Targeted Marketing Strategies Research Articles

Unveiling the Sway of Marketing Mix on Student Decisions: A Study on Higher Learning Institutions


 
 
 
 
 
 This research delves into the pivotal process of student decision-making within higher learning institutions, focusing on the dynamic interplay of marketing mix services encompassing product, price, promotion, and place. Through an analysis of alumni and current student cohorts at a prominent university, this study empirically demonstrates the profound influence exerted by product quality, strategic promotion, and favorable institutional positioning on students' selection choices. Notably, while price emerges as a comparatively weaker determinant, the collective impact of product, place, and promotion proves instrumental in shaping prospective students' decisions. These findings illuminate the complex landscape of student preferences, offering valuable insights for crafting targeted marketing strategies that effectively guide students' educational trajectories.
 Highlight:
 
 Influence of Marketing Mix Dynamics: This study examines the interaction of product, price, promotion, and place in shaping student decisions within higher learning institutions, revealing their collective impact on choice.
 Empirical Insights: Analysis of alumni and current students from a renowned university highlights the significant sway of product quality, strategic promotion, and favorable institutional positioning on student selection choices.
 Role of Price in Decision-making: While price emerges as a less potent factor, the study underscores the pivotal role played by the interplay between product, place, and promotion in molding the educational preferences of prospective students.
 
 Keyword: Student Decision-Making, Higher Learning Institutions, Marketing Mix Services, Product Quality, Strategic Promotion
 
 
 
 
 

Exploring the Relationship between Stochastic Events and Patterns of International Tourism in Kenya

Purpose: The purpose of this research was to investigate the fluctuations in international tourist arrivals in Kenya and identify the underlying factors contributing to these trends. The study aimed to gain a deeper understanding of the impact of stochastic events on tourist arrivals.
 Methodology: The research focused on international tourist arrivals in Kenya across seven geographical areas. It utilized annual time series data from 1980 to 2020 and examined various variables, including tourism arrivals, terrorism, political instability, conferences, and natural disasters. Descriptive statistics, the Kruskal-Wallis test, Lepage change point detection, and a Vector Autoregressive Model (VAR) with Granger causality tests were employed to analyze the data.
 Findings: The findings revealed significant fluctuations in preferred destinations, source markets, purpose of visits, and length of stay among international tourists in Kenya over the study period. Major stochastic events were observed to coincide with significant changes in net arrivals, source markets, or destinations. Notably, terrorism, conferences, and the combined impact of all factors had a significant influence on net arrivals. Political instability, pandemics, and natural disasters were also found to affect international tourism arrivals.
 Recommendations: Based on the results, policymakers are advised to prioritize safety and security measures to mitigate the adverse effects of pandemics and political instability on the tourism industry in Kenya. Additionally, the study recommends the development of targeted marketing strategies to attract resilient source markets. Furthermore, promoting sustainable tourism practices is essential to mitigate the long-term impact of negative events on the industry. These recommendations aim to enhance the resilience and growth of the tourism sector in Kenya.

A Multisector Analysis of Tourist Delight and Positive Visitor Outcomes

ABSTRACT This research provides new insights into how customer delight functions across the tourism experience. Drawing from literatures on delightful experiential encounters, service encounters, cognitive appraisal theory and multisectoral satisfaction index studies, we develop and test a comprehensive model of tourist delight that integrates multisectoral delight, destination delight, and destination loyalty. Results from a novel application of multivariate probit analysis to data from an exit survey administered to 1,240 visitors to Barbados suggest that destination delight is strongly associated with delight across tourism sectors, and that traveler, trip and planning characteristics explain variation in delight. Tourists’ behavioral intentions are associated with delight in all tourism sectors. Delight with accommodation, food and beverage, and the overall destination provide the strongest avenues for economic returns through visitor loyalty. Understanding the key sectors and elements of tourist delight gives tourism stakeholders a practical way to design targeted marketing strategies that yield destination loyalty.

Omnichannel Marketing: Connecting Consumer Experiences from Online to Offline

Omnichannel marketing is a strategic approach that integrates all distribution channels and customer interactions, creating a consistent and seamless experience from online to offline platforms. This research aims to explore how omnichannel marketing can effectively connect consumer experiences from the online to the offline world. This research uses a qualitative approach with descriptive methods. The research results show that the implementation of omnichannel marketing has had a positive impact in connecting customer experiences from online to offline channels. Customer data integration enables a better understanding of customer preferences and behavior, enabling more targeted marketing strategies. Flexibility in payment systems and omnichannel performance analysis capabilities provide valuable insights for companies to improve the effectiveness of marketing campaigns. Overall, omnichannel marketing proves that continuous customer engagement, personalization of experiences, and channel integration can increase customer satisfaction and support a company's business goals.

Penguatan UMKM Desa Pakel Melalui Implementasi Sosial Media Dan Platform E-Commerce

Micro, Small, and Medium Enterprises (MSMEs) are one of the economic sectors that play a crucial role in the economic growth of a country. However, they often face challenges in competing in the increasingly advanced digital era. Therefore, the implementation of digital strategies such as social media and e-commerce platforms has become increasingly relevant to strengthen MSMEs and help them achieve success in the digital economy. This journal concludes by providing recommendations to assist MSMEs in overcoming challenges and maximizing the benefits of implementing social media and e-commerce platforms. These recommendations include training and mentoring in the use of digital technology, the development of targeted marketing strategies, and government policy support to encourage MSME growth through the digital economy.

Google Trends platform: An analysis of the search for accommodation in Meruoca/Ceará-Brazil and its importance for digital marketing

<p>Digital marketing has proven to be an essential tool for several business sectors, and tourism is one of the beneficiaries of this strategy. This article aims to analyze data from searches for words or terms related to accommodation in Meruoca. The research has a descriptive and quantitative character, seeking data, analysis and discussion about the searches for terms related to the main means of accommodation in the city of Meruoca, carried out by users in Brazil during the months of January to December 2019. The results indicate the variations in the interest of users for both means of accommodation throughout the year, providing a more comprehensive view of periods of greater demand and preference. Thus, it is concluded that the use of Google Trends provides important insights for managers in the tourism sector, helping them to develop more efficient and targeted marketing strategies.</p>

Drivers of potential policyholders’ uptake of insurance in Kenya using Random Forest

The low adoption of insurance by potential policyholders in developing countries like Kenya is a cause for concern for insurers, regulators, and other marketing stakeholders. To effectively design targeted marketing strategies to boost insurance adoption, it is crucial to determine the factors that affect insurance uptake among potential policyholders. In this study, the 2021 FinAccess Survey, which interviewed sampled individuals above 16 years in Kenya and machine learning techniques, including Random Forest, XGBoost, and Logistic Regression, were utilized to uncover the factors driving insurance uptake and the reasons for the low adoption of insurance among potential policyholders. Random Forest was the most robust model of the three classifiers based on Kappa score, recall score, F1 score, precision, and area under the operating characteristic curve (approaching 1). The paper explores eight reasons why people currently do not have insurance policies. The results indicated that affordability was the primary driver of uptake with 68.67% of having expressed a desire to possess insurance but are unable to afford it. The highest level of education being the next most significant factor. Cultural and religious beliefs and mistrust of insurance providers were found to have a minimal impact on uptake. These findings imply that offering affordable insurance products and conducting awareness campaigns are critical to increase insurance adoption.

Smart mobile tourism app featuring augmented reality and big data analytics: an empirical analysis using UTAUT2 and PCT models

Purpose Considering the limited understanding of determinants influencing the adoption of smart mobile tourism app (SMTA) featuring augmented reality (AR) and big data analytics (BDA), privacy concern (PC) and the risk of privacy information disclosure (PI) have threatened SMTA adoption. This study aims to propose an expanded consumer acceptance and use of information technology (UTAUT2) model by including new contextual components, integrated with privacy calculus theory (PCT) model to examine the determinants influencing behavioural intention (BI) to use SMTA. Design/methodology/approach Personal innovativeness (IN) and privacy information disclosure (PI) are incorporated in UTAUT2 model to determine its effect on SMTA featuring AR and BDA technologies from smart perspective. Both privacy concern (PC) and privacy risk (PR) derived from PCT model are also included to determine its influences on an individual's willingness to disclose privacy information for better-personalised services. We collected responses from 392 targeted participants, resulting in a strong response rate of 84.66%. These responses were analysed statistically using structural equation modeling in both SPSS 22.0 and SmartPLS 3.0. Findings Findings showed that personal innovativeness (IN), habit (HT) and performance expectancy (PE) significantly affect behavioural intention (BI) while privacy concern (PC) significantly affect privacy information disclosure (PI) to use SMTA. In contrast, effort expectancy (EE), hedonic motivation (HM) and privacy information disclosure (PI) had no significant effects on behavioural intention (BI) while privacy risk (PR) had no significant effects on privacy information disclosure (PI) to use SMTA. Originality/value The study findings help tourism practitioners in better comprehending recent trends of SMTA adoption for establishing targeted marketing strategies on apps to improve service quality. In addition, it enables app development companies acquire app users’ preferences to enhance their app development for leading app usage.

Innovation of Development Model for Health-Preserving Tourism in Yunnan Province, China

The development of health-preserving tourism has become a new trend in the tourism industry, and it has played an important role in promoting social and economic development. Yunnan Province, located in southwestern China, has abundant tourism resources and a long history of traditional medicine and health preservation practices. In recent years, Yunnan Province has made significant efforts to develop health-preserving tourism, and it has achieved remarkable results. However, there are still challenges that need to be addressed, such as the lack of innovation in the development model and the need for better integration of traditional medicine and modern medical practices. This paper aims to explore the innovation of the development model for health-preserving tourism in Yunnan Province. The innovative development model for health-preserving tourism in Yunnan Province should focus on three key areas: product development, marketing, and supporting services. In terms of product development, Yunnan Province should focus on the integration of traditional medicine and modern medical practices to develop new health-preserving tourism products that meet the needs of different tourists. In terms of marketing, Yunnan Province should strengthen its brand image and develop targeted marketing strategies to attract different types of tourists. In terms of supporting services, Yunnan Province should improve the quality of tourism services and infrastructure, such as transportation, accommodation, and medical facilities. The innovation of the development model for health-preserving tourism in Yunnan Province has significant implications for the tourism industry and the local economy. The findings of this study provide insights for policymakers, industry practitioners, and researchers to promote the sustainable development of health-preserving tourism in Yunnan Province and other regions.

Bibliometric Analysis of Internationalization Strategies in Supporting MSME Business Growth in Indonesia

Research on MSME internationalization strategies is interesting and important in increasing global scale business growth. This study aims to analyze the five dimensions of internationalization strategy including market entry, target market strategy, time strategy, allocation strategy, and coordination strategy that supports MSME business growth in Indonesia. There have been no studies that have reviewed this topic using bibliometric analysis. Our analysis includes 180 journal articles published in Scopus, Google Scholar, and Crossref between 2018 – 2023. Researchers use VOSviewer software to classify and visualize research results. Research findings show that Indonesian MSME players combine several internationalization strategies such as market entry strategies, target market strategies, allocation strategies, and coordination strategies synergistically through e-commerce development so that they can help exploit existing competitive advantages or create new competitive advantages in the international market. Research on time strategies and allocation strategies has not been explored much, therefore this can be raised as a further research topic. The implications of this research present a bibliometric analysis of the dimensions of Indonesia's MSME internationalization strategy and propose future research directions.

Consumer preferences and demand for conventional seafood and seafood alternatives: Do ingredient information and processing stage matter?

While plant-based seafood alternatives (PBSAs) are emerging as potential substitutes for conventional seafood products, to date, there is scant evidence on how consumers evaluate PBSAs. We conducted two discrete choice experiments to determine consumer preferences and demand for fresh and processed PBSAs relative to their conventional fish and shellfish equivalents and other potential substitutes. In both experiments, consumers had the option to access additional information on the ingredient lists, as they varied substantially between PBSAs and conventional seafood products. We find that the market share of seafood alternatives is relatively small compared to their animal-based counterparts even though respondents prefer novel PBSAs to traditional plant-based food like tofu. Respondents who choose to see the ingredient list tend to have a higher demand for PBSAs. Differences in preferences are also found across demographics and consumption habits. Results from this study provide important information that can assist stakeholders in designing more targeted marketing strategies or policies concerning seafood alternatives (e.g., labeling).

The market dynamics of specialized food products: a cost-effectiveness analysis of gluten-free pasta with corn and rice flour formulations

As consumer preferences evolve, market segmentation becomes increasingly pronounced, leading to specialized food products’ rising significance. This study undertakes a multi-faceted exploration into the market dynamics of specialized food products, focusing on gluten-free pasta made with corn and rice flour formulations in the context of Kazakhstan’s economic landscape. Utilizing a composite framework that blends elements from cost-effectiveness analysis, supply chain considerations, and consumer behavior models, this research aims to elucidate the multiple dimensions that govern the commercial feasibility and market penetration of gluten-free pasta in Kazakhstan. The study’s salient findings suggest that the market for gluten-free pasta in Kazakhstan is predominantly driven by a mix of socioeconomic factors and health considerations. Interestingly, the cost-effectiveness analysis unveils that economies of scale have not yet been realized in the production cycle, indicating an opportunity for future investment in technology and process optimization. Furthermore, consumer awareness and willingness to pay a premium for specialized food products differ based on demographic factors such as age, education level, and household income, which necessitates targeted marketing strategies for maximizing market reach and profitability. This research holds significant implications for stakeholders across the food industry value chain, from raw material suppliers to end consumers. It offers an integrated lens to examine the complex interplay between production costs, consumer preferences, and market forces, ultimately serving as a guide for policy and strategic decision-making in the growing market of specialized food products.

Machine learning approach to cluster destination image on Social Networks

The increasing use of Instagram as a platform to share travel experiences has led to a vast amount of visual content related to destination images. This study proposes a machine learning approach to cluster destination images on Instagram. The objective is to identify the underlying patterns and themes in travel images shared on Instagram, which could provide useful insights for the tourism industry. The study uses a dataset of 10,000 Instagram images with destination tags and applies a deep learning approach to extract visual features from the images. K-means clustering is then applied to group images based on visual similarities. The results show that machine learning techniques can be used to cluster destination images on Instagram into meaningful categories such as natural landscapes, cultural landmarks, food, and cityscapes. These insights can be used to develop targeted marketing strategies and improve tourism experiences.

Market Profile Analysis of Lokawisata Baturraden

This research studies segmentation and targeting strategies at Lokawisata Baturraden, Banyumas Regency, Central Java Province. This study uses the theory of destination market segmentation, whose indicators consist of demography, geography, psychography, and behavior. Then the research results are also strengthened by targeting theory which explains targeting as a process of evaluating segmentation and focusing marketing strategies on a group of people with the potential to become respondents. Data collection methods were used in the form of questionnaires and interviews. Then the data obtained is processed using SPSS software with frequency and crosstab calculations. The results obtained will be recommendations for developing potential target market strategies.

SWOT Analysis of Shein’s Marketing Strategy

Shein, a Chinese online retailer brand, ​​grew tremendously during the COVID-19 pandemic and becomes a phenomenal company. The company experienced rapid acceleration during the pandemic, reaching a peak in 2021. The paper examines the marketing aspects of Shein's success as an emerging online fashion retailer. Using the SWOT analysis method and incorporating evidence from media press and research, this paper studies the strength side of the company such as its affordable price, segmenting the right target market and successful marketing strategy, its weaknesses in terms of its delivery services, market opportunities created by the COVID-19 pandemic, as well as threats within the fast-fashion industry due to the fierce competitions and the company’s poor performance on ESG. The paper offers recommendations for increasing the transparency of the supply chain and workforce, establishing a sustainability plan, developing a premium version of the brand, and a shift in Shein's public image with Shein's desire to go public in 2022.

Discovering fans and anti-fans among social media users based on their emotional reactions and comments

While audience profiling has been a critical concern in social media marketing, little research has used a systematic methodology to identify fans and anti-fans in social media communities. This study aimed to develop a fan and anti-fan detection model by analysing social media users’ mood responses and comments on fan page posts. The sentiment analysis of comments was conducted using a bidirectional long short-term memory (LSTM) model. A total of 83 posts, 849,820 emoticons and 216,688 comments were collected from two different fan pages over 14 days. Results showed that the proposed model, combining emotional reaction analysis and sentiment analysis of their comments, exhibited better fan and anti-fan identification capability than the single-dimensional behaviour model. It exhibited 96% accuracy, 100% precision and 93% recall in terms of community management. This study provides a novel, accurate and efficient way to identify fans and anti-fans that can help form more targeted marketing strategies for social media managers in a cost-effective manner.

Investor preference analysis: An online optimization approach with missing information

How to derive an investor's preference is vital for investment advisors and online lending platforms for targeted marketing strategies, e.g., market segmentation and financial product recommendation. However, investor preference analysis usually depends on judgments from human investment experts, which are inherently subjective and costly. Intelligent investment advisors (or Robo-advisors), supported by cutting-edge technologies such as machine learning and artificial intelligence, are established to relieve these pending issues. This paper employs an online optimization framework to obtain investors' preferences for further financial product recommendations. This proposed method allows us to update the investor's preference for newly-arriving data sets and tackle the situation where plenty of missing values in investors' records are present. Unlike the black-box-like machine learning approach, our method can provide more managerial implications regarding why one financial product/service is preferred. Real-world data set from an online financial platform is used to compare the existing approaches and shows the stronger and more stable performance of our method when facing different data-missing types and situations with different missing degrees, followed by a recommendation system application.

Competitive targeted marketing in social networks with switching topology: Seed selection and consensus shaping

The boom of social network platforms has greatly enriched people's platform choices for social interactions, which may cause people to switch frequently in different social networks to chase hot points. Meanwhile, as an advertisement integrator, the social network platform enables firms to implement targeted marketing campaigns to steer people's opinions in their own favor. Identifying the seed set and devising targeted marketing strategies emerge as the key points for firms' success. To tackle such a problem, we develop a game-theoretical framework for targeting marketing resources to network agents with switching topology based on their competitive opinion dynamics. In the framework, the dynamics of agents' opinions are driven by not only the targeted marketing behaviors of competing firms but also the social interactions of agents with switching topology. With capturing these features, we determine the consensus condition and weight property in such dynamics. On this basis, the optimal targeted marketing strategies are derived that firms only need to invest the overlapped opinion leaders of switching networks. And specifically, such a leader with more weight in consensus will gain more marketing resources from firms. Besides that, we further carry out comparative static analysis of relevant factors and discuss the equilibrium strategies with no consensus. Finally, two examples are presented to illustrate our theoretical results. In spite of the mathematical characterization, our paper additionally provides some insights into marketing management.

تأثير المتغيرات الديموغرافية على سلوك المستهلك اليمني تجاه التسوق عبر الإنترنت

This study investigates the demographic factors influencing Yemeni consumers' online shopping behavior. The findings reveal that demographic factors, such as gender, age, education, and occupation, do not significantly influence various aspects of online shopping behavior. Instead, findings reveal that income level, employment status, previous online shopping experience, and travel experience abroad have a significant impact on consumers' online shopping behavior. The study's findings suggest that the digital age has created a more level playing field for online shoppers, with traditional demographic factors becoming less influential. It also emphasizes the need for policies that foster digital inclusion and targeted marketing strategies that account for the diverse preferences and expectations of Yemeni consumers. The article concludes by recommending policymakers, businesses, and marketers prioritize digital literacy, expand internet access, bridge the digital divide, and develop marketing strategies that appeal to a diverse consumer base.

Purchasing Decisions in Terms of Perceived Quality and Product Knowledge towards Electrical Products

The present research paper investigates the factors influencing purchasing decisions concerning electrical products, specifically focusing on the role of perceived quality and product knowledge. As consumers’ expectations and preferences evolve, understanding how individuals perceive quality and employ product knowledge is crucial for marketers and manufacturers aiming to develop effective marketing strategies. This study aims to contribute to the existing literature by exploring the relationships between perceived quality, product knowledge, and purchasing decisions in the context of electrical products. The research employs a quantitative approach, utilizing a survey questionnaire administered to a diverse sample of consumers. Multiple dimensions of perceived quality are assessed, including durability, reliability, performance, aesthetics, and customer service. Product knowledge is measured by evaluating consumers’ familiarity with technical specifications, features, and brand reputation. The survey also captures demographic variables and purchase-related information to analyse potential variations in purchasing behaviour. The findings reveal a positive correlation between perceived quality and purchasing decisions, indicating that consumers tend to prioritize products with higher perceived quality when making purchasing choices. Moreover, the study highlights the significant impact of product knowledge on purchasing decisions, indicating that consumers with a higher level of product knowledge are more likely to make informed and favourable purchase decisions. Furthermore, the research identifies specific dimensions of perceived quality that have a stronger influence on purchasing decisions, shedding light on the aspects that hold greater importance for consumers in the context of electrical products. The study also examines the moderating role of demographic factors, such as age, gender, and income, on the relationship between perceived quality, product knowledge, and purchasing decisions. The results of this research paper have implications for marketers, manufacturers, and policymakers. Understanding the determinants of consumers’ purchasing decisions can assist in developing targeted marketing strategies, enhancing product development processes, and providing tailored information to consumers. By addressing the factors influencing perceived quality and product knowledge, stakeholders can improve consumer satisfaction and loyalty, leading to long-term success in the highly competitive electrical products market.