Marketing has evolved from barter-based trade in early societies to the structured marketplace of the industrial revolution, where mass production necessitated targeted strategies (Uyan 2017). The emergence of mass media newspapers, radio and television further transformed marketing by enabling broader reach and engagement. NEW: Condensed historical overview removes broad industrial revolution detail and focuses on digital marketing evolution in higher education institutions (HEIs). The late 20th century saw another shift with the advent of the internet, offering global access and innovative digital marketing strategies (Gillpatrick 2019). The coronavirus disease 2019 (COVID-19) pandemic accelerated this digital transformation, pushing businesses and institutions to integrate online marketing into their core strategies (Ercolano 2020). Higher education institutions have adapted by incorporating online marketing to attract and retain students in an increasingly competitive environment (Pawar 2024). Online marketing, also known as digital marketing, leverages digital communication channels, including social media (SM), email and search engines, to engage prospective students (Karakus et al. 2026). Higher education institutions operate within a commercial landscape where students function as consumers, making enrolment decisions based on available online information (Khan 2025). Marketing managers at HEIs must recognise that prospective students today are more informed, articulate and have access to flexible, affordable degree options (Mogaji 2019). The higher education sector has seen an increase in marketing materials, including advertisements and SM campaigns (Kisiolek, Karry & Halkiv 2021). A study by Paladan (2018) highlights the growing use of digital platforms like Facebook, Twitter, LinkedIn and YouTube in university marketing. Website design and user experience have also become critical in shaping student perceptions. Universities must adopt innovative, student-centred strategies to remain competitive (Kusumawati 2019). Online marketing offers a cost-effective alternative for universities seeking to attract students while managing financial constraints (Paladan 2018). The transition from traditional to online marketing is necessary to address increased competition and evolving student expectations. Higher education institutions, like businesses, must implement strategies that cater to student aspirations to enhance satisfaction and enrolment rates. Prospective students consider various factors when choosing a university, including academic reputation, programme offerings, financial considerations and institutional branding (Echchabi & Al-Hajri 2018). While some prefer on-campus learning, others opt for online programmes due to their flexibility (Valtonen et al. 2021). Digital marketing strategies such as SM engagement, search engine optimisation (SEO), content marketing, email campaigns and virtual campus tours play a crucial role in attracting and informing students (Makrydakis 2021). By effectively leveraging these digital tools, universities can increase visibility, engage with prospective students and communicate their unique value propositions, ultimately driving enrolment (Stephenson, Heckert & Yerger 2015). Africa, and South Africa in particular, is still underrepresented in empirical research on HEI online marketing tactics, despite global advances in digital marketing in higher education. Digital marketing has not yet been fully incorporated into institutional initiatives at many of the region’s colleges, and it is unclear which techniques have the greatest impact on student choices (Khan 2025). The proportional effects of SM, email marketing, SEO, search engine marketing (SEM) and electronic word-of-mouth (eWOM) on student enrolment decisions in African contexts are not well-documented. This study makes a contribution by presenting empirical data from South Africa regarding the impact of various online marketing tactics on student preferences, contrasting their relative efficacy and providing useful advice for academic institutions looking to improve enrolment and recruitment through focused digital marketing campaigns. The term ‘student preference’ in this study refers to prospective students’ positive assessment of and propensity to choose a specific university based on views of institutional value and online marketing contacts (Li et al. 2025). It includes both behavioural intents (probability of participation) and attitudinal components (favourability and perception).

This study adopted a quantitative, cross-sectional survey design guided by a positivist paradigm, appropriate for measuring relationships between predefined constructs. The aim was to assess the influence of five online marketing strategies: SMM, eWOM, email marketing, SEO and SEM on student preference for university. A structured questionnaire was used as the primary data collection tool. The target population comprised first-time entering students aged 18–65 at Nelson Mandela University. A non-probability convenience sampling approach was applied due to accessibility constraints and the large size of the population. First-time students were recruited using convenience sampling. We recognise that the generalizability of this method is limited. Despite ethical approval, the prior defence of ‘accessibility constraints’ clearly refers to limited access to personal student contact information because of data privacy laws. Probability sampling should be used in future research to guarantee a more representative sample. Institutional data protection policies limited access to the complete enrolment list, despite the target demographic being explicitly stated (first-time incoming students). In order to ensure clarity on the sample procedure, this limitation is specifically acknowledged. A total of 448 valid responses were collected, exceeding the minimum sample size recommended for multiple regression analysis with five predictors. Data were collected using a self-administered online questionnaire developed on Google Forms. The instrument was designed based on established scales and previous literature and consisted of six sections measuring: student preference (dependent variable), SMM, eWOM, email marketing, SEO and SEM (independent variables). Each item was measured on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The questionnaire also included demographic questions. This study’s measurement scales are derived from well-known tools. The student preference scale assesses behavioural intents (probability of enrolment) as well as attitudinal elements (favourability and perception). The corresponding institutional or peer-driven influence is measured by each marketing strategy scale.

To ensure content validity, the instrument was developed with reference to existing studies (Baltezarevic 2023) and reviewed by subject matter experts. Exploratory factor analysis (EFA) was conducted to assess construct validity, and all factor loadings exceeded the 0.6 threshold. Internal consistency was tested using Cronbach’s alpha, with all constructs scoring above 0.7, confirming reliability. Given the exploratory character of the study and sample limitations, EFA was carried out even though confirmatory factor analysis (CFA) would have been perfect for confirming the pre-defined scales. Construct validity was validated by this method. Confirmatory factor analysis should be used in future studies with bigger, more representative sample sizes. The online survey link was disseminated via university email channels and during in-person class sessions. Participation was voluntary, and ethical clearance was obtained from the university’s ethics committee. Respondents were assured of confidentiality and anonymity. The data collected were first cleaned and coded to address missing values and ensure consistency across all variables. Data analysis was conducted using Statistica. Descriptive statistics, including means, standard deviations and frequency distributions, were computed to provide an overview of the demographic characteristics of the respondents and responses across each construct.

Inferential statistical techniques were applied to examine relationships among the key variables:

All tests were conducted using a significance level of p < 0.05, meaning relationships or group differences were considered statistically significant if the probability of error was less than 5%.

The study sample consisted of 448 valid responses from first-time entering students. The demographic analysis, as presented in Table 4, revealed that the sample comprised predominantly females (62%) and young respondents aged 18–24 years (97.5%), with smaller proportions in the 25–30 age group (2%), followed by the smallest proportions of respondents (0.2%) in the 31–40 and 41–50 age groups, respectively. Most participants identified as African (93.8%), with minorities including Coloured (4.4%), white (0.9%), Indian (0.5%), and (0.5%) belonging to an unspecified category. The majority of participants identified as African (93.8%), followed by Coloured (4.4%), White (0.9%), Indian (0.5%), and unspecified (0.5%) categories. Regarding study status, most participants were full-time students (98.9%), while 0.9% were part-time students and 0.2% did not specify their study status part-time and 0.2% unspecified. Respondents were primarily affiliated with the Faculty of Business and Economic Sciences (96.9%), with 2.9% in the Faculty of Humanities and 0.2% in the Faculty of Education. Regarding qualifications, 87.1% held a Grade 12 certificate, 5.8% a bachelor’s degree, 5.1% a national diploma or certificate, 0.7% postgraduate qualifications and 1.3% reported holding other qualifications. Reliability was assessed using Cronbach’s alpha, ensuring internal consistency across all online marketing strategies. The results, presented in Table 1, indicate that all constructs exceeded the acceptable threshold (α > 0.7).

A Pearson correlation analysis was conducted to determine relationships between online marketing strategies and student preferences. Results, summarised in Table 2, reveal positive associations, particularly between SEO and student preference (r = 0.554) and SMM (r = 0.477).

A multiple regression model was applied to assess the impact of each marketing strategy on student preference. The model results, displayed in Table 3, indicate that SEO (β = 0.35, p < 0.01) and SMM (β = 0.29, p < 0.01) had the most significant influence.

The ratings of digital marketing tactics by male and female students were compared using a t-test. Compared to female respondents (M = 3.85), male respondents (M = 4.12) gave higher scores. The analysis’s reference group was women, as shown by the negative t-values; the sign has no bearing on how group differences are interpreted.

The correlation matrix revealed statistically significant positive relationships between all five online marketing strategies. These results affirm that online marketing strategies are meaningfully associated with students’ university choices. This conclusion is consistent with Ismagilova et al. (2020) who emphasise the role of online interactions in influencing consumer behaviour. The significant correlation between SMM and student preference aligns with Singh et al. (2021), who argue that direct engagement via platforms like Instagram or Facebook enables institutions to establish brand trust and awareness. Similarly, the positive relationship between SEO and student preference supports findings by Iqbar et al. (2022), who underline the role of discoverability in students’ research processes during university selection. Multiple regression analysis was conducted to assess the predictive power of each online marketing strategy on student preference. The model was statistically significant (p < 0.05), confirming that the independent variables jointly influence the dependent variable. The model accounted for a substantial proportion of variance in student preference, demonstrating the practical relevance of digital marketing in higher education. Social media marketing had the strongest positive influence on student preference (β = 0.29, p < 0.01), validating H1. This supports Baltezarevic (2023), who highlights SMM as an essential driver of engagement among Gen Z. Platforms like TikTok, YouTube and Instagram allow universities to create authentic and relatable narratives that align with students’ values. The interactive nature of these platforms also facilitates two-way communication, which has been linked to increased student trust and commitment. Search engine optimisation was the second most significant predictor (β = 0.35, p < 0.01), validating H4. These results correspond with findings from Makrydakis (2021), suggesting that optimised content increases university visibility during students’ online search process. The positive influence of SEO reinforces the importance of discoverability and information accessibility when students compare institutions. Electronic word-of-mouth demonstrated a moderate positive impact on student preference (β = 0.18, p < 0.05), validating H2. This aligns with Tata et al. (2020), who posits that peer recommendations have higher credibility among prospective students compared to institutional messaging. User-generated content, student testimonials and online reviews enhance institutional reputation and trustworthiness. Although SEM showed a positive association (β = 0.14), it was the least impactful among the five predictors, though still statistically significant, thus validating H5. This suggests that while paid advertisements can increase awareness, students may view them with some scepticism, especially compared to organic results or peer-driven content. Email marketing was found to be a significant, though relatively weaker predictor (β = 0.14, p < 0.05), supporting H3. This outcome suggests that while email campaigns may play a role in influencing student decisions, their effectiveness depends on personalisation and relevance. This echoes Sabbagh (2021), who emphasised that impersonal mass emails are less effective in Generation Z-targeted marketing. The independent samples t-test assessed gender-based differences in perceptions of online marketing strategies. Notably, males reported significantly higher ratings for SEO and SEM compared to females. This suggests a gendered difference in information search behaviour, with males potentially placing greater emphasis on search functionalities and paid advertisements. These findings resonate with Valtonen et al. (2021), who noted that male students tend to be more analytical in their university research process, valuing structured search strategies and ranking data. In contrast, females may rely more on interpersonal communication and social cues, potentially making eWOM or SMM more influential for this group. The t-test did not reveal significant gender differences in perceptions of SMM, eWOM or email marketing, implying a convergence of digital preferences across these strategies. This aligns with Paladan (2018), who reported a narrowing gender gap in online engagement behaviour among university applicants. By analysing the impact of each online marketing technique on student preference and evaluating potential gender variations in perceptions of these strategies, our findings directly meet the study’s secondary objectives. Overall, the findings confirm prior literature advocating for digital marketing in higher education recruitment (Kusumawati 2019). The significant influence of SMM and SEO reinforces their role as primary tools for influencing prospective student perceptions. These strategies directly impact both the awareness and evaluation stages of the student decision-making process. Furthermore, this study adds to the growing body of literature stressing the importance of multi-channel strategies. The significant regression results across all five marketing strategies support Stephenson et al. (2015), who argue that integrated digital campaigns yield stronger outcomes than isolated efforts.

This article provides empirical evidence on the role of online marketing strategies in influencing student preference for universities. The findings indicate that SEO and SMM are the most influential strategies, reinforcing the importance of organic search visibility and digital engagement. Meanwhile, eWOM, email marketing and SEM contribute significantly but to a lesser extent. These results align with existing literature, highlighting the growing reliance of students on peer recommendations, interactive digital experiences and content-driven engagement. Despite the positive impact of SEO and SMM, this article underscores the limitations of short-term paid advertising strategies. While SEM and email marketing provide immediate exposure, they lack the long-term relationship-building capacity observed in organic digital marketing approaches. The results suggest that institutions must shift towards sustainable, student-centred engagement strategies to maintain long-term recruitment success (Stefko, Fedorko & Bacik 2015). From a practical standpoint, universities must develop integrated digital marketing strategies that prioritise SEO-driven content, SM engagement and eWOM credibility management. Institutions that fail to do so risk losing competitive advantage in an increasingly digital recruitment landscape. Moreover, marketing managers should balance organic and paid strategies, ensuring that student engagement, authenticity and institutional reputation remain central to their approach (Mogaji 2019). These findings offer critical insights into higher education marketing strategies, particularly in competitive digital environments. Institutions aiming to enhance their online presence should invest in SEO optimisation, data-driven content marketing and authentic student engagement tactics. Such an approach will not only improve visibility, but also foster long-term trust and student satisfaction (Kisiolek et al. 2021). The fact that 96.9% of respondents were from the Faculty of Business and Economic Sciences is a significant study constraint. This focus restricts how broadly the results can be applied to students in other faculties. To improve the representativeness and application of the findings, future studies should strive to incorporate a more varied sample from various faculties and institutions. While this article provides a strong foundation, there are areas that warrant further investigation. Firstly, future research should explore the role of emerging digital platforms such as TikTok, LinkedIn and AI-driven marketing tools in shaping student preferences.

As SM consumption patterns evolve, understanding which platforms and content types yield the highest engagement will be crucial for university marketing efforts. Secondly, longitudinal studies could provide deeper insights into how online marketing strategies influence student retention and long-term brand loyalty. While this article captures a snapshot of student preferences, further research could examine how engagement with online marketing impacts students’ actual enrolment decisions and satisfaction post-admission. Finally, qualitative studies should complement quantitative research to explore the psychological and behavioural factors driving student preferences. While this article highlights broad trends, understanding the motivations, perceptions and emotional triggers behind students’ decision-making could enable universities to develop more refined, targeted marketing strategies. In line with the secondary objectives, this study revealed gender-based disparities in perceptions, identified the digital marketing tactics that most significantly affect student preferences and provided useful insights for targeted university marketing. By integrating these future research directions, universities can continue refining their digital marketing approaches, ensuring data-driven, student-centric strategies that align with the evolving digital landscape.