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Is AI and Chatbots-based Digital Marketing the
Future? A Natural Language-based Explorative
Study
Chetan Sharma
upGrad Education Private Limited
Shamneesh Sharma
upGrad Education Private Limited
Komal Sharma
Sri Aurobindo College of Commerce and Management
Sandeep Kautish
Chandigarh University
Timilehin Olasoji Olubiyi
West Midlands Open University
Research Article
Keywords: E-Marketing, E-Commerce, Articial Intelligence (AI), Latent Semantic Analysis (LSA), KNIME
(Konstanz Information Miner), Chatbots
Posted Date: February 13th, 2025
DOI: https://doi.org/10.21203/rs.3.rs-5865492/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Additional Declarations: No competing interests reported.
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Abstract
The Internet's widespread growth and diverse range of applications have made digital marketing the
preferred technique in today's marketing landscape. Over the past decade, numerous creative methods
have been created, with expectations for further advancements in the future. This paper presents an
examination of the latest developments in digital marketing methods. The Scopus database is used in
this research, and 4808 articles from 1989 to 2025 are analyzed. Latent semantic analysis, a text mining
technique under the umbrella of natural language processing, is implemented using the KNIME
(Konstanz Information Miner) tool to anticipate future trends. K-Mean clustering technique on the TF-IDF
score to predict the ten clusters that future researchers can explore. The investigation revealed that the
three most signicant trends were articial intelligence, chatbots, and programmatic advertising. The
thorough analysis and classication offer researchers and specialists critical perspectives and
emphasize the increasing importance of chatbots in digital marketing.
1. Introduction
Articial Intelligence (AI) and Chatbots play a crucial role in contemporary digital marketing by improving
consumer interaction, optimizing processes, and customizing user experiences. They offer round-the-
clock customer service, lead generation, personalized content, marketing automation, CRM, predictive
analytics, data management, and conversion optimization. AI algorithms can process large data
volumes, recognize opportunities, and provide customized content instantaneously. Chatbots can serve
as intermediates by gathering and analyzing feedback and data. They can assist rms in making
informed decisions by providing tailored messaging or services. AI systems can analyze vast amounts
of data, offering practical insights. AI and Chatbots are crucial in enhancing client happiness and
increasing conversion rates in contemporary digital marketing. The Internet has changed the lives of
billions in the last three decades.
Beginning with Web 1.0 (syntactic web), we have reached the stage of Web 4.0 (meta web) (Kartajaya et
al., 2021)(Duy et al., 2020). In line with this, marketing has evolved from marketing 1.0 (identity) to
marketing 4.0 (Dash et al., 2021). Digital marketing as a tool started soon after the advent of the
Internet. It is a strategy that uses numerous online channels for improved communication, brand
building, and enhanced customer experience (Tiago & Veríssimo, 2014). Digital marketing is the umbrella
term for any marketing effort using an electronic device or the Internet (Suppatvech et al., 2019). Digital
channels such as search engines, social media, email, and websites are used by businesses to connect
with current and potential customers. "digital marketing" encompasses various tactics, from websites to
the online assets used to build a company's brand, including digital advertising, email marketing, and
online brochures. It can help organizations or individuals reach a larger audience (Kapoor & Kapoor,
2021). It all began in the 1980s, and by the beginning of 2020, almost 60% of the world's population is
online (Tembo & Malik, 2022). Digital and social media marketing (DSMM) has become the backbone of
modern marketing strategies (Dwivedi et al., 2021). DSMM can help companies achieve their marketing
objectives at a low cost and in less time (Appel et al., 2020). Social media, primarily, has driven
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marketers into a new marketing era. Almost 90% of businesses use Twitter for marketing, and more than
50million businesses engage their stakeholders via Facebook (Dwivedi et al., 2021). As a result, the use
of DSMM tools and applications to raise public awareness skyrocketed in the last ve years. In addition,
from the customers’ point of view, online searches and research about products and services have
increased exponentially. This massive shift in consumer behavior has resulted in companies
incorporating digital and social media into their existing marketing plans or a dedicated DSMM strategy.
A generic version of the digital marketing process is depicted in Fig.1.
In this study, the authors' objective is to provide recent research trends for the researchers and future
researchers in digital marketing. Firstly, the study focused on providing information about the growth of
digital marketing publications. Secondly, the authors aim to offer the top journals contributing to digital
marketing growth. Finally, the authors used topic modeling, a natural language processing technique, to
provide information about the most important keywords with their frequency and relevance in the
corpus. In addition, we used the k-mean clustering technique to give various clusters, which are recent
trends on which current researchers are working and need more attention.
The rest of the paper is organized as follows: section 2 provides state-of-the-art studies on digital
marketing. Section 3 describes natural language processing, data collection, and pre-processing steps.
Section 4 provides the results analysis of the study, section 5 explains the research trends, and section 6
concludes the study.
2. Literature Review
Marketers use electronic media to promote their products or services in a "digital marketing" strategy.
Marketing through digital media is the primary goal of digital marketing. Digital marketing is a catch-all
term for marketing products or services via digital technology, such as the Internet, mobile phones, or
display advertising (Nabieva, 2021). Some researchers claim that digital marketing is becoming
increasingly popular in India (Kaushik, 2016). However, according to more research, as more and more
young people shop online, the effectiveness of traditional marketing is dwindling (Todor, 2016). In
keeping more closely with the spirit of a survey, it has been found that the mature segment of clients still
relies on traditional marketing. Mixing traditional and digital marketing can cater to the needs of both
older and younger customers. Researchers examined the signicance of digital marketing and the
advantages it offers. India's government is also working on programs like Digital India, a new way to
connect and inform people worldwide (Mathur, 2016). To date, work in this area supports that SEO is the
most successful method for gaining organic customer trac (Kannan & others, 2017). Much of the
existing research is derived from the seminal work on Consumer behavior trends and patterns, which
says that it can help marketers succeed in digital marketing (Strauss et al., 2014). The authors carried
out an SLR on B2B digital marketing to zero in on relevant literature in the eld (Ram & Zhang, 2021). The
review reveals that topics like electronic marketing orientation (EMO), critical success factors, decision
support systems, digital marketing communication, and sales management have seen less attention.
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Several researchers have conducted numerous surveys and assessments in digital marketing.
Bibliometric analysis is a prominent approach among scholars for literature reviews; in the same series,
digital marketing trends and patterns have been analyzed (Ghorbani et al., 2022). Using bibliometrics,
this study evaluates digital marketing research trends and patterns from 1979 to June 2020. The
examination of 924 Scopus articles revealed the most signicant number of multiple (MCP) and single
(SCP) publications, the top 20 most repeated authors' keywords, and the top 20 most referenced papers
each year. Another research study on the same pattern was carried out in 2021, where researchers used
bibliometric techniques to analyze 925 Scopus publications from 2000 to 2019 (Faruk et al., 2021). The
researchers claimed that the United States, India, and the United Kingdom are the three nations with the
most active research communities in digital marketing. The study also identied three main clusters in
digital marketing research. One is a digital marketing strategy, the second is app development for mobile
marketing, and the last is client demographics. In the most recent digital marketing surveys, researchers
have done another bibliometric analysis of inuencer marketing (Tanwar et al., 2022). The researchers
studied the literature from 2011 to 2019 by using R-Tool. The researchers provide an overview of the
history and development of inuencer marketing research and examine the effectiveness analysis
according to sources, authors, documents, nations, and keywords. The researcher examined the
widespread implementation of cutting-edge tech and data-driven advertising, especially in digital
marketing, which can signicantly impact (Krishen et al., 2021). The study shows how topics, articles,
citations, and co-citation networks develop over time. They also concluded that there is a growing body
of literature on interactive digital marketing worldwide and across academic disciplines. It is a matter of
opinion with which the researcher provided information about the digital marketing and sustainability
eld and explored six areas that lead to better insights about customer orientation and value proposition,
digital consumer behavior, digital green marketing, competitive advantage, supply chain, and capabilities
(Diez-Martin et al., 2019). A systematic review of prospective observational studies found that digital
marketing can help businesses gain and maintain an edge in the market (Denga et al., 2022). The study
aims to familiarize participants with digital marketing concepts and reveal various strategies that give
companies an edge in the marketplace. A recent systematic review investigated the inuencer’s role in
swaying internet users' purchasing decisions. Companies can improve their brand awareness and
product positioning by having inuential people spread carefully crafted messages (León-Castro et al.,
2021). One of the researchers conducted an extensive literature review of 121 articles and concluded
that digital marketing for small and medium-sized enterprises (SMEs) has risen over the past three
years, with academics conducting studies in developed and developing nations (Thaha et al., 2021).
Many different types of SMEs were analyzed, and then individual sectors, such as the hospitality industry,
the food and beverage industry, and the manufacturing industry, were analyzed.
Upon an extensive review of research in the eld of digital marketing, it has been found that most
researchers have chosen either bibliometric analysis techniques or manual systematic reviews. The
present research is based on Latent Semantic Analysis, where the researchers have taken data from the
Scopus database from 1989 to 2022. The Latent Semantic Analysis has been applied in various elds
earlier but not to digital marketing. The researchers formulated research questions based on a literature
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review and then answered these questions based on terms related to documents. The following
questions were proposed for this research:
RQ1: How has digital marketing seen its growth?
RQ2: Which top journals have focused on digital marketing?
RQ3: What are the top keywords contributing to digital marketing research?
RQ4: What are the recent trends for the present and future researchers?
2. Methodology
2.1. Natural Language Processing and Latent Semantic
Analysis
Topic modeling, a powerful text-mining tool based on natural language processing, can examine the
relationships between the data and documents being mined (Usmani et al., 2021). Various researchers
use this technique in their respective elds, such as medical research (Hassan et al., 2020; Selvi et al.,
2019), engineering (Gurcan & Cagiltay, 2019), etc., to predict related topics based on the essential
keywords. These keywords are related to each other regarding their weight in the documents, which is
predicted using the latent semantic analysis. Various authors used different techniques for topic
modeling in their published studies, such as Latent Dirichlet Allocation (LDA), Non-Negative Matrix
Factorization (NMF), Latent Semantic Analysis (LSA), and Parallel Latent Dirichlet Allocation (PLDA).
Latent semantic analysis (LSA) is the most popular method for nding the relationship between
keywords. The software can automatically manipulate natural language, such as voice and text, using
NLP (Natural Language Processing) (Y. Li et al., 2021). It has been half a century since linguistics and
computers merged to create the eld of natural language processing (S. Yu & Lu, 2021). Despite this,
when it comes to computers' ability to process and analyze vast amounts of natural language data, all
context points to natural language processing (Allen et al., 2021).
As a result, the goal is to build a computer that can read and comprehend documents, including their
content and the nuances of their writing in different environments. Topic modeling is used in this study
to extract keywords from abstracts of research papers. Text or data corpus can be modeled to identify
words associated with a particular topic. However, extracting words from a text is more time-consuming
and complicated than extracting them from the document's themes (Mustak et al., 2021). Latent
Semantic Analysis (LSA) is used to investigate the connections between documents and terms as part
of the distributional semantics of natural language processing. They are extracting structured data from
an unstructured text collection using Latent Semantic Analysis (LSA) (Kim et al., 2020). Words are not
randomly selected from a lexicon when writing anything that resembles writing. Latent dimensions are
those that are hidden from view. After reading the text, words are understood. Words with similar
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meanings are often used together to convey the same message. Topic modeling takes a dictionary's
worth of words and distills them to their most essential components, resulting in a bag of words (BOW).
When it comes to NLP, the words that are contained within a corpus are an essential aspect. One of the
characteristics of NLP is that each word is utilized in training the model. We don't have to spend time
sorting through the data using this approach; instead, we can immediately zero in on the most crucial
information. The LSA method presents the results statistically and visually while also connecting the
documents included in the dataset. A collection of documents should demonstrate how individuals
express themselves through various words, topics, or themes (Sharma et al., 2022). In this study, LSA is
implemented to make forecasts regarding potential developments within digital marketing. Figure2
illustrates the research approach that has been used for this study.
2.2. Data Collection
In the study, the data was collected from articles published in various journals, conferences, and book
chapters stored in various digital libraries. Digital library search terms were chosen under study
objectives. The rst step is to gather data to conduct the research and collect the string data formulation
developed following Kitchenham & Charters guidelines (Kitchenham & Charters, 2007). This study used "
Digital Marketing " as a search string to nd the data.
The Scopus database, the world's most comprehensive research database, was examined in this study.
Scopus compiles content from various publications, including scholarly journals, conferences, and
books. Scopus incorporates content from prestigious publishers, including Elsevier, Springer, Emerald,
Inderscience, saga, Wiley, and Taylor & Francis. In the initial pass of string on the Scopus database, the
author fetched 5262 articles as on 14 Jan 2025. Authors included only those studies written in English,
and the corpus was ltered out to remove studies that did not meet the author's name, year, and abstract
requirements. Finally, after applying inclusion/exclusion, 4808 articles from 1989 to 2025 corpus have
been selected to conduct this current study.
3.3. Preprocessing
This research uses LSA, which can be categorized under natural language processing and text mining.
KNIME and Vosviewer, two open-source text processing tools, were also used for this experiment
because they are free to use (W. Liu et al., 2021). KNIME is easy to use, allowing users to share their
workow with buddy researchers (Wratten et al., 2021). Vosviewer tool is used for meta-analysis in
which various information is extracted for analysis, and network analysis is performed using the tool
(Sharma et al., 2022).
The rst step in normalizing data is called pre-processing. Before creating the nal document for
analysis, the abstract from the publication and the keywords provided by the author are combined.
Preprocessing can be started in various ways, and one of them is assigning a POS tag to each word
(Part of Speech) (Yalcin et al., 2022). The following step is to change all words in the corpus to either
lowercase or uppercase, depending on the specic situation. The third step involves removing the
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punctuation marks from the text. Following that, a number lter will remove all the numbers from the
document because a number itself does not convey any information. In the fth step, you should remove
stop words such as is, am, and are from your writing. This lter for stop words eliminates the stop words
that are causing the documents to be devoid of all English words (HaCohen-Kerner et al., 2020). In the
sixth step, the author used the stemming module, which involves reducing all the words to their root
forms. Finally, lemmatization and stem words are converted to their root words (Boban et al., 2020).
When the corpus has been analyzed, the next step is to compile a part-word dictionary for additional
research. Creating a dictionary, also known as an object's vocabulary, requires the utilization of a BOW
creator, which is a necessary step. BOW is used for both the modeling of topics and the clustering of
terms.
3. Result Analysis
3.1. Meta-Analysis
In the meta-analysis, the number of selected studies was counted and analyzed in terms of years, with
the results depicted in Fig.3 showing the number of publications produced each year. Figure4 provides
a visual representation of the publication distribution of the study. According to the information
collected, Springer Conference Proceedings in Business and Economics is the most successful
publisher of articles dealing with digital marketing. The breakdown can be seen in Fig.3, which is
organized by year. The gathering of the necessary documents is an essential component of the process.
Figure 4 represents the most prestigious publications or journals in Digital Marketing that have published
this work and their names. Lecture noted in Networks and Systems is leading the board with 121 articles
which is 2.5% of total corpus and Springer proceedings in business and economics have the 109
publications, the most inuence in this eld, and the good participation rate, according to an assessment
of renowned journals. As a result, researchers in this eld can draw on the work presented in these
proceedings to inform their work.
In this eld of research, the rst article was published in 1989, and further data show extensive growth.
Various authors contribute to this context, and the top 10 researchers and their citations are shown in
Fig.5.
3.2. Term Frequency (T.F.) And Inverse Document
Frequency (IDF)
One of the most common statistical measures in information retrieval and text mining is called Term
Frequency-Inverse Document Frequency (TF-IDF for short).(Al-Obaydy et al., 2022). It is a way of
assigning weights to words in a document based on their frequency and relevance to the document. The
"term frequency" (TF) component of the calculation refers to the number of occurrences of a particular
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word in a given piece of writing. The greater the number of times a word is used in writing, the higher its
total frequency score will be. The "inverse document frequency" (IDF) component of the calculation
takes into consideration the total number of times a given word appears in all of the documents that
make up a corpus (Dash et al., 2023). After preprocessing, relevant keywords are stored in the dictionary
as Bag of Words (BOW). For each word that relates to BOW, the TF-IDF score is calculated (Ravishankar
& Raghunathan, 2017). TF-IDF is a popular algorithm for determining the relevance of a document to a
specic search query, and term frequency is a crucial component of the algorithm. The TF-IDF score for
clustering and document vector generation is derived from the T.F. and IDF and is shown in Table1. The
TF-IDF score for a term (word) "t" in a document "d" can be calculated using the Eq.1:
TF-IDF(t,d) = TF(t,d) * IDF(t) (1)
where,
TF(t,d) = (Number of times the term t appears in the document d) / (Total number of terms in the
document d)
IDF(t) = log_e(Total number of documents / Number of documents with the term t in it)
The corpus culled the top 20 terms used in BOW for this study. BOW used data from 4808 articles in
Scopus to build the dictionary and 19,051 unique tokens. Figure6 depicts the 20 most frequently
occurring tokens out of 19,051. Therefore, it is necessary to create a weighted 19,051*4808 term-
document matrix for the ith term in the jth document of nth documents in the corpus and use it in all
identied topic solutions, which follows the weighting scheme described in the above equations.
BOW contains 19,051 tokens for which the TF-IDF score is calculated, but it is hard to represent all data
in the table, so the top 20 high-loading terms are represented in Table1 against the 4808 documents.
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Table 1
Transformed TF-IDF values Representation of 4808 Documents
Row ID Doc 1 Doc 2 Doc 3 Doc 4 Doc 5 Doc 6 Doc 4808
digit -0.0085 0.0097 -0.0054 -0.0631 -0.0438 -0.0054 …... -0.0014
social 0.0050 0 0.0026 0 0.0027 0 …... 0
understand 0.0026 0.0073 0.0036 0.0053 0 0 …... 0.0066
onlin 0.0015 0.0017 0 0.0023 0 0 …... 0
studi 0.0001 0.0104 0 0 0 0 …... 0.0028
us 0.0015 0.0021 0 0.0027 0 0.0095 …... 0.0019
result 0.0016 0 0.0020 0 0 0.0080 …... 0.0039
media 0.0034 0 0.0029 0 0 0.0015 …... 0.0014
market 0.0064 -0.0333 -0.0302 -0.0295 -0.1026 0 …... -0.0101
nd 0.0040 0 0.0053 0 0 0.0051 …... 0.0034
model 0 0.0168 0 0 0 0 …... 0
consum 0 0.0045 0.0082 0 0 0 …... 0.0234
brand 0 0.0153 0.0069 0 0 0 …... 0.0080
research 0 0 0.0007 0 0 0 …... 0
effect 0 0 0.0155 0.0086 0.0090 0 …... 0.0856
product 0 0 0.0090 0 0 0.0046 …... 0.0090
commun 0 0 0 0.0130 0.0158 0 …... 0
develop 0 0 0 0.0102 0 0.0070 …... 0
provid 0 0 0 0 0 0.0061 …... 0
inform 0 0 0 0 0 0 …... 0
custom 0 0 0 0 0 0 …... 0
3.3. Optimal Topic Solution
Choosing an appropriate dimension has proven to be a signicant problem in this procedure, as it
requires extensive expertise and multiple iterations to achieve the best possible result (Evangelopoulos
et al., 2012). For example, ten topic solutions are estimated to be the optimal number for a 4808-
document corpus (Deerwester et al., 1990). Nevertheless, it may be sucient for predicting
developments in digital marketing. Text mining necessitates topic labeling, which is a crucial step. The
TF-IDF score signicantly impacts the topics in this study because the latent semantic analysis is used.
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Further, K-Mean clustering is done based on the TF-IDF score. Finally, each topic's most heavily loaded
values are clustered according to their occurrences and weightage in term loading.
3.4. Clustering
Papers from the Scopus database on digital marketing can be clustered to gain insights. Clusters are
then formed based on the phrases found in these clusters. K-Means clustering generates a topic solution
after BOW selects the K most frequently occurring terms from the corpus. Ten clusters are the best
option when deciding how many to include in the 4808 document corpus (Deerwester et al., 1990).
Table2 shows the ten best KNIME topics and their high-loading phrases. The TF-IDF scores can now
identify these ten topics or clusters. These labels represent new developments in digital marketing that
can be studied further. The author and subject matter experts work together to complete the topic labels
by hand.
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Table 2
Topic Label with High Loading Terms
Cluster Terms Topic High Loading Paper
Cluster
Imedia, social, behavior, digit, intelligen,
strategus, datum, result, onlin, inform,
strategi, custom, market, Internet, busi,
product, advertis, commun, atrticial
Articial Intelligence
in Digital Marketing (van Esch & Stewart
Black, 2021)
(V. Singh et al.,
2022).
(Sinha et al., 2020)
Cluster
II market, conversat, ecosystem,
hypermarket, techologus, messag,
specialis, phone, applic, interact, busi,
ecien
Chatbot conversation
in Business (Mufadhol et al.,
2020)
(BARI\cS, 2020)
(Illescas-Manzano et
al., 2021)
Cluster
III market, digit, social, messag, applic,
analy, inform, custom, german, scienc,
adolesc, competit, entrant, week, medi,
Social media analytics
and Applications (Bekmamedova &
Shanks, 2014)
(Ayodeji & Kumar,
2019)
(Moon et al., 2022)
Cluster
IV site, brand, price, tourist, perform,
proxim, skill, search, user, region,
destin, property, view, sme, intellig,
googl, programmat
Programmatic
Advertising (Busch, 2016)
(Seitz & Zorn, 2016)
(Kiran & Arumugam,
2020)
Cluster
Vdatum, custom, service, commun,
onlin, model, busi, inform, develop,
channel, consum, media, brand,
companus, effect, qual, advertis,
tourism, social, search, factor, design
Role of Social Media
in Tourism Marketing (Sahin & Sengün,
2015)
(Alghizzawi et al.,
2018)
(Lange-Faria & Elliot,
2012)
Cluster
VI brand, impact, inform, digit, commun,
sale, consum, user, educ, search, shop,
medi, Interact, motiv, intern, destin,
websit, behavio
Impact of Digital
Media Advertisement
on consumer
behaviour
(Sama, 2019)
(Zari, 2021)
(Stephen, 2016)
Cluster
VII digit, market, system, valu, risk,
instagram, person, facebook, environ,
email, websit, driver, applic, enterpris,
mobil, automat,
Automated &
Personalized Email
Marketing
(Goic et al., 2021)
(Mohammadi et al.,
2013)
(Hafaiedh et al.,
2020)
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Cluster Terms Topic High Loading Paper
Cluster
VIII commerc, Social, media, datum,
consum, busi, base, market, strategi,
onlin, content, technolog, Electronic,
manag, brand
Social Media and E-
Commerce (Kwahk & Ge, 2012)
(Linda, 2010)
(M. Singh & Singh,
2018)
Cluster
IX video, package, real, Reel, tiktok,
Instagram, post, publish, vlog, youtube,
ad, fashion, cosmet, whatsapp, expo,
host, facebook, market
Marketing through
videos and reels on
social media
(Ayeni, 2021)
(Stsiampkouskaya
et al., 2021)
(Moriuchi, 2021)
Cluster
Xmarket, studi, social, research, media,
datum, onlin, digit, busi, analysi, listen,
custom, social, inform, strategi,
social listening
analysis and
strategies
(Chaffey & Smith,
2013)
(Saura, 2021)
(Chaffey & Smith,
2017)
In this experiment, authors calculated TF absolute, TF relative, IDF, and TF-IDF scores using the k-Mean
clustering. A graphical representation of the scores achieved is represented in Fig.7.
In this experiment, authors calculated different values, and based on these achieved values, a graphical
representation has been provided by the authors.
4. Research Areas and Current Trends
4.1. Cluster I: Articial Intelligence in Digital Marketing
In digital marketing, articial intelligence differs from human intuition because data drive it. A.I. is an
extension of human intelligence, commonly called robot-driven intelligence (Davenport & Ronanki, 2018).
In general, AI refers to the processes by which machines collaborate with humans to make decisions by
processing data. A digital marketing strategy can encourage consumer behavior and increase customer
interaction. AI-based marketing helps businesses reach the right customer at the right time, including AI-
controlled chatbots, big data, and outputs from cognitive technologies (Yuniarthe, 2017).
Compared to traditional retailers, retailers that use AI-powered marketing perform ve times better.
Changing consumer behavior is a result of digital marketing. Modern consumers expect a more
consistent and personalized experience. AI can assess a large amount of data and determine trends
faster than humans (Wierenga, 2010). Brands and marketers are incorporating Machine Learning and
Articial Intelligence to reduce resources and save time. Through machine learning technologies and
interaction with virtual assistants, articial intelligence can create simulation models and personalize
purchasing processes (Rao et al., 2016). Articial Intelligence has become a popular way for brands to
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interact with customers. Amazon uses a similar system to recommend products based on previous
purchases, views, and searches (Le & Ha, 2021). Marketing automation systems, like CRMs, help us
manage data and cater to customers more eciently (Zhu & Wu, 2011). Articial Intelligence is being
incorporated into different types of businesses every day. The capabilities of these intelligent tools are
developing rapidly to a point where they surpass humans on certain levels (Siau & Yang, 2017).
4.2. Cluster II: Chatbot Conversation in Business
A company's customer service must always be prompt, whether sales, marketing, or support, and
customers will never stick with a business if it does not deliver smooth engagement (Russell, 2002).
Chatbots that A.I. powers can augment customer support in this area by automating customer
communication. Approximately 35% of consumers desire a better communication strategy and
customer service through chatbots (Anik et al., 2016). Across industries, chatbots are becoming
increasingly popular due to their need to be always available. It is also necessary for a business to use a
bot at some point in time if it wants to engage customers round-the-clock and improve their experience
(Schumaker & Chen, 2007). Instant responses to customer requests will enhance customer satisfaction.
Chatbots can be incredibly useful When building good relationships with customers (Jia, 2003).
Engaging and interacting with website visitors can help make solid connections for your business.
Chatbots assist in achieving marketing goals and improving customer service and sales (Setiaji &
Wibowo, 2016).
4.3. Cluster III: Programmatic Advertising
Online advertising is bought and sold automatically through programmatic advertising (W. Li et al.,
2022). By automating the digital advertising process, you can streamline your efforts and consolidate
them into one platform (Rogers, 2017). Every format and channel can be accessed programmatically,
including mobile, desktop, tablet, audio, digital outdoor, and linked T.V. Programmatic platforms have
expanded their inventory and database (White & Samuel, 2019). The platform leverages real-time data to
determine which online audience will be most effective for the campaign. Then, it purchases digital ad
inventory through an auction based on everything accessible across multiple devices in locations the
audience cares about (Seitz & Zorn, 2016). Advertisements are tailored based on each customer's
distinct interests and behaviors. As a result, performance is maximized, intelligent connections are
created, and insights are produced. Programmatic advertising has helped advertisers transition to a
more customer-centric and real-time buying perspective. In addition, it has modied the advertising
process by deploying targeted campaigns to be more need-based (Gertz & McGlashan, 2016). As a
result, better advertising revenue returns have been provided to advertisers. In addition, through
programmatic advertising, brands can provide more accurate and lucid insights into the demands of
their audience. Even if programmatic advertising is still in its infancy in the nation, the trend is pointing
toward advancement in the advertising industry (Shiller et al., 2018).
4.4. Cluster IV: Social Media Analytics
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As soon as social media began to take off, public relations agencies began monitoring customer
feedback on a company's website to track down and address angry customers (Stsiampkouskaya et al.,
2021). Do not turn social media into another Literary Digest poll because of the increasing number of
social media sites and the volume of people using them (Gayo-Avello, 2011). Considering social media's
tremendous power, consider how much data users consume daily. Over a year, Facebook has over one
billion active users clocked in some 20,000 hours of online time. "In 24 hours, YouTube received
approximately 4billion views, which is the equivalent of 500 years of video" (spread among 800million
unique users) (Hoffman & Fodor, 2010). Analyzing social media involves gathering data, deciphering it,
and presenting it. It is necessary to "listen" to various social media sources, archive essential
information, and extract appropriate data at the capture stage (B. Liu, 2011). The company or a third-
party vendor can carry out this technique. Not all of the information gathered will be helpful. Using
modern data analytic tools, the understanding stage selects the relevant data for modeling, removes the
noise and low-quality data, and then evaluates the data that has been retained and derives new insights
from it (W. Yu et al., 2021). This phase's primary goal is to effectively communicate the results of Stage
2. Input-process-output models are well-known and widely used on which our system is built. Concerning
whom we observe, analyze, and sum up summaries of their work to depict, etc., our current stage covers
both of these responsibilities. Systems are developed and tested by data analysts and statisticians
before they are made available to the general public (Sprott et al., 2009).
4.5. Cluster V: Role of Social Media in Tourism Marketing
Social networking has transformed people's lives in the decade after its inception. The Internet has
become a real-time source of information in every facet of our lives, from business to technology to
current events, social interactions, and travel (Constantinides & Fountain, 2008). Additionally, companies
have communicated with consumers nationwide without ever having to meet them (Cooper et al., 1998).
A look at how social media affects travel and how it is being used in nations as different as Turkey,
China, India, Istanbul, Sweden, the UK, Spain, France, Malaysia, Australia, Sri Lanka, and Kuala Lumpur in
France (Királ’ová & Pavl\’\ičeka, 2015). Social media is one of the most ecient ways tourist groups
promote locations and products (Királ’ová, 2014). As a result, tourism organizations increasingly use
social media tools like Yelp, TripAdvisor, Trip Hobo, and more. Given that TripAdvisor has received more
than 200million evaluations and opinions, it is safe to say that social media dominates tourism
marketing (Lv et al., 2021). On the other hand, Facebook has over 800million users who share updates
and photos from their travels (Királová & Malachovsk\`y, 2014).
4.6. Cluster VI: Impact of Digital Media Advertisement on
Consumer Behavior
Digital technology is evolving new methods for doing business over time. Digital technology and its
impact on business cannot be denied by businesses and companies related to conventional industries
(Akhtar et al., 2016). For example, the Nokia mobile phone company became famous and trusted in
1990. Although Nokia has no competitors in the mobile sector due to an aversion to regulating advanced
technology as new-generation shoppers seek out online shopping, traditional retailers face a signicant
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challenge (Al-Dhuhli & Ismael, 2013). Modern lifestyles require further technology adjustments and
adjustments in conventional businesses that reect new life changes. Digital media marketing plays a
signicant role in consumer behavior, especially in the modern world strongly fenced with technology
(Luo, 2021). Therefore, the company must consider social media as part of its digital marketing strategy.
Social media describes the type of media that involves transformation and interaction between online
users. It is common for people to multitask in their waking hours, using multiple media platforms
simultaneously (Boateng & Narteh, 2016). They can browse the Internet, text their friends, and talk while
drinking local coffee. The term "digital native" was recently used to describe these people. Digital natives
are being reached out to today using new tools. For most organizations, the question is not whether to
utilize social media but how much (De Vries et al., 2012). Increasingly, social media marketing is
becoming an essential aspect of marketing.
4.7. Cluster VII: Automated and Personalized Email
Marketing
Email marketing automation transforms the manual distribution of marketing emails into a mechanism
that selects recipients according to predetermined criteria (Kumar, 2021). It then starts a predetermined
sequence of emails being sent to those people. It entails sending customized emails to selected
subscriber groups. It could be a weekly newsletter, details about your goods or services, or general
information about your business (Wesche & Sonderegger, 2021). With email marketing automation, you
may keep establishing a relationship with your subscribers over time. You will choose your campaign's
target audience and goal when you plan it (Cranmer et al., 2021). Not only is email marketing automation
more practical for you, but it also gives your customers a better experience. The type of emails your
consumers receive is based on their behavior (Rosário & Raimundo, 2021). For instance, a website visitor
can be curious about your business but not quite ready for a salesperson to contact them (Shankar et al.,
2021). As a result, people register for a free guide and get a warm welcome email. When a subscriber is
prepared to proceed to the next level, you continue strengthening the relationship while providing them
with valuable and pertinent information. Thanks to email marketing automation, you can offer customers
information tailored to their needs (Goic et al., 2021).
4.8. Cluster XIII: Social Media and E-Commerce
Social media sites like Twitter, Facebook, LinkedIn, Instagram, and YouTube have exploded in popularity
over the last decade, making them more widely available to the general public than ever before (Gyenge
et al., 2021). Businesses can now more easily communicate with their clients thanks to the increasing
use of social networking platforms (Schivinski & Dabrowski, 2016). Traditional media, such as television,
radio, and periodicals, have been replaced by social networking sites in the last decade (Evans et al.,
2021). Four of ve Internet users have at least one social media account, and every seventh person has a
Facebook prole (Rappaport, 2010). As social media and internet users expand, large brands must better
understand online customer behavior (Icha & others, 2015). Due to the rise of social media and the
subsequent shift in media consumption, companies and organizations use social media as a marketing
Page 16/32
strategy and public relations tool (Costa & Castro, 2021). It has led to using social media to promote
online goods and services to potential customers and clients (S. Singh et al., 2017).
4.9. Cluster IX: Marketing Through Videos and Reels on
Social Media
A digital marketing tool is video marketing. It entails using videos to promote your goods or services
online, raise brand awareness and audience participation, and spread your message to new audiences.
Therefore, it pertains to including videos in your content marketing plan. Another idea to remember is
social video, a particular type made with social media sharing and promotion in mind (Masciantonio et
al., 2021). The objective is to produce social media-optimized, shareable video and reel content across
all social networks where your brand is active (Stsiampkouskaya et al., 2021). It can be expensive to
deliver high-quality video material, but the investment is worthwhile.
This movement heavily relies on mobile devices. Optimizing your movies for mobile devices is essential
since more people watch lms on their smartphones. Video material is also no longer limited to a single
network. It has evolved and is now distributed through various platforms, including Facebook, YouTube,
Instagram, TikTok, and Snapchat, to mention a few (Lim et al., 2022). Video campaigns, particularly, as
well as digital marketing in general, are just one component of your branding and advertising strategy.
Produce videos showcasing your brand's overall identity, values, and style. After TikTok criticized
Facebook's attack tactics, a source claimed that Zuckerberg's lobbying was a signicant factor in the
pressure that TikTok experienced in the U.S. market. In addition to other concerns, Facebook has a
reputation for being aggressive. The most common is the Snapchat imitation tactic (Plaisime et al.,
2020). Because of its "Stories" feature, Snapchat gained tremendous popularity. As the engagement of
this feature increased, the market began progressively understanding Snapchat's commercialization
approach. Instagram introduced its own Stories feature during the peak of Snapchat's popularity, and it
earned more positive reviews than Snapchat. Eight months after its debut, Instagram Stories has
eclipsed Snapchat in terms of daily active users, making it the most signicant component of
Instagram's advertising portfolio (Lin & others, 2022). Although the overwhelming number of users is
purportedly to blame for the copycat approach to Snapchat's success, it is the inevitable outcome of the
monopolistic inuence of the prominent market share among all the social media platforms.
4.10. Cluster X: Social Listening Analysis and Strategies
In a world with abundant data, listening to learn, comprehend, and make intelligent decisions has
become rare. Humans are taught listening skills before speaking at a young age, but sadly, many people
listen more to reply than to learn (Crotts, 1999). Interestingly, despite the prevalence of social media
conversations worldwide, very few individuals or organizations pay attention to or take action on them
(Drury, 2008). 90% of the data in the world, according to the International Business Machines (IBM) 2017
Cloud Marketing Report, was produced in the previous two years. Based on that, we may conclude that
rms can use ve (5) years' worth of data to develop their operations in 2020 (Goldman, 2013). Based on
the mentioned, it is vital to inquire as to what businesses are doing with the data that is easily accessible
Page 17/32
to them. Are companies getting more innovative, or do they still rely only on being forceful about the goal
of their brand? Marketing is an age-old art practiced in one form or another since the beginning
(O’Donohoe, 2008). Authorities have given many denitions of the term "marketing." Making products
available when and where needed was the conventional marketing goal. Still, this idea eventually
evolved, and the focus switched to the “satisfaction of human want” from the “exchange” of goods
(Deepak & Jeyakumar, 2019).
5. Future Research Directions
Between 2020 and 2021, the potential reach of digital marketing increased signicantly. Despite the
pandemic's overall impact on our lives for two years, growth in the digital realm has been steady and
impressive (Figueiredo et al., 2021). In 2022, it is expected to rise much more dramatically. One of the
primary reasons for the importance of technology in the business world, particularly for small and
medium-sized enterprises, is its ability to connect people from various geographic locations and
nationalities (Wendt et al., 2021). Furthermore, purchasing transactions such as online commerce will
become more straightforward. Therefore, digital marketing researchers may focus on various research
elds based on current trends from the dataset's information modeling (Saura, 2021). Some of the
research directions in the eld of digital marketing are as follows:
Articial Intelligence technology in Digital Marketing is currently the most researched topic (Mustak et
al., 2021). Particular applications of AI in digital marketing include recommender systems, dynamic
pricing models, customer support chatbots, Pay per Click, and Advertisement Optimizations (V. Singh et
al., 2022).
Researchers can focus on IoT, autonomous marketing, and natural dialogue shortly (Sachdev, 2020).
Information modeling suggests that Programmatic advertisements in digital marketing constitute the
third largest cluster in the present research.
Another research area a researcher can focus on is Automated and Personalized Email Marketing
(Shifhuddin, 2022). Like most other rms operating in the twenty-rst century, email marketing
increasingly relies on machine learning and articial intelligence (Hufbauer & Jung, 2021). Technical
improvements, both digital and analog, have helped pave the way for the development of marketing
automation, which makes it possible to tailor communications to a specic target audience depending
on the information in that audience's prole (Behera et al., 2021). Nowadays, it is not easy to imagine
what life would be like without email. It is estimated that more than 4billion people will have at least one
email account by 2023. It will make email one of the most widespread ways people worldwide
communicate.
Because there is such a large user base, it is only logical for marketing teams to deliver their messages
directly to the target population's inboxes. Social media marketing is a well-known subgenre of digital
marketing, mainly when videos and reels are utilized (Vlasich, 2022).
Page 18/32
Statista reports that the daily average time spent watching online videos by internet users is 1 hour and
43 minutes (Einav, 2022). Therefore, creating video content that is readily available and thoroughly
optimized for every social network on which your business appears is a signicant responsibility (Giertz
et al., 2021). Producing a high-quality lm can be costly and time-consuming, but the benets are
enormous. The success of this industry will depend on the provision of reliable, specialized video
creation and editing technologies.
As a result of the most recent technological revolution, digital marketing has emerged and become
widely used commercially. In today's modern business world, digital marketing strategies have entirely
supplanted their more analog predecessors.
6. Conclusion
Digital marketing has experienced considerable advancements in the previous decade, substantially
impacting billions of people worldwide. Digital marketing, which includes social media, search engine
optimization (SEO), augmented reality (A.R.), virtual reality (V.R.), and chatbots, is leading the way in
changing corporate strategy and how consumers interact. With the increasing availability of
sophisticated tools and strategies, digital marketing will remain crucial for rms to compete effectively
in their marketplaces. Small businesses can now obtain a higher return on investment and conduct
successful advertising campaigns due to the growing availability of digital marketing solutions. The
democratization of advertising has created several opportunities and is expected to inuence the future
of marketing strategies. Shortly, businesses are anticipated to fully utilize digital marketing with the
assistance of technology like articial intelligence (A.I.), search engine optimization (SEO), augmented
reality (A.R.), virtual reality (V.R.), and chatbots. Recent studies have pinpointed a rise in interest in
utilizing Articial Intelligence and Machine Learning in digital marketing to improve marketing tactics.
This research is anticipated to stimulate innovation in digital marketing, potentially transforming
theoretical understanding and practical implementations in the industry.
Declarations
Acknowledgement:
The authors present their appreciation to King Saud University for funding the publication of this
research through Researchers Supporting Program (RSPD2024R809), King Saud University, Riyadh, Saudi
Arabia.
Funding statement:
Competing Interests
The authors have no relevant nancial or non-nancial interests to disclose
Data availability
Page 19/32
The datasets used during the current study are available from the corresponding author on request.
Funding
The research is funded by Researchers Supporting Program at King Saud University, (RSPD2024R809)
Clinical trial number: Not Applicable.
Consent to publish : We provide consent to publish.
Ethics, Consent to Participate : Not applicable.
Author Contribution
Conception and design of the work - Chetan Sharma, Sandeep Kautish , Timilehin Olasoji Olubiyi Data
collection - Chetan Sharma, Shamneesh SharmaData Analysis and interpretation—Komal Sharma,Chetan
Sharma, Sandeep KautishDrafting the article- Sandeep Kautish , Timilehin Olasoji Olubiyi critical revision
of the article -Sandeep Kautish ,
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Figures
Figure 1
Digital Marketing Process
Page 29/32
Figure 2
Proposed Methodology
Page 30/32
Figure 3
Year-wise Publication Analysis
Figure 4
Journals Analysis
Figure 5
Top Authors with Citations
Page 31/32
Figure 6
Top 20 Frequent Terms
Page 32/32
Figure 7
Representation of K-Mean Clustering
