilized base materials (PCBM) is a challenging task. This paper proposed a segmentation and reconstruction paradigm for raw CT slice images using a mask regional convolutional neural network (Mask R-CNN). The results demonstrate that the proposed method effectively segments and reconstructs unclear raw CT slice images, providing a convenient and advanced tool for accurately assessing the meso-scale structures of cement bridges and voids in PCBM prepared with different combinations of cement content and compaction force. An increased cement content or compaction force results in the migration of cement paste, leading to thicker localized cement bridges and blockage of permeable pore space. This phenomenon further disrupts the homogeneous distribution of cement bridges and voids. An increased compaction force tends to elongate and flatten pore throats, while an increased cement content may clog voids, rendering them impermeable. The vertical permeability of the PCBM is significantly greater than the horizontal permeability, which should be considered during material design optimization. The proposed method could serve as a reference for microscopic analysis and structural design optimization of PCBM.