Consumer Segments Research Articles

Factors Influencing Smart Phone Buying Behavior of Consumers: Implications for Building a Marketing Strategy

This research investigated the nature of factors that influence the smartphone buying decision in Arar province in Saudia Arabia. The research is based on a survey which targets individuals who own smartphones. A total of 384 smartphone owners were questioned. The five items that correspond to the study’s independent variables (personal factors, external factors) underwent reliability analysis. The results allowed confirming the reliability of the questionnaire items. The statistical package analysis program SPSS was used to examine the study data. The ACP-method, regression model, was used to evaluate our hypothesis. The ACP resulted in dividing the influencing factors into 3 groups: personal factors, external factors, and gender. Regression model analysis revealed that personal factors, external factors and gender accounted for 41.7% of the variance in buying decisions and were statistically significant. It has also been confirmed that these variables have an impact on smartphone buying behavior. The results of the analysis revealed that internal and external factors, as well as gender, influence the decision to purchase a smartphone. Personal traits have a greater impact on the decision to purchase a smartphone. The findings are important for smartphone marketers to better analyze consumer behavior and segment the market based on the customer’s particular qualities. The same results can be applied to these firms’ product development processes.

Winning Seats: Contingency Selling in Markets with Limited Capacity and Diverse Preferences

This paper examines an emerging pricing model, contingency selling, for markets where the product is sold in advance with attribute uncertainty and capacity constraint. A typical example is tournament ticket sales for sports events where it is uncertain whether a particular team will appear in the game. The model enables the firm to sell tickets in a contingent fashion, i.e., some tickets will be valid only if a certain team gets into the game. When consumers (fans) have sufficiently different preferences for different products (games with different teams), selling these “contingent tickets” better matches the price with the willingness-to-pay of particular consumer segments (fans of specific teams). Exploiting state-based price discrimination and belief heterogeneity, contingent tickets generate a higher margin per seat than the traditional way of selling general tickets, especially when there are supply constraints. We further show that contingency selling continues to be advantageous in the presence of secondary markets where consumers purchase tickets in advance but can trade tickets once they know which team will be in the game. We extend our model to examine the impacts of consumer optimism bias and fan base asymmetry. Finally, we compare contingency selling with alternative selling mechanisms to demonstrate the robustness of our results and illustrate the situations where this new selling model is more (or less) advantageous.

Evaluating multimedia advertising campaign effectiveness

Companies increasingly combine multiple media outlets when launching advertising campaigns. This study employs causal forest to examine the effects of complex multimedia campaigns. The model effectively corrects for selection bias, automatically identifies informative consumer features, and performs automated data-driven consumer segmentation based on the consumer features identified. We analyze a large dataset involving around seven million consumers and four thousand covariates, and provide empirical evidence on the nonlinear effect of repeated ad exposures in the multimedia context, how such effect varies across consumer groups, and the contingent existence of multimedia synergy. We demonstrate that negligence of the selection bias and heterogeneity across segments results in suboptimal conversions and a waste of advertising resources. The analysis procedure that we propose can facilitate decision making for complex advertising campaigns to improve their effectiveness.

The role of ethical orientation in animal welfare choice behaviour: A segmentation study

Consumers are becoming more and more conscious about their consumption choices, demanding more sustainable, healthy and fair options. Meat consumption especially is under scrutiny for environmental as well as ethical reasons. We develop a set of items to measure ethical orientation and apply it to consumers’ choices of meat products with 3000 consumers across six European countries (Denmark, Germany, Spain, Poland, Netherlands, Italy). We find consumer preferences to be similar across countries, with two thirds of consumers showing a preference for animal welfare attributes, especially outdoor access. Consumer segments can be differentiated based on their degree of ethical orientation, environmental concern, level of trust in the food system and price sensitivity. Ethical orientation differentiates between consumers’ self-reported welfare meat purchases. Our results contribute to the literature on consumer behavior regarding farm animal welfare across different meat product and countries.

The bright and dark sides of servitization under extended producer responsibility

Servitization has gained popularity with its concept of selling the functionality or use of a product rather than the product itself. However, the implications of EPR under servitization scenarios are not straightforward. On the one hand, servitization allows the manufacturer to retain ownership of the product, which mitigates the need for costly buyback procedures when the manufacturer is required to repurchase the end-of-use product under EPR. On the other hand, servitization expands the demand of consumers by allowing those who cannot afford the product to still use it by paying per use, thereby increasing the costs for manufacturers when it comes to recycling the product. To investigate the impact of EPR on servitization, we incorporate three distinct characteristics into the model: (i) pay-per-use pricing, (ii) the retention of product ownership, and (iii) repurchasing the end product due to EPR. The manufacturer strategically chooses among the pure sales model, servitization model, or a hybrid model that includes both options. Our findings indicate that without EPR, the manufacturer consistently prefers a hybrid model, as it provides better segmentation of different consumers. However, with EPR, the manufacturer will choose the pure sales model when the recycling rate is sufficiently high. Moreover, we find stringent EPR regulations with higher recycling rate may have negative environmental consequences. Based on these results, we emphasize the need for policymakers to carefully consider and set recycling rate.

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.

Pricing and Service Decisions of Products in a Dual-Channel Supply Chain Introducing Ship-to-Store

This study investigates the optimal pricing and service decisions when implementing ship-to-store (STS) in a dual-channel supply chain comprising one BM store and one manufacturer. Homogeneous products within the market are categorized into mainstream and niche products. The effects of consumer service sensitivity, channel preference, and degree of personalization on optimal decisions and profitability are analyzed, followed by numerical simulation and analysis. It is found that (1) the supply chain should charge high prices for mainstream products and low prices for niche products to maximize profits. (2) Implementing STS is conducive to expanding niche products in the market when facing highly service-sensitive consumer groups. (3) Based on highly service-sensitive consumer segments, supply chains are more profitable implementing STS strategies in a centralized decision-making model than in a traditional dual-channel one; however, under decentralized decision-making, it is only profitable to implement an STS strategy if consumers have a low level of personalization and have a particularly pronounced preference for online direct marketing channels.

Color Matters: A Study Exploring the Influence of Packaging Colors on University Students’ Perceptions and Willingness to Pay for Organic Pasta

The organic food market’s rapid expansion necessitates an understanding of factors influencing consumer behavior. This paper investigates the impact of packaging colors on perceptions and willingness to pay (WTP) for organic foods, utilizing an experimental auction among university students. Drawing on previous research, we explore how colors influence perceived healthiness, premiumness, trust, and sustainability. The results indicate nuanced responses to different colors, emphasizing the need for businesses to adopt tailored packaging strategies. White and green dominate organic food packaging, aligning with associations of freshness and health. However, the study uncovers varied consumer responses, suggesting a more intricate relationship between color, trust, premiumness, and healthiness perceptions. Demographic factors such as age, gender, income, and residence areas influence WTP for organic foods with different colors, emphasizing the importance of diverse consumer segments in marketing strategies. Trust and perceived premiumness significantly influence WTP, highlighting their pivotal role in consumer valuation. The results highlight that green packaging builds trust among non-organic buyers, while organic buyers are influenced by a broader range of colors that emphasize premiumness and healthiness. The study concludes that businesses in the organic food market should carefully consider color choices in branding and packaging to effectively communicate product qualities and align with consumer values.

Multiple-perspective consumer segmentation using improved weighted Fuzzy k-prototypes clustering and swarm intelligence algorithm for fresh apricot market

Leveraging clustering technology for consumer segmentation is crucial for discerning the nuanced differences among fresh apricot consumer groups and subsequently executing precise marketing strategies. To achieve a more comprehensive and lucid consumer segmentation and identify typical characteristics of apricot consumers in different clusters, this research constructs a novel multiple-perspective segmentation indicator system for fresh apricot consumers. Given the diverse degrees of importance and types of consumer segmentation variables, and the inherent sensitivity of the original Fuzzy k-prototypes (FKP) algorithm to clustering centers, we proposed the weighted Fuzzy k-prototypes (WFKP) algorithms for mixed data (MD) optimized by the particle swarm optimization (PSO) algorithm (MDPSO-WFKP) and mixed data sparrow search algorithm (SSA) (MDSSA-WFKP), both incorporating information entropy weighting for mixed attributes. We test the proposed algorithms on four University of California Irvine machine learning repository (UCI) datasets and the consumer segmentation dataset, and the performance of all selected evaluation indexes shows significant improvement. These findings unequivocally validate the efficacy of the proposed methodologies. Since the MDSSA-WFKP algorithm has the best comprehensive effect on the evaluation indexes, we use it to conduct in-depth apricot consumer segmentation research and find that the apricot consumers can be subdivided into three groups with differentiation: ‘Buddhist-like youths’, ‘Upscale attribute enthusiasts’, and ‘Quality-oriented consumers’. Finally, this paper gives the corresponding marketing suggestions based on the characteristics of the segmented groups.

Meat fans' and meat reducers' attitudes towards meat consumption and hybrid meat products in the UK: a cluster analysis

SummaryMeat reduction, rather than total elimination, is emerging as a popular trend that fulfils most consumer demands. In this study, using survey results from 802 United Kingdom residents, we identified distinct groups of consumers based on their meat consumption habits and explored their attitudes towards meat reduction and their perception of hybrid meat products. Two distinct consumer segments were identified, named ‘fans’ and ‘reducers’. ‘Fans’ were unwilling to reduce their meat consumption, considering it a right that makes them feel good. They were more optimistic regarding environmental concerns and less enthusiastic about the idea of hybrid meat products. On the other hand, ‘reducers’, although they consumed meat, expressed feeling bad about it and reported high levels of environmental concerns. These differences were also reflected in their willingness to eat and buy hybrid meat products, as well as the desired type of meat and meat‐to‐plant‐based ratio in their chosen hybrid meat.

MULTILEVEL MODAL VALUE ANALYSIS FOR INTERPRETING CATEGORICAL K-MEDOIDS CLUSTERS DATA

Consumer segmentation plays a crucial role for business owners in developing their enterprises. K-Medoid is commonly used for segmentation functions due to its low computational complexity. However, K-Medoid has limitations, such as the variability in cluster sizes across different iterations and the challenge of determining the optimal number of clusters. The Davies-Bouldin Index (DBI) is a metric used to evaluate the number of clusters by calculating the ratio between the within-cluster distance and the between-cluster distance. Most segmentation studies typically stop at the formation of clusters without further interpretation, particularly when dealing with categorical data. This study aims to modify the use of K-Medoid and propose a method for interpreting clusters with categorical data. The research began with questionnaire design and the data collecting from 100 respondents, which was normalized in the second stage. Clustering used K-Medoid with variations K values from K=2 to K=10, with each K value tested 10 times. The clustering results were evaluated using the DBI to select the optimal clusters. Data interpretation conducted using modal values, calculated as the ratio of the number of times a specific attribute variable was selected by respondents to the total number of data points in the cluster. Utilization and hierarchical visualization of modal values proposed in this study offer insights into the dominant variables within an attribute and also depict the relationships between attributes based on the ranking of modal values. These advantages facilitate business analysts in labeling clusters for developing consumer-driven business strategies.

Segmentation of Chinese consumer preference for wine extrinsic attributes based on stratification and weighted clustering algorithm

Chinese consumers are unfamiliar with the sensory attributes of wine and often pay more attention to the extrinsic attributes of wine that can most directly present information. Therefore, it is crucial to study consumer preferences for extrinsic attributes. This paper proposes a consumer segmentation method based on stratification and weighted clustering algorithm, leveraging data from an anonymous online survey (N = 3179). The AFR model is constructed based on the RFM model to segment wine consumers from the perspective of customer value. This model removes Monetary indicator and adds a new indicator Amount to eliminate multicollinearity caused by the Frequency and Monetary indicators of RFM model. In addition, factor analysis is used to assign weights to clustering indicators, addressing the problems of unequal weighting and strong correlation among the indicators. This allows for a simple, efficient, and precise segmentation of wine consumers at different levels. The method proposed in this article segments consumers into two layers and six categories: the potential layer includes product-oriented type, amorphous type, and cheap-fine type, while the mature layer comprises the rational type, reputation-price type and random type. This research will provide wine producers and distributors with a simple and efficient basis for production and marketing decision-making. Additionally, it can serve as a reference for consumer segmentation in food and other fields.

Leftover love: Exploring the behavioural heterogeneity of household meal wasters

Meal leftovers are a major component of household food waste. However, there is a limited understanding of leftover food management behaviours, which can assist in reducing food waste in household settings. This study segments households with distinctive behaviours associated with meal leftover management. Principal component analysis and cluster analysis were used for consumer segmentation. Data on meal leftovers and food management behaviours were collected using a national-level survey of 1728 Australian households. We identified two heterogenous groups of households regarding leftover meal management, ‘Self-sufficient savers’ (59% of the sample) and ‘Naïve discarders’ (41% of the sample). The findings indicate that inefficient cooking and storing behaviours lead to cooked meals being discarded. ‘Naïve discarders’ dispose of leftover meals either immediately or after storing them because they do not have sufficient cooking skills to repurpose leftovers or judge the safety of the food after storing them. They also lack meal-planning skills. In contrast, the ‘Self-sufficient savers’ demonstrated efficient cooking skills and storing behaviours. They read and followed the storage and meal portioning instructions. Results highlight the need for customised interventions targeting consumers with low cooking and storing knowledge to contribute towards overall food waste minimisation.

Methodological Aspects of Analysis of Competition in Regional Markets of Services for Children with Disabilities

The modern development of social services markets is aimed at introducing competition mechanisms into them. These trends create the need to develop and use a methodology for assessing competition, taking into account the characteristics of each market. Existing methods for calculating various indices are labor-intensive for social markets and do not take into account their specifics: a significant share of government institutions, a narrow segment of consumers, and significant differentiation in prices for services. The article proposes a methodology for analyzing competition in the market of psychological and pedagogical support for children with disabilities, based on the use of two groups of indicators: objective and subjective indicators. To analyze subjective assessments, a coefficient of satisfaction with the price-quality ratio is proposed. Statistical indicators are used as objective assessments. The proposed methodology was tested using the example of markets for psychological and pedagogical support for children with disabilities in a number of regions of Russia. The results obtained allowed us to draw a conclusion about the state of competition in the markets under consideration and identify “problem” regions. The presented methodology allows us to obtain relatively adequate results of the analysis of competition in the market of psychological and pedagogical support for children with disabilities and can be extended to other markets in the social sphere with appropriate adjustments.

Consumer segmentation with large language models

Consumer segmentation is vital for companies to customize their offerings effectively. Our study explores the application of Large Language Models (LLMs) in marketing research for consumer segmentation. We developed a workflow leveraging LLMs to perform clustering analysis based on consumer survey data, with a focus on text-based multiple-choice and open-ended questions. Firstly, we employed a LLMs model to embed text for clustering, demonstrating that LLMs enhance clustering accuracy over traditional models. Secondly, we created persona chatbots using LLMs, which achieved over 89% accuracy in simulating consumer preferences. Our findings underscore the potential of our LLMs framework in marketing research.

Constructing a combined brand identity

Identity has acquired the status of a key concept in social, cultural and economic contexts, because it affects the formation of human values, beliefs, preferences and behavior, in particular, consumer behavior. Moreover, consumption itself has long been a stable form of organizing social life, as it focuses on almost all social positions, becoming an almost ideal identifier. Because of this, the research of the target audience is considered the basis of bran­ding, because its coincidence with the brand identity significantly increases consumer loyalty to the brand. On the other hand, brands tend to become part of the consumerʼs self-concept, influencing his values and lifestyle. It highlights the different roles of identity as an influencer (consumer on the brand) and as an object of influence (brand on the consumer). The search for ways to achieve a balance between consumer identity and brand identity outlined the research hypothesis, that systemic and structural app­roach will allow taking into account all elements of the consumerʼs identity in the process of brand identity formation and bring the "brand-consumer" relationship to a new emotional level through the construction of a complex, joint identity. Based on the methods of scientific ana­lysis and synthesis, conceptualization and struc­tural modelling, the key components of consumer identity and brand identity were identified. The structure of the consumerʼs identity has been improved by supplementing it with goals and ambitions of the consumer. A categorical apparatus of brand management has been developed due to the introduction of the concept of "combined brand identity", the starting condi­tion for the construction of which is the analysis of the consumerʼs identity. The result of the proposed system-structural approach is a gene­ralized matrix that allows forming a combi­ned brand identity through a combination of diffe­rent level factors of consumer identity and constituent elements of brand identity. The appli­cation of a system-structural approach will provide a detailed analysis of various consumer segments and will allow determining which elements of the consumerʼs identity should be reproduced in the brand for each segment. This will help to transform the brandʼs partnership with the consumer into a vital priority based on emotional intelligence

Driving Consumer Choices: The Influence of Pidgin Language in Pepsi Advertising

This study explores the influence of using pidgin English in Pepsi advertisements on consumer purchase decisions. Pidgin English, a creole language widely spoken in various regions, serves as a significant cultural and linguistic marker. The research investigates how incorporating this vernacular into marketing strategies impacts consumer perception, engagement, and ultimately, purchasing behavior. A mixed-methods approach, combining quantitative surveys and qualitative interviews, was employed to gather data from a diverse demographic. Findings suggest that advertisements utilizing pidgin English resonate more deeply with certain consumer segments, enhancing brand relatability and fostering a sense of cultural identity. This linguistic strategy appears to enhance emotional connection and trust, leading to a higher propensity to purchase. The study concludes that leveraging local languages like pidgin can be a powerful tool in marketing, providing brands with a competitive edge in linguistically diverse markets. Implications for marketers include the necessity of cultural sensitivity and the potential benefits of localizing advertising content to reflect linguistic diversity.

The role of conventional meat unnaturalness in cultured meat acceptance: A test of holistic mindset

The eventual reality of a cultured meat market is increasingly uncertain, primarily due to low consumer acceptance levels influenced by perceptions of unnaturalness towards cultured meat. Previous research has shown limited success in increasing acceptance through naturalness messages. In this research, we employ a counter-messaging strategy that highlights the unnaturalness of conventional meat and show that it is effective at increasing willingness to try cultured meat among a particular consumer segment. Across two experimental studies, we show that this counter-messaging strategy is successful at increasing willingness to try cultured meat among holistic mindset consumers—i.e., those holding beliefs to the effect that considering the whole is fundamental to understanding the parts of a phenomenon. We establish this effect by both measuring (Study 1) and priming (Study 2) analytic-holistic mindset and also establish the mechanism driving this interactive effect (Study 2): positively-valenced feelings. Our findings are informative theoretically, extending mindset and counter-messaging theory to the context of cultured meat and, practically, offering crucial pointers to stakeholders interested in the promotion of cultured meat.

Who has an appetite for insects? Identifying segments of early adopters of insect-based food and their product attribute preferences: Insights from a choice experiment study in Germany

Despite well-documented scepticism among Western consumers about eating insects, there are groups of potential early adopters who will form the initial market for insect-based food. Understanding the different preferences of these potential early adopters is key to developing effective targeted marketing actions. This study aimed to identify segments of potential early adopters and their distinct product attribute preferences. Via an online survey in Germany (N = 922), we conducted discrete choice experiments using real packaging of insect-based meatballs and crackers. Latent class logit analysis identified four consumer segments for each product, three of which showed a willingness to buy the products. While these potential consumer groups varied in their price sensitivity and insect labelling preferences, they all attached the greatest importance to naturalness claims. Interestingly, sustainability and nutritional information were not top priorities for all consumers; instead we observed the importance of an institutional trust indicator, especially for unconvinced consumers. In conclusion, the results of the present study provide interesting insights for both researchers and practitioners to make informed marketing decisions in the development and labelling of insect-based products.

Data-Driven Market Segmentation: K-means Clustering and STP Analysis in Mainland China's Sportswear Industry

Post-Covid-19, mainland China's economy is gradually recovering, leading to increased sportswear sales. This study integrates STP theory (segmentation, targeting, positioning) with the K-means clustering algorithm to analyze the sportswear market in mainland China. Consumer data was collected via a questionnaire survey, pre-processed, and cleaned before applying K-means clustering to segment consumers into four distinct groups. The analysis reveals significant differences in purchasing habits across these segments. This data-driven approach offers sportswear companies insights for targeted marketing strategies, enhancing the precision and effectiveness of their marketing efforts in mainland China.