Market Segmentation Research Articles

Market segments and pricing of fine wines over their lifecycle

Is the fine wine market segmented? The literature shows that this is the case for ordinary wines, and this has significant implications for hedonic analysis. We apply a finite mixture model to a 20-year dataset of auction prices. Our results reveal the existence of five segments in the fine wine market that differ in both their composition and the effects of scores, age, and market conditions on prices. This segmentation helps reconcile the divergent findings in existing research: some wines are highly sensitive to market conditions and have yielded significant returns (investment wines), while prices of other wines are primarily influenced by age (collectible wines) or quality (drinking wines). Our findings enhance the understanding of fine wine pricing and offer new insights into the age-price relationship, which is generally linear but varies by segment and vintage quality.

The Gap Between the Actual Cost and Tariffs of Global Surgical Procedures: A Retrospective Cross-sectional Study in Qazvin Province, Iran.

Iran's healthcare system has a significant discrepancy between the national tariff and the cost of global surgical procedures (GSPs). This study aimed to compare the actual costs of GSPs with national tariffs in Iran's public hospitals. This retrospective cross-sectional study was conducted in 2017. Using the census method, 6126 GSPs performed in three public hospitals were investigated in this study. Additionally, national tariffs from the Supreme Council of Health Insurance were obtained. The tariff-cost gap was the discrepancy between a GSP's actual costs and tariff. Multiple linear regression analysis determined factors affecting the tariff-cost gap. The average actual cost of GSPs was 637 USD, while the average tariff was 495 USD. The reimbursement covered only 78% of the costs. The gap was higher in older (B=1.05, 95% CI: 0.76-1.35, P<0.001), females (B=26.7, 95% CI: 15.5-37.9, P<0.001), patients with a longer stay (B=81.2, 95% CI: 77.5-84.8, P<0.001), and procedures performed by full-time surgeons (B=67.3, 95% CI: 56.9-77.5, P<0.001). Furthermore, neurosurgery had the highest effect on forecasting the gap between actual costs and tariffs among surgical specialties (B=346.9, 95% CI: 214.3-479.5, P<0.001). Public hospitals suffer from large financial losses due to the national tariff for many GSPs not covering their actual costs. It is suggested that tariffs be increased for certain customer segments that can bear higher costs and global tariffs be adjusted to match actual service delivery costs.

AI in Neuromarketing: Understanding Consumer Emotions and Behavior through Machine Learning

This paper explores the integration of artificial intelligence (AI) and machine learning in the emerging field of neuromarketing, which combines neuroscience with marketing strategies to decode consumer emotions and behaviors. By employing advanced techniques such as EEG, fMRI, and facial recognition, the research highlights how these technologies can analyze subconscious responses to marketing stimuli, providing deeper insights than traditional methods. The study emphasizes the transformative role of AI in enhancing predictive analytics, real-time personalization, and customer segmentation. Furthermore, it discusses the potential benefits of AI-driven insights for crafting effective marketing campaigns that resonate with consumers and improve engagement. The paper also addresses the challenges faced in this field and proposes future research directions to explore the intersection of AI and consumer behavior analysis.

APPLYING SOFT COMPUTING METHODS IN BUSINESS ANALYTICS USING HYBRID GENETIC ALGORITHMS

In the era of big data and advanced analytics, the application of soft computing techniques has emerged as a powerful tool in solving complex business problems. This paper presents the use of hybrid genetic algorithms (HGAs) in business analytics to address challenges related to optimization, prediction, and decision-making processes. Traditional algorithms often struggle with large, nonlinear, and dynamic datasets typical of business environments. The incorporation of soft computing techniques such as genetic algorithms (GAs) and their hybridization with other methods like fuzzy logic and neural networks can help overcome these limitations. The problem addressed in this research is optimizing decision-making in marketing strategies, focusing on maximizing return on investment (ROI). Standard methods face difficulties in navigating through vast datasets and discovering optimal solutions. The hybrid genetic algorithm proposed in this study combines the exploration strength of GAs with the exploitative precision of local search techniques. The model was tested using a real-world dataset of marketing expenditures and revenues from a retail company. The HGA achieved an ROI improvement of 25%, significantly outperforming standard GAs and traditional optimization methods, which yielded only a 12% improvement. The flexibility and efficiency of this approach make it ideal for various business applications, including supply chain optimization, customer segmentation, and product pricing.

Fair Competition Review System and cross-regional capital flow: Evidence from China

Breaking local protectionism and market segmentation to promote the free flow of production factors between regions is crucial for building a unified national market in China. Introduced in 2016, the Fair Competition Review System (FCRS) restricts local governments’ abuse of administrative power to limit competition. The system corrects market failures and weakens regional barriers, establishing market competition by evaluating policies and regulations for anti-competitive practices. This study investigated the impact of the FCRS on cross-regional capital flow through subsidiaries in different regions. Using data from Chinese-listed companies between 2011 and 2022, this empirical study found that capital flow significantly increased after the system's implementation. Cross-regional flow was stronger in areas with higher reduced administrative barriers, lower tax burdens, and lower capacity utilization rates. This suggests that the primary mechanisms driving capital flow were expansion and cost-efficiency improvements. Heterogeneity tests indicate that the system exhibited a stronger impact on promoting cross-regional capital flow for private enterprises and those with low investment efficiency and weaker profitability. These findings suggest that the FCRS effectively regulates administrative monopolies, reduces regional barriers, and facilitates freer capital flow between regions, contributing to a unified national market in China.

Determining the leaders of Ukraine’s insurance market based on the adaptation of the DEA method

The escalating wartime risks in Ukraine has led to a rapid reduction in insurance coverage in life and non-life segments. The purpose of the study is to rank Ukraine’s insurance companies based on an adaptation of the DEA method to the insurance market conditions. The study utilized the Supervisory Statistics of the National Bank of Ukraine as data. The study also used ranking of insurance companies based on technical efficiency criteria. The output indicators include the profitability of total capital (Output1) and the occupied share of the insurance market (Output2). The input indicators comprise the volume of total assets of insurers (Input1), the share of equity capital in assets (Input2), the level of gross payments (Input3), and the level of payments to insurance reserves (Input4). The ranking of insurance companies is based on minimizing the distance of each from the bounds of technical efficiency by solving a set of optimization problems. Based on the modeling results, a list of market leaders (7 companies) was formed for the end of the third quarter of 2023. They served nearly 37% of the insurance market in Ukraine, accumulating 41.5% of the total assets of this financial market sector. So, the primary issue for insurance companies is the loss of solvency due to the absence of adequate levels of insurance reserves. Therefore, priority measures should include strengthening regulatory constraints in this financial market segment to facilitate its qualitative renewal.

Future of Hygieia Health Private Limited

The case revolves around the dilemma faced by the existing co-founders of Hygieia Health Private Limited (hereafter, Hygieia) in managing a potential startup with an amazing product but declining investor confidence due to the sudden exit of one of the co-founders. Hygieia, a tech services company that started in September 2016, represented the medical healthcare sector that served the niche customer segment—pregnant women. The startup was considered to be one of the promising businesses by early-stage tech funders. However, one of the co-founders suddenly announced that he would leave the company without parting with his stake in it. This created a major problem for the other founder members in moving ahead. The case examines the factors contributing to the crises and dilemmas faced by Hygieia’s co-founders, which led them to devise a strategic plan to overcome these challenges. It emphasizes the difficulties encountered by a growing startup and highlights the importance of having a strong team to sustain and expand the business. Additionally, the case explores relevant valuation concepts in a startup context. It provides insights into the significance of human capital in a tech startup using the people, opportunity, context and deal (POCD) framework, explores various financing options available for startups and outlines key valuation methods.

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.

Market segment evaluation based on fuzzy tools

The paper suggests a hybrid model with simplified and extended schemes for evaluating market segments based on strategic diagnostics methods and fuzzy multi-criteria analysis tools. The developed model's novelty and originality consist of forming a system of evaluation criteria based on the GROT criteria of I. Ansoff and the five forces of the M. Porter model (and in the case of an extended calculation scheme – with their decomposition into sets of relevant sub-criteria) and the use of the latest Fuzzy SBWM method (Fuzzy Extension of Simplified Best-Worst Method) to determine their weighting factors. Expert linguistic evaluations on a defined 7-level term set, followed by their transformation into fuzzy numbers with triangular membership functions, are used to evaluate market segments for each identified criteria (sub-criteria). The Fuzzy SAW method determines fuzzy integral estimates of market segments based on these sub-criteria. A practical case of evaluating the confectionery market segments of Ukraine for the simplified calculation scheme is given. The systematic approach makes it possible to determine the attractiveness of market segments for forming strategic recommendations based on the application of portfolio analysis methods, for developing and implementing diversification and logistic strategies.

Importance-Induced Customer Segmentation Using Explainable Machine Learning

Abstract Customer segmentation plays a critical role in enhancing a company's product penetration rate in the market. It enables numerous downstream applications such as customer-oriented product development and trend analysis. Previous approaches to customer segmentation have relied either on survey-based methods or data-driven approaches. However, these methods face challenges such as high human labor requirements or the generation of noisy segments. To address these challenges, this paper proposes a new methodology based on data-driven network construction and an importance-enhanced framework. The framework incorporates two techniques: (1) the utilization of a neural network model to compute feature importance values and (2) the proposal of a novel network connection rule. This framework addresses the limitation of the previous approach, sentimentpolarity- based networking, by connecting customers based on feature importance. We further validated the effectiveness of the framework using three real-world datasets and demonstrated the proposed method outperformed the previous approach.

Framework for developing entrepreneurial business models: Theory and practical application

A framework for developing entrepreneurial business models is crucial for guiding entrepreneurs from conceptualization to practical application in a dynamic market environment. This review outlines a comprehensive framework that integrates theoretical concepts with practical approaches to help entrepreneurs create robust and adaptable business models. The framework is grounded in a synthesis of contemporary entrepreneurship theories and practical insights drawn from case studies of successful ventures. It begins with a theoretical exploration of foundational business model components, including value proposition, customer segments, revenue streams, and cost structures. By leveraging established models such as the Business Model Canvas and the Lean Startup methodology, the framework provides a structured approach for entrepreneurs to design and validate their business models. A key component of the framework is its emphasis on iterative development and validation. Entrepreneurs are encouraged to employ lean principles, such as rapid prototyping and customer feedback loops, to refine their business models. This iterative process helps in adapting to market changes and customer needs, reducing risk, and improving the likelihood of success. The framework also incorporates strategic analysis tools to evaluate market opportunities, competitive landscape, and potential challenges. Practical application is emphasized through detailed guidelines and tools for each stage of business model development. This includes methodologies for conducting market research, identifying key resources and partnerships, and establishing operational processes. Additionally, the framework addresses the importance of financial planning and risk management, offering practical advice on funding strategies and contingency planning. The paper also highlights the significance of contextual factors, such as industry trends, regulatory environment, and technological advancements, in shaping business models. By incorporating these elements, the framework provides a holistic approach to entrepreneurial business model development. In conclusion, the proposed framework serves as a valuable guide for entrepreneurs seeking to develop and implement effective business models. It combines theoretical insights with practical tools and strategies, enabling entrepreneurs to navigate the complexities of the business landscape and achieve sustainable success. This approach not only enhances the strategic planning process but also fosters innovation and adaptability in an ever-evolving market environment.

BAAS: Banking as a service brings Industry disruption

Banking as a Service (BaaS) has emerged as a transformative force in the financial sector, challenging traditional banking models and offering innovative solutions. BaaS enables non-financial institutions to integrate banking services into their own products and platforms, leveraging the technology and infrastructure of licensed banks. This disruption has far-reaching implications for both established financial institutions and fintech startups. For traditional banks, BaaS presents an opportunity to expand their reach and generate new revenue streams by partnering with non-financial businesses. By offering their banking services through BaaS platforms, banks can tap into new customer segments and diversify their income sources. However, this also poses challenges, as banks must adapt to the changing competitive landscape and ensure their technology infrastructure can support BaaS offerings. For fintech startups, BaaS provides a cost-effective and efficient way to enter the financial market. By leveraging BaaS solutions, these startups can focus on developing innovative products and services without the need for significant upfront investments in banking infrastructure. This has led to a surge in fintech innovation, with startups offering a wide range of financial products and services, from payments and lending to wealth management. The disruption caused by BaaS is also driving a shift towards open banking, which promotes the sharing of customer data and financial information between banks and third-party providers. This increased transparency and competition is benefiting consumers by providing them with more choice and flexibility in their financial products and services.

Developing and validating an instrument for assessing E-entrepreneurial idea feasibility

Despite the critical importance of e-entrepreneurship idea generation for e-venture success, there remains a significant lack of valid and reliable instruments to evaluate the feasibility of e-entrepreneurial ideas proposed by undergraduates during entrepreneurship learning. This study addresses this gap by creating and validating a scale based on the Business Model Canvas with a sample of 299 participants. Following established scale development procedures, a 13-item scale with four factors emerged: value creation and customer relationship management, cost structure, business viability, and customer segmentation and personalization. The scale demonstrates satisfactory internal consistency and criterion-related validity, providing a reliable tool for assessing e-entrepreneurial idea feasibility in higher education, benefiting both educators and researchers.

Artificial Intelligence in SME financial decision-making: Tools for enhancing efficiency and profitability

Small and medium-sized enterprises (SMEs) face significant challenges in financial decision-making due to resource constraints, limited access to capital, and unpredictable cash flow. To overcome these challenges, many SMEs are turning to artificial intelligence (AI) to improve efficiency and profitability. AI, through tools like machine learning and predictive analytics, has transformed how SMEs manage their finances, offering solutions that enhance accuracy, speed, and data-driven decision-making. AI-driven accounting tools are revolutionizing routine tasks such as bookkeeping, invoicing, and expense tracking. By automating these functions, SMEs can reduce human error, save time, and allocate resources more effectively. AI’s ability to integrate real-time data ensures that financial records remain up to date, enabling more accurate cash flow forecasting. For example, AI can predict liquidity needs based on historical data, seasonal trends, and market conditions, helping SMEs maintain healthy cash flow and avoid financial shortfalls. In addition to improving efficiency, AI plays a crucial role in optimizing revenue and profitability. AI-based pricing models allow SMEs to adjust prices dynamically, responding to market demand and competitor behavior in real-time. This data-driven approach ensures that businesses maximize revenue without compromising customer satisfaction. Moreover, AI helps SMEs identify high-value customers by analyzing purchasing patterns, preferences, and behaviors. By focusing on customer segmentation and tailored marketing strategies, SMEs can boost sales and customer retention. Despite the clear benefits, AI adoption in SMEs is not without challenges. High implementation costs, limited technical expertise, and concerns over data privacy can hinder the integration of AI tools. However, scalable, cost-effective AI solutions are becoming increasingly available, making it easier for SMEs to incorporate AI into their financial processes. As AI continues to evolve, it will play an even more significant role in helping SMEs navigate financial complexities, improve decision-making, and enhance long-term profitability.

Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques

The proliferation of big data and the growth of e-commerce have intensified the challenges associated with extracting actionable data for personalised recommendations and decision-making. With data-driven marketing strategies, understanding and predicting customer behaviour has become paramount for maintaining competitive advantage. This study leverages business analytics tools, focusing on Recency, Frequency, and Monetary (RFM) Analysis, alongside K-Means and Hierarchical (Agglomerative) Clustering algorithms, to segment customer transactional data. Data normalisation, a critical step for accurate clustering, was performed using log transformation and the Power Transformer technique with the Yeo-Johnson parameter, the latter proving more effective for handling both positively and negatively skewed data, enhancing data normalisation and suitability for analysis. This study reveals that RFM Analysis with Hierarchical Clustering outperforms K-Means Clustering, achieving a Silhouette Score of 0.47 and a Calinski–Harabasz Index of 3787.1, indicating a more accurate identification of customer segments. RFM Analysis alone generated eight clusters, while integrating RFM Analysis with both Hierarchical Clustering and K-Means generated three similar-sized clusters with interchanged labels. These metrics highlight the proficiency of Hierarchical Clustering in identifying unique customer segments and customising marketing strategies. The findings indicate that the RFM-Hierarchical Clustering approach enhances segmentation precision and facilitates more refined and effective marketing strategies.

Implementation of K-Nearest Neighbors Algorithm in Analyzing Public Interest in Shoping at Supermarkets

People's shopping patterns and behaviors continue to develop along with technological advances and lifestyle changes, thus requiring retail business actors, especially supermarkets, to better understand their customers' interests and preferences. In this context, accurate analysis of customer shopping interests is very important to improve customer satisfaction and optimize marketing strategies. One solution that can be implemented to analyze people's shopping interests is the application of the K-Nearest Neighbors algorithm, a simple yet effective nearest neighbor-based classification method for recognizing patterns from existing data. This study aims to apply the K-Nearest Neighbors algorithm to classify people's interest in shopping at supermarkets. This study also evaluates the effectiveness and performance of the algorithm in the context of business decision-making in the retail sector. The research methodology includes collecting data on people's shopping interests, data pre-processing, implementing the K-Nearest Neighbors algorithm, and evaluating model performance using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this study indicate that the K-Nearest Neighbors algorithm is able to achieve an accuracy of 88%, with precision, recall, and F1-score all reaching 92.86%. These results indicate that the K-Nearest Neighbors model is very effective in classifying people's shopping interests, with a low error rate. The resulting confusion matrix also shows the model's ability to identify customers who are interested in shopping with little prediction error. This study concludes that we can rely on the K-Nearest Neighbors algorithm to analyze people's shopping interests in supermarkets. This model not only shows good performance in classification but also has great potential to be implemented in recommendation systems and customer segmentation in the real world. This study contributes to the development of consumer behavior analysis methods in the retail sector, as well as providing a basis for further research to explore other algorithms or combinations of techniques to improve the accuracy and effectiveness of classification models.

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The article highlights the current state of the world market of industrial hemp, presents forecasts for its development, it is stated that the last decade has been characterized by the active development of hemp production in many countries of the world. It is emphasized that this niche plant is called a culture of the 21st century due to a number of advantages that are inherent to it, and also that there are no generalized official statistics on the cultivation of industrial hemp in the world as of 2024. The geography of growing industrial hemp in the world is presented. It is noted that the cultivation of industrial hemp in Ukraine is subject to licensing, the level of the narcotic substance in the varieties that are allowed to grow is the lowest in the world, according to forecasts, the state can become one of the leaders in the production of industrial hemp in the world by 2050. It is highlighted that hemp raw materials are used in the most diverse branches of the economy, in the process of processing different parts of hemp, a wide range of products with high added value can be obtained. A number of other factors that testify to the economic attractiveness of hemp production are also presented, including: short growing season; the possibility of obtaining a significant harvest of biomass even on lands that are unsuitable for other crops; the possibility of cultivation without herbicides; low need for water use; expediency of introduction into crop rotation; the possibility of using all parts of the plant and others. Attention is focused on the multi-vector possibilities of hemp processing; the key factors that encourage the growth of the world market of hemp products are highlighted; the key segments of the global industrial hemp market are highlighted food and beverages, personal care products and paper. Examples of the main areas of hemp processing in Ukraine and the profitability of its cultivation in Ukraine in 2023-2024 are given. It was revealed that the opening of industrial parks, which are aimed exclusively at the cultivation and processing of industrial hemp, is a confirmation of the development of the hemp growing niche in Ukraine. It was concluded that the production of a niche culture of hemp and its processing is accompanied by a high level of economic efficiency

SEGMENTATION OF YOUNG ADULT TOURISTS VISITING CROATIAN CITIES: A HUNGARIAN CASE STUDY

Croatia's allure as a cultural, natural, and historical destination in the global tourism landscape has garnered increasing interest from international travelers, including Hungarian tourists. This study addresses the research gap in demographic segmentation and destination preference among Hungarian adults aged 18-34, a crucial yet understudied market segment within Croatian tourism. Our analysis integrates descriptive statistics and ordinal regression to assess how tourism preferences influence satisfaction. A two-step clustering method identifies demographic clusters, further analyzed to determine their visitation patterns to 11 Croatian cities, revealing significant travel behaviour insights. The findings reveal distinctive clusters with varied travel behaviours and preferences, indicating that tailored marketing strategies could significantly enhance the tourism experience. The travel patterns suggest a move towards personalized travel experiences and an inclination towards autonomy and comfort in accommodation choices. The study enriches the discourse in tourism segmentation and destination analysis, suggesting that understanding the intricate preferences of demographic segments can lead to more effective marketing approaches. Practically, it offers actionable insights for tourism operators, marketers, and policymakers to fine-tune their strategies to cater to this demographic, potentially leading to increased visitation and deeper engagement with Croatian tourism offerings.

Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management

Small and medium-sized enterprises (SMEs) are vital contributors to global economies, but they often face unique financial challenges that threaten their long-term stability. This review presents a comprehensive analysis of forecasting financial stability in SMEs, focusing on strategic budgeting and revenue management as key pillars. Strategic budgeting plays a crucial role in aligning financial plans with business goals, managing risks, and adapting to market changes. Techniques such as zero-based budgeting, activity-based budgeting, and rolling budgets offer SMEs flexibility in allocating resources and ensuring optimal cash flow management. Meanwhile, effective revenue management involves the use of pricing strategies, sales channel diversification, and customer segmentation to optimize revenue streams and improve profitability. The interplay between strategic budgeting and revenue management is critical for creating dynamic forecasting models that help SMEs anticipate financial challenges and make informed decisions. By integrating both strategies, SMEs can better manage risks, enhance financial predictability, and achieve long-term sustainability. This review also explores the role of technological tools, such as financial software and predictive analytics, in supporting financial decision-making processes. Tools like customer relationship management (CRM) systems and AI-driven forecasting platforms enable SMEs to optimize revenue potential and refine budgetary practices. The review provides recommendations for SMEs to implement best practices in financial planning, suggesting regular budget reviews and proactive revenue management strategies to respond to market fluctuations. By leveraging strategic budgeting and revenue management, SMEs can enhance their financial resilience and improve their capacity to navigate economic uncertainties. The review concludes with insights into emerging trends in financial forecasting, emphasizing the importance of continuous adaptation to maintain financial stability.

Customer Acceptance for Self-Service Technologies in Malaysian Fast-Food Restaurants: An Empirical Study

Recently, the fast-food industry has witnessed an increase in adopting self-service technologies (SSTs) intended to offer customers increased convenience and efficiency. This study seeks to find out the factors influencing customer acceptance of SST in fast food restaurants in Melaka, Malaysia, using the Technology Acceptance Model. For that purpose, a structured questionnaire was presented to 350 customers of fast food and this data was analyzed by descriptive and inferential statistics. A test of the hypotheses revealed that perceived ease of use (PEOU) and perceived usefulness (PU) are highly significant predictors of customers' attitudes toward using SSTs (ATU). More specifically, PU indicates stronger effects on acceptance such that customers are more willing to use SSTs when obvious practical benefits would be achieved from them regarding faster services and greater accuracy in placing orders. The findings also indicate that younger, highly educated customers with previous experience with SSTs are more likely to accept this innovation. These insights go a long way in helping the operator of fast food to design a better customer experience using technology-driven solutions. This covers the improvement of user interfaces, the emphasis on the practical benefits of SSTs, and tailoring marketing strategies to diverse customer segments.