1. Introduction
The term artificial intelligence was first used in the 1950s at Dartmouth College. It is then founded as a research discipline based on the hypothesis that any part of learning and all other characteristics of intelligence may, in theory, be so thoroughly described that it can be simulated by a machine [1]. Since then, artificial intelligence has been applied in different areas to improve productivity [2-4]. In the field of marketing, AI technologies have also been changing how businesses and organisations attract their target audience. By 2030, artificial intelligence in the marketing field is anticipated to produce a 47 billion dollar market at a compound annual growth rate of 28.6% [5]. As AI technology has flourished, so have its applications in marketing, with tremendous potential to increase marketing channels, deliver marketing strategy, boost marketing results, and provide better segmentation, targeting, and positioning [6].
There is a lack of integrated research on the state of work generated on artificial intelligence in the field of marketing [7]. And there is a significant disconnect between what artificial intelligence marketing technologies are capable of and how they are being applied in real-world marketing situations [8]. This article aims to aid a larger marketing community, including companies, organisations, business owners, marketing workers, marketing researchers, and artificial intelligence product developers, in developing a greater comprehension of artificial intelligence in marketing. This is done in an effort to enhance artificial intelligence technologies for marketing through a variety of analysis techniques. This in-depth study of linked literature specifically tries to accomplish the following objectives: first, the study aims to provide companies, organisations, and marketing and AI technology specialists with useful advice. Second, it intends to encourage interaction and cooperation amongst stakeholders in various areas of marketing. Third, it helps to comprehend research in artificial intelligence in marketing and development from the perspectives of current technologies. At last, it attempts to bridge the gap among the research, development, implementation, and assessment of artificial intelligence in marketing.
Thus, by centring on marketing, this study presents an overview of current studies on artificial intelligent technologies using the method of bibliometrics [9,10] and content analysis [11–13]. This study aims to find out the following four points throughout the process of searching and analysing the literature in the field of AI in marketing: 1. the distribution of existing AI marketing-related research; 2. the AI marketing technologies studied in the selected articles; 3. the benefits the technologies have on marketing; 4. the effect recent research have on AI marketing.
2. Methodology
2.1. Bibliometrics and content analysis
Bibliometrics is a tool broadly used since 1969 for tracking the overall state of scientific research at a certain degree of specialisation in order to observe the state of a certain field of study [9,10]. Content analysis is a method to examine the subject in articles used by researchers since 2009 to review publications on a certain topic [11–13]. This study chose bibliometrics and content analysis as the analysis methods.
2.2. Source database
Bibliometric research must come from a source database [9]. The ScienceDirect database and the IEEE Xplore database were chosen as the source databases of the scientific literature of this study. ScienceDirect database and IEEE Xplore were selected because high-quality journals from the social sciences and natural sciences are included in these databases and they are web-based databases that allow for in-depth bibliometric analysis [9].
2.3. Searching and selection criteria
Bibliometric research utilizes scientific parameters including articles and citations [9]. To acquire the parameter for this study, the source database was searched using various keyword combinations such as "AI", "artificial intelligence", and "marketing". A total of 136 publications were initially found by selecting the most relevant article titles after three rounds of searches on ScienceDirect; five non-English articles were disregarded in the preliminary screening. The following search criteria were applied in order to accomplish the aim of this research:
Only English-language journal publications presenting empirical studies were chosen.
Non-English publications were excluded.
Theoretical or conceptual articles were excluded.
Reports of personal user experiences and articles reporting insufficient or no data were excluded.
Research without human participants was excluded.
Research unrelated to marketing or artificial intelligence was excluded.
Quantitative Research with fewer than 20 participants was excluded.
Additionally, in the process of selection, these criteria were applied:
Research must focus on the impact of artificial intelligence on marketing.
Research must have enough human participants.
Research must be based on empirical studies.
Research must not be a literature review.
During the selection process, 116 research papers were filtered and 11 research papers were selected for this analysis. Of those filtered articles, literature review articles were thoroughly read to gain a better understanding of this topic. Qualified articles were examined to identify these factors: bibliometrics, place of study, demographic of the sample, artificial intelligence technologies, and the impact on marketing. A list of indicators is advanced to conduct this review: bibliometrics of the article including the date of publishment, authors and journal of the article, place of the study conducted, participants of the study including the age and industry of the marketers, technologies mentioned in the survey, and the marketing effects.
3. Results
3.1. Study sites
Artificial intelligence in marketing has been studied all over the world. Study sites from the 11 selected articles are located in 9 countries including India, the US, South Africa, Saudi Arabia, China, Serbia, Indonesia, Egypt, and the UK.
3.2. Participants and sample sizes
The sample sizes of the research on artificial intelligence in marketing differ hugely. The number of participants ranges from 30 [14] to 492 [15]. The geographical background and social status of the participants also range vastly, ranging from Europe [16,17], Asia [15,18], Africa [19,20], to North America [14,21] and from students [16], scholars [17] to senior managers [22]. For instance, Keegan et al. [17] analysed the adoption of artificial intelligence in business-to-business marketing, and this study sampled people from experts on AI research as well as suppliers of AT technologies. However, most of the participants are between 22 to 35 years old as these people are more familiar with AI applications [23] and therefore there is a lack of research on participants who are older than 50 years old (See Table 1).
Table 1. An overview of the place of study and participant demographics of artificial intelligence in marketing research.
Study Site Location | n | Article | Participants |
India | 2 | [18,23] | 276 millennials between 22-37 years old |
349 managers of 27 B2B organisation that uses artificial intelligence-based customer relationship management | |||
US | 2 | [14,21] | 368 participants between 25–48 years old that are users of an AI application |
30 marketing managers, product or brand managers, and sales managers | |||
South Africa | 1 | [22] | 306 senior executives including General managers, senior managers, managers, and junior managers. |
Saudi Arabia | 1 | [19] | 131 participants of diverse backgrounds |
China | 1 | [15] | 492 citizens who had used a tax AI assistant service |
Serbia | 1 | [16] | 61 millennial students between 19 - 35 years old |
Indonesia | 1 | [24] | 399 consumers of a company |
Egypt | 1 | [20] | 168 AI experts at IT departments of 20 international AI-powered organisations in Egypt |
UK | 1 | [17] | 20 participants including B2B firms deploying AI solutions, AI suppliers, and scholars that are experts on AI in B2B |
To sum up, participants of existing research on artificial intelligence in marketing have diverse backgrounds, occupations, and social statuses but they come from relatively homogenous age groups.
3.3. Collaborations in AI in marketing studies
The majority of the research papers on this topic are the achievements of more than one author. Only 3 articles of the 11 are written by one author [14,19,21]. Some studies are even collaborations of scholars in different countries [18].
3.4. Industries
Research in this field has been conducted in various industries including pharmacy, tourism, entertainment, e-commerce, manufacturing, mining, banking, and telecommunications [14,20,22,23].
3.5. Technologies
Artificial intelligence applications have been utilised in numerous aspects of marketing. The selected studies conducted investigations on applications including virtual assistants, chatbots, machine learning, e-marketing tool, and expert systems (See Table 2).
4. Conclusion
The use of artificial intelligence in marketing is anticipated to increase fast in the coming years as artificial intelligence technology is developing quickly. In particular, companies and organisations may greatly benefit from the use of AI technology in marketing to increase sales, cut costs, improve firm productivity, boost innovation, and achieve organisational goals.
One of the limitations of this study is related to the choice of scientific literature databases and articles. Firstly, the terms utilised in the search include terms like “marketing and artificial intelligence”. This is normal for any search engine or technique. This review can therefore omit research articles that do not have the term “marketing” or “AI” on their titles, or articles that are in another language. Articles that are not in the chosen database could be omitted. Conference proceedings are also not included in this study even though they might contain more current or even active research initiatives given their distinct filtering methods and analysing procedures. Thus, the scope of this review is constrained.
In-depth journals, peer-reviewed conference proceedings, and other reliable databases may be added to the search scope in subsequent evaluations. Additional search terms like certain artificial intelligence technologies such as virtual reality or their marketing applications may provide better pertinent results. Artificial intelligence in marketing studies that do not involve human subjects or concentrate on developing models and applications, such as those in this study, must be carefully distinguished from research that includes teachers, students, or other human participants.
Artificial intelligence in marketing intersects with a few newly developing subfields as an interdisciplinary field, including machine learning, hardware manufacturing, e-commerce, and recommendation systems. Future studies could choose other disciplines and investigate how artificial intelligence technologies are utilised in their marketing, which can be a subfield of artificial intelligence in marketing. Researchers could also use a different online scientific literature database and different filtering methods to choose the target articles. They may also decide to look beyond peer-reviewed journal articles and into conference proceedings. Future evaluations may also focus on specific AI technologies used in the marketing field due to the integrations of new cutting-edge technologies like machine learning and virtual reality.
References
[1]. Dick S. Artificial Intelligence. Harvard Data Science Review. 2019 Jul 3 (cited 2022 Nov 8). Av ailable from: https://hdsr.mitpress.mit.edu/pub/0aytgrau/release/3.
[2]. Amisha M P, Pathania M, Rathaur V. Overview of artificial intelligence in medicine. Family Med Prim Care 8(7), 2328 (2019).
[3]. Chen L, Chen P, Lin Z. Artificial Intelligence in Education: A Review. IEEE Access 8, 75264–7 8 (2020).
[4]. Eli-Chukwu N C. Applications of Artificial Intelligence in Agriculture: A Review. Eng Technol Appl Sci Res 9(4), 4377–83 (2019).
[5]. Artificial Intelligence in Marketing Market Research Report, by Application (Ad Optimization, Content Curation), by Technology (Adaptive Learning, Advanced Analytics), by Vertical (B FSI, Government), by Deployment (On-Cloud) - Global Forecast till 2030 [Internet]. Market Research Future (MRFR); 2022 Nov (cited 2022 Nov 8) p.100. Report No.: MRFR/ICT/5106 -HCR. Available from: https://www.marketresearchfuture.com/reports/artificial-intelligencemarketing-market-6568.
[6]. Vlačić B, Corbo L, Costa e Silva S, Dabić M. The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research 128, 187–203 (2021).
[7]. Mariani M M, Perez-Vega R, Wirtz J. AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology and Marketing 39(4), 755–76 (2022).
[8]. De Bruyn A, Viswanathan V, Beh YS, Brock J K U, von Wangenheim F. Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing 51, 91–105 (2020).
[9]. Okubo Y. Bibliometric Indicators and Analysis of Research Systems: Methods and Examples [Internet]. 1997 Jan (cited 2022 Nov 9). (OECD Science, Technology and Industry Working Papers; vol. 1997/01). Report No.: 1997/01. Available from: https://www.oecd-ilibrary.org/science-and-technology/bibliometric-indicators-and-analysis-of-research-systems_208277770603.
[10]. Thelwall M. Bibliometrics to webometrics. Journal of Information Science 34(4), 605–21 (2008).
[11]. Gao F, Luo T, Zhang K. Tweeting for learning: A critical analysis of research on microblogging in education published in 2008-2011: Tweeting for learning. Br J Educ Technol 43(5), 783–801 (2012).
[12]. Mogil J S, Simmonds K, Simmonds M J. Pain research from 1975 to 2007: A categorical and bibliometric meta-trend analysis of every Research Paper published in the journal, Pain. Pain 142(1), 48–58 (2009).
[13]. Hung J L, Zhang K. Examining mobile learning trends 2003–2008: a categorical meta-trend analysis using text mining techniques. J Comput High Educ 24(1), 1–17 (2012).
[14]. Chen J. The Augmenting Effects Of Artificial Intelligence On Marketing Performance 173.
[15]. Wang C, Teo T S H, Janssen M. Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector. International Journal of Information Management 61, 102401 (2021).
[16]. Arsenijevic U, Jovic M. Artificial Intelligence Marketing: Chatbots. In: 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) [Internet]. Belgrade, Serbia: IEEE; 2019 (cited 2022 Nov 10). p.19–193. Available from: https://ieeexplore.ieee.org/document/9007330/.
[17]. Keegan B J, Canhoto A I, Yen D A wan. Power negotiation on the tango dancefloor: The adoption of AI in B2B marketing. Industrial Marketing Management 100, 36–48 (2022).
[18]. Chatterjee S, Rana N P, Tamilmani K, Sharma A. The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management 97, 205–19 (2021).
[19]. Fayed A E. Artificial Intelligence for marketing plan: the case for e-marketing companies. MMI (1), 81–95 (2021).
[20]. Younis R A A, Adel H M. Artificial Intelligence Strategy, Creativity-Oriented HRM and Knowledge-Sharing Quality: Empirical Analysis of Individual and Organisational Performance of AI-Powered Businesses. SSRN Journal [Internet]. 2020 (cited 2022 Nov 10); Available from: https://www.ssrn.com/abstract=4127128.
[21]. Moriuchi E. Okay, Google!: An empirical study on voice assistants on consumer engagement and loyalty. Psychol Mark 36(5), 489–501 (2019).
[22]. Bag S, Gupta S, Kumar A, Sivarajah U. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management 92, 178–89 (2021).
[23]. Devang V, Chintan S, Gunjan T, Krupa R. Applications of Artificial Intelligence in Marketing. EAI 25(1), 28–36 (2019).
[24]. Dharmaputra R T, Fernando Y, Aryshandy G, Ikhsan R B. Artificial Intelligence and Electronic Marketing Outcomes: An Empirical Study. In: 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS) [Internet]. Makasar, Indonesia: IEEE; 2021 (cited 2022 Nov 10). p.1–6. Available from: https://ieeexplore.ieee.org/document/9649533/.
Cite this article
Mao,Y. (2023). Current state and future development of artificial intelligence in marketing. Applied and Computational Engineering,6,536-541.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Dick S. Artificial Intelligence. Harvard Data Science Review. 2019 Jul 3 (cited 2022 Nov 8). Av ailable from: https://hdsr.mitpress.mit.edu/pub/0aytgrau/release/3.
[2]. Amisha M P, Pathania M, Rathaur V. Overview of artificial intelligence in medicine. Family Med Prim Care 8(7), 2328 (2019).
[3]. Chen L, Chen P, Lin Z. Artificial Intelligence in Education: A Review. IEEE Access 8, 75264–7 8 (2020).
[4]. Eli-Chukwu N C. Applications of Artificial Intelligence in Agriculture: A Review. Eng Technol Appl Sci Res 9(4), 4377–83 (2019).
[5]. Artificial Intelligence in Marketing Market Research Report, by Application (Ad Optimization, Content Curation), by Technology (Adaptive Learning, Advanced Analytics), by Vertical (B FSI, Government), by Deployment (On-Cloud) - Global Forecast till 2030 [Internet]. Market Research Future (MRFR); 2022 Nov (cited 2022 Nov 8) p.100. Report No.: MRFR/ICT/5106 -HCR. Available from: https://www.marketresearchfuture.com/reports/artificial-intelligencemarketing-market-6568.
[6]. Vlačić B, Corbo L, Costa e Silva S, Dabić M. The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research 128, 187–203 (2021).
[7]. Mariani M M, Perez-Vega R, Wirtz J. AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology and Marketing 39(4), 755–76 (2022).
[8]. De Bruyn A, Viswanathan V, Beh YS, Brock J K U, von Wangenheim F. Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing 51, 91–105 (2020).
[9]. Okubo Y. Bibliometric Indicators and Analysis of Research Systems: Methods and Examples [Internet]. 1997 Jan (cited 2022 Nov 9). (OECD Science, Technology and Industry Working Papers; vol. 1997/01). Report No.: 1997/01. Available from: https://www.oecd-ilibrary.org/science-and-technology/bibliometric-indicators-and-analysis-of-research-systems_208277770603.
[10]. Thelwall M. Bibliometrics to webometrics. Journal of Information Science 34(4), 605–21 (2008).
[11]. Gao F, Luo T, Zhang K. Tweeting for learning: A critical analysis of research on microblogging in education published in 2008-2011: Tweeting for learning. Br J Educ Technol 43(5), 783–801 (2012).
[12]. Mogil J S, Simmonds K, Simmonds M J. Pain research from 1975 to 2007: A categorical and bibliometric meta-trend analysis of every Research Paper published in the journal, Pain. Pain 142(1), 48–58 (2009).
[13]. Hung J L, Zhang K. Examining mobile learning trends 2003–2008: a categorical meta-trend analysis using text mining techniques. J Comput High Educ 24(1), 1–17 (2012).
[14]. Chen J. The Augmenting Effects Of Artificial Intelligence On Marketing Performance 173.
[15]. Wang C, Teo T S H, Janssen M. Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector. International Journal of Information Management 61, 102401 (2021).
[16]. Arsenijevic U, Jovic M. Artificial Intelligence Marketing: Chatbots. In: 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) [Internet]. Belgrade, Serbia: IEEE; 2019 (cited 2022 Nov 10). p.19–193. Available from: https://ieeexplore.ieee.org/document/9007330/.
[17]. Keegan B J, Canhoto A I, Yen D A wan. Power negotiation on the tango dancefloor: The adoption of AI in B2B marketing. Industrial Marketing Management 100, 36–48 (2022).
[18]. Chatterjee S, Rana N P, Tamilmani K, Sharma A. The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management 97, 205–19 (2021).
[19]. Fayed A E. Artificial Intelligence for marketing plan: the case for e-marketing companies. MMI (1), 81–95 (2021).
[20]. Younis R A A, Adel H M. Artificial Intelligence Strategy, Creativity-Oriented HRM and Knowledge-Sharing Quality: Empirical Analysis of Individual and Organisational Performance of AI-Powered Businesses. SSRN Journal [Internet]. 2020 (cited 2022 Nov 10); Available from: https://www.ssrn.com/abstract=4127128.
[21]. Moriuchi E. Okay, Google!: An empirical study on voice assistants on consumer engagement and loyalty. Psychol Mark 36(5), 489–501 (2019).
[22]. Bag S, Gupta S, Kumar A, Sivarajah U. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management 92, 178–89 (2021).
[23]. Devang V, Chintan S, Gunjan T, Krupa R. Applications of Artificial Intelligence in Marketing. EAI 25(1), 28–36 (2019).
[24]. Dharmaputra R T, Fernando Y, Aryshandy G, Ikhsan R B. Artificial Intelligence and Electronic Marketing Outcomes: An Empirical Study. In: 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS) [Internet]. Makasar, Indonesia: IEEE; 2021 (cited 2022 Nov 10). p.1–6. Available from: https://ieeexplore.ieee.org/document/9649533/.