Individual Consumer Preferences Research Articles

Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies

Purpose: The aim of the study was to examine the consumer behavior analysis in the age of big data for effective marketing strategies. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that consumer behavior analysis in the age of big data presents a transformative opportunity for businesses to enhance their marketing strategies and better understand their target audiences. The studies discussed demonstrate the effectiveness of leveraging Big Data analytics in various domains such as social media sentiment analysis, personalized marketing, predictive analytics, customer segmentation, location-based marketing, and real-time personalization. These strategies enable businesses to tailor their marketing efforts more precisely, leading to increased consumer interest, improved engagement, and higher conversion rates. Unique Contribution to Theory, Practice and Policy: Social Identity Theory, Information Processing Theory & Technology Acceptance Model (TAM) may be used to anchor future studies on Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies. Promote the adoption of advanced data analytics tools and techniques by businesses to enhance their marketing strategies. Companies should invest in robust data infrastructure, analytics platforms, and talent to effectively collect, analyze, and interpret consumer data. By leveraging predictive analytics, sentiment analysis, customer segmentation, and real-time personalization, businesses can tailor their marketing efforts to individual consumer preferences and behaviors, leading to improved engagement, customer satisfaction, and ultimately, increased sales and profitability. Advocate for the development of regulatory frameworks that balance consumer privacy rights with the benefits of data-driven marketing. Policymakers should collaborate with industry stakeholders to establish guidelines and standards for ethical data collection, usage, and storage.

E-commerce and consumer behavior: A review of AI-powered personalization and market trends

In the dynamic landscape of electronic commerce (e-commerce), understanding and adapting to evolving consumer behavior is critical for the sustained success of online businesses. This review delves into the intersection of e-commerce and consumer behavior, focusing on the transformative role of Artificial Intelligence (AI)-powered personalization and its impact on market trends. The advent of AI has revolutionized the way e-commerce platforms engage with and cater to individual consumer preferences. AI-powered personalization techniques leverage advanced algorithms to analyze vast datasets, enabling the delivery of highly tailored and relevant content, product recommendations, and user experiences. This review explores the intricate mechanisms of AI-driven personalization, examining how it enhances customer engagement, satisfaction, and loyalty. Furthermore, the study investigates the prominent market trends shaped by AI in e-commerce. From chatbots and virtual assistants facilitating seamless customer interactions to predictive analytics optimizing inventory management, AI is driving innovation across various facets of the online retail landscape. The analysis delves into the integration of machine learning algorithms in predicting consumer preferences, streamlining the purchasing process, and fostering a more personalized shopping journey. As e-commerce continues to evolve, the review also explores the challenges and ethical considerations associated with AI-powered personalization. Issues such as data privacy, algorithmic bias, and the delicate balance between customization and intrusiveness are examined to provide a comprehensive understanding of the broader implications of AI in shaping consumer behavior. Ultimately, this review offers valuable insights into the symbiotic relationship between e-commerce and consumer behavior, shedding light on the transformative power of AI-powered personalization and its influence on emerging market trends. As businesses navigate the digital landscape, understanding and harnessing the potential of AI-driven strategies become imperative for staying competitive and meeting the evolving expectations of tech-savvy consumers.

LEVERAGING BIG DATA FOR PERSONALIZED MARKETING CAMPAIGNS: A REVIEW

The burgeoning field of big data analytics has revolutionized the landscape of marketing, offering unprecedented opportunities for personalized marketing campaigns. This review aims to synthesize the current state of knowledge on leveraging big data for personalized marketing, elucidating the objectives, methodologies, key findings, and conclusions drawn from recent research in this domain. The primary objective of this review is to explore how big data analytics can be effectively utilized to tailor marketing strategies to individual consumer preferences, behaviors, and patterns. Methodologically, the review adopts a comprehensive approach, examining a wide range of studies that employ various big data tools and techniques, including machine learning algorithms, data mining, and predictive analytics, in the context of personalized marketing. Key findings indicate that big data analytics significantly enhances the ability of marketers to understand and predict consumer behavior, leading to more effective targeting and segmentation strategies. The integration of big data has shown to improve customer engagement, satisfaction, and loyalty by delivering more relevant and timely marketing messages. However, challenges such as data privacy concerns, the need for advanced analytical skills, and the potential for data inaccuracies are also highlighted. In conclusion, while big data presents substantial opportunities for personalizing marketing campaigns, its effective implementation requires careful consideration of ethical implications, investment in technological infrastructure, and ongoing skill development. Future research directions include exploring the impact of emerging technologies like artificial intelligence and the Internet of Things (IoT) on personalized marketing, and developing frameworks for ethical data usage in marketing practices. This review underscores the transformative potential of big data in reshaping personalized marketing strategies, offering valuable insights for both practitioners and researchers in the field.
 Keywords: Big Data, Marketing Strategies, Consumer Behavior, Data Analytics, Personalized Marketing, Market Segmentation, Privacy Concerns, Ethical Challenges, Digital Transformation, Artificial Intelligence (AI).

From hamburgers to holidays: Modelling the climate change impact of reducing meat consumption according to UK consumer preferences

The climate impact of food is rapidly gaining attention. Studies focus on global and national impacts, marginalising the role and preferences of individual consumers. Diet is highly personal and for changes to be widely adopted they should be consistent with peoples' lifestyles, preferences, tastes, and knowledge. We construct global scenarios of reducing meat consumption and model their carbon mitigation potential to 2050. We conduct a simple survey to explore UK consumers' preferences for different approaches to reducing meat consumption, which are used to develop scenarios, and units for communicating the associated portion sizes and emissions savings. Results from our survey suggest that modest reductions in meat consumption are more resonant (e.g., eating three portions per week) rather than absolutes (becoming vegan for one month) or abstractions (consuming 170 cal of meat per day). Similarly, tangible analogies such as number of airline flights are preferred over raw emissions abated. We find that reducing meat intake to recommended healthy levels (92 cal per day) and avoiding ruminant meat could almost halve production-phase GHG emissions from the food system. Our survey illustrates how such information can be rephrased for more engaging communication: “reducing your meat intake to three times per week is equivalent to avoiding six short-haul return flights each year”.

Fair cost standards for social services

Current regulations in the field of social services do not allow meeting the requirements of the state standard in terms of pooling and allocating necessary resources objectively. The research aims to create a model of regional information system according an algorithm, which assumes developing a five-level classifier of social services, identifying typological groups of services providers, and applying an industry-average cost of homogeneous services. This model serves a basis for automated formation of norms that guarantee the quality of social service required by the standard. The research methodology rests on the theory of public goods and institutional economics. The methods include structural, normative, and comparative analysis. The data comes from statistical, accounting, and analytical reporting of the social welfare institutions of the Sverdlovsk oblast. Creation and practical implementation of the suggested model of digitalisation of the processes related to the development of normative regulatory measures permitted the authors to customise the existing method of formation of normative regulators of the services quality in line with the preferences of individual consumers; to ensure completeness, accuracy, and timeliness of substantiating the expenditures for fulfilling the state task by social welfare institutions and autonomous non-profit organisations of social service. The findings contribute to the research of the systemic effects of the product, spatial, and time customisation of the social welfare system

Ethical consumption: why should we understand it as a social practice within a multilevel framework?

This article discusses the importance of a multilevel and intertwined understanding of ethical consumption given its conjunction with other social practices. Although the literature on ethical consumption is vast, the role of sociotechnical regimes including technological and cultural elements, infrastructure, market and regulation has been mainly overlooked in this literature. This may be so because ethical consumption practices that refer to other-oriented consumption practices are mainly considered in the view of the motivations and preferences of individual consumers. Due to the insufficiency of individualistic approaches in explaining stimulators and inhibitors of ethical consumption, there might be "various constraints" and "competing demands" in society which limit the formation of "ethical consumption". Therefore, to avoid an oversimplified view of ethical consumption, this paper contributes with a theoretical discussion on combining social practice theory (SPT) with a multi-level perspective (MLP). Although the SPT is a very well-structured framework in consumption studies, the necessity of a combined approach concerns the often-insufficient attention paid to structural prerequisites of various consumption forms in social practice theories. By understanding ethical consumption practices according to a multi-level framework, the paper emphasizes the importance of structural factors at macro- and mesolevels. It also contributes attention to how ethical consumption grows due to dialectical processes between levels, showing that niche practices can simultaneously challenge and rely on existing regimes.

(Bounded) Exit, voice, and politics: pandemic education in US and South Korea

ABSTRACT This article theorizes how crises and markets shape the responses of consumers and producer organizations. We advance four propositions: 1) if a crisis requires major revisions in operational rules, less-exit sensitive (i.e., monopoly-like) organizations shall revise to aggregate preferences of organized producers; and more-exit sensitive (market-oriented) organizations, to the preferences of individual consumers; 2) if a sustained crisis widens the gap between incumbent organizations and consumer preferences, more consumers shall exit to alternative organizations; 3) if the incumbent organization controls valued resources, consumers shall select more-bounded exit options, which retain formal-legal ties with the incumbent; and 4) compared to less-bounded exit, more-bounded exit options shall enhance consumers’ collective voice about incumbent organizations. We inductively derive our theory from an exploratory analysis of educational organizations in US and South Korea during the Covid-19 pandemic in 2020. Many consumers were dissatisfied with regular-public schools and considered various exit alternatives, including non-regular schools (e.g., private, charter [US], homeschooling [US], special-purpose [Korea]) and private supplemental education. Compared to less-bounded exit (e.g., private schools), more-bounded exit options (e.g., charter, PSE) grew more, and contributed more to consumer voice about regular-public schools, during the pandemic.

Ethical consumption: why should we understand it as a social practice within a multilevel framework?

This article discusses the importance of a multilevel and intertwined understanding of ethical consumption given its conjunction with other social practices. Although the literature on ethical consumption is vast, the role of sociotechnical regimes including technological and cultural elements, infrastructure, market and regulation has been mainly overlooked in this literature. This may be so because ethical consumption practices that refer to other-oriented consumption practices are mainly considered in the view of the motivations and preferences of individual consumers. Due to the insufficiency of individualistic approaches to explain stimulators and inhibitors of ethical consumption, there might be other components in society to lead (un)ethical consumption decisions. Therefore, to avoid an oversimplified view of ethical consumption, this paper contributes with a theoretical discussion on combining social practice theory (SPT) with a multi-level perspective (MLP). Although the SPT is a very well-structured framework in consumption studies, the necessity of a combined approach concerns the often-insufficient attention paid to structural prerequisites of various consumption forms in social practice theories. By understanding ethical consumption practices according to a multi-level framework, the paper emphasizes the importance of structural factors at macro- and mesolevels. It also contributes attention to how ethical consumption grows due to dialectical processes between levels, showing that niche practices can, at the same time, both challenge and depend on existing regimes.

Learning consumer preferences from online textual reviews and ratings based on the aggregation-disaggregation paradigm with attitudinal Choquet integral

Online reviews contain a wealth of information about customers’ concerns and sentiments. Sentiment analysis can mine consumer preferences and satisfaction over products/services. Most existing studies on sentiment analysis only considered how to extract attribute types or attribute values of products/services from textual reviews, but ignored the role of attribute-level ratings in reflecting consumer preferences and satisfaction. Based on sentiment analysis and preference disaggregation, this paper unifies the quantitative and qualitative information extracted from attribute-level ratings and textual reviews, respectively, to obtain attribute types and attribute values of products/services. To acquire individual consumer preferences concerning product/service attributes, this paper proposes a method within an aggregation-disaggregation paradigm based on the attitudinal Choquet integral to transform overall online ratings into the form of pairwise comparisons. Compared with the additive value function used in most studies, more consumer preferences in terms of the importance of attributes, the interactions between pairwise attributes, and the tolerance of consumers to make compensation between attribute values in the aggregation process can be deduced by our proposed method. Several real cases on TripAdvisor.com are given to show the applicability of the proposed method.

The Effect of Online C2C Markets on New Product Purchasing Behavior: An Empirical Analysis of Japanese Selling Apps

This study quantitatively investigated the effect of purchases and sales on selling apps on the value of new product purchases, based on questionnaire data from 20,000 consumers. To sufficiently address endogeneity, individual consumer preferences were incorporated into the model and a GMM (generalized method of moments) model employing instrumental variables, was utilized. An analysis on six categories showed a substitution effect where an increase in the value of purchases on selling apps led to a decrease in the value of new product purchases for three categories of hair products: cosmetics, and fragrances, consumer electronics, smartphones, and toys and hobbies. Conversely, for sales, a complementary effect was found for all categories where an increase in the value of selling app sales (the value of items offered for sale and then sold) led to an increase in the value of new product purchases. The absolute value of the complementary effect was higher than that for the substitution effect for all categories except toys and hobbies. Subjective consumer assessment showed that the number of people who thought the value of new product purchases would increase due to selling app use was higher than those who thought it would decrease. The empirical analysis shows that selling app transactions complement, rather than substitute in the new product market. This implies that companies that develop products, which can be easily transacted, searched for, and accepted on selling apps can increase sales by using selling apps in marketing strategies such as information transmission, branding, and customer loyalty.

The effect of culture on energy efficient vehicle ownership

We provide an empirical analysis on the relation between culture and revealed environmental preferences. Switzerland's citizens share the same set of institutions but belong to multiple population groups, which differ by culture and language across distinct geographical locations. This unique setting allows us to disentangle the effect of culture on individual consumer preferences from institutional characteristics. We analyze the effect of culture on energy efficient vehicle registration, using municipality level data and applying a spatial fuzzy Regression Discontinuity Design at the internal French/German language border. Our results indicate that French-speaking municipalities have a 3 to 6 percentage points higher share of energy efficient vehicles, compared to their German-speaking counterparts. These findings suggest that French-speakers place a higher value on the environment, which may be due to their higher sense of collectivism and altruism.

Traditional versus fast fashion supply chains in the apparel industry: an agent-based simulation approach

In the past, most companies in the European apparel industry focused on minimizing manufacturing costs in the design of supply chains in conjunction with long-distance shipping from production sites in the Far East and relatively long production cycles. Today, for some market segments, the speed of production cycles is more important than the cost because short throughput time allows the flexibility to adjust to rapidly changing fashion trends in these market segments. Accordingly, choosing the most beneficial supply chain strategy has become an established research topic. However, apparel markets are complex systems. Therefore, attempts to reduce the underlying complexity in order to model these markets have limited existing research to the consideration of only selected aspects of markets (e.g., considering only homogeneous buyers, a single period, a single product, or a single manufacturer in the absence of competition) rather than taking a more comprehensive view. These limitations can be overcome by applying an agent-based simulation approach—an approach that can account for a wider range of factors, including several competing manufacturers utilizing different supply chain strategies, individual consumer preferences and behavior, word-of-mouth communication, normative social influence, and first-hand experience, as well as advertising. In this paper, the capability potential of such agent-based market simulation is demonstrated by investigating two supply chain strategies (fast fashion vs. traditional fashion) with varying product and communication strategies (product attributes and advertising) in several market scenarios.

An explorative optimization algorithm for sparse scheduling in-home energy management with smart grid

PurposeElectricity utilization at electricity peak hour may differ from every single administration region, for example, mechanical region, business territory and residential zone. This paper introduces a demand-side load management (DSM) strategy, which is one of the utilization of smart grid (SG) that is fit for controlling loads inside the residential working so that the client fulfillment is augmented at least expense.Design/methodology/approachIn this paper, a heuristic algorithms-based energy management controller is intended for a residential region in a SG. Here, Antlion Optimization technique is used for DSM techniques such as load shifting, peak clipping, and valley filling in the residential sectors for 24 h with the help of stochastic function to determine the detection of random distribution of the load.FindingsThis proposed algorithm offered the greatest fulfillment and least expense caused by the consumers when compared to the traditional cost by taking the individual consumer preferences for the loads and the ideal time scheduling for the load, which is obtained from the rebuilding trap.Originality/valueSimulation results demonstrate that the comparison of the cost incurred by the users obtained by the DSM techniques is satisfiable.

Assessing the role of product category involvement and relationship proneness in the satisfaction–loyalty link in retailing

PurposeProduct category involvement and relationship proneness are crucial in explaining relationship outcomes. Nevertheless, the authors know little about their roles in the formation of loyalty, especially in the retail industry. Individual consumer traits and preferences are likely to play a critical role in the success of relationship marketing. Yet, relationship marketing studies have fallen short of considering such individual differences. The purpose of this paper is to examine the mediating effects of product category involvement and relationship proneness on the relationship between satisfaction and loyalty in retail clothing stores.Design/methodology/approachData were obtained using a survey of 220 consumers. Partial least squares structural equation modeling was employed to test the proposed theoretical model.FindingsThe results show that satisfaction significantly affects product category involvement and relationship proneness, which, in turn, significantly affect purchase intention and word-of-mouth (WOM). The results also show that product category involvement and relationship proneness partially mediate the impact of satisfaction on purchase intention and WOM.Research limitations/implicationsProduct category involvement and relationship proneness play a critical role in explaining the satisfaction–loyalty link. Future research could consider the role of potential moderating variables.Practical implicationsRetail managers should not only focus on improving customer satisfaction to achieve customer loyalty, but should also consider the importance of product category involvement and relationship proneness, and their role in the formation of customer loyalty both in traditional and online environments.Originality/valueTo the best of the authors’ knowledge, this study is the first to explore the mediating effects of product category involvement and relationship proneness on the relationship between satisfaction, purchase intention and WOM in the retail industry.

Brewology Research Project: identifying the consumer values and motivations implicated in the rise of microbreweries in the Midwest

•This mixed-method study is a collaborative effort that draws on faculty expertise in marketing, tourism and communication. The study seeks to identify the consumer values and motivations implicated in the rise of microbreweries in the Midwest. Over the course of a few decades, craft beers have gained significant market share relative to large-scale commercial brands. The study aims to identify salient factors underlying the popularity of craft beers, taking into consideration both individual consumer preferences as well as how these intersect with socio-economic megatrends such as the “buy local” movement and agri-tourism.•The popularity of microbreweries is further evidenced in an increasing number of beer festivals and associated regional events. As these have become fixtures on annual calendars, additional questions regarding group affiliation and how it informs conceptions of self among craft beer enthusiasts’ demands exploring. Thus the proposed study further aims to shed light on social dimensions of these trends in a regional setting. Overall, results will not only fill gaps in knowledge concerning regional consumers, but will provide an understanding of shifting socio-cultural trends as reflected in the microbrewery movement at local levels.

Optimising healthy and safe fish intake recommendations: a trade-off between personal preference and cost.

Individuals may perceive personalised dietary advice as more relevant and motivational than national guidelines. Personal preference and food cost are factors that can affect consumer decisions. The objective of this study was to present a method for modelling and analysing the trade-off between deviation from preference and food cost for optimised personalised dietary recommendations. Quadratic programming was applied to minimise deviation from fish preference and cost simultaneously with different weights on the cost for 3016 Danish adults (whose dietary intake and body weight were recorded in a national dietary survey). Model constraints included recommendations for EPA, DHA and vitamin D and tolerable levels for methyl mercury, dioxins and dioxin-like polychlorinated biphenyls. When only minimising deviation from preference, 50 % of the study population should be recommended to increase fish intake, 48 % should be suggested to maintain current consumption and 2 % should be suggested to decrease fish consumption. When only minimising cost, the vast majority (99 %) should be recommended to only consume herring, which is the least-expensive fish species. By minimising deviation from preference and cost simultaneously with different weights on the cost, personalised optimal trade-off curves between deviation from fish intake preference and fish cost could be generated for each individual in our study population, except for twenty-two individuals (0·7 %) whose contaminant background exposure was too high. In the future, the method of this paper could be applied in the personal communication of healthy and safe food recommendations that fit the preferences of individual consumers.

Influence of Demographics on Consumer Preferences for Alternative Fuel Vehicles: A Review of Choice Modelling Studies and a Study in Portugal

The significant energy consumed by road transportation and the difficult market penetration of Alternative Fuel Vehicles (AFVs) has led to a substantial body of research aiming to understand consumer preferences and future demand for AFVs. The individual characteristics of consumers are one of the explanatory factors of these preferences. In this context, the main purpose of this work is to present a comprehensive state-of-the-art review of how consumer demographics influence their preferences concerning AFVs. This review focuses on papers that applied Choice Modelling techniques to elicit individual consumer preferences for AFVs through stated preference surveys. Age, gender, income, level of education, family size, driving habits and number of vehicles per household were selected for analysis. This study also adds to the literature by analyzing the influence of demographic characteristics on preferences of Portuguese consumers. Very few studies addressed the influence of demographics on preferences for vehicle attributes. Considering the influence of consumers’ income and age, no consistent results were found. However, when age and consumers’ nationality were crossed, a potential trend of consumers’ age influence was unveiled. Regarding gender, level of education and family size, it was observed that consumers with higher education levels, women and consumers with larger families have higher preferences for AFVs.

Disparities in wage flexibility in the Polish economy

The paper focuses on the examination of wage flexibility in the Polish economy, indicating the sectoral differences. In spite of the fact that wages depend on many variables, labour productivity (according to the traditional approach) was selected as the most influential determinant of wages. As a result, the paper describes wage flexibility as how wages respond to changes in labour productivity. The research shows that wages are the most flexible in manufacturing. The survey also discovers the lack of wage flexibility in agriculture and its strong diversification in the services sector. Disparities amongst services sections are determined by the specificity of individual sections and consumer preferences. A rising minimum wage attracts the attention of economists in the current economic climate in Poland, as the minimum wage plays a significant role as the institutional factor of wage flexibility. The paper consists of two parts. The first, theoretical, part of the article presents the issues of wage flexibility and its determinants. The efficiency wage theory, which emphasizes a positive relationship between wages and labour productivity, and the wage-requirements curve are also presented in the first part of the paper. In turn, the methodological part of the article includes a study of wage flexibility in the Polish economy on the macroeconomic scale and on a sectoral basis. The division of the economy according to PKD 2007 was applied, while the empirical materials were Eurostat and the Central Statistical Office database.

Guidance for optimizing drinking water taste by adjusting mineralization as measured by total dissolved solids (TDS)

Dissolved minerals, usually measured as total dissolved solids (TDS), are the major determinant of the taste of tap water. The mineral content of drinking water is largely affected by the source water's mineral content, except where membrane treatment removes minerals which then requires remineralization. The aim of this research is to provide guidance to water utilities about TDS differences that allow consumers to differentiate waters and to determine how changing TDS affects the acceptability of drinking water. Panels of consumers and/or trained professionals performed taste tests to determine their ability to discriminate between two water samples with different TDS. Results reveal that to discriminate between waters based on the taste of TDS, a value of ≈∆TDS >150 mg/L is necessary. This provides a baseline target ∆TDS when treating tap water. Panelists provided taste-liking scores, using a 0–10 scale, for individual water samples of varying TDS. Taste-liking scores decreased with increased level of TDS, with an estimated overall rate of −0.23 liking units/100 mg/L TDS. However, there are caveats to this general guidance: specific mineral composition, the direction of change of TDS, the presence of odourants, organic carbon content, temperature, and varying taste abilities and preferences of individual consumers.

Brand competitiveness

PurposeThis paper aims to introduce the concept of consumer-based brand value (CBBV), a change in the perspective of brand-equity and brand value from one where the brand is considered separately from other brands. The purpose of this paper is threefold: highlighting the theoretical difference between brand equity and brand value (Raggio and Leone, 2007); conceptually linking brand equity with brand value and then with brand competitiveness; and demonstrating a straightforward method for scholars and practitioners to measure brand value and then to forecast and simulate brand competitiveness.Design/methodology/approachOn a sample of 190 postgraduate students, the authors apply the conjoint analysis and best-worst scaling to illustrate our CBBV model. Following Keller (1993), the authors operationalise brand equity as the outcome of consumers’ internal mental processes, in the form of brand preference, and show how preferences data can be transformed into estimates of brand value in the form of price premium. Finally, the model allows market share simulation based on manipulation of branding and brand features.FindingsThe paper is more a conceptual piece, highlighting the distinction between brand equity and brand value. It also demonstrates a relatively new measurement technique for transforming measures of brand equity, in the form of brand preferences, into estimates of brand value. The paper used airlines as a service-provider example, but the technique can be applied to many hospitality and service environments.Research limitations/implicationsThe study demonstrates how brand equity drives brand value via consumer utility, and proposes a CBBV–competitiveness chain. The authors convert individual consumer preference data to brand value with subsequent preference-based market segmentation, and estimate competitiveness in two ways: market share ceteris paribus and price premium for brand indifference. They also demonstrate how market simulation can be performed so that it allows forecasting of competitiveness (market share) based on product attributes that affect brand value.Practical implicationsThe CBBV–competitiveness chain constitutes a (new) mindset in the marketing of hospitality, tourism and other services. The study provides a method to measure and test the components of this model and determine brand competitiveness. It used airlines in the example, but the method can be applied to many different settings such as, for example, hotel management group (Starwood, Accor, Intercontinental, Hilton). The authors show how the method allows for benefit-based market segmentation, market-share forecasting and estimation of price premium.Originality valueThe CBBV chain provides a conceptual link between brand equity, brand value and brand competitiveness. When equity and related constructs are often conceptualised as relative to competition, they are rarely actually measured in that manner. The study shows how brand equity measured as preferences can be transformed into brand value and competitiveness relative to the competition. The combination of conjoint analysis and best–worst scaling is relatively new to the hospitality and services industries. The authors show that these tools can be applied to these industries without depending on costly software or high-priced consultants.