Targeted Marketing Strategies Research Articles

Segmenting the Higher Education Market: An Analysis of Admissions Data Using K-Means Clustering

One of the affecting factors of marketing strategy in educational institutions is determining clear marketing objectives including suitable segmentation. This study develops a market segmentation for private HEIs considering the potential academic records of targeting schools and geographic factors. K-Means clustering method is exploited to cluster around 260 data with eight attributes. The elbow method and silhouette coefficient are used to determine the appropriate number of clusters. Findings suggest that schools within Cluster 1 and Cluster 4, with approximately 42% market share, are the primary market segment for deploying a robust and targeted marketing strategy.

Research on e-commerce user segmentation and customized marketing strategy based on cluster analysis

Abstract E-commerce user segmentation is the basis for enterprises to accurately formulate marketing strategies and successfully manage their customer base. With the rapid development of e-commerce, this paper improves the traditional K-means clustering algorithm and proposes a SAPK-means algorithm, which effectively excludes the noisy data and isolated points in the dataset and obtains the high-quality initial clustering center. Company A’s e-commerce platform is used to apply the SAPK-means algorithm for customer segmentation, and the results are analyzed in detail before proposing targeted marketing strategies. The customized marketing strategy’s effect is evaluated through sales and platform user satisfaction. Through experimental testing, this paper concludes that the five types of segmented customer groups account for 9.46%, 18.43%, 36.95%, 21.91%, and 13.24% of the total number of samples, respectively, known as the “platinum customer group”, “gold customer group “Platinum customer group”, “Gold customer group”, “Silver customer group”, “Copper customer group” and “Iron customer group”, respectively.

Identifying Factors Affecting Variety-Seeking Behavior of Customers in Online Retail Markets: A Qualitative Study

Objective: This study aims to identify the factors influencing variety-seeking behavior in customers of online retail markets. Methodology: This is a qualitative and applied study. Data were collected through semi-structured interviews with 12 experts and experienced individuals in the field of online businesses and digital retail. Purposeful sampling was used to select participants, and the data were analyzed using grounded theory with open, axial, and selective coding. Maxqda software was employed for data analysis. Findings: The study revealed that factors such as the weaknesses of online businesses, marketing culture, competitor appeal, purchase motivation, and the desire to experience new products directly influence customers' variety-seeking behavior. Pricing strategies, online marketing, and customer loyalty were also found to play a crucial role in either reducing or increasing this behavior. Conclusion: Variety-seeking behavior in online retail markets is a complex phenomenon influenced by various factors. By improving service quality, creating a unique shopping experience, and utilizing targeted marketing strategies, online businesses can enhance customer loyalty and benefit from reduced variety-seeking behavior.

ПОКОЛІННЯ ЗУМЕРІВ ЯК КАТЕГОРІЯ СПОЖИВАЧІВ РОЗДРІБНОЇ ТОРГІВЛІ: ДОСЛІДЖЕННЯ ПОВЕДІНКИ, СЕГМЕНТАЦІЯ АУДИТОРІЇ В УКРАЇНІ

In today's fast-paced market environment, understanding and analyzing consumer behaviour across different generations are critical components of successful business management. Of particular importance is the examination of the Zoomer Generation, representing the youthful segment of consumers who have come of age amidst technological advancements, the internet revolution, and globalization. Zoomers constitute a relatively new market segment with unique characteristics that are evolving rapidly. With a significant portion of Ukrainian youth already possessing financial capabilities, Zoomers are poised to become a primary target audience for numerous brands in the foreseeable future. Therefore, given the rapid advancements in technology and shifts in socio-cultural landscapes, comprehending the behaviour and preferences of the Zoomer generation stands as a crucial element in devising effective business development strategies within the retail sector. The aim of the article is to investigate the behaviour traits of Zoomers and their segmentation within the Ukrainian market to foster successful engagement with this consumer segment amidst emerging opportunities in retail trade. Methodology. The research builds upon the theoretical and methodological frameworks established by both local and international scholars in the realm of marketing research on consumer behaviour and the practical deployment of segmentation strategies. The information base encompasses a range of sources including scientific, economic, and reference literature, contributions from prominent domestic and international scholars, methodological resources, information portals, and relevant periodicals. Various methods and approaches were employed, such as system analysis and theoretical synthesis to delve into the characteristics of the Zoomer generation, analytical and synthetic methods to assess market volumes concerning the Zoomer demographic, in-depth interviews to delve into the consumer behaviour intricacies of Ukrainian Zoomers, and multiple segmentation techniques for effectively categorizing the Zoomer generation's audience. Results. The article highlights the distinguishing features of Zoomers as consumers, setting them apart from other generations with their agility, adaptability, openness to gender and racial equality, extensive tech literacy, embracement of the new, collaborative mindset redirecting mass criticism into collective actions, yet facing challenges in cultivating critical thinking skills due to constant media multitasking leading to chronic distractions. Market assessments focusing on the Zoomer audience in Ukraine revealed high ownership of smartphones (96%), extensive social media usage (83%), and a preference for online shopping (62%), with YouTube, Instagram, and TikTok being the preferred social platforms. Furthermore, a series of in-depth interviews uncovered insights into Ukrainian Zoomers' preferences, showcasing a strong inclination towards immediate consumption, elucidating primary motives driving both online and offline purchases. Criteria for segmentation (age, gender, ethnicity, education, income, lifestyle, and psychographic traits) were identified for Generation Z, delineating their profiles and furnishing valuable insights into their preferences, behaviours, and needs. This segmentation aids marketers in tailoring targeted marketing strategies to effectively engage with this audience. The scientific novelty lies in deepening the understanding of the behaviour of Generation Z consumers in the Ukrainian market, which, unlike existing ones, distinguishes their complex characteristics for effective interaction with this consumer group in the context of new opportunities for retail trade. The practical significance is determined by the application of the obtained results to develop more effective marketing strategies for Zoomer consumers, ensuring increased competitiveness of retail companies.

Effectiveness of online advertisement on the behavior of stripling in purchasing the lifestyle products: A comparative study of urban and rural college students

The 21st century has shown remarkable changes in the Indian marketing landscape due to internet adoption and technological advancements. The young generation's embrace of online activities has transformed the consumer behavior. With 840 million internet users, including 448 million active users on social media, online advertising has become a powerful tool to attract attention and drive product purchases. Simultaneously, E-commerce platforms have revolutionized access to goods and services. In this view, this study focuses on understanding the impacts of online advertisements on the purchasing behavior of the young generation, specifically in terms of lifestyle products. Researchers explored how online advertisement has influenced decision-making processes and uncovered the motives driving internet usage among young adults. Additionally, the study assessed the perceptions of trustworthiness between online and offline products. 200 respondents from rural and urban colleges were analyzed to identify the key factors determining the purchasing decisions among young adults. Price, discounts, perceived needs, and timeliness are some of the factors explored. The findings revealed the impacts of online advertisements on product purchases among college students in both rural and urban settings. Insights into young adults' motivations and preferences are invaluable for businesses to effectively engage with this dynamic consumer base. Armed with these findings, businesses can develop targeted marketing strategies that resonate with the evolving digital landscape, ensuring success in India's digitally-driven market.

The Impact of Social Media Marketing on the Mobile Game Industry--Take Glory of the Kings as an Example

Glory of the Kings is in a leading position in the mobile game industry, and its successful marketing strategy is worth learning from other mobile game manufacturers. This paper takes the marketing strategy of Glory of the Kings as an example to study how the mobile game industry should carry out more targeted marketing strategies according to the characteristics of different social media after the maturity of social media platforms. For social media with solid relationship characteristics, it is necessary to build a friend mechanism around the relationship link between users, strengthen the social attributes of games, enhance user stickiness, and carry out social marketing. For social media with weak relationship characteristics, marketing strategies closer to users should be adopted to stimulate users' interest and sense of participation, and interactive marketing should be conducted. Key opinion leaders should be utilized for content-heavy social media platforms to gain higher brand exposure and reach more target customers. It can also establish social media based on interest community, which can not only accumulate users but also feed back the game ontology and two-way drainage, complement each other to promote long-term operation and continue to expand the influence and possibility of game IP.

User Segmentation Based on Purchasing Habits and Preferences on the Amazon Platform Using K-Means Clustering

As a large company, Amazon operates an online marketplace with a diverse user base exhibiting varied purchasing habits. This diversity challenges Amazon to provide tailored services and marketing strategies for each user with distinct characteristics. Therefore, this research aims to assist Amazon in segmenting its users based on their characteristics, enabling the implementation of targeted marketing strategies and service provision for each user. The study employs the K-Means Clustering method to segment Amazon platform users based on their purchasing behavior, site feature interactions, and preferences. The research process involves Knowledge Data Discovery (KDD) stages, including data processing, attribute selection, and applying the K-Means Clustering algorithm. The analysis results reveal five distinct user clusters, each with unique characteristics reflecting user behavior and preferences. These clusters depict variations in purchasing frequency, interactions with site features, and responses to product recommendations. This user segmentation provides valuable insights for Amazon to develop more focused marketing strategies, enhance personalized services, and improve overall customer satisfaction.

Consumer Perceptions and Purchase Intentions towards Organic Foods: Evidence from Eastern Mediterranean Region of Türkiye

This study aimed to investigate consumer perceptions towards organic foods and explore the relationship between purchase intentions and socio-demographic characteristics. Between February and July 2021, we conducted an online survey in the Eastern Mediterranean Region of Türkiye, specifically in the provinces of Adana, Osmaniye, Kahramanmaraş, and Hatay. The survey included 384 randomly selected respondents within the age range of 18 to 71. Factor analysis revealed five significant factors: "Environmental Awareness," "Attitude," "Trust," "Purchase Barrier," and "Purchase Intention." Notably, t-test result showed a significant difference in purchase intention, with female consumers exhibiting a more positive preference. We identified organic tomato-pepper pastes and olive oil as the most consumed organic foods. Moreover, the statement "Organic foods are produced with environmentally friendly methods" received the highest average value under " Environmental Awareness." In conclusion, this research fills a gap in the literature by providing insights into consumer perception and purchase intention toward organic foods in Türkiye. The observed gender-based differences emphasize the need for targeted marketing strategies. The popularity of specific organic products indicates market expansion opportunities. Understanding consumer preferences and market dynamics is crucial for stakeholders in the organic food industry to tailor their approaches effectively. Further research and market analysis are essential to adapt strategies and meet evolving consumer demands in the dynamic organic food market.

Clustering-based Categorization of Music Users through Unsupervised Learning

The process of categorizing music users without the need for explicit guidance, known as unsupervised learning, has been explored through a technique called clustering. This innovative approach involves the use of algorithms to group music users based on their preferences, behaviors, or other relevant characteristics, thereby uncovering patterns and structures within the music consumption landscape. By identifying distinct clusters of music users, this method facilitates the creation of personalized recommendations, targeted marketing strategies, and tailored music experiences, ultimately enhancing user satisfaction and engagement. Through unsupervised learning, the clustering-based categorization of music users has the potential to revolutionize the music industry by enabling precise segmentation and understanding of diverse user segments. By leveraging this approach, music streaming platforms and other industry stakeholders can gain valuable insights into user behavior, preferences, and trends, empowering them to develop more effective content curation techniques, user interfaces, and promotional campaigns. Additionally, the application of unsupervised learning in music user categorization opens up opportunities for the development of more sophisticated recommendation systems, capable of delivering highly personalized and relevant music suggestions to individual users.

European farmers' perceptions and intentions to use bio-based fertilisers: Insights from the theory of planned behaviour and perceived utility

Bio-based fertilisers, derived from diverse biological waste and processing technologies, have shown promising efficiency. However, the successful introduction of newly developed bio-based products in the market remains a challenge due to insufficient information on optimal marketing strategies. In this study, we examine farmers' perceptions regarding the acceptance of bio-based fertilisers in the EU. This research utilizes the Theory of Planned Behaviour (TPB) and an extended version with Perceived Utility as a mediating variable, using two distinct Structural Equation Models (SEMs) for analysis. Data were collected via an online questionnaire distributed across the EU 27 (n=332). Our findings reveal that farmers make rational decisions about fertiliser use, striving to maximize utility. Intentions to adopt bio-based fertilisers are strongly linked to attitudes, subjective norms, and perceptions of utility. Social networks and expert acceptance play a crucial role in shaping farmers' intentions. To enhance utility perception and encourage adoption, targeted marketing strategies should address key social groups, providing comprehensive information on the benefits and costs of implementation. Moreover, ensuring price stability through effective regulatory tools and mechanisms is vital for successful integration into the market. These insights offer valuable guidance to policymakers and the fertiliser industry, facilitating the introduction of bio-based fertilisers into the EU market.

Analysis of Walmart's Marketing Strategy Based on STP Theory

Walmart is a retail industry leader, and its growth and success have given the company a measure of reference significance. Analyzing the retail industry's current market condition, resolving the challenge, and providing strategic reference for other businesses may all be accomplished by studying Walmart's approach to marketing. This study examines the following aspects of Walmart's marketing strategy through literature review and analysis method: market segmentation, market target, and market positioning, with an eye on the theory underlying each of them. The research concluded that Walmart used population, geography, and the purchasing power of different consumer groups to divide the market into distinct segments, and that the company used both a generic marketing strategy and a more targeted marketing strategy to meet customer demand for its low prices and convenient locations. Walmart has made tactical tweaks to the aforementioned marketing methods to account for the ever-shifting nature of the market. In order to pursue the sustainable development of businesses, the paper adopts a more complex and humanized segmentation principle, modifies the market target strategy and market positioning, and upgrades the positioning to energy conservation and environmental protection.

Relationship between E-Commerce Strategies and Customer Loyalty among Multinational Enterprises in Kenya: A Case of Jumia

Purpose: The purpose of this study was to examine the relationship between customer orientation strategies and customer loyalty among multinational enterprises on ecommerce platforms in Kenya. The study specifically focused establishing the relationship of e-marketing, e-service quality and e-trust on customer loyalty among Multinational Enterprises in Kenya.
 Methodology: The research design adapted in this study was descriptive research design. The population comprised of 1378 of ecommerce managers and ecommerce users. The study employed Taro Yamane’s formula to derive a sample of 310 managers. Primary data was collected using questionnaires on a Likert scale of 1-5, based on the three research questions. Statistical Package for Social Sciences (SPSS) software was used to analyze data using mean, standard deviation, frequency distributions, Pearson coefficient of correlation and simple linear regression analysis techniques. The results were then presented in tables and figures.
 Findings: In terms of influence of e-marketing on customer loyalty in Multinational Enterprises in Kenya, there was a statistically significant positive relationship between e-marketing and customer loyalty, (r=0.519, p<0.05). Linear regression analysis showed that 27% of variability in customer loyalty was explained by integration of e-marketing, which statistically and significantly had a positive effect on effective customer loyalty R2 = .27, 𝛽=.524, 𝑡= 10.419, 𝑝 < .05). In terms of influence of e-service quality on customer loyalty among Multinational Enterprises in Kenya, there was a statistically significant positive relationship between e-marketing and customer loyalty, (r=0.514, p<0.05). Linear regression analysis showed that 26.5% of variability in customer loyalty was explained by integration of e-service quality, which statistically and significantly had a positive effect on effective customer loyalty R2 = .265, 𝛽=.629, 𝑡= 10.147, 𝑝 < .05). In terms of the influence of e-trust on customer loyalty among Multinational Enterprises in Kenya, there was a statistically significant positive relationship between e-trust and customer loyalty, (r=0.597, p<0.05). Linear regression analysis showed that 35.6% of variability in customer loyalty was explained by integration of e-trust, which statistically and significantly had a positive effect on effective customer loyalty R2 = .356, 𝛽=.707, 𝑡= 12.505, 𝑝 < .05). In conclusion, Jumia uses various technologies to make e-commerce lively to customers.
 Unique contribution to theory, practice and policy: The study recommended that Jumia should invest more in personalized and targeted marketing strategies, focus on enhancing their website and mobile app interfaces to make them more user-friendly and intuitive. In addition, Jumia should prioritize building trust with their customers by ensuring the security of their personal and financial information. It is also recommended that for Jumia Kenya should implement security measures and communicate them effectively to customers to reassure them of the safety of their data.

Early warning of telecom enterprise customer churn based on ensemble learning.

Analyzing customers' characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Understanding consumer face mask consumption: a MaxDiff-based cluster analysis

This study aims to develop a consumer typology based on the importance of product attributes in consumer face mask consumption during the COVID-19 pandemic. An online survey with a MaxDiff experiment was employed to measure the importance of eight face mask attributes. Data were collected from 563 U.S. consumers during April 2022. Hierarchical Bayes analysis, K-means clustering, Chi-square tests, and ANOVA were used for data analysis. Results suggested that protection and comfort were the most important attributes, while appearance and brand were the least important. Based on the tradeoffs made in their purchase decision-making process, three distinct groups of face mask consumers were identified, named protection-focused, utilitarian-driven, and eco-conscious consumers. A consumer profile was established for each group, reflecting their mask-wearing behaviors, mask type preferences, and demographics. This study provides implications for face mask manufacturers and retailers to develop effective production and target marketing strategies for future respiratory pandemics.

Unlocking Purchase Preferences: Harnessing Psychographic Segmentation, Promotion and Location Strategies

This research examines the impact of Psychographic Segmentation, Promotion, and Location on consumers' Purchase Decisions. Data were collected through an online questionnaire distributed to 90 respondents who had visited hotels in Denpasar, Bali. Statistical techniques such as validity and reliability tests, correlation analysis, and multiple regression analysis were employed for data analysis. The findings indicate that Psychographic Segmentation significantly influences Purchase Decisions. Categorizing buyers based on psychological traits, lifestyle, and values plays a crucial role in shaping consumer preferences and motivations. Promotion also has a significant impact on Purchase Decisions, as effective promotional strategies create awareness and persuade target customers. Additionally, the Location of a business emerges as a critical factor, with strategic placement influencing customers' buying decisions. The regression analysis reveals that Psychographic Segmentation, Promotion, and Location collectively explain approximately 75.1% of the variance in Purchase Decisions. However, it is important to note that factors beyond the study's scope, such as product quality, pricing, brand reputation, and customer service, may also influence purchase decisions. These insights provide valuable guidance for businesses in developing targeted marketing strategies to enhance their competitive advantage. Understanding consumers' psychographic profiles, implementing effective promotions, and strategically locating operations can significantly influence Purchase Decisions, leading to increased sales and overall business success. Future research could explore additional variables to gain a more comprehensive understanding of consumer behavior in purchase decisions.

Suggestions for Lawsons Entry into the Global Market: Evidence from Operations and Strategy of Lawson Convenience Stores in China

Convenience store as a retail format meets urban populations demand of a more efficient and expedient lifestyle. This retail model originated in Japan, and Lawson, as the second largest Japanese convenience store chain, has reached saturation in the domestic market and is now looking to expand internationally for future growth. This essay examines Lawsons development in China and provides suggestions for its future growth in the global market based on its operations and strategies in China. Through the analysis of literature research, market data and Lawsons official annual reports, this paper gives a comprehensive analysis of Lawsons successful experience in the Chinese market and offers advice on the firms global development, emphasizing detailed market research and entry mode, a well-structured supply chain and technological investment, and targeted marketing strategies.

Assessing data phishing risks associated with unencrypted apps on smartphones with non-parametric test and random forest model: Insights from Kuwait phishing scam calls

The outbreak of covid-19 has helped M-commerce to strengthen its roots and helped to increase the number of customers in different industries. Likewise, Electronics product companies also developed M-commerce platforms such as mobile applications (Apps) regardless of their website to sell their products based on customers’ interests and likes. In order to create personalized and targeted marketing strategies, companies collect data from users of M-commerce platforms. For this, they have taken the data from the Handheld device users who installed the companies’ applications with or without the consent of the users which raises ethical concerns related to privacy and security. Unlike other studies this research explores the reason of increased data phishing cases in Kuwait from 2020 and the findings are supported by statistics using machine learning and python language. This paper examines the ethical implications-data phishing chances from users if M-commerce platforms of electronics Apps which are data unencrypted installed in the smartphones. The study employs a mixed-methods approach that includes semi-structured interviews with industry experts and customers, Checking the M-commerce Data privacy details, and analysis of relevant literature. Using the study's survey findings, the hypothesis was tested and a machine learning model was developed that predicts the likelihood of data theft when two specific apps are installed on a smartphone. The research pointed out that the null hypothesis is true and there is approximately 82% of chance if the smart phone users installs data unencrypted apps in the device.

Investigating the conceptual link between appearance-related self-discrepancies and retail therapy shopping behavior through motivational routes

PurposeThe purpose of this study was to propose and examine a conceptual model delineating how two different types of appearance-related self-discrepancies (i.e. the ideal appearance self-discrepancy vs. the ought appearance self-discrepancy) influence retail therapy shopping behavior through motivational route (i.e. approach motivation vs. avoidance motivation).Design/methodology/approachThis study utilized the online survey for data collection. Using a national sample of 532 US consumers who had retail therapy shopping experience, the conceptual model was tested through various statistical techniques, including confirmatory factor analysis, exploratory factor analysis and structural equation modeling.FindingsResults revealed that the ought appearance self-discrepancy positively influenced retail therapy shopping behavior through avoidance motivation and emotion-focused coping strategy, whereas the ideal appearance self-discrepancy did not influence retail therapy shopping behavior. The results also suggested that the effect of two different types of appearance-related self-discrepancies on motivations in retail therapy shopping context varied depending on the gender (i.e. male vs. female).Originality/valueResults suggest implications regarding potential target market strategies to retailers and provide a better understanding of retail therapy shoppers' characteristics and psychological mechanisms for consumer researchers.

A Study on Consumer Profile and Preference for Virgin Coconut Oil in Bangalore City

Aim: This research endeavors to examine the fundamental factors driving consumer preferences and actions concerning Virgin Coconut Oil (VCO) in Bangalore City. It seeks to assess the impact of demographic variables, health perspectives, and brand recognition on consumer choices and behavior toward VCO.
 Study Design: A quantitative, descriptive research design.
 Place and Duration of Study: The research was carried out in Bangalore City, during June and July of 2023. The primary data were collected from a random sample of Sixty respondents.
 Methodology: A diverse group of participants were surveyed, spanning different age groups, educational backgrounds, and occupations. The survey encompassed aspects such as age, gender, education, occupation, income, and family size. Conjoint analysis facilitated the measurement of attribute importance scores, providing insights into consumer preferences for factors including price, brand, and health benefits.
 Results: Analysis of the collected data unveiled noteworthy trends The majority of the respondents (61.7%) were males, and a significant proportion (43.3%) held master's degrees. Employed individuals constituted the largest occupation category (58%). Conjoint analysis revealed that health benefits held the highest importance score (39.979), followed by price (37.011) and brand (23.010).
 Conclusion: This study accentuates the pronounced role of health benefits in driving consumer choices regarding VCO. The findings illuminated the demographics of VCO consumers in Bangalore City and emphasize the need for targeted marketing strategies aligned with health-conscious consumer preferences. Insights gained from this research can significantly guide product development and branding initiatives, fostering a competitive advantage in this potential market. Further exploration and validation of these predictors are warranted to enhance the efficacy of marketing strategies and cater effectively to the evolving consumer landscape.

Dairy purchase behaviors: Increasing understanding of Chinese consumers using a consumer involvement segmentation approach

Understanding consumers' purchase behaviors is fundamental to the success of the dairy industry. With its economic importance, the Chinese market is critical to dairy producers in most countries around the world. However, understanding consumers in this market is particularly challenging as these consumers often have a different relationship with dairy products to consumers elsewhere in the world, given the country's historical dairy-related scandals. This special relationship can be characterized by what consumer behavior researchers call "high involvement," indicating that Chinese dairy consumers often attempt to reduce the level of risk associated with buying dairy products. Surprisingly, while this relationship impacts several important aspects of purchase behavior, examination of the concept of involvement in the dairy sector has not been widely considered. Of note, there is no understanding of how Chinese consumers vary in their involvement levels and their implications on their dairy purchase behaviors. Nor have there been involvement-based insights provided on how dairy companies can position their marketing mix to suit the needs of these consumers better. Thus, this study proposes a new approach to understanding Chinese consumer dairy decisions by introducing "consumer involvement" as a segmentation tool through which individuals' behavioral consequences can be predicated according to their involvement profile. Based on an online survey administered in Shanghai using 1,073 dairy consumers, principal component analysis confirmed involvement with dairy is a multidimensional construct, which has 4 factors: pleasure value, symbolic value, risk importance, and risk probability. A 2-step cluster analysis identified 4 consumer clusters based on their involvement profile: face-concerned dairy lover, carefree dairy consumer, cautious dairy lover, and confused dairy consumer. According to a one-way ANOVA test and cross-tabulation with χ2 test, these consumer segments behave differently in relation to the extensiveness of decision making, cue utilization, trust of information sources, and consumption behavior. The outcomes of this paper further explain why measurements aiming to restore consumers' trust for dairy products do not work among some consumers, as individuals may exhibit diverse attitudes toward such information due to their heterogenous involvement levels. The study also provides suggestions for market practitioners and organizations to develop effective target market strategies and policies according to different consumer clusters.