Machine Learning in Customer-Centric Web Design: The Website of

a Portuguese Higher Education Institution

Vitor Monteiro Pinto

1,2,3 a

, Fernando Paulo Belfo

1,4 b

, Isabel Pedrosa

1,4,5 c

and Lorenzo Valgimigli

Polytechnic Institute of Coimbra, Coimbra Business School, Quinta Agrícola, 3045-601 Coimbra, Portugal

Neuron – Data Science and AI, University of São Paulo, R. da Reitoria, 374, São Paulo – SP, Brazil

GenD – Data, Design & Digital – Edificio Gluework, Calle Edison 3, Madrid, Spain

CEOS.PP, ISCAP, Polytechnic of Porto, Rua Jaime Lopes Amorim, S/N, Matosinhos, Porto, Portugal

ISTAR-ISCTE, Av. das Forças Armadas, Lisboa, Portugal

Department of Computer Science and Engineering, University of Bologna, Via dell’Università 50 Cesena, Italy

Keywords: Higher Education, DigItal Marketing, Web Analytics, Data Mining, Machine Learning, Customer Experience,

Web Design, CRISP-DM.

Abstract: Prospective students interact with the brand of higher education institutions (HEI) via several channels

throughout their journey to choose a course to enroll. The institutional website is among these channels and

the way it is designed might influence how engaged these visitors are. Web analytics tools allow collecting

high amounts of user behavior data, which can generate insights that help to improve higher education

institutions website and the students’ incentives to apply for a course. Techniques of Data Mining are

presented as a proposition to help generating insights with an applied case study of a Portuguese HEI. The

CRISP-DM method was used to generate suggestions to improve user engagement. The tools applied from

Google Tag Manager, Analytics, BigQuery and RapidMiner allowed to collect, storage, transform, visualize

and model data using the machine learning algorithms Naïve Bayes, Generalized Linear Model, Logistic

Regression, Fast Large Margin and Decision Tree. The main results showed that: the course pages do attract

volume of users, but their engagement is low; the general undergraduate course page is more successful to

bring users who see course content and that; masters and other course pages do attract engaged users who see

undergraduate that content.

1 INTRODUCTION

Competitiveness, as an aspect of evolution, plays its

role in the sector of higher education. The large number

of people is one of the reasons why individuals try to

be highlighted by competing in education, which

influences universities on looking to offer the best

quality with a reasonable price (Nuriadi, 2021).

Higher Education Institutions (HEI) are

encouraged to improve themselves to increase the

number of course applicants to fulfil available places

of courses, to achieve higher levels of revenue,

notoriety and quality of the student body.

https://orcid.org/0009-0008-1741-7837

https://orcid.org/0000-0002-7653-6413

https://orcid.org/0000-0003-4713-2759

https://orcid.org/0000-0003-0309-771X

Tiago and Verissimo (2014) have shown that

human environments were heavily impacted by the

rapid growth of web platforms, leading to changes in

activities, habitats and interactions. Thus, it is

essential to re-strategy their marketing activities

digitally. Data mining techniques can be used to

perform efficient and insightful analysis for this case

(Osman, 2019).

This study describes the case of a Portuguese

higher education institution, with the typical

opportunities and challenges on Digital Marketing of

the sector. The general objective of the project was to

increase the number of applicants for the courses at

Pinto, V., Belfo, F., Pedrosa, I. and Valgimigli, L.

Machine Learning in Customer-Centric Web Design: The Website of a Portuguese Higher Education Institution.

DOI: 10.5220/0012209500003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 1: KDIR, pages 387-394

ISBN: 978-989-758-671-2; ISSN: 2184-3228

387

the institution improving enrollment intention, more

specifically the percentage of users who checked

course pages. To achieve this objective, it can be

helpful to understand the users’ path from the first

contact with HEI’s brand, the difficulties and the

values perceived until finally enrolling. (Kalbach,

2021).

In this project, the digital environment of

Portuguese HEI was analyzed as much as the website

considering the customer journey to define specific

goals. The internal data of the case-study HEI was

mapped, scoped, collected, stored and dissected using

data mining techniques on visitor’s behavior data.

The project was conducted by the method CRISP-

DM, which has shown to be flexible enough to be

used in different industries. The tools used were

Google Tag Manager for data collection, Google

Analytics and Google BigQuery for storing and

transforming data, and RapidMiner for statistical

modelling. Finally, the results and conclusions were

presented with suggestions for future expansions of

this study.

2 LITERATURE REVIEW

2.1 Digital Marketing for Higher

Education Institutions

Human environments have been heavily impacted by

the popularization of web technologies leading to a

need to adapt in marketing strategies by including the

factor of digital channels (Tiago & Veríssimo, 2014).

Digital Marketing might have different

approaches when described: Nurtirtaway et al (2021)

focused on the innovativeness of going beyond

traditional conventional analogical transactions by

digitalizing the distribution channels to reach

consumers more effectively, with more

personalization and cost efficiency using channels as

search engines, emails, websites, blogs and social

media. (Forghani, 2021).

When focusing in HEI, digital marketing can be

helpful with: targeting prospective students, enrolling

student engagement, providing placements, designing

curriculum, offering career counselling, developing

alumni contacts and professional students network

(Harbi and Ali, 2022). The advantage that online

marketing offers for academic communications is a

collaborative two-ways channel that breaks space and

time limitations (Bateman, 2021).

Del Rocio Bonilla et al (2020) put on view in a

study that Instagram, an image and video-oriented

online social media, was more efficient and effective

when engaging students in comparison to posters,

events, places and sports. Kusumawati (2019)

foregrounded the use of social media at the moment

of considering which course they will enroll in.

Rajkumar et al (2021) argued about the

importance of Digital Marketing to uplift results of

student admission decision making process. Oré

Calixto’s (2021) study focused on the positive effect

of these strategies on the customer relationship

management (CRM) operations. Basha (2019)

investigations revealed that 62% of students selecting

higher education institutions were impacted by digital

marketing.

Stone and Woodcock (2013) highlight the

importance of Business Intelligence and Customer

Insights to understand and support their new

marketing interactions. Mapping their interactions

and behaviors can be the key to understand behaviors

and to define improvement plans (Kalbach, 2021)

2.2 Customer-Centric Experiences and

Web Analytics

When running an organization with selling goals it is

important to understand what might persuade

consumers to buy and the same applies for

prospective students. As much as a consumer, when

they find the value that they were looking for, the

purchase might occur. Some of these values can be:

functional, social, emotional, epistemic or conditional

according to Sheth et al (1991).

However, it can be complicated for an

organization to see and to prioritize value driving

improvements throughout their channels, products,

processes, departments, roles, metrics, strategies, and

goals.

Customer Journeys put on a big picture, side by

side, the components of the company and the path of

the customer making their impact clearer. It can

identify the contribution of valuable content on the

website or of an event at the final application result.

(Kalbach, 2021).

Each stage of the customer path can typically go

through some phases as suggested by Pedowitz Group

in the shape of a Loop (Holiday, 2018).

The Loops consists of onboarding, adoption,

value realization, loyalty and advocacy. They are

condensed in two interconnected parts, the customer

retention and acquisition.

Customer acquisition can be in the phase of

“awareness”, by which potential consumers are put in

contact with the brand, even when they are not

interested on the product or in the service. In this stage

the goal is to be positively recognized by the audience.

KDIR 2023 - 15th International Conference on Knowledge Discovery and Information Retrieval

388

Potential customers might be in the following

phase, “consideration and evaluation”. At this

moment they can be interested in knowing more

about the product or they might be already

considering purchasing it. They might be comparing

different products to finalize purchases.

Finally, they can be in the “decision” phase, by

which they decided to buy the product or service.

To properly address the most relevant users and

behaviors during the whole journey it is needed to

gather data from inside and outside the website.

For the website data there are Web Analytics

tools, to collect and present usage data to highlight

what works well or not and what can be further

leveraged, improved or removed (Palomino et

al.2021).

2.3 Data Mining Techniques

As technologies evolve, the amount of data collected

and stored grows exponentially. Being able to extract

valuable insights from them is considered a key

aspect of growth of any brand now (Osman, 2019).

Data mining techniques are presented as a solution to

understand and examine these big data assets

effectively to solve problems and generate insight

using the collection, extraction, analysis and

statistical methods. It is important to mapping the

goal to be reached to identify the best technique to be

applied. Some of the techniques applied are

association, classification, clustering, decision trees,

predictions and neural networks.

In this work, the classification was applied. The

purpose of it was to predict the behavior of the

following cases to be given a label as, for example,

high or low purchase propensity. Another example

would be using it for labelling users who are more

likely to return for another visit on the website.

All these techniques can be applied individually

or in combinations to achieve valuable insights. To

choose and to apply them using a method to orient the

work can be help improving the quality of the result.

3 METHODOLOGY

Extracting valuable information from large datasets

requires analytical models and applications.

CRISP-DM is the de-facto standard project

methodology, since its release in 2000. It is widely

accepted, easy and structured, reliable and an

industry-independent process model (Schroer et al.

2021).

CRISP-DM projects are composed by six phases.

The first phase, Business Understanding, convers the

assessment of the business situation, mapping data

sources available and addressing specific data mining

goals and techniques and setting a plan for the project.

Data Understanding means comprehending the

data source, assessing the quality, and defining

transformation strategies before applying the models.

The data quality indicators are ID-ness, Missing

Values, Stability and Correlation. Mandalapu and

Gong (2019) explain them as:

“Correlation is […] the linear correlation between

attribute and target column. The percentage of ID-

ness implies the percentage of different values

present in a column. [...] Stability is the measure

of constant values in an attribute. […] Missing

value measure is the percentage of values missing

in an attribute.”

Attributes that have ID-ness and stability lower

than 90%, correlation between 1% and 40 %, and less

than 70% of missing values were considered as

adequate for analysis.

Then, in the Data Preparation stage, low quality

attributes are excluded from the dataset, new

attributes are added such as classifications and the

integration of tables is undertaken.

In the Modelling phase the technique of modelling

is selected with proper justifications considering

evaluation criteria and parameter settings defined.

More than one method can be applied. The methods

applied were Naïve Bayes, Generalized Linear

Model, Logistic Regression, Fast Large Margin and

Decision Tree. Naïve Bayes is “a simple probabilistic

classifier that calculate a set of probabilities by

summing the frequency and value combination of

given dataset.” (Peling et al., 2017), whereas the

Generalized Linear Model is used to identify the

correlation between two or more variables with

cause-effect relations (Uyanık & Güler, 2013). The

Fast Large Margin is based on Support Vector

Machine and can work with large number of

attributes (Saputro et al., 2021). Decision Trees select

the combination of attributes that influence the most

a selected targeted variable (Osman, 2019)

The indicator used to evaluate which model to

choose was the Area Under Receiver Operating

Characteristics curve (ROC’s AUC) because it

aggregates different analysis from the confusion

matrix and such as sensitivity which measures the

proportion of correct positive classification, and

specificity, which measures the proportion of correct

negative classifications (Zhu et al., 2010).

In the Evaluation stage the results are discussed

by seeing the business objectives.

Machine Learning in Customer-Centric Web Design: The Website of a Portuguese Higher Education Institution

389

The Deployment stage is associated with the

creation of a user guide, software components and

planning the monitoring and performance. This

project does include the application result prediction

no classification nor clustering algorithms frequently

and automatically on a database and thus it is

considered that it does not apply to this project.

4 RESULTS

The results were organized in the first 5 stages of

CRISP-DM.

4.1 Business Understanding

The top 10 Portuguese HEI presented around 18

million sessions in November 2022. The first

university counts for 23% of them in number of

sessions in this period according to SimilarWeb, a

platform about competitor’s website information. The

case-study university is ranked in 10

place in

number of sessions in the period accounting for 4%

of the sessions, similar to the 8

and the 9

place, with

5% each of them (SimilarWeb, 2022).

When compared to the average session duration,

those 10 Portuguese HEI sessions spend 2.3 times

more than the global average of 2:25 minutes (Bailyn,

2022), which might designate that the public of

Portuguese HEI do value more online contents than

in other regions.

The case-study HEI achieved the 7

place out of

the 10 top Portuguese HEI websites in terms of

average session duration and average number of

pageviews per session, which demonstrates that it is

possible to generate more engaged experiences on the

website. As seen that the Portuguese users apparently

value the website content spending more time than

the average in the world, it might indicate that

improving the content of the case-study HEI can lead

to positive results in the value of the brand.

The case-study HEI has 7 websites about its

internal faculties and departments, counting 1.12

million sessions and 65% of them are from the central

institution that manages all faculties. The engineering

school ranked as top 1 in volume and engagement. It

counts with 65% of all faculty website sessions, 56%

more time spent on average per session. The

agriculture school seems to be focused on a niche,

because it is the one with the lowest volume of users,

only 1% of sessions on faculty websites, but the

engagement is higher than the engineering school

with similar average session duration and 1.56 more

pageviews per session. The business school and the

education schools seem similar because their sessions

are the shortest in time, around 70% of the average

session duration. The health school and the

technology and business school count shows sessions

with less pageviews than the average.

Beyond the central website, the case-study

institution counts with several online and offline

channels of contact with prospective and current

students such as the academic service department of

each faculty onsite and by email, the university

communications department email, higher education

events, the university website, the student space

website, websites of each of the six websites and their

respective social media profiles on Facebook and

Instagram.

The website is informative, providing content

about the institution, their courses, their internal

organizations, events, facilities, the city and services.

For those interested ones, the email and the telephone

contact are displayed for more information. By the

time the study occurred, it did not allow collecting the

contact of visitors who are the most interested in the

course directly as a next move to provide a closer

approach to them. It did not allow applying to courses

directly nor asking to be contacted by a school

representative to know more about the course nor

leaving a proactive message by those visitors who are

interested. Then, it can be classified at the perspective

of a prospective student as a channel used in the

consideration and evaluation phase. It has as

objective enhancing user engagement as a showroom.

Considering that the institution is taking the first

steps to use data to shape their digital strategy with

this project, it was decided to work only with the

central website as a data source to be analyzed having

in mind that it is the most important in volume of

users.

The focus of this study was the undergraduate

courses, which is the type of course with the highest

number of active users with course pageviews – 37%

in the analyzed period.

The period from April to June 2023 was delimited

for this study because it can be considered long

enough, but short enough to generate the first insights

with the possibility to be extended in future analysis.

It is considered that those who saw content about

undergraduate courses are the most interested in

them. In this project the goal was to compare the

sessions of those who see undergraduate course

content with those who did not via classification

analysis using a classification algorithm.

It was defined that the goal-content is the course

page views, which describes course objectives,

enrollment conditions and curricular units. The

KDIR 2023 - 15th International Conference on Knowledge Discovery and Information Retrieval

390

objective of this work is increasing the percentage of

users who saw course pages comparatively to all

users, in the period (34,84%).

4.2 Data Understanding

Google Analytics and BigQuery accounts of the case-

institution were the web analytics tool used as data

source. It counts with storage data regarding user

variable types as user acquisition, device, content,

geography, demographics, sessions, users, events and

among others which are measured as number of users,

of sessions and events, including pageviews. The total

number of events was 1855874, 140781 engaged

sessions. It means those who spent 10 seconds or

more in a session, who performed 1 conversion event

or that saw 2 or more pages or in their first visit

(Analytics Help, 2023a, 2023b). 123724 active users,

who have engaged sessions or that had their first visit

in the website (Analytics Help, 2023), in the period.

From all users, those who accessed the website from

Portugal were 85%. They have been chosen as the

only ones filtered in the analysis as a first step.

International prospective students can be studied in a

future work.

The week-by-week evolution of users showed a

number commonly from 10 thousand to 15 thousand

by period, stable engaged sessions by user around 1

and pageviews by user around 4 and more.

These indicators were aggregated into a group of

users for comparison focusing on:

 Device Category, namely desktop, mobile,

tablet and smart tv with respectively 50%, 49%,

0.9% and 0.1% of active users.

 Region, in where the user did the session

restricted by Portugal users, which is the main

public for the courses and that count for 82,3%

of users. The regions with the highest

concentration of active users are respectively

“Lisbon” (35%), “Coimbra District” (20%),

“Porto District” (16%), “Setubal” (13%),

“Aveiro District” (5%), “Leiria District” (4%)

and “Braga” (3,5%) and others (3%).

 Medium of the first session, as the organic

searching engine used to achieve the website or

if the URL has been written directly on the

browser search bar as described as “(none)”

when accessing the website for the first time or

when using a link on an external website to

reach get to the website for the first time. The

most representative Mediums of the first session

in terms of total active users are: “organic”

(78%), “(none)” (11%), “referral” (11%),

“email” and “Website” (less than 1%).

 Source of the first session, as the previous

website, the searching engine used to achieve

the website or if the URL has been written

directly on the browser search bar as described

by (direct) when accessing the website for the

first time. Among the top values of this

dimension there are: “google” (74%), “(direct)”

(11%), “esec.pt” (6%), “bing” (4%), “isec.pt”

(2%) and others (3%).

 Medium of the session, in comparison to the

Medium of the first session, it considers the

medium used in the session itself. The largest

number of active users are found in the groups,

respectively, “organic” (75%), “referral” (13%),

“(none)” (11,85%), others (0.15%).

 Source of the session, in comparison to the

source of the first session, it considers the source

used in the session itself. The most frequent

values were: “google” (70%), “(direct)” (12%),

“education school website” (6%) and others

(12%).

 Landing Pages, is the first page of the website

they arrive to via the link they clicked on or the

URL that they searched in the browser. The

Landing Page URL with the most users is the

“Homepage” (10.3%), the contact page for the

health school (3,3%), the general page for

technical courses (3,05%), the page of the

university canteens (2,9%), and the page with

the full list of courses offered by the university

(2,48%) and others less common landing pages

(77,7%).

The quality of each dimension has been analyzed

in terms of number of values, % of Missing Values,

ID-ness, and stability. The Source of session from the

first session, the Source of the session and the

Landing page presented high number of values,

higher than 150. which might indicate the need to

group values in categories. The Medium of first the

session and The Medium of the session, showed

higher levels of stability, which might indicate that it

is possible to group less relevant values together as

“others”. There were no dimensions with more than

0.56% missing values. It is possible to consider

grouping regions, as they count for 21 regions. The

Table 1 brings the analysis.

To reduce the number of values of “Region”,

“Source of the first session”, “Source of the session”

the values with fewer active users were grouped

together and for “Landing Page’s Page Path” it was

created a categorization based on the analysis of the

website and the URLs as further explained on the

section of data preparation.

Machine Learning in Customer-Centric Web Design: The Website of a Portuguese Higher Education Institution

391

The same analyses of the quality of the variables

have been performed with the metrics. It is possible

to see that some groups concentrate the number of

users as seen that the deviation can be 5,2 the average

of users per group in Table 2.

When analyzing the correlations above 0.4 it was

possible to see some insights such as: the main

positive correlations were that the “Course Offer

Page” and “No Course” filter is of 0.73 and that goes

up to 0.82 when the search feature is used. On the

other hand, the filter most used is to see “technical

courses” only (correlation = 0.63).

Table 1: Quality of data of dimensions.

The “Type of course” page used the most is the

Undergraduate one (correlation = 0.75). The

undergraduate courses appear as the main course

pages used as landing page in comparison to the other

type of courses (correlation = 0.58). The groups with

more active users tend to be those that see more pages

(correlation of 0.72). To see more pages is correlated

with seen more sessions (0.54) which is correlated

with not seeing course pages (0.6). This shows a

profile of users who see more pages, do more sessions

but without landing on course pages. Users who

arrive for the first time by direct, organic or referral

usually prefer doing sessions with the same medium

(correlations respectively 0.65, 0.61, 0.52).

Table 2: Quality of data of metrics.

When comparing faculties, it was possible to see

that the undergraduate courses of education school

followed by the engineering school are the most

common course pages used as landing page

(correlations = 0.5 and 0.4 respectively).

The negative correlations below -0.4 were

described as relevant as well. It was possible to see

that desktop and mobile showed a fairly different

experience with a negative correlation of almost of -

0.96. The “Medium of the first session” and the

“Medium of the session” also showed that the values

“(none)”, “organic” and “referral” behave differently

with negative correlations from lower than -0.4.

It was not found any correlation that explained

clearly what causes higher percentages of users who

saw course pages, and thus, it is needed to make

further analysis with multivariate methods to get to

them. Positive correlations might indicate groups of

attributes that could be merged and the negative

correlations might show which should not be due to

divergent behaviors.

4.3 Data Preparation

The data preparation stage aims to improve the

quality of variables used in terms of relevance

identified in the Data Understanding stage excluding

less influential variables, grouping together less

relevant attributes or performing samplings.

The Landing Page – Page attribute showed a high

number of values, which have been grouped by

identifying patterns in the URL and the understanding

of the sections of the website. The page paths of the

URLs, which are the passages between “/” symbols,

have been separated in columns. Only the Page Path

2, 3 and 4 showed stability less than 90% and less than

70% of missing values. For the modelling dataset, the

variable Landing Page has been substituted by its

categories. By the analysis of the sections of the

website it was possible to identify 4 levels of

categories counting with 20, 30, 31 and 26 values

each respectively.

Then, the values with less active user have been

grouped in “others” until the maximum limit of 15%,

with exception of “Medium of the session” as

“(none)” because it showed positive correlation with

the value “(none)” of the “Medium of the first

session” and negative correlation with the attributes

“referral” and “organic” of the same variable. Other

exceptions were the values “education school” and

“engineering school” from the variable “Page

Subcategory 1”, which showed positive correlations

with the “Page Category” value “Course Page”.

KDIR 2023 - 15th International Conference on Knowledge Discovery and Information Retrieval

392

4.4 Modeling

After the data has been prepared, the modeling

technique applied was classification of users with

more than 50% of users who saw course pages and

those with less than that.

The target was the “percentage of users who

viewed course pages in comparison to all users”. The

unit of analysis used was the group of sessions for all

the combination of attributes. The variable “total

active users” was excluded to have an analysis not

influenced by the volume of traffic, but by the

characteristics of the group.

The models with the highest AUC (0.883) were

the Generalized Linear Model and Logistic

Regression. Both presented the same sensitivity

(99,3%) and similar specificity of 53%, but the first

one was 0.1% higher. This model presented an

accuracy of 86% as seen in the Table 3 and finally, it

was the chosen one.

4.5 Evaluation

In this section the attributes with the positive or

negative correlations are presented. The attribute

associated with the groups that see less the course

pages is of those whose landing page were whether

undergraduate course pages (coefficient = -4.243),

mainly those from the schools of health, engineering,

and business followed by agriculture and education

one in this order (respective coefficients = -0.583; -

0.377; -0.353; -0.116; -0.078). But it is possible to see

that the course pages, specifically do not bring

engaged sessions when compared to the “general

undergraduate course page” (coefficient = 3,658).

Considering that the course pages represent the most

important type of landing page, making up to 36% of

all active users, it is important to consider improving

the design of the page.

The source of the users who see course pages

usually come on the first visit via organic search by

google (coefficient = 0.634) and then might return via

direct search (coefficient = 0.468).

In sequence, we see that the course pages of

masters and courses other than undergraduate and

technical programs bring more users who see

undergraduate course pages (correlations of 0.441

and 0.402). It might indicate that those courses can be

an interesting topic to be used in communications to

attract to undergraduate courses even though they are

not directly about the central theme. Results from

residual groups of sessions were not analyzed because

they do not reach a minimum volume of number of

sessions.

The “referral” source seems not to help bringing

sessions that see course pages (coefficient = -0.466),

in special those that come from the education school

website (coefficient = -0.37). However, it is important

to highlight that this is the school that brings more

users via its website when compared to the other

schools.

Table 3: Measures of error from each method.

5 CONCLUSIONS

HEI need to attract a large number of applicants in

their programs to promote the quality of the student

body, revenue and notoriety. Digital Marketing

appears as a solution. This solution needs to be

contextualized with the applicant experience with

data such as that from web analytics. Data mining can

help generating insights with this data using the

CRISP-DM method.

This study was focused on a Portuguese HEI’s

website. After understanding its business and the

available data using Google Tag Manager, Analytics,

BigQuery, and RapidMiner for collecting, preparing

and applying machine learning techniques to get

insights.

It was possible to see that the most common

landing page, the course page, can be improved to

generate sessions that lead to more engagement; that

the general undergraduate course page is the one that

might attract the most interested students in seeing

course content and so, it can be incentivized as a

landing page in campaigns. The same can be inferred

about master courses and other courses.

As future research, it can be identified the most

relevant segments of users and behaviors of the

website, those with a higher propensity of returning

for a second session, ways to make the course page

and course unit pages more engaging and how to

improve brand awareness for the institution as seen

that it takes to higher engagement rates.

Machine Learning in Customer-Centric Web Design: The Website of a Portuguese Higher Education Institution

393

REFERENCES

Analytics Help. (2023, July 12). [GA4] Automatically

collected events. https://support.google.com/analytics/

answer/9234069?sjid=10976514797204882090-EU#fi

rst_visit

Analytics Help. (2023, July 12). [GA4] Analytics

dimensions and metrics. https://support.google.com/

analytics/answer/9143382?sjid=109765147972048820

90-EU#engaged-sessions&zippy=%2Csession

Bailyn, E. (2022, March 16). Average Session Duration:

Industry Benchmarks. FirstPageSage. https://first

pagesage.com/seo-blog/average-session-duration-by-

industry/

Basha, A. (2019). A study on the effective digital marketing

strategy in education sector at Bangalore city. IJRAR

6(1), 161-169.

Bateman, T. S. (2021). Using Academic Social Networks to

Enhance the Student Experience in Online Education.

Online Learning, 25(4), 296-323.

del Rocío Bonilla, M., Perea, E., del Olmo, J. L., & Corrons,

A. (2020). Insights into user engagement on social

media. Case study of a higher education institution.

Journal of Marketing for Higher Education, 30(1), 145-

160.

Forghani, E., Sheikh, R., Hosseini, S. M. H., & Sana, S. S.

(2022). The impact of digital marketing strategies on

customer’s buying behavior in online shopping using

the rough set theory. International journal of system

assurance engineering and management, 1-16.

Harbi, A. M., & Ali, M. M. (2022). Adoption of Digital

Marketing in Educational Institutions: A Critical

Literature Review. IJCSNS, 463.

Holiday, R. (2018). Growth Hacker Marketing: A Primer

on the future of PR, marketing, and advertising. New

York, NY: Portfolio/Penguin.

Kalbach, J. (2021). Mapping experiences: A complete guide

to customer alignment through journeys, blueprints,

and diagrams. Beijing: O’Reilly.

Kusumawati, A. (2019). Impact of digital marketing on

student decision-making process of higher education

institution: A case of Indonesia. Journal of E-Learning

and Higher Education, 1(1), 1-11.

Mandalapu, V., & Gong, J. (2019). Studying Factors

Influencing the Prediction of Student STEM and Non-

STEM Career Choice. In M. Desmarais, C. F. Lynch,

A. Merceron, & R. Nkambou (Eds.), The 12th

International Conference on Educational Data Mining

(pp. 607–610).

Nuriadi, N. (2021). The Effectiveness Of Application Of

Marketing Strategies In Private Higher Education.

AKADEMIK: Jurnal Mahasiswa Humanis, 1(3), 104-

113.

Nurtirtawaty, I. G. A. S., Murni, N. G. N. S., Bagiastuti, N.

K., & Ruki, M. (2021). Digital marketing strategy

through mobile application to increase room sales At

Ibis Styles Bali Legian Hotel. Journal of Applied

Sciences in Travel and Hospitality, 4(2), 93-100.

Oré Calixto, S. (2021). The effect of digital marketing on

customer relationship management in the education

sector: Peruvian case.

Osman, A. S. (2019). Data mining techniques.

Palomino, F., Paz, F., & Moquillaza, A. (2021, July). Web

Analytics for User Experience: A Systematic Literature

Review. In International Conference on Human-

Computer Interaction (pp. 312-326). Springer, Cham.

Peling, I. B. A., Arnawan, I. N., Arthawan, I. P. A., &

Janardana, I. G. N. (2017). Implementation of Data

Mining To Predict Period of Students Study Using

Naive Bayes Algorithm. International Journal of

Engineering and Emerging Technology, 2(1), 53.

https://doi.org/10.24843/IJEET.2017.v02.i01.p11

Rajkumar, S. G., Joseph, D. C. S., & Sudhakar, D. J. C.

(2021). Digital Marketing Communication Strategies

and Its Impact On Student Higher Education Decision

Making Process–A Review Of Relevant Academic

Literature. Psychology and education, 13.

RapidMiner. (2023, July 13). Quality Measures.

https://docs.rapidminer.com/latest/studio/operators/cle

ansing/quality_measures.html

Saputro, B., Ma’mun, S., Budi, I., Santoso, A. B., & Putra,

P. K. (2021). Customer churn factors detection in

Indonesian postpaid telecommunication services as a

supporting medium for preventing waste of IT

components. IOP Conference Series: Earth and

Environmental Science, 700(1), 012015.

https://doi.org/10.1088/1755-1315/700/1/012015

Schröer, C., Kruse, F., & Gómez, J. M. (2021). A

systematic literature review on applying CRISP-DM

process model. Procedia Computer Science, 181, 526-

534.

SimilarWeb. (2022, November). Top Education Websites

Ranking in Portugal in November 2022. SimilarWeb.

https://www.similarweb.com/top-websites/portugal/sc

ience-and-education/education/

Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why

we buy what we buy: A theory of consumption values.

Journal of business research, 22(2), 159-170.

Stone, M., & Woodcock, N. (2013). Social intelligence in

customer engagement. Journal of Strategic Marketing,

21(5), 394-401.

Tiago, M. T. P. M. B., & Veríssimo, J. M. C. (2014). Digital

marketing and social media: Why bother?. Business

horizons, 57(6), 703-708.

Uyanık, G. K., & Güler, N. (2013). A Study on Multiple

Linear Regression Analysis. Procedia - Social and

Behavioral Sciences, 106, 234–240. https://doi.org/

10.1016/j.sbspro.2013.12.027

Zhu, W., Zeng, N., & Wang, N. (2010). Sensitivity,

specificity, accuracy, associated confidence interval

and ROC analysis with practical SAS implementations.

NESUG Proceedings: Health Care and Life Sciences,

Baltimore.

KDIR 2023 - 15th International Conference on Knowledge Discovery and Information Retrieval

394