Market Segmentation Research Articles

EXPRESS: From Social to Purchase: Customer Selection in Social Group Buying

Social scope group buying has emerged as an increasingly popular promotional strategy and has served as a new customer acquisition tool. In the service industry, companies use social group buying (SGB) to recruit new customers and promote full-price products. Through SGB activities, customers can trade their social capital to form buying groups, experience SGB-offered sample products, and further alleviate uncertainty regarding expensive full-price products before making a final purchase. We investigate this novel SGB phenomenon by examining customers’ decisions throughout the “experience-conversion” process. In collaboration with a leading online educational platform, we examine customers’ grouping behavior during SGB activities and analyze their subsequent purchases. Our analysis reveals an interesting pattern in which non-grouped customers have a higher proportion of full-price product purchases than grouped customers. We postulate that, in addition to observations from operational data, unobserved social information is important for gaining a deeper understanding of the customer behaviors underlying this pattern. Employing a Bayesian learning framework, we model customers’ three-stage discrete-choice decision-making processes and quantify two influential social information factors: social cost and social learning. By incorporating social information, our Bayesian learning model demonstrates improved effectiveness in assisting companies with accurately predicting final purchases in the conversion process. We provide actionable insights into how companies can employ our model to strategically tailor SGB designs by customer segments to increase overall purchase rates. Our study sheds light on the significance of social information in enhancing customer management and refining SGB design.

IPO Pricing in Germany and Switzerland in the New Millennium: Tests on Underpricing and Long-Term Performance

We provide an updated analysis of initial public offerings (IPOs) by Swiss and German companies after the turn of the millennium up to 2020. We compare the characteristics of initial pricing to those observed in prior studies, test conventional hypotheses about underpricing cross-sectionally, and analyze the long-run performance of IPO firms. Our sample included 72 IPOs in Switzerland and 307 IPOs in Germany. We found an average (median) underpricing of 13.27% (7.19%) for Swiss IPOs and an average (median) of 21.50% (3.7%) for German IPOs; the averages dropped to 6.45% and 5.82%, respectively, after excluding the hot-issue market in 2000. Among the cross-sectional explanations, interesting effects can be observed with respect to the bookbuilding width and transparency standard of the listed market segment, as well as the reputation of the underwriter and the value of the greenshoe option. We documented strong post-issue underperformance for holding periods up to 120 months, which were, however, only present in the Swiss sample for median, not mean, returns.

Evaluating the research and development (R&D) efficiency of the Chinese construction industry

PurposeThe construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.Design/methodology/approachBased on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.FindingsResults suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.Originality/valueThis study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

Do you love traveling to the beach or mountain? Predicting personality traits and choice behaviour

ABSTRACT Several personality-predicting tools are being used by managers in their strategic decision-making process. The most common are the Big Five model and the Myers – Myers-Briggs Type Indicator model, respectively. This study deviates and tests a novel personality prediction model. Specifically, it utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm as a feature extraction method to map personality traits based on consumers’ textual data from the Kaggle MBTI Personality Type dataset. This study uses realistic data gathered from traveller reviews and comments on hotel stays from TripAdvisor’s widely recognised platform to predict different personality traits and their travel destination preferences, i.e. beach or mountain. The contribution of this study lies in the identification of multiple dimensions of peoples’ personality traits and their relationship with their preferences for travel locations. This proposed trait classification mechanism can assist marketing managers in effectively carrying out customer segmentation, setting realistic goals, and optimizing marketing strategies.

Oneiros: The Sports Club: navigating the growth path

Learning outcomes After completion of the case study, the students will be able to analyze the challenges and opportunities associated with business expansion in any business, assess the significance of leveraging existing strengths versus exploring new markets for organizational growth, evaluate the implications of regional consolidation versus national expansion strategies, develop strategic thinking and decision-making skills in a competitive business landscape and understand the nuances of market dynamics, brand recognition and operational challenges in diverse geographical regions. Case overview/synopsis Oneiros – The Sports Club stood poised at a pivotal juncture, grappling with a strategic quandary that encapsulates the divergent visions of its leadership. The narrative unfolded in Surat, a vibrant city in the state of Gujarat where contrasting viewpoints champion the familiar stability of local success against the lure of uncharted state and national territories. Ms Hemali Shah advocated for consolidating the club’s triumphs in Gujarat, emphasizing on the parameters of brand loyalty and operational mastery. In stark contrast, Mr Robin Patel envisioned a bold expansion strategy across the state, aiming for brand recognition and a paradigm shift to regional prominence. Caught amidst these competing visions, Mr Sumit Lathia who was aspiring for the club’s national presence navigated the complexities of market dynamics and business model, oscillating between preserving familiarity and embracing the allure of ambition. This case study highlights the nuanced strategic dilemmas faced by Oneiros, offering a captivating exploration of growth strategies in a competitive landscape. With insights from various perspectives within the organization, this case study navigates the complexities of growth, market dynamics and the balance between familiarity and ambition. This case study offers valuable insights and practical applications for students pursuing regular Master of Business Administration (MBA) and executive MBA programs, as well as undergraduate and postgraduate studies in entrepreneurship and strategic management. By examining the strategic decisions and operational challenges faced by Lathia and Oneiros – The Sports Club, students can gain a deeper understanding of key concepts such as public–private partnerships, market expansion strategies, customer segmentation and revenue diversification. Complexity academic level This case study is positioned within the decision-making or business development modules of the curriculum. It serves as a platform to apply theoretical concepts of strategic decision-making, market analysis and growth strategies to a real-world scenario. This case study prompts students to critically evaluate expansion dilemmas and devise strategic solutions. Supplementary materials Teaching notes are available for educators only. Subject code Strategy.

Factors Influencing Hotel Revenue Management in Times of Crisis: Towards Financial Sustainability

(1) Background: Facing the challenges of a post-pandemic period and the Ukraine War and recognising the gap in scientific research on the application of revenue management (RM) in the Portuguese hotel industry, the main objective of this study is to identify the most effective and least appropriate RM practices for use in periods of low demand and crises, reflecting the financial sustainability perspective. The theoretical framework of this study focuses on the main RM practices, grouping them into price and non-price strategies. (2) Methods: A quantitative methodology was employed, collecting information from Portuguese hotels through an online questionnaire, and statistical analysis using Mann–Whitney and Chi-square tests was conducted. (3) Results: Hotels offered discounts during the pandemic, but room rates were reduced during the recovery period. These findings also revealed that commonly used techniques were the best available rate (BAR) and rate fences, particularly during the pandemic. Quality, brand image, strategic partnerships, and marketing actions are recognised as essential. However, loyalty programs, length of stay (LOS) control, rate parity, and bundled services are not commonly implemented despite their importance during periods of low demand. Larger hotels, five-star hotels, and members of international chains applied more RM practices than smaller four-star independent hotels. (4) Originality: This study provides original and valuable insights into increasing hotel revenues and occupancy rates during future periods of low demand, which benefit financial sustainability.

OPTIMIZING THE EFFECTIVENESS OF DIGITAL MARKETING CAMPAIGNS WITH THE USE OF E-COMMERCE COMPANY DECISION SUPPORT SYSTEMS

Despite the rapid growth of the e-commerce sector in Indonesia reflected by a significant increase in the value of e-commerce transactions by 2022, the effectiveness of digital marketing campaign strategies is often faced with several challenges, including large and complex data volumes, lack of understanding of digital consumer behavior, and difficulty in measuring campaign effectiveness. This research aims to optimize the effectiveness of digital marketing campaigns by utilizing a Decision Support System (SDM), with the main focus of understanding the role of SDM in assisting decision-making in the context of digital marketing campaigns and developing a SDM model that can improve the effectiveness of these campaigns.This research uses the literature review research method which reviews or critically examines knowledge, ideas, or findings contained in academically oriented literature, formulating theoretical and methodological contributions to a particular topic. The integration of digital marketing campaigns and Decision Support Systems (SDM) increases marketing effectiveness by making the right decisions. Implementation using APIs and integration examples involves audience mapping, campaign effectiveness measurement, and campaign optimization. The application of SPK in e-commerce involves planning, development, implementation, and maintenance stages to improve marketing campaign effectiveness. Optimization of SPK integration with a focus on in-depth data analysis, customer segmentation, content personalization, and continuous monitoring can improve the effectiveness of e-commerce campaigns.

The Role of AI in Personalizing Insurance Policies

This article analyzes the transformative impact of artificial intelligence on the insurance industry through four key innovations. It examines how AI has enabled real-time risk assessment and dynamic pricing through personalized policy optimization, integrated terabytes of daily customer data for precise underwriting, implemented advanced analytics frameworks for enhanced fraud detection and customer segmentation, and automated operations for improved efficiency. The findings demonstrate that AI implementation has achieved an 82% improvement in risk prediction accuracy, reduced operational costs by 67%, and increased customer satisfaction by 76%. However, these technological advancements necessitate robust ethical governance frameworks, particularly in data privacy and algorithmic fairness. The research comprehensively analyzes AI's role in reshaping insurance operations while emphasizing the critical balance between technological innovation and ethical implementation. These insights offer valuable guidance for insurance providers pursuing digital transformation while maintaining regulatory compliance and customer trust.

Critical decisions at the early stage of start-ups: a systematic literature review

AbstractThis systematic literature review (SLR) identifies and ranks the decisions that can be considered critical for entrepreneurs at the early stage of the start-up process. The sample (N = 56) derived from the SLR contained primary publications from the A + and A journals of the VHB-Journal ranking. Deductive and inductive approaches were used to define the critical decision domains. Then, the relevance of the domains was evaluated using the frequency of the selected studies. A key implication for founders is to understand that not all decisions that could be taken in the early stage may be critical for success. Most critical seems to invest time and effort in the, what we label the internal business and human core (Core People and Product Definition) and the external business and human core (Market segment selection and Partnerships). This study depicts an integrated view of the fragmented field of critical decisions in the context of early-stage start-ups by using a SLR. Thus, it creates value for theory-building and practitioners in entrepreneurship and innovation.

EXPRESS: Identifying Competitors in Geographical Markets Using the CSIS Method

Identifying the most relevant competitors in a geographical market is crucial for businesses with a significant offline presence, such as hotels, restaurants, and retail stores. The specific location of a business and the geographical density of potential competitors are critical factors in determining the competitive structure. However, this task can be challenging when the potential number of competitors is large and the competition is asymmetric. In this study, we apply the Conditional Sure Independence Screening (CSIS) method to a system of demand functions for competitor identification. This method offers significant computational efficiency by estimating a marginal regression for each potential competitor, rather than a full model consisting of all potential competitors. To validate the effectiveness of the CSIS method and explore the boundary conditions of its performance, we conduct extensive simulation analyses under different spatial data-generating processes. Our findings demonstrate that the CSIS method outperforms multiple other variable selection methods and remains robust under spatial misspecifications. Then we apply the CSIS method to hotel competition in two U.S. geographical regions, illustrating how the competitive structure varies across geographical densities and market segments. Finally, we highlight how managers can strategically use the results and outline the potential of the method for other non-geographical applications.

GO-TO-MARKET STRATEGIES FOR CONSUMER TECHNOLOGICAL PRODUCTS WITH SUBSCRIPTION MONETIZATION MODEL

This study examines go-to-market (GTM) strategies for consumer technological products utilizing subscription monetization models. As the digital landscape evolves, businesses are increasingly adopting subscription-based approaches to generate recurring revenue and establish long-term customer relationships. Our research investigates the key components of successful GTM strategies in this context, focusing on market segmentation, value proposition development, pricing strategies, distribution channel selection, and customer acquisition and retention tactics. Through a comprehensive analysis of scientific papers, industry reports, and case studies, we identify best practices and trends in subscription-based GTM strategies for consumer tech products. Our findings highlight the importance of market segmentation, value proposition development, pricing strategies, marketing and communication, customer acquisition and retention practices in optimizing customer lifetime value. This study contributes to the growing body of literature on subscription business models and provides practical insights for managers and entrepreneurs in the consumer tech industry.

The Effectiveness of Green Marketing in Starbucks: An Assessment

Green marketing practices have become increasingly significant as businesses aim to reduce their environmental impact and cater to the growing number of environmentally conscious consumers. The primary goal of green marketing extends beyond appealing to eco-conscious buyers; it also aims to generate positive environmental and societal effects. Green marketing integrates sustainability principles across various aspects of marketing, including product design, packaging, messaging, production processes, and promotion. Its importance lies in enhancing profitability, fostering brand loyalty, and attracting a new market segment that prioritizes sustainability. For instance, businesses may promote reduced emissions in manufacturing or use post-consumer recycled materials in packaging. Some also demonstrate environmental responsibility by donating a portion of sales proceeds to eco-friendly initiatives, such as tree-planting campaigns. This study examines how Starbucks' green marketing practices have contributed to its overall success, profitability, brand reputation, customer loyalty, and employee engagement. Additionally, it investigates challenges and limitations Starbucks may have faced in implementing these practices and explores potential areas for improvement. The study’s findings will provide valuable insights for other businesses aiming to adopt green marketing strategies and contribute to the broader discourse on sustainability's role in business.

Developing innovative financial products for sustainable economic growth

In today’s competitive landscape, technology firms increasingly turn to advanced data analytics as a strategic tool for boosting revenue growth and operational efficiency. This review explores how leveraging sophisticated analytical techniques can enable these organizations to extract actionable insights from vast datasets, thereby enhancing decision-making processes and driving business performance. Advanced data analytics encompasses a range of methodologies, including predictive analytics, machine learning, and big data analysis, which collectively empower firms to identify trends, optimize resources, and tailor products to meet evolving customer demands. One of the primary benefits of utilizing advanced data analytics is the ability to enhance revenue growth through improved customer targeting and segmentation. By analyzing customer behaviors and preferences, technology firms can develop personalized marketing strategies that resonate with their target audiences, resulting in increased conversion rates and customer loyalty. Moreover, data analytics facilitates the identification of new market opportunities, enabling firms to innovate and expand their product offerings effectively. Operational efficiency is another critical area where advanced data analytics proves invaluable. By monitoring key performance indicators (KPIs) and operational metrics, technology firms can streamline their processes, reduce costs, and minimize waste. Predictive analytics, in particular, allows organizations to anticipate potential disruptions in their operations, enabling proactive measures to mitigate risks and maintain smooth workflows. This capability not only enhances productivity but also fosters a culture of continuous improvement. Additionally, advanced data analytics supports informed strategic planning by providing insights into market dynamics and competitive positioning. Firms can leverage these insights to make data-driven decisions, allocate resources effectively, and align their business strategies with market trends. In conclusion, advanced data analytics serves as a catalyst for revenue growth and operational efficiency in technology firms. By embracing these analytical techniques, organizations can harness the power of data to drive innovation, improve customer engagement, and achieve sustainable competitive advantage. As technology continues to evolve, the strategic implementation of advanced data analytics will be essential for firms seeking to thrive in an increasingly data-driven world. Keywords: Advanced Data Analytics, Revenue Growth, Operational Efficiency, Technology Firms, Predictive Analytics, Customer Targeting, Strategic Planning.

State and trends in the development of the microfinance market in Russia

The relevance of the article is due to the insignificant contribution of microfinance to the development of the Russian economic system, especially in the context of supporting small and medium-sized businesses. However, the regulator sees it as a promising sector of the financial market and considers its progressive development necessary. The purpose of the work is to study the current state and identify trends in the development of the microfinance market in Russia. The methodological basis of the study includes empirical observation and analysis. During the work, official statistical data and scientific publications on the topic of microfinance have been used. The article provides a review of current legislative documents regulating the microfinance sector, and analyses the state of the market based on statistical data. It is substantiated that some elements of the monetary and financial policies pursued in the Russian Federation can be applied for the successful development of the microfinance sector, contribute to its effective and balanced work, and improve accessibility and sustainability. In the course of the work, key directions for the development of the microfinance market are formed: improving regulation and supervision; promoting financial literacy of the population; application of digital technologies; expansion and diversification of the product portfolio, development of partnerships, social entrepreneurship, which contribute to the sustainable development of this segment of the financial market. Particular attention is paid to a detailed analysis of improving the regulatory framework for microfinance in Russia from the point of view of legislation on bankruptcy of individuals, stimulating innovation and digitalisation, and supporting social entrepreneurship.

Labour market segmentation, self-employment, and Hukou reform

Abstract This study aims to examine the impact of the Hukou system, a labour mobility-restricting institution, on the decision to engage in non-agricultural self-employment in China, reflecting the dynamic process of labour transition from contractual work to self-employment within the country’s evolving labour market. By using the China Family Panel Studies (CFPS) dataset, the results show that urban migrants, who own rural Hukou but migrate to large cities, are more likely to turn to self-employment than local citizens conditional on individual characteristics. Taking advantage of the 2014 Hukou reform, we provide supporting evidence that Hukou discrimination is a key factor in explaining the higher self-employment rate among representative urban migrants. Further heterogeneous analysis suggests that low-income migrants experience greater constraints imposed by the Hukou system, while high-income level migrants are more likely to turn to self-employment voluntarily.

Handling Noise Data with PCA Method and Optimization Using Hybrid Fuzzy C-Means and Genetic Algorithm

The significance of machine learning (ML) and data mining techniques particularly clustering is examined in this research, in managing large data sets for customer segmentation in the retail sector. The research emphasizes the challenges posed by data noise and proposes a solution using Principal Component Analysis (PCA) to improve accuracy. This study introduces a hybrid approach that combines Fuzzy C-Means (FCM) with genetic algorithms for optimization in customer segmentation, and suggests further research on the optimal number of clusters and data noise elimination. By addressing data noise, the proposed PCA-based method achieved a higher accuracy rate of 98% compared to 93% without PCA. This finding underscores the effectiveness of PCA in noise reduction, improving clustering accuracy. This research contributes to the advancement of customer-focused business strategies through better data analysis and interpretation. The proposed approach has potential applications in areas including data analysis, pattern recognition, and image processing, highlighting its relevance in the contemporary business environment.

Search Steering in Two-Sided Platforms

Many two-sided platforms are known to steer customers toward certain products based on their willingness to pay. In this article, we study platforms’ incentives to adopt this type of market segmentation and present conditions under which this can generate distortions that negatively impact the surplus from buyers and sellers. In our environment, a monopolistic platform matches sellers with buyers. Upon being matched, each pair of buyer and seller negotiates prices. If they choose to transact, the platform receives a commission fee proportional to the value of the transaction plus a flat fee per transaction. The platform is assumed to have full information about customers’ and sellers’ outside options. We show that, as long as the market is in excess supply or as long as there is a crossing between the demand and supply curves , the platform’s optimal matching is suboptimal from the perspective of buyers and sellers.

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types─manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies.

INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE

This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.

Innovations and practices in hospitality management

Intensification of competition and transformation of consumer tastes have determined the constant need for innovations in hospitality management. Companies are constantly looking for new ways to improve the quality of services, reduce costs, and create a segment of regular customers. The optimal combination of technological innovations, customer loyalty management algorithm and online booking platforms in the field of hospitality allow you to build a strategy of competitive advantages and ensure sustainable development. The materials of the presented research are aimed at identifying the impact of technological innovations on the level of customer service and, as a result, an opportunity to influence their loyalty. The concept of integration of the main elements of innovation in management and marketing is proposed, based on the analysis of modern trends and the effectiveness of using online booking platforms in the field of hospitality. The proposed hypothesis is based on several key aspects. The fundamental factors that affect changes in the hospitality sector are due to intensifying competition and the transformation of consumer tastes. In addition, an integral part of successful management in the s hospitality sector is the constant implementation of innovations in management, which allows companies to adapt in accordance with the transformation of the external environment. Effective management of communications with customers has consisted in the optimal combination of technological innovations and algorithms for managing customer loyalty. The proposed hypothesis clearly indicates that the research is aimed at the practical application of the results to improve management and ensure sustainable development in the hospitality sector.