FACULTAD DE CIENCIAS DE LA EMPRESA Escuela Académico Profesional de Administración y Negocios Internacionales Tesis Analysis of Digital Marketing and Its Effect on the Positioning of Peruvian Universities in 2023 Yilda Lisset Canaza Calsina Aldo Jeampiere Torvisco Becerra Harold Delfín Angulo Bustinza Para optar el Título Profesional de Licenciado en Administración y Negocios Internacionales Arequipa, 2024 Esta obra está bajo una Licencia "Creative Commons Atribución 4.0 Internacional" . INFORME DE CONFORMIDAD DE ORIGINALIDAD DE TRABAJO DE INVESTIGACIÓN A : Decano de la Facultad de Ciencias de la Empresa DE : Harold Delfín Angulo Bustinza Asesor de trabajo de investigación ASUNTO : Remito resultado de evaluación de originalidad de trabajo de investigación FECHA : 5 de Octubre de 2024 Con sumo agrado me dirijo a vuestro despacho para informar que, en mi condición de asesor del trabajo de investigación: Título: Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 URL / DOI: https://doi.org/10.14349/sumneg/2024.V15.N33.A5 Autores: 1. Yilda Lisset Canaza Calsina – EAP. Administración y Negocios Internacionales 2. Aldo Jeampiere Torvisco Becerra – EAP. Administración y Negocios Internacionales Se procedió con la carga del documento a la plataforma “Turnitin” y se realizó la verificación completa de las coincidencias resaltadas por el software dando por resultado 10 % de similitud sin encontrarse hallazgos relacionados a plagio. Se utilizaron los siguientes filtros:  Filtro de exclusión de bibliografía SI X NO  Filtro de exclusión de grupos de palabras menores SI X NO Nº de palabras excluidas : 10  Exclusión de fuente por trabajo anterior del mismo estudiante SI NO X En consecuencia, se determina que el trabajo de investigación constituye un documento original al presentar similitud de otros autores (citas) por debajo del porcentaje establecido por la Universidad Continental. Recae toda responsabilidad del contenido del trabajo de investigación sobre el autor y asesor, en concordancia a los principios expresados en el Reglamento del Registro Nacional de Trabajos conducentes a Grados y Títulos – RENATI y en la normativa de la Universidad Continental. Atentamente, https://doi.org/10.14349/sumneg/2024.V15.N33.A5 SUMA DE NEGOCIOS, 15(33), 119-129, julio-diciembre 2024, ISSN 2215-910X Doi: https://doi.org/10.14349/sumneg/2024.V15.N33.A5 Research Article Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 Yilda Lisset Canaza Calsina 1 , Aldo Jeampiere Torvisco Becerra 2 y Harold Delfín Angulo Bustinza 3 1 Bachelor in Administration, Student, Universidad Continental, Arequipa, Peru. E-mail: yildalissetcanaza@gmail.com 2 Bachelor in Administration, Student, Universidad Continental, Arequipa, Peru. E-mail: jeantorviscobecerra@gmail.com 3 Ph.D. in Economics and International Business, Professor and researcher, Universidad Continental, Arequipa, Peru (corresponding author). E-mail: hangulo@continental.edu.pe A RT ICL E I N FOR M AT ION: A BST R AC T Received: May 7, 2024 Accepted: August 7, 2024 Online: August 30, 2024 Codes JEL: JM31, I23, C83, L83, 057 Keywords: Digital marketing, positioning, content distribution, customer acquisition, customer conversion, strategies, quality. Introduction/Objective: Any change and strategic advertising and/or commercial plan- ning carried out by means of digital media is called digital marketing, which helps uni- versities to position themselves, which is why Peruvian universities create and share relevant content on digital platforms. The objective of this study is to determine how digital marketing affects the positioning of Peruvian universities. Methodology: A quantitative approach and a deductive, non-experimental research de- sign were used. A survey instrument was applied to 433 students studying at the Uni- versidad Continental (located in Arequipa, Cusco, Huancayo, and Lima), and the Partial Least Squares Structural Equation Modelling (PLS-SEM) model was used, a multivariate statistical method for analysing the independent variable of digital marketing and the dependent variable of positioning. Results: The study found that the variation in units of Customer Acquisition, Customer Conversion, and Content Distribution generates a variation in positioning of 0.36, 0.24, and 0.20 units, respectively. Conclusions: Customer Acquisition is the most determining factor for positioning, fol- lowed by Conversion and, finally, Content Distribution. Additionally, it is established that Customer Acquisition primarily impacts the Service level, while Content Distribution has a greater influence on the Staff level. Palabras clave: Marketing digital, posicionamiento, distribución de contenido, captación de clientes y conversión de clientes, estrategias, calidad. Análisis del marketing digital y su efecto en el posicionamiento de las uni- versidades peruanas en 2023 R E SU M E N Introducción/objetivo: Todo cambio y planificación estratégico publicitario o comercial que se realice en los medios digitales se denomina marketing digital, lo que ayuda a las universidades a posicionarse, por lo que las universidades peruanas crean y comparten contenido relevante en las plataformas digitales. El objetivo de este estudio es determi- nar cómo el marketing digital afecta la posición de las universidades peruanas. © 2024 Fundación Universitaria Konrad Lorenz. This is an Open Access article under the license CC BY-NC-ND (http://creativecommons. org/licenses/by-nc-nd/4.0/). SUMA DE NEGOCIOS https://doi.org/10.14349/sumneg/2024.V15.N33.A4 mailto:yildalissetcanaza@gmail.com mailto:jeantorviscobecerra@gmail.com mailto:hangulo@continental.edu.pe http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ https://orcid.org/0009-0003-8173-1744 https://orcid.org/0009-0006-3316-4206 https://orcid.org/0000-0002-1360-4378 120 Yilda Lisset Canaza Calsina et al. SUMA DE NEGOCIOS, 15(33), 119-129, julio-diciembre 2024, ISSN 2215-910X Metodología: Se empleó un enfoque cuantitativo y el diseño de investigación fue deducti- vo, no experimental. Se aplicó la herramienta de encuesta a 433 estudiantes que estudian en la Universidad Continental (Arequipa, Cusco, Huancayo y Lima), y el modelo utilizado es el de Partial Least Squares Structural Equation Modeling (PLS-SEM), un método estadís- tico multivariado para analizar la variable independiente marketing digital y la variable dependiente posicionamiento. Resultados: El estudio tuvo como resultado que la variación en unidades de captación de clientes, conversión del cliente y distribución de contenidos genera una variación en el posicionamiento de 0.36, 0.24, y 0.20 unidades respectivamente. Conclusiones: la adquisición de clientes es el factor más determinante para el posiciona- miento, seguido por la conversión y, finalmente, la distribución de contenido. Además, se establece que la adquisición de clientes impacta principalmente el nivel de servicio, mientras que la distribución de contenido influye más en el nivel de personal. Introduction The use of mobile devices in Peru in recent years has in- creased; according to the “The Supervisory Board for Private Investment in Telecommunications” (OSIPTEL, by its acro- nym in Spanish, 2023), four out of every five Peruvian house- holds own a smartphone, laptop or tablet, which represents 84.1% of the country’s families with at least one mobile de- vice connected to the Internet. Likewise, according to the Movistar Telefónica Foundation report (2021), mobile devic- es are the main tool to connect to the Internet; the primary use they give is social networks (91.7% Internet users) and information search platforms (60.4% Internet users). During 2021, Peru invested around $16,373,094 dollars in digital ad- vertising; its universities and higher education ranked third with a participation of 2.14% (Admetricks, 2022). Likewise, according to Data Reportal platform (2024) in the latest re- port of Peru Digital 2024, digital advertising will have an interannual increase by 2024, showing a growth in the use of social networks by 8.5%, searches by 13% and digital pro- grammatic advertising by 11.4%. The term “positioning” derived from the competitive- ness in the market; Coca (2007) mentions that positioning strategy is essential for the market segmentation process since marketing strategies will be developed depending on the company, brand, and product/service positioning; it also refers to how present they are positioned in the minds of po- tential clients and customers, unlike the competition. Fur- thermore, according to Seminario et al. (2020), positioning is a strategic tool that has gradually increased over the years; strategies that satisfy the needs and desires of the customer are used and implemented in more than 80% of companies. For Kerin & Hartley (2020), positioning refers to the charac- teristics that customers see compared to the competition. Digital marketing acts as an instrument that allows you to easily reach an informed and interested audience by creating news, reviews, discussions and promotions re- lated directly or indirectly to your published work. Digital marketing refers to technologies or tools such as web appli- cations, websites, email (both traditional and mobile) and social networks. The key to digital marketing is to attract the masses, and there has to be an interaction between the advertising campaign and the masses who receive it; some of the characteristics of digital marketing are interesting content and environments where people can find informa- tion; for example, social networks are increasingly investing in traditional advertising methods and almost all popular websites already contain effective advertising formulas (Salazar et al., 2017). Businesses can gain benefits from digital marketing ser- vices such as product management, e-commerce, influencer marketing, SEO, SEM, search engine marketing, content mar- keting, campaign marketing, social media marketing, direct email marketing, spot display, and digital books. According to Salonen et al. (2024), the benefits of marketing in digital content management improve when the content is correctly oriented; likewise, more significant investments improve the capacity of marketing strategies. Content distribution is an important factor since it is considered a fundamental digital marketing tool; it is used to communicate about a service or product competently through various channels that provide valuable information regarding the brand to the target audi- ence. When searching on the Internet, users look for specific information, so their attention has to be captured through content that provides value (Porras, 2019). Furthermore, Sor- do (2022) states that it is essential since it is the development of creating, sharing, publishing, and promoting high-quali- ty, interesting, and innovative content. According to Lajam and Mohamed (2024), the distribution of content is reflected through texts, videos, and audio, which reach out to users through devices that operate within the network. Customer acquisition is vital for a company or institu- tion; the biggest challenge for the trader is to implement the most appropriate system to achieve this defined objective; companies that follow this plan every day have a thorough and detailed sales performance and automatically increase their intervention in the market and the brand position with great potential for expansion; customer acquisition highlights that “it consists of developing a key process of building long-term solid relationships with people or or- ganisations that ensure the notoriety of the company’s ac- tivities, it is considered that to establish relationships with customers you have to “focus on attracting, developing, maintaining and retaining users (Loor et al., 2021) 121 Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 Conversion is a process established in advance for the website visitor to complete the action they want to take; that is, people stay and finally confirm and do something on the website. Converting visitors into customers is more valuable than the traffic generated by the website (Selman, 2017). There are studies that show that to position yourself in the market, you have to have marketing strategies. Siguenza et al. (2020), in their research, conclude that virtual marketing tools implementation is essential to increase positioning. Likewise, Urrutia and Napán (2021) explain in their study that there is a significant and positive relationship between positioning and various networks that make up digital mar- keting. Furthermore, Chaowarat and Shafiq (2023) aver that companies that use effective digital marketing strategies obtain better product placement, faster revenue growth and greater brand awareness; the study also emphasised the rel- evance of digital marketing techniques in producing these positive results in companies. For all of the above, this study aims to demonstrate that “digital marketing influences the positioning of higher ed- ucation institutions in Peru” in the case of the Universidad Continental in its four locations in Peru, determining the effect on the positioning dimensions in digital marketing dimensions. Additionally, it determines how digital market- ing strategies could affect the positioning of the Universidad Continental in Peru. The general hypothesis proposed is “The use of digital marketing positively affects the positioning of the Universi- dad Continental in the Arequipa headquarters in 2023.’’ Specific hypotheses are the following: • The digital marketing dimensions positively affect the positioning dimensions of Continental University 2023. Table 1. Description and abbreviation of the variables of the structural model and observe in detail, which means they chose to study and continue in the institution. The results of the questionnaire revealed that the most influential factors in students’ de- cision-making are closely related to the level of advanced technology available. This preference underscores the im- portance of integrating cutting-edge technologies into the educational process. To reach its target market, the Univer- sidad Continental invested in digital and traditional mar- keting in order to capture its market segment and position itself as one of the universities with greater use of technolo- gy; the European Commission has published a report on the quality of education and student welfare. Inclusion and exclusion criteria Inclusion. The research criteria for data collection were considered all students in the face-to-face modality of the four locations (Arequipa, Huancayo, Lima, Cusco). The to- tal number of students at the University was estimated at 32,524, and a distribution based on the total number of stu- dents per school was made for applying the questionnaire, Huancayo has 21,029 students and the questionnaire was applied to 279 students, Cusco has 6,022 students and 87 were surveyed, Arequipa has a total of 3,811 students, of which 46 were surveyed and in Lima there are 1,662 stu- dents and 21 answered the survey. The surveys were con- ducted randomly, considering all university careers of dif- ferent academic cycles from the freshman to the last year of study for the 2023 period. Exclusion. The criteria that will not be considered for this research are students of the following modalities: semi- face- to-face, distance learning, and graduates of the Univer- sidad Continental in 2023. Expert instrument validation The questionnaire was validated by Percy Hansel Cárde- nas Vargas, master’s in digital marketing and Master in Communication and Marketing, DNI (national identity doc- ument) 44753386, who currently holds the position of pro- fessor at the Universidad Continental; the instrument will be valid in Arequipa in 2023. His cell number is 915163877. Type, scope, and design The method of the research study is quantitative because data collection is carried out in order to measure, analyse, and test hypotheses based on numerical measurements and statistical analyses to test theories (Hernández & Baptista, 2014). The type of research is basic and explanatory, and the design is not experimental since the variables studied will not be manipulated. In addition, it states that the study is Source: own elaboration. Methodology Data Participants The study selection is exclusively for face-to-face mo- dality students at the Universidad Continental to know conducted over a specific time period. Procedure A pre-survey was randomly conducted with students from different campuses, focusing on the face-to-face mo- dality, across various faculties and academic years. The ob- jective was to validate the study instrument. For review and approval, it underwent a process of observations and sug- gestions from the expert and advisor, who provided com- ments that refined the collected data and led to a thorough investigation of the study topic. Variable Variable description Grades Abbreviation Y1 Image Valuation from 1 to 5 IM Y2 Product Valuation from 1 to 5 PR Y3 Service Valuation from 1 to 5 SE Y4 Staff Valuation from 1 to 5 PE X1 Content distribution Valuation from 1 to 5 DC X2 Customer acquisition Valuation from 1 to 5 CC X3 Customer conversion Valuation from 1 to 5 CO 122 Yilda Lisset Canaza Calsina et al. SUMA DE NEGOCIOS, 15(33), 119-129, julio-diciembre 2024, ISSN 2215-910X To begin data collection, the consistency matrix and op- erationalisation of variables in the questionnaire were sent to an expert in digital marketing for validity and approval. Once the instrument was approved, closed surveys were conducted using a simple random probabilistic sampling model to determine the number of students at different campuses of the Universidad Continental. Data collection took place in April and May 2023 at the Arequipa campus. Following the expert and advisor’s rec- ommendation, data collection was extended nationally to enhance the study’s impact. Consequently, in December, surveys were conducted in person to achieve better infor- mation reach and perception from students at the Cusco, Huancayo, and Lima campuses. The questionnaire was distributed through an online platform in two modalities: first, a link was shared via a WhatsApp QR code to access the questionnaire; second, printed questionnaires were distributed. This dual approach aimed to improve efficiency and appreciation in data col- lection, acknowledging that students use both digital and traditional media. This strategy allowed for a broader reach and adherence to the established sample size. Additionally, students were assisted during the questionnaire completion process to ensure accuracy. The strategy positively impact- ed understanding, explanation, and communication, with prior explanations given about the study’s topic and meth- odology. Participants were also informed about the study’s general and specific objectives. To achieve more accurate re- sults, we considered increasing the number of respondents. Participants took about 10 minutes to complete the questionnaire. The total number of participants by campus was: Huancayo with 279 students, Cusco with 87 students, Arequipa with 46 students, and Lima with 21 students, in 2023. Ethical considerations Before starting the questionnaire, participants are in- formed about the study’s objectives, reliability, and the anonymity of the data (only general data was collected, not personal data), with the aim of gaining students’ time and trust so they can answer the questions with complete trans- parency and honesty. Data analysis For the present study, the model used is the Partial Least observations based on a formative approach, which is val- idated considering the convergence, collinearity, signifi- cance, and relevance of the observations used to measure latent variables. Subsequently, after the measurement mod- el is validated, a structural model is designed considering the interaction between the measured latent variables. Fi- nally, to validate these results, the collinearity, significance, and relevance of the latent variables within the structural model are evaluated. Structural equation modelling, a multivariate statistical analysis technique, is used to analyse how each dimension of the “digital marketing” variable affects the dimensions of the “positioning” variable. Based on this, closed surveys were used through a simple probabilistic and random sam- pling model. These surveys were applied to 433 students from the Universidad Continental at the branches of Areq- uipa, Huancayo, Lima, and Cusco so that these data are pro- cessed in the R - Studio software. Structural analysis is a data analysis technique that al- lows identification of patterns in the data, linking them to each other; this is a fundamental stage of design process in which the calculation, design and verification of the struc- ture of the dimensions studied is carried out. This scientific technique allows us to determine whether the structure is adequate to fulfil its purpose or objective and to validate or reject the hypotheses (Gimena, 2004). The dimensions of the independent variable, digital mar- keting, are: Customer Conversion, Customer Acquisition, and Content Distribution; while the dimensions determine the development of the dependent variable of positioning: Image, Product, Staff and Service. In addition, abbreviations were used for the structural model, the descriptions of the abbreviations are on Table 1. These were taken into account in the 433 surveys, which consisted of 25 questions. 11 questions were used for the independent variable and 14 questions for the dependent variable. A Likert scale was used indicating 5 “totally agree”, 4 “agree”, 3 “neither agree nor disagree”, 2 “disagree” and 1 “totally disagree”. The preservation of the “students” privacy and confiden- tiality is one of the ethical aspects considered in the current research after obtaining the informed consent of the study subjects: likewise, manipulation of data is rejected, and efforts are made to guarantee transparency in the dissemi- nation of the results. Squares Structural Equation Modelling (PLS-SEM), which is a multivariate statistical method classified as the second generation, so it is not based on the existence of normal dis- tribution and is robust against collinearity; the fact that the data provided by the surveys are categorical does not pose a problem for their processing. Moreover, since dimensions are not measured directly, this statistical method allows us to contain each observation of the dimensions in latent variables. The same variables allow their effect on other latent variables corresponding to the dimensions of the de- pendent variable to be analysed. For the respective data processing and analysis, a mea- surement model has to be designed in principle, which consists of modelling the latent variables considering their Evaluation of the measurement model The convergence of values for each latent variable con- structed by considering training measures is evaluated by means of a regression where each of these variables explains each of the alternative measures for each latent variable. The results show that the indicator, which is the result of the coefficient of estimation, is greater than 0.7, as can be seen from Table 2, and indicates that the degree of convergence of each latent variable with its alternative mea- sure is high in all cases; considering this, it is accepted that the convergence of each latent variable with its alternative measure is valid. 123 Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 Table 2. Convergence indicator Variable Indicator Content distribution 0.955 Customer acquisition 0.951 Customer conversion 0.954 Services 0.966 Image 0.957 Product 0.971 Staff 0.945 Source: own elaboration. Collinearity Another important assumption that must be considered when estimating a structural model is the non-existence of collinearity between the components used for the formative measurement of each latent variable. In effect, it is consid- ered that a VIF (variance inflation factor) indicator lower than 3 or 5 is a favourable signal to consider the non-pres- ence of collinearity between the components of each latent variable. That said, it is observed that each VIF of the compo- nents used for the formative measures is less than 3, as seen on Table 3; therefore, based on this result, it is asserted that the formative construction of each latent variable does not contemplate the problem of collinearity and consequently the latent variables are well identified. Significance and relevance Another very important consideration is that the ob- servations used for the construction of latent variables are statistically significant since each of them must contribute to the construction of the latent variables; Table 4 shows the statistical T at 95% confidence, of which it can be stated that all observations of the variables “Content Distribution”, “Customer Engagement”, “Services” and “Product” are sig- nificant since they have a T-Stat greater than 1.96. On the other hand, it is also interpreted based on Table 3 that the observations corresponding to IM2 for measuring the variable “Image” are not significant at 90%, 95%, and 99% con- fidence; the other observations to measure this variable are, however, valid. Likewise, it is observed that the observations of PE3 to measure the variable “Personal” are not significant at 95% confidence. However, it is substantial at 90% confidence as the observations of CO4; additionally, it can be stated that the other observations to measure the variables “Personal” and “Customer conversion” are significant at 95% confidence. Table 3. VIF (variance inflation factor) for each measured variable Content Distribution DC1 DC2 DC3 DC4 1.466 1.778 1.557 1.464 Customer acquisition CC1 CC2 CC3 1.425 1.355 1.191 Customer conversion CO1 CO2 CO3 CO4 1.659 1.639 1.593 1.372 Services SE1 SE2 SE3 1.759 1.829 1.68 Image IM1 IM2 IM3 IM4 1.983 2.207 2.211 1.846 Product PR1 PR2 PR3 1.535 1.819 1.5 Staff PE1 PE2 PE3 PE4 2.144 2.586 2.86 2.298 Source: own elaboration. 124 Yilda Lisset Canaza Calsina et al. SUMA DE NEGOCIOS, 15(33), 119-129, julio-diciembre 2024, ISSN 2215-910X Indicator FIL (Formative Indicator Loadings) Given that the results show that the CO4, IM2, and PE3 observations are not significant at the 95% confidence lev- el, it is crucial to establish a criterion for evaluating their potential omission from the formative measurement mod- el. To this end, Table 5 presents the “Formative Indicators Loadings” (FIL) indicator, which must exceed the value of 0.5 to avoid the mandatory exclusion of observations that are not significant at the 95% level. This precaution is necessary because their omission could reduce the pressure of the variable measurement or an overfitting of the model. Based on the above, it can be argued that the FIL indicator for all cases is greater than 0.5, therefore, the decision criterion does not require elimination of the observations regarding CO4, IM2 and PE3 since they do not represent a major problem in the efficiency of the construction of latent variables. Table 4. Significance and relevance of the measure for each latent variable T Stat. T Stat. DC1 -> Content Distribution 2.649 SE3 -> Services 3.892 DC2 -> Content Distribution 4.495 IM1 -> Image 5.941 DC3 -> Content Distribution 3.543 IM2 -> Image 1.269 DC4 -> Content Distribution 6.992 IM3 -> Image 4.573 CC1 -> Customer acquisition 13.505 IM4 -> Image 4.078 CC2 -> Customer acquisition 6.014 PR1 -> Product 9.146 CC3 -> Customer acquisition 5.327 PR2 -> Product 6.48 CO1 -> Customer acquisition 2.187 PR3 -> Product 3.729 CO2 -> Customer acquisition 6.164 PE1 ->Staff 3.428 CO3 -> Customer conversion 6.187 PE2 -> Staff 2.656 CO4 -> Customer conversion 1.91 PE3 -> Staff 1.914 SE1 -> Services 7.855 PE4 -> Staff 3.548 SE2 -> Services 3.041 Source: own elaboration. Table 5. Indicator FIL (Formative Indicator Loadings) Indicator FIL T. stat 95% Indicator FIL T. stat 95% DC1 -> Content distribution 0.688 10.317 SE3 -> Services 0.799 18.202 DC2 -> Content distribution 0.841 21.025 IM1 -> Image 0.874 29.31 DC3 -> Content distribution 0.759 15.926 IM2 -> Image 0.777 18.672 DC4 -> Content distribution 0.806 21.706 IM3 -> Image 0.859 25.839 CC1 -> Customer acquisition 0.919 49.317 IM4 -> Image 0.827 22.653 CC2 -> Customer acquisition 0.717 16.56 PR1 -> Product 0.879 31.071 CC3 -> Customer acquisition 0.617 11.572 PR2 -> Product 0.865 26.145 CO1 -> Customer conversion 0.732 14.067 PR3 -> Product 0.7 14.494 CO2 -> Customer conversion 0.856 24.81 PE1 -> Staff 0.864 21.284 CO3 -> Customer conversion 0.825 21.365 PE2 -> Staff 0.863 24.753 CO4 -> Customer conversion 0.584 11.076 PE3 -> Staff 0.871 23.646 SE1 -> Services 0.922 36.284 PE4 -> Staff 0.873 25.314 SE2 -> Services 0.792 19.488 Source: own elaboration. 125 Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 Structural model evaluation Collinearity The constructed latent variables do not present exact linear relationships, given that the VIF indicator of each of them is less than 3, as seen on Table 6, for all cases. In fact, it can be stated that there is no presence of collinearity between the variables evaluated, therefore, the estimates made do not present instability in the estimated parameters of the structural model. Table 6. VIF (Variance inflation factor) of the independent latent variables Var. Latent Exogenous VIF Content Distribution 2.083 Customer acquisition 2.161 Customer conversion 1.888 Source: own elaboration. Results For a complete interpretation, as seen in Figure 1, a diagram is presented below where everything is specified in relation to the calculation of the formative and structural measurement model, for which it is important to detail that the Content Dis- tribution variables (CD), Customer Acquisition (CC) and Cus- tomer Conversion (CO) are dimensions of the Digital Marketing variable and, on the other hand, the variables of Image (IM), Product (PR), Services (SE) and Staff (PE) are Positioning dimen- sions. It is also important to clarify that the diagram shown in Figure 1 is a product of a structural model estimation using partial least squares. Taking into account that the assumptions of the measurement and structural model were validated, the model interpretation presented in Figure 1 is still considered. Mainly, it is observed that the Content Distribution level has a more significant impact on the Staff level, followed by the Image, Product and Service level; it should be noted that its effect on the Service level is significant at 90% of confi- dence, but the other effects are significant at 95% and 99%. Additionally, it can be said that if CD increases by one level, then the level of PE, IM, PR, and SE rise by 0.246, 0.216, 0.231, and 0.109 parts of a level, respectively. Regarding the Customer Acquisition (CA) level, it is ob- served that this has a significant effect at 90%, 95% and 99% confidence on all the latent dependent variables; it is also notable that the level of CA has a more significant influence above the level of SE, followed by PR, IM and PE. Additionally, it can be said, based on the diagram, that if CA increases one level, then the levels of SE, PR, IM, and PE increase by 0.395, 0.368, 0.366, and 0.310 parts of a level, respectively (Table 7). Likewise, it is observed that Customer Conversion (CC) significantly explains all the dependent latent variables at 90%, 95% and 99% confidence levels; Individually, its effect is greater to explain at the SE level, followed by the PR, IM and PE levels; regarding the detail of the effect, it is interpreted that, if CC increases by one level, then PR, IM and PE increase by 0.286, 0.247, 0.220 and 0.194 parts of a level, respectively. Regarding the level of adjustment of the regressions, it is observed that the latent variable best explained by the group of independent variables is first the Product level, with 53.4% of R2; the second regression with the highest R2 corresponds to Image, followed by Service and Staff, with R2 goodness of fit of 51.7%, 49.1% and 43.3%, respectively. This result is relevant given that it is a sign that the variations in the Positioning dimensions are explained on average by 49.38% by the Digital Marketing dimensions; therefore, there are other aspects different from this last variable that are important to explain the “Positioning” variable. Given the results, it can be said that, regarding the rel- evance of the independent latent variables, the range in Customer Acquisition has more impact on the dependent latent variables since its average effect is around 0.36 parts of the level, followed by Customer Conversion with 0.24 and Content Distribution with 0.20 level parts. In this case, if you want to improve the Positioning level, it is important to consider that making decisions in favour of Customer Acquisition contributes more to the objective, followed by Customer Conversion and Content Distribution. Table 7. Results of the hypotheses tested for the model variables Hypothesis β Decision H1: DC and IM 0.232 Accepted H1 H2: DC and PR 0.216 Accepted H2 H3: DC and SE 0.109 Accepted H3 H4: DC and PE 0.246 Accepted H4 H5: CC and IM 0.366 Accepted H5 H6: CC and PR 0.368 Accepted H6 H7: CC and SE 0.395 Accepted H7 H8: CC and PE 0.310 Accepted H8 H9: CO and IM 0.220 Accepted H9 H10: CO and PR 0.247 Accepted H10 H11: CO and SE 0.286 Accepted H11 H12: CO and PE 0.194 Accepted H12 Source: own elaboration. Discussion The present study, based on a structural analysis of dig- ital marketing at Universidad Continental, incorporates di- mensions of digital marketing that are relevant for position- ing through its background and literature reviews. Through the PLS-SEM model supported by previous studies, two vari- ables are considered “digital marketing” and “positioning the university”. Based on the study’s results, the dimensions of digital marketing: content distribution, customer acqui- sition, and customer conversion positively affect the posi- tioning dimensions; in contrast to the results obtained, the most striking feature is the connection between customer acquisition and service. The connection and dynamic be- tween the student and the University are appreciated based on its track record and reputation in technology, innovation, infrastructure, and educational quality. Regarding content distribution and staff, students have a positive perception of the staff due to the presence of research professors and studies in university teaching. Therefore, students show ap- preciation through the service. W =0.282*** W =0.403*** W =0.202** W =0.383*** W =0.274*** W =0.667*** W =0.303*** W =0.447*** W =0.116* W =0.188* W =0.498*** β =0.232*** 95% CI [0.123, 0.366] β =0.216*** 95% CI [0.109, 0.342] β =0.366*** 95% CI [0.225,0.504] β =0.22*** 95% CI [0.89, 0.341] β =0.109* 95% CI [0.006, 0.237] β =0.368*** 95% CI [0.242, 0.481] β =0.247*** 95% CI [0.119, 0.374] β =0.246*** 95% CI [0.131, 0.378] β =0.395*** 95% CI [0.254, 0.525] β =0.286*** 95% CI [0.152, 0.42] β =0.31*** 95% CI [0.181, 0.44] β =0.194** 95% CI [0.064, 0.318] W = 0.095 W =0,343*** W=0.323*** W =0.417*** W=0.226*** W=0.532*** W=0.432*** W=0.328*** W=0.592*** W=0.243** W=0.341*** W=0.248** W=0.215* W=0.348*** Figure 1. Structural model of digital marketing and positioning Source: own elaboration. 1 2 6 Y ild a L isse t C a n a z a C a lsin a e t a l. SU M A D E N E G O C IO S, 1 5 (3 3 ), 1 1 9 -1 2 9 , ju lio -d iciem b re 2 0 2 4 , ISSN 2 2 1 5 -9 1 0 X 127 Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 When analyzing the background and literature reviews of digital marketing, it is observed that it has a positive im- pact on positioning. Accordingly, based on other studies, Tiwari et al. (2023), indicate that digital media are the most used tools as a channel for digital marketing; this study shows that digital media are currently the most influential channels, which can be used to analyse customer profiles and needs. There- fore, it is important to encourage the use of digital mar- keting to achieve business success; compared with other studies where results were obtained; Cuellar et al., (2023) The development of a digital marketing plan ensures the implementation of solid and effective strategies aligned with organisational objectives and the changing demands of the market. Salonen et al., (2024). The he findings of this study confirm that digital marketing content highlights the importance of rational content, transactional content, and interactive content. The results indicate that these three types of content generate a high level of engagement. The aim is to make the content more attractive and attract the attention of the customer. These findings imply that uni- versities should consider positive and negative outcomes to formulate client acquisition strategies, content distribution, and customer conversion in order to have a positive effect on the positioning of universities which are implementing strategies to improve the Image. For a deeper analysis of the dimensions of digital mar- keting, the most accepted is the acquisition of clients, since according to the results, we see that the Universidad Conti- nental has innovative and informative advertising because it is constantly updating its digital media and platforms in order to achieve a higher ranking through the product/ser- vice offered by the university to students interested in plans and programmes. In the context of customer conversion in digital market- ing, it is observed that students continuously explore and navigate the university’s digital media in search of imme- diate attention. The university efficiently responds to fre- quently asked questions and addresses students’ personal and academic needs by offering relevant updates through its digital platforms. This strategy allows the institution to evaluate its customer conversion rate while reinforcing its commitment to high-quality education. Additionally, the university stands out for its modern infrastructure and fa- cilities, strengthening its position in the ranking of the top universities in Peru. The dimension of digital marketing, distribution of con- tent based on surveys, the content published on virtual platforms is of high educational value that remains in force and is easily accessible and visualised in their digital and traditional media, where they provide us with information through social media, radio and advertising boards for fur- ther content distribution, The European Commission has published a study on the role of the European Union in the field of education and training, which is being carried out by the European Commission the Universidad Continental has 298 registered researchers and investigations regarding university teaching, with years of experience, the respon- dents state that teachers are qualified and fit to teach the required courses, The aim is to have a positive effect on the positioning of the Universidad Continental. Regarding the “customer acquisition” dimension and the indicators: Service, Product, Staff, and Image of the Position- ing variable, the results were 0.395, 0.368, 0.366, and 0.310. Thus, Philipp (2017) states that imposing undefined service standards decreases the value of loyal customers with the company, which is why he proposes directing advertising focused on specialised customer service; similarly, the re- sults of the research of Aguirre et al. (2021), indicates that there is a moderately significant relationship between “ser- vice quality” and “customer retention”, this means that if the quality of service increases, so will customer retention, based on the “Spearman’s Rho” coefficient, which is 0.691 (r = .691**, p = .000). Regarding the product, Iyer et al. (2021) indicates that the connection between customer acquisition and products is complicated and has many aspects, influ- enced by various factors that trigger the obtaining of a new product. Ocampo et al. (2021) highlight in their results that the employees’ role in attracting customers is fundamental in a local cafeteria. Having trained and dedicated staff focusing on improv- ing service quality, customer satisfaction, and customer ac- quisition is crucial. Regarding corporate Image, according to the research by Ramos and Neri (2022), customers believe that the company is very focused on creating a clear image and fulfiling the commitments they made in their adver- tisements or promotions; however, we also believe that the company shows a moderate interest in making its products different from those of the competition. A valuation of 0.657 was found according to the “Spearman’s Rho” coefficient, which indicates the 4 p’s. According to the findings, the level of content distribu- tion has the most significant impact on the Staff level; in second place is the Image, followed by Product and finally, the Service level, which, as observed, presents high signif- icance levels. Thus, in similar studies such as that of Firs- tianti and Fajar (2023), a significant and positive relationship with a value of p < 0.05 is evident between staff performance and the integration of digital content to increase business growth. With respect to the Distribution of Content on the Image level, the research by Bui et al. (2023) highlights that the patterns of digital marketing content used, whether dis- tributed for social, informational or entertainment purpos- es, have a moderate impact in relation to brand perception, identifying a value of p > 0.5 with a significant level. On the other hand, it is also noted that the higher the level of Con- tent Distribution, the higher the level of product will be, in- creasing by 0.231; this is also reflected in the research by Ur- rutia and Napán (2021), which at a higher level of marketing of content referring to networks design and management, there is a higher level of acquisition of online products, this through the operation of digital marketing strategies and tools. Finally, regarding the Service level in which Content Distribution has less influence with respect to the other in- dicators, the study made by Matak et al. (2020) evidences the opposite, given that customers’ knowledge of the informa- tion on the services offered on social networks increases by 128 Yilda Lisset Canaza Calsina et al. SUMA DE NEGOCIOS, 15(33), 119-129, julio-diciembre 2024, ISSN 2215-910X presenting a more dynamic distribution of the structure of social network marketing. Regarding the Customer Conversion (CO) dimension, it has a greater effect on the Service level, secondly on the product level, followed by the Image and staff level, likewise, as evidenced in the results obtained if the customer conver- sion increases by one level, then Services, Product, Image and Staff increase by 0.286, 0.247, 0.220 and 0.194 parts of a level, respectively. The research of Ariste et al. (2023) indi- cates that customer conversion has a significant impact on business management, which increases the service contract through social networks by 31.11%. Secondly, in relation to the Product level, the study by Joko (2023) states that sales conversions in electronic media improve the optimisation of products, which in the case study are cars. Likewise, they also improve customer experience and perception. In rela- tion to the Staff level and the implications of Customer Con- version, in the research of Otis and Wu (2018), a conversion mechanism is developed that includes customer preferenc- es, staff division, and staff extra-organisational entities for the valuation of their work in which urban employees are more favoured by conversions in capital. Finally, there is the Image level; this indicator is influenced to a lesser extent by Customer Conversion; the results indicate that it only in- creases by 0.194 parts of the level each time the CO increas- es. In contrast, Monroy et al. (2021) affirm that audience conversion is related to content marketing strategies that, in turn, seek to increase consumer loyalty and commitment. Conclusions Regarding the hypotheses raised, the dimensions of digital marketing, such as Customer Acquisition, Custom- er Conversion, and Content Distribution, are concluded to have an influence on positioning. Likewise, of the three di- mensions presented, Customer Acquisition has the greatest influence on positioning, followed by Customer Conversion and thirdly, Content Distribution. Likewise, in relation to the positioning dimensions, it is concluded that Customer Acquisition influences to a great- er extent the Service level and to a lesser extent the Image level. On the other hand, Content Distribution has a greater influence on the Staff level and less influence on the Service level. Finally, Customer Conversion has greater effects on the Service level and occurs to a lesser extent at the Staff level. For this research, the limitations that were presented were the collection of information, and the fact that no scientific articles and no research on digital marketing and positioning in the education sector were found; therefore, more research is needed, The European Commission has published a report on the Fifth Framework Programme for Education and Training, which is available in English, French, German, Italian, Portuguese and Spanish. Digital marketing would not be the only variable that affects positioning, by which we find variables that slightly resemble our research such as advertising, data analysis, digital technology, strategic marketing, among other variables, but on the other hand it was not found in the same education sector, but in other financial sectors, The European Commission has been working on this subject since the beginning of the 1980s. In the present research study based on theory and prac- tice, digital marketing contributes to the educational train- ing of professional students from different fields of study from all Peruvian universities that apply digital marketing; the Universidad Continental must, therefore, invest in cus- tomer acquisition to improve its positioning. Today, digital marketing is the most relevant and effective means to pub- licise proposals and inform students. To this end, surveys were conducted to measure the use of digital marketing at universities. However, the dimensions considered in this study were investigated to measure the use of digital marketing and its effect on positioning; to achieve this, to explain in depth it is necessary to address other dimensions of digital marketing in order to explain the positioning of universities; this study has not managed to explain more than 55% on average, It is, therefore, preferable to consider new dimensions because as evidence of the results obtained we find that only part of the university’s positioning is explained. The European Commission has already published a report on the European Union’s position in the field of education and training. There are other factors that affect the positioning of the study, which is based on a series of studies carried out in the field of education and training. Financing This research is financed exclusively by the authors themselves. Conflict of interests There is no conflict of interest since everyone works for the same company. Authors’ contribution Yilda Lisset Canaza Calsina: Conceptualisation, data cura- tion, formal analysis, investigation, methodology, writing – original draft, validation, resources, (writing – review and editing, software). Aldo Jeampiere Torvisco Becerra: Con- ceptualisation, data curation, formal analysis, investigation, methodology, writing – original draft, validation, resources, (project administration, supervision). Harold Delfín Angulo Bustinza: Conceptualisation, data curation, formal analysis, investigation, methodology, writing – original draft, valida- tion, resources, (visualization). 129 Analysis of digital marketing and its effect on the positioning of Peruvian universities in 2023 References Admetricks. (2022). Ranking mensual de inversión publicitaria. Admetricks. https://iabperu.com/wp-content/ uploads/2022/03/16.-Reporte-mensual-Peru-Febrero-2022.pdf Aguirre, J., Pillaca, C., & Agnoli, R. Q. (2021). 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Datos generales de la institución 1.2. Actividades principales de la empresa Figura 1. Actividades económicas COVICE EIRL 1.3. Reseña histórica de la empresa Figura 2. Construcciones usuales en las ciudades del Perú Figura 3. Construcción de viviendas sin inspección de personal técnico 1.4. Organigrama de la empresa a. Organigrama de la empresa COVICE EIRL 1.5. Visión y Misión Visión Misión 1.6. Bases legales o documentos administrativos 1.7. Descripción del área donde realiza sus actividades profesionales 1.8. Descripción del cargo y de las responsabilidades del bachiller en la empresa CAPITULO II: ASPECTOS GENERALES DE LAS ACTIVIDADES PROFESIONALES 2.1. Antecedentes o diagnóstico situacional Figura 4. Evolución del PBI y participación del sector construcción 2.2. Identificación de oportunidad o necesidad en el área de actividad profesional 2.3. Objetivos de la actividad profesional 2.4. Justificación de la actividad profesional 2.5. Resultados esperados CAPITULO III: MARCO TEÓRICO 3.1. Bases teóricas de las metodologías o actividades realizadas Figura 5. Índices de precios al consumidor nacional: variación según divisiones de consumo, Diciembre 2022 CAPITULO IV: DESCRIPCIÓN DE LAS ACTIVIDADES PROFESIONALES 4.1. Descripción de actividades profesionales 4.1.1. Enfoque de las actividades profesionales 4.1.2. Alcance de las actividades profesionales 4.1.3. Entregables de las actividades profesionales Tabla 1. Pasos de la designación del Proyecto que desarrollara el bachiller en la empresa COVICE EIRL. 4.2. Aspectos técnicos de la actividad profesional 4.2.1. Metodologías 4.2.2. Técnicas 4.2.3. Instrumentos 4.2.4. Equipos y materiales utilizados en el desarrollo de las actividades 4.3. Ejecución de las actividades profesionales 4.3.1. Cronograma de actividades realizadas. Tabla 2. Diagrama de Gantt del proyecto asignado al bachiller para la empresa COVICE EIRL. 4.3.2. Proceso y secuencia operativa de las actividades profesionales. Figura 6. Proceso del desarrollo del proyecto planteado por el Bachiller CAPITULO V: RESULTADOS 5.1. Resultados finales de las actividades realizadas Tabla 3. Gastos de almacenamiento de la base de datos física Tabla 4. Gastos de almacenamiento de la base de datos virtual Tabla 5. Proyección anual de gastos de almacenamiento de la base de datos Figura 7. Proyección anual de gastos de almacenamiento de la base de datos Tabla 6. Gastos de los materiales de la base de datos física Tabla 7. Gastos de los materiales de la base de datos virtual Tabla 8. Proyección anual de gastos de materiales de oficina de la base de datos Figura 8. Proyección anual de gastos de materiales de oficina de la base de datos Tabla 9. Gastos del personal de recaudación de datos de la base de datos física Tabla 10. Gastos del personal de recaudación de datos de la base de datos virtual Tabla 11. Proyección anual de los gastos de recaudación de información de la base de datos Figura 9. Proyección anual de los gastos de recaudación de información de la base de datos Tabla 12. Gastos totales de la base de datos física Tabla 13. Gastos totales de la base de datos virtual Tabla 14. Proyección anual de los gastos de la base de datos Figura 10. Proyección anual de los gastos de la base de datos Tabla 15. Datos insertados en la base de datos física Tabla 16. Datos insertados en la base de datos virtual Tabla 17. Proyección anual de los datos insertados en la base de datos Figura 11. Proyección anual de los datos insertados en la base de datos Tabla 18. Distribución de datos de la base de datos física Tabla 19. Distribución de datos de la base de datos digital Tabla 20. Comparación del tiempo de distribución de la base de datos Figura 12. Comparación del tiempo de distribución de la base de datos 5.2. Logros alcanzados Logros personales Logros propuestos por la empresa 5.3. Dificultades encontradas 5.4. Planteamiento de mejoras 5.4.1. Metodologías propuestas 5.4.2. Descripción de la implementación 5.5. Análisis 5.6. Aporte del bachiller en la empresa CONCLUSIONES RECOMENDACIONES REFERENCIAS BIBLIOGRÁFICAS ANEXOS Figura 13. Base de datos de la empresa COVICE EIRL en el apartado de fecha Figura 14. Base de datos de la empresa COVICE EIRL en el apartado de distrito Figura 15. Base de datos de la empresa COVICE EIRL en el apartado de Zona Figura 16. Base de datos de la empresa COVICE EIRL en el apartado de tipo de construcción Figura 17. Base de datos de la empresa COVICE EIRL en el apartado de datos relevantes para proyectos de inversión Figura 18. Matriz financiera para proyectos construidos de la empresa COVICE EIRL Figura 19. Matriz financiera de proyectos por construir de la empresa COVICE EIRL Figura 20. Gráficos de precios promedio por distrito y Zonificación automatizados con los datos de la empresa COVICE EIRL Figura 21. Gráficos de medidas promedio por distrito y Zonificación automatizados con los datos de la empresa COVICE EIRL Figura 22. Pantalla de inicio de la aplicación creada para la empresa COVICE EIRL Figura 23. 1ra parte de la ventana móvil de ingreso de datos de la aplicación creada para la empresa COVICE EIRL Figura 24. 2da parte de la ventana móvil de ingreso de datos de la aplicación creada para la empresa COVICE EIRL Figura 25. Gráficos de precio promedio de inmuebles por distritos desde la aplicación creada para la empresa COVICE EIRL. Figura 26. Base de datos observable desde la aplicación creada para la empresa COVICE EIRL. Figura 27. Constancia de Trabajo emitida por el gerente de la empresa COVICE EIRL Figura 28. Figura 29. Figura 30. Figura 31. Figura 32. Figura 33.