Is AI and Chatbots-based Digital Marketing the Future? A Natural Language-based Explorative Study

Is AI and Chatbots-based Digital Marketing the Future? 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A Natural Language-based Explorative Study Chetan Sharma, Shamneesh Sharma, Komal Sharma, Sandeep Kautish, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5865492/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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 significant trends were artificial intelligence, chatbots, and programmatic advertising. The thorough analysis and classification offer researchers and specialists critical perspectives and emphasize the increasing importance of chatbots in digital marketing. E-Marketing E-Commerce Artificial Intelligence (AI) Latent Semantic Analysis (LSA) KNIME (Konstanz Information Miner) Chatbots Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Artificial 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 firms 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 marketers into a new marketing era. Almost 90% of businesses use Twitter for marketing, and more than 50 million 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 five 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 significance 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 traffic (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 field (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. 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 significant 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 identified 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 influencer 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 influencer 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 significantly 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 field 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 influencer’s role in swaying internet users' purchasing decisions. Companies can improve their brand awareness and product positioning by having influential 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 field 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 fields earlier but not to digital marketing. The researchers formulated research questions based on a literature 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 fields, 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 finding 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 field 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 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. Figure 2 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 first 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 find 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 filtered 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 workflow 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 first step in normalizing data is called pre-processing. Before creating the final 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 specific situation. The third step involves removing the punctuation marks from the text. Following that, a number filter will remove all the numbers from the document because a number itself does not convey any information. In the fifth step, you should remove stop words such as is, am, and are from your writing. This filter 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. Figure 4 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 influence in this field, and the good participation rate, according to an assessment of renowned journals. As a result, researchers in this field can draw on the work presented in these proceedings to inform their work. In this field of research, the first 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 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 specific 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 Table 1 . 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. Figure 6 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 identified 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 Table 1 against the 4808 documents. 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 find 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 significant 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 sufficient for predicting developments in digital marketing. Text mining necessitates topic labeling, which is a crucial step. The TF-IDF score significantly impacts the topics in this study because the latent semantic analysis is used. 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 ). Table 2 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. Table 2 Topic Label with High Loading Terms Cluster Terms Topic High Loading Paper Cluster I media, social, behavior, digit, intelligen, strategus, datum, result, onlin, inform, strategi, custom, market, Internet, busi, product, advertis, commun, atrtificial Artificial 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, efficien 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 V datum, 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 ) 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 X market, 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: Artificial Intelligence in Digital Marketing In digital marketing, artificial 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 five 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 Artificial Intelligence to reduce resources and save time. Through machine learning technologies and interaction with virtual assistants, artificial intelligence can create simulation models and personalize purchasing processes (Rao et al., 2016 ). Artificial Intelligence has become a popular way for brands to 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 efficiently (Zhu & Wu, 2011 ). Artificial 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 modified 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 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 4 billion views, which is the equivalent of 500 years of video" (spread among 800 million 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 efficient 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 200 million 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 800 million 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 significant challenge (Al-Dhuhli & Ismael, 2013 ). Modern lifestyles require further technology adjustments and adjustments in conventional businesses that reflect new life changes. Digital media marketing plays a significant 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 five Internet users have at least one social media account, and every seventh person has a Facebook profile (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 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 films 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 significant 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 significant 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 influence 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 firms can use five (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 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 definitions 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 significantly. 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 fields based on current trends from the dataset's information modeling (Saura, 2021 ). Some of the research directions in the field of digital marketing are as follows: Artificial 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 firms operating in the twenty-first century, email marketing increasingly relies on machine learning and artificial 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 specific target audience depending on the information in that audience's profile (Behera et al., 2021 ). Nowadays, it is not easy to imagine what life would be like without email. It is estimated that more than 4 billion 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 ). 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 significant responsibility (Giertz et al., 2021 ). Producing a high-quality film can be costly and time-consuming, but the benefits 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 firms 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 influence the future of marketing strategies. Shortly, businesses are anticipated to fully utilize digital marketing with the assistance of technology like artificial 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 Artificial 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 financial or non-financial interests to disclose Data availability 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 , References Akhtar, N., Tahir, M., & Asghar, Z. (2016). Impact of social media marketing on consumer purchase intention. 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Application of artificial intelligence (AI) in search engine optimization (SEO). 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT) , 96–101. Zari, T. S. (2021). Digital advertising and its impact on Consumer Behaviour. International Research Journal of Humanities and Interdisciplinary Studies , 2 (5), 121–127. Zhu, C., & Wu, G. (2011). Research and analysis of search engine optimization factors based on reverse engineeing. 2011 Third International Conference on Multimedia Information Networking and Security , 225–228. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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04:46:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1646226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5865492/v1/ada85a63-4f51-496f-9c40-573b43797d59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Is AI and Chatbots-based Digital Marketing the Future? A Natural Language-based Explorative Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial 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 firms 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.\u003c/p\u003e \u003cp\u003eBeginning with Web 1.0 (syntactic web), we have reached the stage of Web 4.0 (meta web) (Kartajaya et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)(Duy et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In line with this, marketing has evolved from marketing 1.0 (identity) to marketing 4.0 (Dash et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 \u0026amp; Ver\u0026iacute;ssimo, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Digital marketing is the umbrella term for any marketing effort using an electronic device or the Internet (Suppatvech et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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 \u0026amp; Kapoor, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It all began in the 1980s, and by the beginning of 2020, almost 60% of the world's population is online (Tembo \u0026amp; Malik, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Digital and social media marketing (DSMM) has become the backbone of modern marketing strategies (Dwivedi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). DSMM can help companies achieve their marketing objectives at a low cost and in less time (Appel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Social media, primarily, has driven marketers into a new marketing era. Almost 90% of businesses use Twitter for marketing, and more than 50\u0026nbsp;million businesses engage their stakeholders via Facebook (Dwivedi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, the use of DSMM tools and applications to raise public awareness skyrocketed in the last five years. In addition, from the customers\u0026rsquo; 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003cp\u003eThe rest of the paper is organized as follows: section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2\u003c/span\u003e 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.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eMarketers 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, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some researchers claim that digital marketing is becoming increasingly popular in India (Kaushik, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, according to more research, as more and more young people shop online, the effectiveness of traditional marketing is dwindling (Todor, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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 significance 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, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To date, work in this area supports that SEO is the most successful method for gaining organic customer traffic (Kannan \u0026amp; 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., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The authors carried out an SLR on B2B digital marketing to zero in on relevant literature in the field (Ram \u0026amp; Zhang, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eSeveral 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., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). 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 significant 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., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 identified 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 influencer marketing (Tanwar et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The researchers studied the literature from 2011 to 2019 by using R-Tool. The researchers provide an overview of the history and development of influencer 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 significantly impact (Krishen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 field 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., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). 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 influencer\u0026rsquo;s role in swaying internet users' purchasing decisions. Companies can improve their brand awareness and product positioning by having influential people spread carefully crafted messages (Le\u0026oacute;n-Castro et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eUpon an extensive review of research in the field 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 fields earlier but not to digital marketing. The researchers formulated research questions based on a literature review and then answered these questions based on terms related to documents. The following questions were proposed for this research:\u003c/p\u003e \u003cp\u003eRQ1: How has digital marketing seen its growth?\u003c/p\u003e \u003cp\u003eRQ2: Which top journals have focused on digital marketing?\u003c/p\u003e \u003cp\u003eRQ3: What are the top keywords contributing to digital marketing research?\u003c/p\u003e \u003cp\u003eRQ4: What are the recent trends for the present and future researchers?\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Natural Language Processing and Latent Semantic Analysis\u003c/h2\u003e \u003cp\u003eTopic 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., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Various researchers use this technique in their respective fields, such as medical research (Hassan et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Selvi et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), engineering (Gurcan \u0026amp; Cagiltay, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), 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 finding 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., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has been half a century since linguistics and computers merged to create the field of natural language processing (S. Yu \u0026amp; Lu, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs 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., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 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 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).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen 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., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, LSA is implemented to make forecasts regarding potential developments within digital marketing. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the research approach that has been used for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Collection\u003c/h2\u003e \u003cp\u003eIn 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 first step is to gather data to conduct the research and collect the string data formulation developed following Kitchenham \u0026amp; Charters guidelines (Kitchenham \u0026amp; Charters, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This study used \" Digital Marketing \" as a search string to find the data.\u003c/p\u003e \u003cp\u003eThe 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 \u0026amp; 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 filtered 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Preprocessing\u003c/h2\u003e \u003cp\u003eThis 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., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). KNIME is easy to use, allowing users to share their workflow with buddy researchers (Wratten et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first step in normalizing data is called pre-processing. Before creating the final 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., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The following step is to change all words in the corpus to either lowercase or uppercase, depending on the specific situation. The third step involves removing the punctuation marks from the text. Following that, a number filter will remove all the numbers from the document because a number itself does not convey any information. In the fifth step, you should remove stop words such as is, am, and are from your writing. This filter for stop words eliminates the stop words that are causing the documents to be devoid of all English words (HaCohen-Kerner et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 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., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result Analysis","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Meta-Analysis\u003c/h2\u003e \u003cp\u003eIn the meta-analysis, the number of selected studies was counted and analyzed in terms of years, with the results depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showing the number of publications produced each year. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which is organized by year. The gathering of the necessary documents is an essential component of the process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e 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 influence in this field, and the good participation rate, according to an assessment of renowned journals. As a result, researchers in this field can draw on the work presented in these proceedings to inform their work.\u003c/p\u003e \u003cp\u003eIn this field of research, the first article was published in 1989, and further data show extensive growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVarious authors contribute to this context, and the top 10 researchers and their citations are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Term Frequency (T.F.) And Inverse Document Frequency (IDF)\u003c/h2\u003e \u003cp\u003eOne 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., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). 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 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., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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 \u0026amp; Raghunathan, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). TF-IDF is a popular algorithm for determining the relevance of a document to a specific 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 Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The TF-IDF score for a term (word) \"t\" in a document \"d\" can be calculated using the Eq.\u0026nbsp;1:\u003c/p\u003e \u003cp\u003eTF-IDF(t,d)\u0026thinsp;=\u0026thinsp;TF(t,d) * IDF(t) (1)\u003c/p\u003e \u003cp\u003ewhere,\u003c/p\u003e \u003cp\u003eTF(t,d) = (Number of times the term t appears in the document d) / (Total number of terms in the document d)\u003c/p\u003e \u003cp\u003eIDF(t)\u0026thinsp;=\u0026thinsp;log_e(Total number of documents / Number of documents with the term t in it)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 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. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e 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 identified topic solutions, which follows the weighting scheme described in the above equations.\u003c/p\u003e \u003cp\u003eBOW 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 Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e against the 4808 documents.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTransformed TF-IDF values Representation of 4808 Documents\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econsum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eproduct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecommun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edevelop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprovid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecustom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026hellip;...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Optimal Topic Solution\u003c/h2\u003e \u003cp\u003eChoosing an appropriate dimension has proven to be a significant problem in this procedure, as it requires extensive expertise and multiple iterations to achieve the best possible result (Evangelopoulos et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For example, ten topic solutions are estimated to be the optimal number for a 4808-document corpus (Deerwester et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Nevertheless, it may be sufficient for predicting developments in digital marketing. Text mining necessitates topic labeling, which is a crucial step. The TF-IDF score significantly impacts the topics in this study because the latent semantic analysis is used. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Clustering\u003c/h2\u003e \u003cp\u003ePapers 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., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e 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.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTopic Label with High Loading Terms\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Loading Paper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedia, social, behavior, digit, intelligen, strategus, datum, result, onlin, inform, strategi, custom, market, Internet, busi, product, advertis, commun, atrtificial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArtificial Intelligence in Digital Marketing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(van Esch \u0026amp; Stewart Black, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(V. Singh et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(Sinha et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarket, conversat, ecosystem, hypermarket, techologus, messag, specialis, phone, applic, interact, busi, efficien\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChatbot conversation in Business\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Mufadhol et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(BARI\\cS, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Illescas-Manzano et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarket, digit, social, messag, applic, analy, inform, custom, german, scienc, adolesc, competit, entrant, week, medi,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial media analytics and Applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Bekmamedova \u0026amp; Shanks, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Ayodeji \u0026amp; Kumar, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Moon et al., 2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esite, brand, price, tourist, perform, proxim, skill, search, user, region, destin, property, view, sme, intellig, googl, programmat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgrammatic Advertising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Busch, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Seitz \u0026amp; Zorn, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Kiran \u0026amp; Arumugam, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edatum, custom, service, commun, onlin, model, busi, inform, develop, channel, consum, media, brand, companus, effect, qual, advertis, tourism, social, search, factor, design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRole of Social Media in Tourism Marketing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Sahin \u0026amp; Seng\u0026uuml;n, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Alghizzawi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Lange-Faria \u0026amp; Elliot, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebrand, impact, inform, digit, commun, sale, consum, user, educ, search, shop, medi, Interact, motiv, intern, destin, websit, behavio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpact of Digital Media Advertisement on consumer behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Sama, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Zari, \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Stephen, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster VII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edigit, market, system, valu, risk, instagram, person, facebook, environ, email, websit, driver, applic, enterpris, mobil, automat,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomated \u0026amp; Personalized Email Marketing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Goic et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Mohammadi et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Hafaiedh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster VIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecommerc, Social, media, datum, consum, busi, base, market, strategi, onlin, content, technolog, Electronic, manag, brand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial Media and E-Commerce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Kwahk \u0026amp; Ge, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Linda, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(M. Singh \u0026amp; Singh, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster IX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evideo, package, real, Reel, tiktok, Instagram, post, publish, vlog, youtube, ad, fashion, cosmet, whatsapp, expo, host, facebook, market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarketing through videos and reels on social media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Ayeni, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Stsiampkouskaya et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Moriuchi, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarket, studi, social, research, media, datum, onlin, digit, busi, analysi, listen, custom, social, inform, strategi,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esocial listening analysis and strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Chaffey \u0026amp; Smith, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Saura, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Chaffey \u0026amp; Smith, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn 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.\u0026nbsp;7.\u003c/p\u003e \u003cp\u003eIn this experiment, authors calculated different values, and based on these achieved values, a graphical representation has been provided by the authors.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research Areas and Current Trends","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Cluster I: Artificial Intelligence in Digital Marketing\u003c/h2\u003e \u003cp\u003eIn digital marketing, artificial intelligence differs from human intuition because data drive it. A.I. is an extension of human intelligence, commonly called robot-driven intelligence (Davenport \u0026amp; Ronanki, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). 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, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared to traditional retailers, retailers that use AI-powered marketing perform five 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, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Brands and marketers are incorporating Machine Learning and Artificial Intelligence to reduce resources and save time. Through machine learning technologies and interaction with virtual assistants, artificial intelligence can create simulation models and personalize purchasing processes (Rao et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Artificial Intelligence has become a popular way for brands to interact with customers. Amazon uses a similar system to recommend products based on previous purchases, views, and searches (Le \u0026amp; Ha, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Marketing automation systems, like CRMs, help us manage data and cater to customers more efficiently (Zhu \u0026amp; Wu, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Artificial 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 \u0026amp; Yang, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Cluster II: Chatbot Conversation in Business\u003c/h2\u003e \u003cp\u003eA 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, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). 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., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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 \u0026amp; Chen, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Instant responses to customer requests will enhance customer satisfaction. Chatbots can be incredibly useful When building good relationships with customers (Jia, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). 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 \u0026amp; Wibowo, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Cluster III: Programmatic Advertising\u003c/h2\u003e \u003cp\u003eOnline advertising is bought and sold automatically through programmatic advertising (W. Li et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By automating the digital advertising process, you can streamline your efforts and consolidate them into one platform (Rogers, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 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 \u0026amp; Samuel, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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 \u0026amp; Zorn, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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 modified the advertising process by deploying targeted campaigns to be more need-based (Gertz \u0026amp; McGlashan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Cluster IV: Social Media Analytics\u003c/h2\u003e \u003cp\u003eAs 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., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). 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 4\u0026nbsp;billion views, which is the equivalent of 500 years of video\" (spread among 800\u0026nbsp;million unique users) (Hoffman \u0026amp; Fodor, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). 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, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). 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., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Cluster V: Role of Social Media in Tourism Marketing\u003c/h2\u003e \u003cp\u003eSocial 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 \u0026amp; Fountain, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additionally, companies have communicated with consumers nationwide without ever having to meet them (Cooper et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). 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 (Kir\u0026aacute;l\u0026rsquo;ov\u0026aacute; \u0026amp; Pavl\\\u0026rsquo;\\ičeka, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Social media is one of the most efficient ways tourist groups promote locations and products (Kir\u0026aacute;l\u0026rsquo;ov\u0026aacute;, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a result, tourism organizations increasingly use social media tools like Yelp, TripAdvisor, Trip Hobo, and more. Given that TripAdvisor has received more than 200\u0026nbsp;million evaluations and opinions, it is safe to say that social media dominates tourism marketing (Lv et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, Facebook has over 800\u0026nbsp;million users who share updates and photos from their travels (Kir\u0026aacute;lov\u0026aacute; \u0026amp; Malachovsk\\`y, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Cluster VI: Impact of Digital Media Advertisement on Consumer Behavior\u003c/h2\u003e \u003cp\u003eDigital 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., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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 significant challenge (Al-Dhuhli \u0026amp; Ismael, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Modern lifestyles require further technology adjustments and adjustments in conventional businesses that reflect new life changes. Digital media marketing plays a significant role in consumer behavior, especially in the modern world strongly fenced with technology (Luo, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 \u0026amp; Narteh, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Increasingly, social media marketing is becoming an essential aspect of marketing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Cluster VII: Automated and Personalized Email Marketing\u003c/h2\u003e \u003cp\u003eEmail marketing automation transforms the manual distribution of marketing emails into a mechanism that selects recipients according to predetermined criteria (Kumar, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 \u0026amp; Sonderegger, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 (Ros\u0026aacute;rio \u0026amp; Raimundo, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, a website visitor can be curious about your business but not quite ready for a salesperson to contact them (Shankar et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.8. Cluster XIII: Social Media and E-Commerce\u003c/h2\u003e \u003cp\u003eSocial 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., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Businesses can now more easily communicate with their clients thanks to the increasing use of social networking platforms (Schivinski \u0026amp; Dabrowski, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Traditional media, such as television, radio, and periodicals, have been replaced by social networking sites in the last decade (Evans et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Four of five Internet users have at least one social media account, and every seventh person has a Facebook profile (Rappaport, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As social media and internet users expand, large brands must better understand online customer behavior (Icha \u0026amp; 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 strategy and public relations tool (Costa \u0026amp; Castro, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has led to using social media to promote online goods and services to potential customers and clients (S. Singh et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.9. Cluster IX: Marketing Through Videos and Reels on Social Media\u003c/h2\u003e \u003cp\u003eA 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., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can be expensive to deliver high-quality video material, but the investment is worthwhile.\u003c/p\u003e \u003cp\u003eThis movement heavily relies on mobile devices. Optimizing your movies for mobile devices is essential since more people watch films 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., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). 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 significant 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., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 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 significant component of Instagram's advertising portfolio (Lin \u0026amp; 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 influence of the prominent market share among all the social media platforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.10. Cluster X: Social Listening Analysis and Strategies\u003c/h2\u003e \u003cp\u003eIn 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, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Interestingly, despite the prevalence of social media conversations worldwide, very few individuals or organizations pay attention to or take action on them (Drury, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). 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 firms can use five (5) years' worth of data to develop their operations in 2020 (Goldman, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Based on the mentioned, it is vital to inquire as to what businesses are doing with the data that is easily accessible 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\u0026rsquo;Donohoe, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Authorities have given many definitions 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 \u0026ldquo;satisfaction of human want\u0026rdquo; from the \u0026ldquo;exchange\u0026rdquo; of goods (Deepak \u0026amp; Jeyakumar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Future Research Directions","content":"\u003cp\u003eBetween 2020 and 2021, the potential reach of digital marketing increased significantly. 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., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, purchasing transactions such as online commerce will become more straightforward. Therefore, digital marketing researchers may focus on various research fields based on current trends from the dataset's information modeling (Saura, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some of the research directions in the field of digital marketing are as follows:\u003c/p\u003e \u003cp\u003eArtificial Intelligence technology in Digital Marketing is currently the most researched topic (Mustak et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers can focus on IoT, autonomous marketing, and natural dialogue shortly (Sachdev, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Information modeling suggests that Programmatic advertisements in digital marketing constitute the third largest cluster in the present research.\u003c/p\u003e \u003cp\u003eAnother research area a researcher can focus on is Automated and Personalized Email Marketing (Shifhuddin, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Like most other firms operating in the twenty-first century, email marketing increasingly relies on machine learning and artificial intelligence (Hufbauer \u0026amp; Jung, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 specific target audience depending on the information in that audience's profile (Behera et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nowadays, it is not easy to imagine what life would be like without email. It is estimated that more than 4\u0026nbsp;billion people will have at least one email account by 2023. It will make email one of the most widespread ways people worldwide communicate.\u003c/p\u003e \u003cp\u003eBecause 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, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatista reports that the daily average time spent watching online videos by internet users is 1 hour and 43 minutes (Einav, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, creating video content that is readily available and thoroughly optimized for every social network on which your business appears is a significant responsibility (Giertz et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Producing a high-quality film can be costly and time-consuming, but the benefits are enormous. The success of this industry will depend on the provision of reliable, specialized video creation and editing technologies.\u003c/p\u003e \u003cp\u003eAs 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.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eDigital 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 firms 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 influence the future of marketing strategies. Shortly, businesses are anticipated to fully utilize digital marketing with the assistance of technology like artificial 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 Artificial 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research is funded by Researchers Supporting Program at King Saud University, \u0026nbsp;(RSPD2024R809)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e : We provide consent to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate\u003c/strong\u003e : Not applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design of the work - Chetan Sharma, Sandeep Kautish , Timilehin Olasoji Olubiyi Data collection - Chetan Sharma, Shamneesh SharmaData Analysis and interpretation\u0026mdash;Komal Sharma,Chetan Sharma, Sandeep KautishDrafting the article- Sandeep Kautish , Timilehin Olasoji Olubiyi critical revision of the article -Sandeep Kautish ,\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkhtar, N., Tahir, M., \u0026amp; Asghar, Z. 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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 significant trends were artificial intelligence, chatbots, and programmatic advertising. The thorough analysis and classification offer researchers and specialists critical perspectives and emphasize the increasing importance of chatbots in digital marketing.\u003c/p\u003e","manuscriptTitle":"Is AI and Chatbots-based Digital Marketing the Future? 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