The influence of websites user engagement on the development of digital competitive advantage and digital brand name in logistics startups

Abstract

Logistics startups gradually rely on digital marketing strategies to acquire a competitive advantage. The main aim of Logistics startups is to increase their digital brand name and user engagement in order to acquire a competitive advantage. To the completion of this target, various digital marketing strategies could be implemented to ensure a differentiating factor. A three-stage data-driven methodology was adopted to evaluate the contribution between the parameters and to reflect strategies that can be presented to improve the web-site's user engagement and digital brand name. The first part of the study collects data from nine logistics startups' websites over a period of 180 days. The second part of the study employs Fuzzy Cognitive Mapping (FCM) to develop an exploratory diagnostic model that visually depicts the cause-and-effect relationships between the metrics under consideration. In the last part of the study, a predictive simulation model has been created to present the intercorrelation between the examined metrics and to present possible optimization strategies. According to the findings of this study, Logistics startups' websites must be developed with fewer web pages and need to be focused on the customers' target. Additionally, in contradiction with other industries' websites, logistics startups must maintain a steady flow of digital advertisements to optimize brand name and profit.

Full text

The influence of websites user engagement on the development of digital
competitive advantage and digital brand name in logistics startups
Damianos P. Sakas
a
, Dimitrios P. Reklitis
a,
*, Nikolaos T. Giannakopoulos
a
, Panagiotis Trivellas
b
a
BICTEVAC LABORATORY Business Information and Communication Technologies in Value Chains laboratory, Department of Agribusiness and Supply Chain
Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 118 55 Athina, Greece
b
Organizational Innovation and Management Systems, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social
Sciences, Agricultural University of Athens, 11855 Athens, Greece
ARTICLE INFO
Article History:
Received 8 January 2023
Revised 7 June 2023
Accepted 8 June 2023
Available online xxx
ABSTRACT
Logistics startups gradually rely on digital marketing strategies to acquire a competitive advantage. The main
aim of Logistics startups is to increase their digital brand name and user engagement in order to acquire a
competitive advantage. To the completion of this target, various digital marketing strategies could be imple-
mented to ensure a differentiating factor. A three-stage data-driven methodology was adopted to evaluate
the contribution between the parameters and to reflect strategies that can be presented to improve the web-
site’s user engagement and digital brand name. The first part of the study collects data from nine logistics
startups’websites over a period of 180 days. The second part of the study employs Fuzzy Cognitive Mapping
(FCM) to develop an exploratory diagnostic model that visually depicts the cause-and-effect relationships
between the metrics under consideration. In the last part of the study, a predictive simulation model has
been created to present the intercorrelation between the examined metrics and to present possible optimiza-
tion strategies. According to the findings of this study, Logistics startups’websites must be developed with
fewer web pages and need to be focused on the customers’target. Additionally, in contradiction with other
industries’websites, logistics startups must maintain a steady flow of digital advertisements to optimize
brand name and profit.
© 2023 The Authors. Published by Elsevier España, S.L.U. on behalf of AEDEM. This is an open access article
under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Keywords:
Startups
Competitive advantage
Big data
Brand name
JEL classification:
M30
M31
M37
M13
M10
1. Introduction
Investing in logistics start-ups has increased steadily over the last
ten years, with a $3.5 billion increase in 2017 solely (Wyman, 2017).
Taking this information into consideration, digital marketing could
play a crucial role in the development, establishment, and expansion
of logistics startups and in creating a competitive advantage between
them (Tajpour & Hosseini, 2021;Moroni, Arruda & Araujo, 2015;Rua
& Santos, 2022;Wongsansukcharoen & Thaweepaiboonwong, 2023).
More specifically 36% of all startups do not have a website (Shepherd,
2021) and 56.9% of the startups that had a website do not have a mar-
keting department (Campaignmonitor, 2021). This blissful ignorance
can also describe why more than 90% of those startups fail in the first
year (Shlomo & Maital, 2021). Conventional technology like the
World Wide Web, mobile devices, artificial intelligence (AI), and Big
Data record fluctuating client interests and usage habits, accelerating
commercial network expansion (Wirtz et al., 2019).
Digitalization and online markets are becoming extremely signifi-
cant in the logistics operations sector for both enterprises and organi-
zations since they affect traditional frameworks, corporate designs,
and sectoral borders (Barrett et al., 2015). Consequently, the main
question that startup entrepreneurs attempt to respond to is how the
company can survive the first year of operation. A lot of different
responses have been given, some of them focus on the entrepreneur’s
passion, purpose, and philosophy, while others focus on pragmatic
solutions such as creating a website and developing a digital market-
ing strategy (Aminova & Marchi, 2021;Rafiq, Melegati, Khanna,
Guerra & Wang, 2021).
The fact that the vast majority of startups fail in the first year of
operation (Shlomo & Maital, 2021) provides a fertile ground for
research. The main points of the study can be located in the adoption
of a Fuzzy cognitive map that helps to a no-cost examination of users’
behavior in logistics startup websites as well as the implementation
of a predictive simulation for digital marketing campaigns in order to
* Corresponding author.
E-mail addresses: d.sakas@aua.gr (D.P. Sakas), drekleitis@aua.gr (D.P. Reklitis),
n.giannakopoulos@aua.gr (N.T. Giannakopoulos), ptrivel@aua.gr (P. Trivellas).
https://doi.org/10.1016/j.iedeen.2023.100221
2444-8834/© 2023 The Authors. Published by Elsevier España, S.L.U. on behalf of AEDEM. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)
European research on management and business economics 29 (2023) 100221
www.elsevier.es/ermbe

increase brand name and profit and optimize user experience that
can also be adopted from marketeers for free. This is crucial since the
vast majority of startups either have budget restrictions or even
worse do not have a marketing department (Saura, 2021). Addition-
ally, there is limited relative research on the past that incorporates
the digital marketing strategy with the competitive advantage for
startups in general (Teixeira et al., 2018).
Negrutiu et al. (2020) highlighted through their research that a
digital competitive advantage for logistic firms, in the air forwarding
sector, could be obtained from the existence of both sales and dis-
patch departments. Our paper also studies logistic firms’digital
advantage but instead focuses merely on startups and the exploita-
tion of big data. On this basis, Cichosz et al. (2020), researched the
enhancement of digital logistics firms’operation efficiency, so as to
gain a competitive advantage. Their study indicated that such a factor
could be the increasing utilization of IT applications and strategies.
To this point, our research aims to explore the potential benefits of
big data and logistic startups’user engagement to define such IT
applications and strategies capitalization. Zielske et al. (2022) pointed
out that fresh logistics startups (with fewer than 5 years of opera-
tion), should promote their customers’value to obtain improved
financial results. The authors are keen on determining the ways to
achieve enhanced customer value and financial results for logistic
startups, mainly focusing on improving their digital brand name and
their website users’engagement levels. Those facts make this
research important since the main goal of startups is to create a brand
name as fast as possible in order to exist and flourish; for this pur-
pose, this study provides detailed optimization scenarios.
This research paper focuses on the utilization of digital marketing,
big data, and web analytics in achieving a digital competitive advan-
tage for logistic startups in a cost-efficient way since these are valu-
able tools to reach potential customers and keep the existing ones
(Sakas et al., 2022a;Wymbs, 2011;Sakas, Reklitis & Trivellas, 2023).
Subsequently, this study is organized as Section 2 illustrates the liter-
ature review; Section 3 describes the sample selection and research
methodology included in this paper.; Section 4 illustrates the results
of the statistical correlation analysis, which is depicted in the FCM
and an agent-based model (ABM) has been developed based on the
statistical analysis. Finally, Section 5, discusses the results; and Sec-
tion 6 presents the conclusions, including implications and future
research.
2. Literature review and hypotheses development
2.1. Digital marketing in logistics startups
This research paper focuses on digital marketing, big data, and
web analytics since are valuable tools to reach potential customers
and keep the existing ones (Chaffey & Ellis-Chadwick, 2016;Vogel-
zang, 2016). In order to obtain more visibility and brand awareness,
digital marketing promotes services and goods through smartphone
applications and websites (ITIF, 2021). Logistics startups benefit
greatly from the development of corporate websites and iOS/Android
apps. Digital marketing is described as the implementation of digital
technologies to build and maintain integrated and calculable commu-
nication that assistances to attract and retain clients while making
better relationships with them (Beier, 2016). Marketing managers of
logistics companies use digital technology to create a wide range of
web advertisements in order to acquire customers’attention and
improve brand loyalty (Sakas et al., 2022a;Wymbs, 2011;Tsai et al.,
2011;Akkaya, 2021;Ahmed et al., 2015;Nuseir, 2016). Another
aspect of digital marketing is social media and its ability to imple-
ment easier viral marketing which is one of the most powerful ways
of reaching new customers and creating a snowball effect (Petru
,
Pavl
ak & Pol
ak, 2019;Pucciarelli et al., 2017;Wu and Liu, 2021;Choi
et al., 2020). A more in depth analysis of the ways to promote the
digital brand name of firms is needed, based on the specific features
of logistics startups.
For startups and Small-Medium Enterprises (SMEs) in general, is
crucial to attract as fast as possible new customers and build brand
awareness to secure their sustainability in a competitive environ-
ment (Tardan et al., 2017;Dinesh & Sushil, 2019). For instance, when
customers begin to use corporate websites to book a parcel delivery
or a collection of a parcel or to track a parcel via an iOS/Android app
or computer. This behavior creates various web analytics that can be
used by marketers in order to optimize website efficiency and user
experience (Sakas et al., 2022b). User experience can be defined as
the total impression of a user with a website, in regard to how user-
friendly is the corporate website (Saura et al., 2017;Zheng et al.,
2015). Two essential factors in this arena are simplicity and speed.
People are increasingly searching for websites that are simple,
straightforward, and personalized to their preferences and interests
and all of those are necessary for the establishment of a positive cus-
tomer experience (Sakas et al., 2022a;Gardner, 2011;Butkiewicz
et al., 2015). It is highlighted by these studies that, increased user
engagement of a firms’customers can lead to enhanced levels of
brand name and sustainability.
Previous research indicates that when websites load fast, this can
boost user experience and increase the brand name (Sakas et al.,
2022a). The previous elements in order to be effective must be incor-
porated into a general digital marketing strategy (Sakas et al., 2022a;
Saura et al., 2017;Butkiewicz et al., 2015). The user experience aspect
of a marketing strategy can be described as the combination of corpo-
rate goals, technical background, and consumer demands (Butkiewicz
et al., 2015;Petrescu, Vermeir, Burny & Petrescu-Mag, 2022). In order
to utilize those data in the marketing strategy, logistics startups need
to collect and analyze data from third-party websites that redirect
the customer to their websites, social networks, and other sources to
create new personalized services for their clients (Renzi et al., 2015).
According to previous research, startups that fail to implement an
effective user experience strategy could suffer from low corporate
culture as well as fewer sales and failure in the business model (Hok-
kanen et al., 2016;Jayaram et al., 2015). The implementation of such
strategies can lead to the competitive differentiation of SMEs and
logistics startups (Jayaram, Manrai & Manrai, 2015;Bruton & Ruba-
nik, 2002;Thomas, 2019). In this direction, our research aims to pro-
pose an efficient digital marketing strategy for logistics startups to
gain a digital competitive advantage from the analysis of their web-
site user engagement.
2.2. Logistics startups, competitive advantage, and differentiation
Competitive differentiation can be defined as a strategic position-
ing strategy that a company can use to distinguish its goods and serv-
ices from those of its competitors (Stubbs, 2014;Fern
andez, L
opez-
L
opez, Jard
on & Iglesias-Antelo, 2022). One of the main drawbacks of
a company’s competitive differentiation is that every company tries
to be more innovative or improved than the competitors (Stubbs,
2014;Hausladen & Zipf, 2018). However, competitive differentiation
is the outcome of exceeding consumers’expectations (Albert, Mer-
unka & Valette-Florence, 2008). Since the majority of startups fail in
the first year of operation (Shlomo & Maital, 2021), the utilization of
marketing strategies and technics seems to be a necessity to acquire
competitive advantage and differentiation; and there are various
examples in different countries (Bruton & Rubanik, 2002;Friar &
Meyer, 2003;Potjanajaruwit, 2018). However, for startups in the
logistics market, a specific plan based on digital marketing strategies
for enhancing their website user engagement is necessary, for them
to achieve a digital competitive advantage in their field.
In contradiction with traditional marketing strategies, digital mar-
keting provides a cost-effective solution for startups since, can attract
a larger audience, faster, and with less budget (Gulati, 2019;L
opez-
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221
2

Buenache, Meseguer-Martínez, Ros-G
alvez & Rosa-García, 2022). In
digital marketing, competitive differentiation can be achieved by
advertising the right product to the right customer at the best time
(Albert, Merunka & Valette-Florence, 2008). Competitive differentia-
tion can lead to a competitive advantage. A competitive advantage
can be defined as an attribute that allows a business to gain market
share and outperform other companies in the same industry (Hagiu
& Wright, 2020). At the same time, digital competitive advantage can
be described as increased market share in the e-commerce environ-
ment, through unique digital improvements of firms’website perfor-
mance.
In this respect, user experience can play a crucial role in the acqui-
sition of competitive advantage since the user experience strategy
can assist a logistics startup in critically considering how its consum-
ers communicate with the corporate website while conducting pack-
age tracking (Hokkanen et al., 2016;V€
a€
at€
aj€
a & Paananen, 2012).
More specifically, the correct implementation of a user experience
strategy can provide the ability to deliver experiences that the com-
petition can’t match and also demonstrates to their consumers that
the company concentrates on their needs and satisfaction (Hokkanen
et al., 2016;V€
a€
at€
aj€
a & Paananen, 2012). In this manner, our research
aims to provide specific digital marketing processes to increase the
user engagement of logistics startups’websites.
2.3. Big data of logistics startups
From social sciences and business studies (Salganik, 2019;Ere-
velles, Fukawa & Swayne, 2016) to psychology (Harlow & Oswald,
2016) and healthcare (Andreu-Perez, Poon, Merrifield, Wong & Yang,
2015), big data analysis is becoming increasingly popular. There are
various definitions and attempts to describe big data. Big data could
be defined as vast amounts of complicated, and unstructured infor-
mation that require complex techniques and methods to acquire,
store, distribute, manage, and analyze this information (Favaretto, De
Clercq, Schneble & Elger, 2020). Given the fact that most startups col-
lapse in the first year of operation (Shlomo & Maital, 2021) and have
been reluctant to adopt the new technology of big data analytics,
exposing them to the risk of being left behind (Coleman et al., 2016).
Startups and SMEs face various challenges in implementing big data
and analytics. The most profound is:
Lack of understanding (Beaver et al., 2010;Sendra-Pons et al.,
2022). A clear example is that 36% of all startups don’t have a web-
site (Shepherd, 2021). That highlights the ignorance of using big
data.
Lack of expertise. The vast majority of startups don’t have a dedi-
cated digital marketing department (Campaignmonitor, 2021).
That leads to fewer sales which leads to less profit.
Lack of organizational models (Beaver et al., 2010;Turienzo et al.,
2023). Many startups and SMEs do not have a clear organizational
structure which leads to confusion. The main prerequisite for a
successful implementation of a big data analytics strategy is an
appropriate management structure (McAfee & Brynjolfsson,
2012).
The implementation of big data analytics seems to be a necessity
for the vitality of startups. The above challenges have led to the
increasing demand of effective context for logistic startups that are
able to provide them a digital competitive advantage in their market.
Some of the big data analytics and techniques are text analytics, video
analytics, social media analytics, and predictive analytics (Gunase-
karan et al., 2017;Gandomi & Haider, 2015;Shah et al., 2018). Predic-
tive analytics can be applied to almost any discipline (Schulte-Althoff,
F€
urstenau & Lee, 2021;Neubert, 2018;Sodero, Jin & Barratt, 2019;
Waller & Fawcett, 2013). Predictive analytics refers to a group of
techniques that forecast future results based on historical data
(Gandomi & Haider, 2015). Text analytics refers to the extraction
technics that can be used to obtain data from a text, such as from
financial websites, and social networks (Hu & Liu, 2012;Chung,
2014). Generally, big data analytics could potentially assist logistics
startups to achieve a digital competitive advantage, if adopted prop-
erly, through analytical modeling of the firms’environment and its
strategic processes.
A range of methodologies and techniques are used in video ana-
lytics to extract, gather and analyze useful data from video content
(Gandomi & Haider, 2015;Jiang, Ananthanarayanan, Bodik, Sen &
Stoica, 2018). Those techniques can be applied in stored videos, such
as YouTube videos, or in real-time videos, such as political speeches
(Walsh, O’Brien & Slattery, 2019;Beraldo & Milan, 2019). Finally, the
extraction and interpretation of data from social media platforms are
defined as social media analytics (Moe & Schweidel, 2017). The
extraction and interpretation of data from social media platforms are
defined as social media analytics. Social media is a general term
applied to various mobile applications (Android/iOS) and websites
that allows the creation and sharing of content by users (Lee, 2018).
A mere focus is put on the experience and engagement of website
users, thus it indicates a path for developing effective digital market-
ing strategies for achieving a digital competitive advantage for firms.
The examination of big data analytics provides valuable insight
into the users’experience in a logistics website (Sakas & Reklitis,
2021a;Sakas et al., 2022a;Sakas et al., 2023). The abundance of big
data generated by a variety of websites and social media has been a
great source of knowledge for user experience strategists. By evaluat-
ing these huge amounts of data, developers may offer more efficient
solutions to their clients (Park, 2019). The most significant benefitof
utilizing Big Data for user experience is that it encompasses all
aspects of data supplied by visitors (Palomino et al., 2021). For
instance, it is useful to know the average time that a visitor spends
on their website as well as how many web pages are accessed until
they find the resolution (Palomino et al., 2021;Merendino et al.,
2018). The usage of big data and web analytics is beneficial since
those data contain useful information gathered without any possible
cognitive biases (Chitkara & Mahmood, 2020). The above examples of
Big Data capitalization highlight the importance of their role in
achieving a digital competitive advantage over firms’antagonists.
2.4. KPIs, web analytics, and user engagement in logistics startups
Web analytics plays a crucial role in startups’and SMEs’success
since a website’s traffic performance can be extracted and get mea-
sured in order to optimize digital marketing strategy (Chitkara &
Mahmood, 2020;Moral, Gonzalez & Plaza, 2014). Additionally, the
analysis and implementation of web analytics have a positive impact
on startups’digital advertisement campaigns because the correct
(available) customer can be reached at the correct time (Chitkara &
Mahmood, 2020;Moral, Gonzalez & Plaza, 2014). The web analytics
that is extracted from logistics startups’websites and used in quanti-
tative form can be described as key performance indicators (KPIs)
(J€
arvinen, Tollinen, Karjaluoto & Jayawardhena, 2012;Saura, Palos-
S
anchez & Cerd
aSu

arez, 2017;Chaffey & Patron, 2012).
A key performance indicator (KPI) can be defined as a perfor-
mance measurement that assesses a company’s growth in specific
activities that it manages (Saura, Palos-S
anchez & Cerd
aSu

arez,
2017). Web analytics KPIs are often used to evaluate various objective
targets with the performance of a website (Saura, 2021). There are
two types of web analytics KPIs, behavioral, such as average visit
duration, and the technical, such as fully loaded time (Sakas et al.,
2022b). The user’s behavior and activity are extracted in quantitative
form. For instance, has been gathered metrics such as organic traffic,
average visits duration, pages per visit, paid traffic, user engagement,
and global rank.
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221
3

The user’s behavior on a website is described as User Engagement
(Sakas et al., 2022b). ``User engagement’’ relates to the comprehen-
sion of the person’s feelings, that is, the user’s emotions in specific sit-
uations like love and death, as well as the comprehension of the
person’s“cognitive engagement,”which explains regular occurrences
(Bobek, Zaff, Li & Lerner, 2009). The user engagement metric is made
from several behavioral metrics, including “Average Time on Site”,
“Pages/Vitis”,“Total Visitors”and “Bounce Rate”(Drivas, Kouis, Kyr-
iaki-Manessi & Giannakopoulou, 2022;Saura, Palos-S
anchez & Cerd
a
Su
arez, 2017). On the other hand, the technical programming factor
of a startup such as a website’s loading time can provide valuable
insights for sales (Kaur, Kaur & Kaur, 2016). According to previous
research, a customer will not wait for a webpage to load for 45 s to
buy a product despite how good the product offered by this website
(Kaur, Kaur & Kaur, 2016;Sakas et al., 2022b).
Moreover, regarding the measurement of digital competitive advan-
tage, specific website metrics have been chosen as KPIs that can present
the efficiency of a company’s digital performance. These metrics are the
global rank and organic traffic variables, which shows a firm’swebsite
rank and the amount of traffic that visits its website through search
engines. The enormous amount of digital marketing campaigns, com-
bined with a lack of understanding of web metrics, makes it difficult for
KPIs to meet the necessary criteria (Kirsh & Joy, 2020). This research
investigates the Web analytics KPIs presented in Table 1.
2.5. Research approach
In order to proceed to further analysis of how logistic startups can
achieve a competitive advantage from their website usage, a review
of relevant empirical studies needs to be referred to. E-commerce
industries have proven to be benefited from low supplier power, thus
highlighting the sector’s path for acquiring a competitive advantage
over firms’competition (Purbasari et al., 2020). By investing in rela-
tionships with their suppliers and keeping supplier costs low, logistic
startups can achieve a competitive advantage. Moreover, Negrutiu
et al. (2020) state that startups in the freight forwarding sector could
capitalize on new technologies and sustainable retail activities to
improve customers’utility and experience in order to achieve a com-
petitive advantage in digital commerce. Our study also seeks to inves-
tigate the utilization of e-commerce and new technologies through
big data analytics to improve logistics startups users engagement,
thus gaining them a competitive advantage in the digital world.
Regarding logistic startups’financial approach, an improvement
could be noted by adopting innovative financial methods like block-
chain finance and thus, according to Korpysa et al. (2021),an
increased corporate performance could be set leading to a competi-
tive advantage for the firm in the market sector. Moreover, Zielske
and Held (2022) found that the usage of various innovative agile
methods of process management contributed to enhanced connec-
tion with the customer by decreasing corporate response time to
market. This means that new firms operating in the logistics sector
should seek customers engagement to achieve a competitive advan-
tage. The methodological framework adopted by the authors aims to
provide sufficient insights to logistics firms and especially startups to
achieve such an advantage.
Additional related research examined the marketing content of
various organizations and discovered that using emotions in adver-
tisements can lead to an improvement in brand equity and competi-
tive advantage (Hutchins & Xiomara, 2018). Other studies highlight
the necessity of the analysis and the implementation of big data in
the corporate marketing strategy in order to acquire an advantage
(Sun, Hall & Cegielski, 2020) and a link found between correctly
placed advertisements with the forecast purchase intention that
leads to gain market share (Lin, Paragas & Bautista, 2016;Grewal,
Bart, Spann & Zubcsek, 2016;V
azquez-Martínez, Morales-Mediano &
Leal-Rodríguez, 2021). A connection between the effect of advertise-
ments of firms and big data utilization is the basis of our study, hence
the authors propose a plethora of procedures based on logistics start-
ups’digital marketing initiatives to enhance their brand equity.
To acquire digital competitive advantage and differentiation,
social media marketers benefit from the opportunity to gather, ana-
lyze and use, social media big data and web analytics (Dwivedi et al.,
2021). This study from Dwivedi et al. (2021), opens the way for our
research methodology that is based on exploiting web analytical data
from logistics startups’websites. Hence, having analyzed the above
relevant studies the authors were mainly focused on exploring the
characteristics that synthesize a digital competitive advantage for
startups in the logistic sector, by providing a handful of intel to sup-
port the enhancement of the startups’financial performance.
2.6. Research hypotheses
Logistics startups operate in a competitive environment, and they
must discover and analyze all those factors that affect their profitabil-
ity, and brand name. In order to accomplish higher levels of brand
name, startups have to take into consideration the KPIs that affect
the user engagement of their websites with the visitors. Some of the
main KPIs that affect user engagement are Bounce Rate, Average
Time on Site, Pages per Visit, and the total visitors. It is extremely cru-
cial for startups to understand the effectiveness of their organic traf-
fic, as this parameter has a significant impact on user engagement, as
well as the inverse.
This study attempts to address this field by investigating the fac-
tors that affect user engagement, as well as the web analytics that
affects the brand name and the profit. The findings of this study could
produce useful suggestions on the effectiveness of logistics startups’
website activities. The findings could allow:
Table 1
Presentation of the extracted Web analytics KPI’s.
Web Analytics KPIs Description of the Web Analytics KPIs
Global Rank This KPI generated from the platform’s total traffic.
The smaller the number of global rank, the greater
the websites fame and brand name. (Drivas, Kouis,
Kyriaki-Manessi & Giannakopoulou, 2022;Atten-
tioninsight, 2021).
Organic Traffic Organic traffic relates to users who enters on the
website via an unpaid search engine (Baye, De los
Santos & Wildenbeest, 2016a,2016b).
Bounce Rate Bounce rate generated when a visitor accesses a web-
site and then instantly leaves without watching
anything else in this the website. Low numbers of
bounce rate indicate that the webpage is much
more productive (Wang, Li, Cai & Liu, 2021;Dolma,
Kalani, Agrawal & Basu, 2021).
Average Time on Site This KPI calculates the average time a visitor spends
on a website in seconds (Semrush, 2022).
Pages per Visit When visitors access a website, they visit a number of
pages, the total amount of pages that accessed per
visitor is calculated by the KPI named “Pages per
visits”(Plaza, 2011).
Paid Traffic The KPI Paid Traffic is produced exclusively through
paid ways. When a person clicks an advertisement
in google search redirects to the company’s website
(Fossen & Schweidel, 2019).
Total Visitors The KPI Total Visitors refers to the total amount of
user accessed a website per day. This amount is
generated while each user’s IP address is counted
via a cookie (Parmenter, 2015).
Social Traffic The KPI "Social traffic" refers to traffic that generated
from social media platforms (Facebook, Instagram,
Twitter) to the logistics website’s(Drivas, Kouis,
Kyriaki-Manessi & Giannakopoulou, 2022;Fossen &
Schweidel, 2019).
Gross Profit This KPI monitors the gross profit performance pro-
vided by the companies (Cascade, 2021).
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221
4

Marketeers to extract, analyze and use web analytics and big
data which could be beneficial to reorganize and reform the com-
pany’s marketing plan. Startups can benefit from the correct imple-
mentation of a digital marketing strategy because through
advertisements and campaigns, startups can reach potential cus-
tomers faster (Ghasemaghaei and Calic, 2020;Tardan, Shihab &
Yudhoatmojo, 2017;Shlomo & Maital, 2021).
Website developers to gain a comprehensive knowledge of the
effects of their website’s user engagement on brand name and sales
in order to create a better website that fits the purpose. For instance,
it’s unimportant how good and innovative are the selling products if
the website needs 45 s to load a page. Additionally, through the
knowledge acquired from web analytics and big data, they could
more actively contribute to the company’s operations and strategies.
Decision-makers take into consideration the added value of those
analytics and incorporate them into sales and marketing strategies to
optimize profit and create future digital investments (Mariani & Fosso
Wamba, 2020).
As a result, five hypotheses have been developed in order to
broaden the knowledge about the importance of the implementation
of web analytics and its effects on a brand name, profit, and user
engagement.
Hypothesis 1. (H1). The “Organic Traffic”of Logistic Startups websites
affects the “Global Rank”variable of Logistic Startups websites through
their “Total Visitors”metric.
This hypothesis focuses on determining whether “Organic Traffic”
and “Total Visitors”as parameters of User Engagement impact the
“Global Rank”in order to find potential intercorrelations between
the examined web analytics. Furthermore, the purpose of this
hypothesis is to identify if only those two parameters of user engage-
ment affect the total rank without taking into consideration other
engagement parameters.
Hypothesis 2. (H2). The “Paid Traffic”of Logistic Startups websites
affects the “Global Rank”variable of Logistic Startups websites through
their “Search Traffic”metric.
This hypothesis focuses on determining whether “Global Rank”is
affected by the intercorrelation of “Search Traffic”from social media
and “Paid Traffic”. This hypothesis is important because will produce
interesting results on digital advertising in logistics startups. It is essen-
tial for startups; especially the ones with a limited budget; to identify if
there is a necessity to place paid advertisements to search engines or
social media and up to what extent and if this advertisement will result
in better website ranking and brand name consequently.
Hypothesis 3. (H3). The “Pages per Visits”metric of Logistic Startups
websites affects the “Global Rank”of Logistic Startups websites through
their ``Average Time on Site’’ metric.
This hypothesis attempts to identify whether the “Global Rank”is
affected by the intercorrelation of two User engagements’parame-
ters, the “Pages per Visits”and “Average Time on Site”. It is crucial
for startups to identify what is the main User engagement parameter
that plays a crucial role in the digital brand name (Global Rank).
Hypothesis 4. (H4). The “Bounce Rate”of Logistic Startups websites
affects the “User engagement”variable of Logistic Startups websites
through their “Organic Traffic”metric.
According to previous research, “User Engagement”can be
defined as the total of all behavioral metrics on a webpage such as
Average Time on Site, Total Visitors, Pages per Visits. This hypothesis
attempts to identify what are the implications when a user leaving
from a startup website (“Bounce Rate”) and what are the effects on
``User Engagement’’ and “Organic Traffic”metrics.
Hypothesis 5. (H5). The “User Engagement”of Logistic Startups web-
sites affects the “Gross Profit”variable of Logistic Startups websites
through their “Global Rank”metric.
This hypothesis is if the total ``Gross Profit’’ is affected by fluctuations
of the “User Engagement”and “Global Rank”metrics and to what
extent. This hypothesis attempts to identify also it is beneficial for a
startup to invest in a creation of a better website with an emphasis on
the development of the user engagement parameters. The following
Fig. 1 illustrates the conceptual framework of the examined hypothesis.
Fig. 1. Conceptual framework of the involved metrics that lead to the formulation of the examined hypothesis.
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3. Methodology
This research paper attempts to implement a methodological
approach for establishing a holistic framework for logistics startup
marketing departments regarding the benefits of advertising on
search engines. In this respect, data has been gathered from the nine
biggest logistics startups (Seedtable, 2023;Explodingtopics, 2022);
based on size and profit; and analyzed with the assistance of analyti-
cal tools. This allowed us to identify the potential relationship
between the examined metrics gathered from the logistics startups’
websites.
Furthermore, an exploratory model has been implemented in
order to measure the important correlations of web analytics using
Fuzzy Cognitive Mapping (FCM) (Kireev, Rogachev & Yurin, 2019).
From there, we moved on to the creation of an agent-based simula-
tion and prediction model which used the above statistical analysis
results to analyze the effect of the aforementioned metrics and pre-
dict the behavior of the users on the websites for the next 180 days.
As a result, the first step of the methodology is primarily directed to
advise logistics startups marketing managers about significant
behavioral Web analytic metrics that have a significant impact on
their brand name and profitability. As a result, following data collec-
tion, we analyzed their integration with the study’s selected KPIs.
The second step of the methodology concentrated on the histori-
cal values of the examined metrics and their statistical analysis,
where variabilities in the web analytic metrics of startups websites
seem to create interesting predictions for fluctuations in startups’
user engagement, profit, and global rankings. This could also be
determined using FCM that can provide accurate guidelines for start-
ups’digital advertisements related to a particular decision-making
process. The methodology’sfinal step was to create and run the pre-
dictive model with the web analytics’intercorrelations extracted
from SPSS. Finally, this research attempts to discover plausible rela-
tionships between logistics startups’websites’organic traffic, user
engagement metrics, profit, and ranking web analytic metrics.
3.1. Sample selection and data retrieval
At this point, the authors conducted a daily, 6-month longitudinal
study to better understand the users’behavior on a logistics startup’s
website (Zhu, Dong & Luo, 2021) after assessing the subject several
times throughout the period. Consequently, two of the main charac-
teristics of the empirical research are the “recreation”of the study
and the “generalization”of the finding on a larger scale (Hubbard &
Lindsay, 2002;Zhu, Dong & Luo, 2021). In this study, the authors
extracted big data from 9 logistics startups of 2022 (Seedtable, 2023;
Explodingtopics, 2022); based on size and profit; with the assistance
of the web analytics tools Alexa and SEMRush which satisfies this
characteristic, since other researchers can follow the same process
and extract the same data. Additionally, empirical research has been
used since is a reliable method of investigation that eliminates the
possibility of data misinterpretation and isolates any possible cogni-
tive bias which is crucial for big data studies (Zhu, Dong & Luo, 2021).
At this point in the research, we gathered web analytics data from
nine logistics startups’websites. Web analytics data gathered from
all those websites relates to metrics from website users and their
behavior in the webpage such as pages per visit and average time on
site. To accomplish this, the authors, used decision support systems
as well as two platforms that extract these analytics, SEMrush, and
Alexa. Those data have been observed and extracted daily for a period
of 180 consequent days, which allows a more accurate analysis of the
examined metrics.
The logistics startups web analytics that gathered from SEMrush
and Alexa are: Wolt, Urbantz, Sennder, Paack, Lalamove, Byrd,
FourKites, Centrica, Cargo.one. Since the focus is located on the
user experience, the authors extracted the behavioral big data
(average time on site, pages per visit) from the two web analytics
platforms in order to assess the experience without making a ques-
tionnaire and asking questions that might lead to various opinions
and consequently to biased results (Power, Cyphert & Roth, 2019).
As Table 2 illustrates, we gathered behavioral data for 4.717.930
visitors in total over a six-month period for 9 logistics startup web-
sites. Some other behavioral KPIs that were used for this purpose
were “organic traffic”,“bounce rate”,“average time on site”and
“pages per visit”. Those data were gathered for every company
daily from the web analytics platforms and after six months we ini-
tiate the statistical analysis process followed by the creation of a
fuzzy cognitive map and agent-based model.
3.2. Diagnostic and exploratory model development
The gathering of web analytics from the startup’s webpages, fol-
lowed by statistical analysis, revealed the presence of correlations
between the extracted data. An FCM is a macro-scale analysis model
that has been developed to illustrate the power of the correlations,
which could be used in the development of an effective marketing
strategy as well as on the decision-making process (Salmeron, 2009).
The primary goal of developing the FCM is to create a visual illustra-
tion of the positive or negative intercorrelations, as well as the cause-
and-effect relationships between the web analytics that are under
consideration (Salmeron, 2009). By illustrating the correlation
between the metrics, FCM offers the ability of a macro perspective on
the development of a better digital marketing strategy.
3.3. Adoption of an agent-based model
After conducting a macro-scale analysis which depicted an overall
description of the study, a micro-scale analysis was also required in
order to provide a complete picture of the situation. ABM could
indeed produce an effective simulation by displaying the dynamic
relationships between key variables (Giabbanelli, Gray & Aminpour,
Table 2
Descriptive Statistics of the 9 logistics startups companies’websites, during six months.
Mean Min Max Std. Deviation
Logistics Startup’s Organic Traffic 286,053.37 2621 1,917,808 581,396.62
Logistics Startup’s Paid Traffic 61,434.42 0.00 746,343 159,267.07
Logistics Startup’s Average Time on Site 1098.64 2.00 3683.00 998.57
Logistics Startup’s Bounce Rate 0.545 0.076 0.889 0.191
Logistics Startup’s Pages/Visit 3.028 1.110 10.245 2.003
Logistics Startup’s Total Visits 1,007,893.01 1559.00 4,717,930.00 1,372,863.01
Logistics Startup’s Global Rank 237,118.25 5671.19 648,538.87 184,626.53
Logistics Startup’s User Engagement 104,404,650,028.76 4231.89 665,427,256,800.00 179,098,783,737.02
Logistics Startup’s Profit Logistics Startup’s Social Traffic 6.869 23,430.92 .38 0.00 18.85 153,764.00 6.812 43,064.64
N= 180 observation days for 9 logistics startups websites.
The Pearson’s Coefficients for the (H1) are shown in Table 3.
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D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

2017). ABM offers the options to estimate the influence of logistics
startups websites’user engagement parameters such as average time
on site, and pages per visit on their global rank and profit, during a
180-day duration.
An ABM works from the interaction of various characters (agents)
which are characterized by specific characteristics and run inside the
system elicited from predefined “if-then”rules (Barbati, Bruno &
Genovese, 2012). Various previous research that implemented rele-
vant methodological approaches has adopted this method, especially
in the digital marketing field (Kavak,Padilla, Lynch & Diallo, 2018).
The implementation of ABM is advantageous for marketing managers
as the simulation that is produced, promotes the improvement of the
existing marketing plans (Zhang & Vorobeychik, 2019;Kavak, Padilla,
Lynch & Diallo, 2018). In general, an Agent-based model monitors
organic traffic and then produces with the assistance of the statistical
analysis behavioral data of the user’s activity on the website (Sakas &
Reklitis, 2021a).
4. Results
4.1. Statistical analysis
This chapter exhibits the outcomes of the data collection plat-
forms. IBM SPSS Statistics 23 was utilized for the statistical analysis
of this article. The findings in question are made up of raw data (gath-
ered from nine logistics startups webpages), as shown in Table 1,
which have been examined using statistical analysis software. After
the data gathering of the logistics websites, the extracted data from
every category was merged in order to conduct a per sector data
analysis. Table 2 displays the descriptive statistics for the collected
web analytics metrics, more specifically, the standard deviation, min,
max, and mean. These values are the outcome of web analytics met-
rics during a 180-day duration. For instance, the “Logistics Startup’s
Pages/Visit”means, is the mean of all nine startups’webpages over
180 days.
Regarding the (H1) on the Table 3, a significant positive correla-
tion with r¼0:945 was found between Visitors and Organic traf-
fic, indicating that when the Organic traffic reach higher levels more
users access the websites. Additionally, significant negative correla-
tions have been found between the Global Rank and the Organic traf-
fic with r¼:584  and between the Global Rank and Visitors
with r¼:501 :When Organic traffic and Visitors reach higher
levels the Global rank takes lower prices. This is a good sign since
lower levels of global ranks provide a better brand name to the com-
pany. For instance, when the global rank is 2 (lower level) is better
than a company with a global rank of 15 (higher level). The Regres-
sion for the (H1) is illustrated in Table 4. Regression analyses are
statistically significant, with p values of less than 5%. The model is
not significant and with every 1% rise of Global Rank and total Visi-
tors, organic traffic decreases by 14,8% and increases by 87.1% respec-
tively.
As for the (H2) on the Table 5, a significant positive correlation
with r¼0:724 was found between Paid Traffic and Social traffic,
showing that, when an advertisement is placed on a search engine
benefits the company not only with visitors that access the webpage
through the advertisement but also with more visitors from the web-
site’s social networks. Moreover, a significant negative correlation
has been found among the Global Rank and the Paid traffic with r¼
:475 . That practical means, the paid advertisements provide a
beneficial result to the company’s brand name through climbing the
site on higher levels, for instance from 12th place to 11th. Finally, a
significant positive correlation with r¼0:624  was found
between Global rank and social traffic, showing that, when an adver-
tisement is placed on a social media website profits the company’s
brand name. The Regression for the (H2) is illustrated in Table 6.
Regression analyses are statistically significant, with p values of less
than 5%. The model is significant and with every 1% rise of Paid Traffic
and Social Traffic, Global Rank decreases by 4,9% and by 58.9% respec-
tively.
In regard to the third hypothesis (H3) on the Table 7, a significant
positive correlation with r¼ 0:370 was found between Global
rank and Average Time on Site, representing that company’s brand
name can benefit if the user stays more time on the website. Addi-
tionally, non-significant correlations have been found between the
Global Rank and the Pages per Visits with r¼:046 and between
the Average Time on Site and Pages per Visits with r¼0:054:That
practically means, it’s unimportant for the company’s brand name
how many web pages will be viewed per visitor on the website. The
Regression for the (H3) is illustrated in Table 8. Regression analyses
are statistically significant, with p values of less than 5%. The model is
not statistically significant and by every 1% rise of Average Time on
Site and Pages per Visits, Global Rank increases by 37.4% and
decreases by 6.6% respectively.
Regarding the, the (H4) on the Table 9, a significant positive corre-
lation with r¼0:694  was found between Organic traffic and
User Engagement, meaning that when a visitor has a positive feeling
while browsing on a website will access it again. Additionally, non-
significant correlations have been found between the Bounce Rate
and the Organic traffic with r¼0:030, and between the User
Engagement and Bounce Rate with r¼0:107:When a user
doesn’tfind the content attractive or doesn’t feel good on the web-
page (user engagement) will leave from the webpage (Bounce Rate),
which illustrates the user’s expected behavior. The Regression for the
(H4) is illustrated in Table 10. The model is not significant and with
Table 3
Coefficients between the examined metrics for H1.
Correlations Global Rank Visitors Organic traffic
Global rank 1
Visitors 0.501** 1
Organic traffic0.584** .945** 1
** Correlation is significant at the 0.01 level (1-tailed).
Table 4
Regression for H1.
Variables Standardized Coefficient R
2
F p Value
Constant (Organic Traffic) −.909 255.089 .812
Global Rank 0.148 .760
Visitors .871 <0.001
The Pearson’s Coefficients for the (H2) are illustrated in Table 5.
Table 5
Coefficients between the examined metrics for H2.
Correlations Global Rank Paid Traffic Social traffic
Global Rank 1
Paid Traffic0.475** 1
Social Traffic0.624** .724** 1
** Correlation is significant at the 0.01 level (1-tailed).
Table 6
Regression for H2.
Variables Standardized Coefficient R
2
F p Value
Constant (Global Rank) −.391 16.338 <0.001
Paid Traffic0.049 .760
Social Traffic0.589 <0.001
The Pearson’s Coefficients for the (H3) are displayed in Table 7.
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D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

every 1% rise of Organic Traffic and Bounce Rate, User Engagement
increases by 69.8% and decreases by 12.7% respectively.
As for the final Hypothesis (H5) on the Table 11, a significant neg-
ative correlation with r¼:381  was found between User
Engagement and Global Rank, meaning that when a user has a posi-
tive feeling for the logistics startup website, will access it again and
this behavior contributes to better Global rank levels which directly
contribute to the brand name. Additionally, a significant negative cor-
relation with r¼:433  was found between User Engagement
and Profit:That interestingly illustrates, when a user spends more
time on a website or browses into different pages on the website has
fewer possibilities to financially contribute, buy a product or a ser-
vice. Practically means, that the websites need to be more technically
effective, easy to use, and goal-oriented (to sales or services) instead
of amusing the customers. Finally, a non −significant correlation had
been found between the Global rank and the Gross Profit. The Regres-
sion for the (H5) is illustrated in Table 12. The model is significant and
with every 1% rise of Global Rank and Gross Profit, User Engagement
decreases by 38.3% and increases by 43.5% respectively. The Durbin-
Watson test, according to Savin and White (1977), is the most com-
mon way to assess the assumption of independence. The test statistic
has a value between 0 and 4, and lower values suggest that consecu-
tive residuals are positively connected. The Durbin-Watson test was
applied for all hypotheses to assess for linear regression dependence.
The Durbin-Watson test for the first hypothesis is 2189. The Durbin-
Watson test for the H2 is 1038, the H3 is 1072, and the H4 is 1994.
Finally, the Durbin-Watson test for H5 is 1484. The hypotheses H2
and H3 are close to 1000 which means that although the hypothesis
is accepted, generalizations must be made with caution.
Additionally, the connection between the independent and the
dependent variables is characterized as an nth-degree polynomial in
x in polynomial regression. The fitting of a nonlinear connection
between the value of x and the conditional mean of y is described by
polynomial regression. It was typically equivalent to the least-
squares approach. According to the Gauss Markov Theorem, reduces
the variance of the coefficients (Lindgren et al., 2011). This is a form
of Linear Regression in which the dependent and independent varia-
bles have a curvilinear connection, and the data is fitted with a poly-
nomial equation (Boryga & Grabo
s, 2009). In this case, the
dependence between input and output variables can be described by
a higher degree polynomial.
4.2. Fuzzy cognitive map formation
The statistical correlations discovered in Section 3.1 were used to
construct the FCM. Figure 2 illustrates the FCM model that has been
developed with the assistance of a Mental Modeler. Blue lines repre-
sent positive correlations and red lines the negative ones. The thick-
ness of a line represents how strong the correlation is. The plus sign
indicates a positive connection between the two measurements,
whereas the minus sign indicates a negative correlation. This
approach was utilized in this article since it has been widely included
in previous research on Search Engine Optimization (SEO) and Search
Engine Marketing (SEM) (Giabbanelli, Gray & Aminpour, 2017;Sakas
et al., 2022b). For instance, as indicated in Fig. 2, User Engagement
can be increased by a rise of organic traffic and can decrease by the
percentage of the users that exit the page (bounce rate).
4.2.1. Adoption of fuzzy cognitive map scenarios to analyze the data
Following the creation of the FCM model, 5 scenarios were con-
ducted to evaluate the projected discrepancies in the KPIs of logistics
startups' websites throughout various stages of the human interac-
tion with the website. The hyperbolic tangent function was adopted
for all cases since provides the ability to illustrate values that can be
negative and fall inside the range (1, 1) (Kokkinos, Lakioti, Papa-
georgiou, Moustakas & Karayannis, 2018). Figures 3,4 and 5 illustrate
three optimization scenarios. Fig. 3 illustrates, after a lot of trials, the
User Engagements optimization scenario for the logistics startups
industry according to the extracted web analytics and the statistical
analysis. To achieve 10% better user engagement and 10% more visi-
tors, startups need to reduce the Pages per Visits metric by 36% and
this can be accomplished by creating a website with fewer pages and
easy to use. Additionally, an expected increase by 8% in Average Time
on Site can be observed as well as an increase in the Bounce Rate by
20%. Interestingly, a decrease in the “Gross Profit”by 5% is expected
and will be explained in the next section.
Table 7
Coefficients between the examined metrics for H3.
Correlations Global Rank Pagers per Visits Average Time on Site
Global Rank 1
Pagers per Visits 0.046 1
Average Time on Site 0.370** .054 1
** Correlation is significant at the 0.01 level (1-tailed).
Table 8
Regression for H3.
Variables Standardized Coefficient R
2
F p Value
Constant (Global Rank) −.141 4.201 <0.001
Average Time on Site .374 .006
Pages per Visits 0.066 .614
The Pearson’s Coefficients for the (H4) are illustrated in Table 9.
Table 9
Coefficients between the examined metrics for H4.
Correlations User Engagement Bounce Rate Organic traffic
User Engagement 1
Bounce Rate 0.107 1
Organic traffic .694** .030 1
** Correlation is significant at the 0.01 level (1-tailed).
Table 10
Regression for H4.
Variables Standardized Coefficient R
2
F p Value
Constant (User Engage-
ment)
−.498 25.336 .051
Organic Traffic .698 <0.001
Bounce Rate 0.127 .205
The Pearson’s Coefficients for the (H5) are illustrated in Table 11.
Table 11
Coefficients between the examined metrics for H5.
Correlations User Engagement Global Rank Gross Profit
User Engagement 1
Global Rank 0.381** 1
Gross Profit0.433** .006 1
** Correlation is significant at the 0.01 level (1-tailed).
Table 12
Regression for H5.
Variables Standardized Coefficient R
2
F p Value
Constant (User Engage-
ment)
−.334 12.796 .005
Global Rank 0.383 .002
Gross Profit .435 <0.001
8
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

Figure 4 illustrates a website development optimization scenario.
More specifically, examined what will happen if the website develop-
ers manage to develop a user-friendly website, with less information,
that reduces the Average Time on Site by 25% and increase Organic
Traffic by 5%. The bounce rate will increase by 15% which is expected
since when more visitors access the website increase the possibility
of exiting. Also, a decrease in Pages per Visit by 9% can be observed.
This scenario aligns with previous research (Krrabaj, Baxhaku &
Sadrijaj, 2017;Pakkala, Presser & Christensen, 2012), it illustrates
that the Google algorithm takes into consideration the total Pages
Fig. 2. Fuzzy Cognitive Map illustrates the correlations between the examined metrics (mentalmodeler.com, accessed on 14 December 2022).
Fig. 3. Engagement Optimization Scenario (mentalmodeler.com screenshot).
9
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

per Visit when constructing the rankings. Developers should consider
it while developing a logistics startups’website since there is no need
to make it complicated.
Finally, Fig. 5 illustrates a Profit Optimization Scenario. More
specifically, examined how the logistics startups will gain 5%
more gross profit. According to this scenario, in order to accom-
plish this goal, startups need to place advertisements in search
engines and social media by 7% and 4% accordingly. Additionally,
increases in user engagement can be observed by 2% as well as in
brand name by 15%.
4.3. Adoption of agent-based model
ABMsprovidedoubleattentiontotheuserbehaviorinsidethe
platform as well as the system operations overall (Barbati, Bruno &
Genovese, 2012;Bonabeau, 2002). The Agent-Based Model
depicted in Fig. 6 is built up of unique features that interact inside
a“cause-and-effect”system to provide an outflow of correlations
(Aguilar, 2005). The application of ABM provides the ability for the
monitoring of both user behavioral data (User engagement) and
their effects on the examined website (Global rank, profit) (Barbati,
Fig. 4. Website Development Optimization Scenario (mentalmodeler.com screenshot).
Fig. 5. Profit Optimization Scenario (mentalmodeler.com Screenshot).
10
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

Bruno & Genovese, 2012). As a consequence, various findings of the
correlations from these interactions are produced. Because ABM
emphasizes the presence and discovery of connections between
distinct system elements, this developmental method prioritizes
the identification of conditions that impact the agents’behavior
inside the model (Sakas & Reklitis, 2021a). As a result, the adoption
of Agent-Based Models enables businesses to completely under-
stand the insights supplied by data in terms of user interaction
with their websites as well as opportunities for expansion (Giabba-
nelli, Gray & Aminpour, 2017). The system Anylogic 8.7.9 with the
computer language “Java”has been used to create the entire
model.
Fig. 6. ABM for the optimization of logistics startups ’User Engagement, Global Rank, and gross profit through Digital Marketing and SEO.
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D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

The ABM visualizes the impact of display advertisements on visi-
tors and their interactions with the logistics startups ‘websites. The
circular blocks depict the agent's initial status before any move.
Movements among the blocks are depicted with the arrows on the
ABM, and such movements come from changes to the required cir-
cumstances dictated on the bottom of the picture in Fig. 3. The Pois-
son distribution has been adopted for this study for two main
reasons (Consul & Jain, 1973). The specified variables for the ABM
were implemented with the poison distribution and are the outputs
of the statistical analysis illustrated in Section 4.1. Additionally, the
findings from the statistical correlations illustrated above are applied
in the ABM for the agents to travel from block to block.
The model on the Fig. 6, initially depicts potential users in the
upper center block, who are separated into two groups. Users that
accessed the logistics startups’websites through a paid advertise-
ment are divided into “Paid Traffic”(e.g., google ads.) and “Social
Traffic”(social media advertisements, e.g. Facebook ads.). On the
other hand, a website can generate users directly from a search
engines search depicted as “Search Traffic”and from “Referral Traffic”
which are users that entered a website through a link from another
website. Next, all those types of traffics constitute the general
“Organic traffic”of the web page. On the top right-hand side “Bounce
rate”can be identified. “Bounce rate”is the percentage of the visitors
that exit from the website without searching anything else. After a
user enters the website (“Organic Traffic”) produce some behavioral
analytics are divided into “Average Time on Site”,“Pages/Visits”and
“Total Visitors”. All those parameters are constituting the “User
Engagement”parameter above. Continuously, the model agents
move to the next block and produce the “Global Rank”before they
exit the website. Finally, inside the “Global Rank”block runs the pro-
cedure of “Gross Profit”with a triangular calculation method (Any-
logic, 2022). Those variables are the outcome of the statistical
analysis of the web analytics and the behavior of the users in the
websites. The Agent-based model produces an agent population dis-
tribution (Fig. 7a and b) and two time charts (Fig. 8a and b) that pres-
ent the fluctuations in logistics websites’User engagement
parameters in correlation to web ranking and profit as a result of
changes in paid and nonpaid traffic.
Figure 7(a) and (b) depict the execution of the ABM model shown
in Fig. 6. The study explores the movement of the users in the Agent-
based model for 180 days period. The agents embody the potential
users moving through the model until they access the ending block
named “User engagement”and provide an outcome for the parame-
ters “User Engagement”and the variables “Global rank”and “Gross
Profit”in Fig. 7. The gray agents represent the users that access the
website through a paid traffic source, blue agents represent the user
that generated from a search traffic source. The red dots are the users
that left from the website (bounce rate). The yellow agents illustrate
users that contribute to user engagement, the light blue agents illus-
trate the users that contribute to the Global Rank and the green
agents embody the users of the webpage that contribute to the
increase or decrease of profit. This logical, since, once the paid adver-
tisement campaign was launched, the website's Global Rank
increased, and consumers reached the startup's website directly, as
the corporate brand name grew.
Figure 8a and b illustrate the results of the predictive model.
According to Anylogic the chart “displays the history of contribution of
several data items into a total during the latest time horizon as stacked
areas”(Anylogic, 2022). The values of the simulation results are indi-
cated on the vertical axis. The horizontal axis depicts the period over
which the simulation takes place (180 days) for the logistics startups’
websites. Figure 8a illustrates the placement of an advertisement in
social media at the time “0”. We can observe that the social traffic
from the advertisement increased as well as the general organic traf-
fic of the website. When the advertisement stopped on the day “5”,
the social traffic and organic traffic dropped. Additionally, on day “8”
the second placement of a search engine advertisement took place
and remained until the end of the simulation. As can be observed, to
maintain good visibility and high organic traffic, logistics startups’
websites need to keep a steady stream of advertisements and keep
them going in order to be effective in contrast to the FinTech industry
websites (Bapat, 2018;Tien, Cheng & Pei-Ling, 2018).
Figure 8b illustrates the results of the main examined metrics
after the placement on an advertisement in the day “0”. There is a
sudden spike in global rank that remained until the day “120”which
decreases. A decrease in global rank is beneficial since a website rank-
ing in 10th place is better than a website ranking in 100th place.
Additionally, an increase in user engagement can be observed after
day “8”on the model and correlates with a placed advertisement in
social media as can be observed in Fig. 8a. As can be observed on the
day “120”with the sudden drop of Global Rank, a brand name has
been created. Finally, there is no correlation between Engagement
and the Brand name with the profitability.
Marketers and developers need to create easy-to-use and dedi-
cated to the result websites in order to enhance engagement. For
instance, the customer must have easy access to tracking numbers or
booking parcels for delivery, which will lead to the creation of brand
loyalty (Zheng, Cheung, Lee & Liang, 2015). This is crucial because the
needs of logistics websites customers are diametrically opposite from
other websites such as online clothing stores. For instance, in online
clothing stores, such as Zara and Mango, the importance is located on
creating different and strong emotions to maximize the user experi-
ence, which will lead to a sale (Goldsmith & Flynn, 2004). Conse-
quently, it is crucial for a logistics startup website to place paid
advertisements both on google and social media regularly to main-
tain high levels of visibility.
5. Discussion
The purpose of this research is to develop an effective “modus
operandi”to investigate the influence of brand name and user
engagement on profit and vice versa to identify options that can be
used to optimize the digital marketing and advertising strategy.
Fig. 7. (a) Experiment’s population allocation with 1000 agents for a period of
180 days. Day 12. (b) Population allocation with 1000 agents for 180 days. Day 54.
12
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

Throughout this study, some interesting findings, that were extracted
from the results section, need to be highlighted. It has been found
that, throughout the simulation, logistic startups’website social and
paid traffic is following organic traffic’s variation during the first days
of advertisements’placement with a small lag of response. Further-
more, users’engagement seems to increase with the increase of the
organic traffic of logistic startup websites with a small-time delay,
underlining their connection, and finally, their gross profit is strongly
affected by the variation of user engagement and global rank (brand
name) metrics, also with a small-time delay.
More specifically, this study highlights that it is beneficial for a
logistic startup to create a good brand name (Nasiopoulos et al.,
2021), which can be accomplished by gathering more visitors on the
website from an organic source (google search or direct access) (H1).
For logistic startups to maintain and develop a brand name and loy-
alty, a steady stream of advertisements is needed both in social media
and search engines, in contradiction with other industries such as
clothing stores and tourism that can create advertisements seasonally
(H2) (Zheng et al., 2015;Fitz-Gibbon, 1990;Palos-Sanchez, Saura,
Velicia-Martin & Cepeda-Carrion, 2021;Ram
on Cardona, Martorell
Cunill, Prado Rom
an & Serra-Cantallops, 2021;Solakis, Pe~
na-Vinces &
Lopez-Bonilla, 2022). Marketers and developers are urged to create
easy-to-use and dedicated-to-the-result websites in order to enhance
engagement. For instance, the customer must have easy access to
tracking numbers or booking parcels for delivery, which will lead to
the creation of brand loyalty (H3) (Zheng et al., 2015). Consequently,
it is crucial for a logistics startup website to place paid
advertisements both on Google and social media regularly to main-
tain high levels of visibility.
In contradiction with other sectors’websites, for instance, online
clothing stores, which are dedicated to the users’experience and
focused on how to keep the customer on their website as much as
possible (H4) (Zheng et al., 2015), logistics startups must emphasize
on solving the customers’problems fast and satisfy their need for eas-
ier to use and user-friendly websites. Additionally, brand name and
profit can be enhanced by increasing the organic traffic of logistic
startups’websites. In a similar logic, paid advertising can boost vari-
ous kinds of traffic data that can increase startup websites’user
engagement (H5) (Sakas et al., 2022c;Pucciarelli et al., 2017). The
shift in logistic startups’focus to increase website users’engagement
and overall traffic can really provide a digital competitive advantage
in their sector since it has been found that user engagement and
brand name (like global rank) metrics can increase their gross profit.
The results of our research are aligned with the existing literature
and provide further insights regarding its differentiation, while
highlighting new approaches. Byun et al. (2020) research utilized big
data from mobile applications of logistics startups to enhance users’
engagement, aiming to improve mobile app usability and logistics
startups’brand name. The present study utilized big data analytics
from logistics startups’websites, regardless of the device used
(mobile or desktop), and also indicated that through improving the
engagement of their website users, these startups can enhance their
brand name and their profitability. It has been stated that logistics
startups are discerned by innovation and efficient models that
Fig. 8. (a). The time chart presents the history of the contribution of the parameters: Organic traffic, Social traffic, Paid traffic from the time that placed an advertisement for
180 days. (b). The time chart presents the history of the contribution of the parameters: Global rank, User Engagement, and Gross Profit from the time that placed an advertisement
for 180 days.
13
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

promote sustainability and profitability (Oliveira-Dias et al., 2022),
mainly due to higher levels of IT innovation applications. The findings
of our research are aligned with these statements, and furthermore, it
provides a potential framework, through big data and IT applications,
for logistics startups to enhance their profitability levels through
higher brand name recognition. Moreover, based on big data origi-
nating from website customers, Huang and Wang (2022) found that
the decision-making process of logistics startups can be optimized
significantly. Throughout our paper, it becomes clear that apart from
the optimization of logistics startups’decision-making, other advan-
tages arise from the utilization of a context based on improving web-
site user engagement, such as the promotion of their brand name,
that can potentially establish a digital competitive advantage in the
field.
6. Conclusions
This research collected and analyzed nine logistics startups’web-
site data, resulting in the identification of intriguing insights that
marketing managers, advertisers, and decision-makers may utilize in
order to optimize their digital marketing and advertising strategy.
The research concentrated on improving logistics startups’brand
name, user engagement, and profit with the assistance of digital
advertising and organic traffic. In general, the research accomplished
the following objectives: (1) a better understanding of the big data
and web analytics metrics and their usefulness for developers and
marketers, (2) the detection of existing correlation between the met-
rics under consideration, (3) the implementation of these findings to
better understand consumers’behavior in the context of digital mar-
keting and digital advertising.
6.1. Theoretical implications
Decision support systems and simulation models enable continu-
ous monitoring of customers’digital behavior across real-time mar-
ket data, with the goal of eliminating digital marketing inefficiencies
and improving user engagement. For instance, digital marketing met-
rics (global ranking and gross profit) have a beneficial impact on traf-
fic-related Key Performance Indicators (organic and paid traffic) and
behavioral-related KPI metrics (pages per visit). In this logic, market-
ing efforts should be directed toward paid ads and targeting specific
keywords in order to improve profit and brand recognition; which
will lead eventually to the sustainability of a startup company since
the vast majority of them fail in the first year of operation (Shlomo &
Maital, 2021). Furthermore, resources need to be allocated to produc-
ing unique content in order to increase user engagement (Byun et al.,
2020). Big data-powered trigger-based customer journeys help busi-
nesses to foresee potential developments and concentrate on deliver-
ing exceptional digital experiences (Terragni & Hassani, 2019).
Additionally, in contrast to other industries, which can create adver-
tisements seasonally (Zheng et al., 2015;Fitz-Gibbon, 1990), logistic
startups require a consistent stream of advertisements in both social
media and search engines in order to maintain and develop a digital
brand name and customer loyalty.
Digital advertising campaigns; as a part of a general digital mar-
keting strategy, are centered on boosting logistics startups’websites
visibility, resulting in higher corporate profit. Paid marketing mes-
sages, such as banner ads, are an essential aspect of digital advertis-
ing; and this research focused specifically on paid ads from search
engines as well as from social media and their effects on startups’
websites user engagement. Additionally, the findings of this study
highlight the necessity of monitoring and analyzing the web analytic
metrics for the optimization of corporate brand name through the
global rank metric (Sakas & Reklitis, 2021a;Drivas, Kouis, Kyriaki-
Manessi & Giannakopoulou, 2022).
Finally, we suggest that in order to establish their company in the
market, startup marketing managers should monitor changes in their
web pages’organic traffic and global rankings in relation to user
engagement metrics and profit. As a consequence, decision-makers
will have a better view of advertising campaigns for their products,
and services. The current study’sfindings lend credibility to various
big data research on the digital marketing process of logistic startups.
Behl et al. (2019) have published a study expressing the valuable
potential of big data on marketing capacities in favor of startups. The
authors emphasize the startups’ignorance of big data capabilities,
which reduces the effectiveness of a big data-driven digital marketing
approach in startups. Furthermore, the current study aligns with the
Cavicchioli and Kocollari (2021) and Roumeliotis et al. (2022) studies,
in which important key marketing metrics are investigated, resulting
in efficient digital marketing strategies, such as increased lead gener-
ation and conversion rates. Finally, this study highlights that custom-
ers’behavioral web and social analytics, on the other hand, can
provide added value to startups’digital marketing strategies.
6.2. Practical implications
The findings of the present research highlight the necessity of
logistics startups to devote more resources in understanding the
behavioral patterns of digital customers using big data analytics. By
doing so, logistics startups can be able to follow existing trends and
identify the aspects that significantly influence customers’online
purchase behavior based on their web and social media engagement
(Negedu & Isik, 2020). The integration of behavioral analytics into
concrete digital advertising strategies is a tough assignment that
involves numerous components, including the behavioral KPIs
(Saura, Palos-S
anchez & Cerd
aSu

arez, 2017).
More specifically, marketers and decision-makers must extract,
evaluate, and utilize web analytics and big data to restructure the
startup’s marketing strategy. Startups may advantage from the
proper execution of digital marketing strategy since advertising and
campaigns allow them to contact potential clients more quickly (Tar-
dan, Shihab & Yudhoatmojo, 2017;Shlomo & Maital, 2021;Mariani &
Fosso Wamba, 2020).
Additionally, in order to construct a user-friendly website that
suits their business purpose, website developers need to obtain a
thorough understanding of the implications of their website’s user
engagement on gross profit and digital brand name. Furthermore,
with the knowledge gained from big data, they could contribute to a
greater degree to the startups’operations. Finally, we suggest that in
order to establish their company in the market, startup marketing
managers should monitor changes in their web pages’organic traffic
and global rankings in relation to user engagement metrics and
profit. As a consequence, decision-makers will have a better view of
advertising campaigns for their products, and services.
6.3. Future research and research limitations
The vitality of a startup is based on reaching new consumers and
maintaining the existing ones. The first parameter can be accom-
plished with digital advertising; the second one is the adoption of a
well-designed and user-friendly website. In order to develop an
effective website, the user engagement analytics must be examined
in correlation to the technical parameters of a website (Sakas et al.,
2022b). Since technical parameters examination are crucial for iden-
tifying a company’s website usability; their investigation in correla-
tion to behavioral analytics can provide valuable insights to
marketers. It could be interesting for instance, to identify the effects
of a slow-loading startup’s webpage on the purchase rate or the cor-
relation between the total webpage size with the total parcel search
time as studied before in the agri-logistics sector (Sakas & Reklitis,
2021b). Additionally, it would be beneficial to examine the effects of
14
D.P. Sakas, D.P. Reklitis, N.T. Giannakopoulos et al. European research on management and business economics 29 (2023) 100221

the logistics startups’social media on the overall user experience and
interactivity of their websites.
Moreover, since a correctly placed advertisement is crucial for
maximizing market share (Lin, Paragas & Bautista, 2016;Grewal,
Bart, Spann & Zubcsek, 2016) and the usage of emotions in ads pro-
motes the brand name (Hutchins & Xiomara, 2018), it could be bene-
ficial to examine the effectiveness of the advertisement with the
assistance of neuromarketing tools. For instance, it could be beneficial
to examine where the customers focus on a website in order to make
it easy to use and user-friendly as studied in other industries’web-
sites and social media (Gonz
alez-Mena et al., 2022). Additionally, it
could be examined if a well-designed logistics startup website can
reduce customers’frustration and anger while searching for a parcel
with the assistance of an EEG. Eye-tracking could be used to identify
where the customer looks frequently to place the advertisements
and EEG could be used to illustrate a consumer’s emotional reaction
to a specific advertisement (Vences, Díaz-Campo & Rosales, 2020).
Finally, the first limitation relies on the quantity of the examined
websites. More websites could be examined to acquire better insights
into the sector. Secondly, the data gathered from the ``SEMrush’’ data
extraction webpage. More, behavioral data could be beneficial in order
to examine the logistics startups’user engagement in depth. Addition-
ally, social analytics can be extracted and analyzed in correlation to
behavioral and technical factors in order to get a holistic approach to
the logistics startups’digital entities. Further research can be held with
the assistance of interviews and questionnaires for a more holistic qual-
itative and quantitative approach (Reklitis et al., 2017;Loor-Zambrano,
Santos-Rold
an & Palacios-Florencio, 2022). Finally, the research is lim-
ited to the logistics startups sector, further research to other startup
sectors could be beneficial. The interviewees’opinions extracted from
questionnaires in relation to their actual behavior on a website as
extracted from web and social analytics could provide valuable insights
to marketers, managers, and startup owners.
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