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Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising

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Abstract

Advertising is a way in which a company introduces possible customers to a company’s product/service, the main objective is possibly to convince them to buy their product or use their service. The significance of Advertising is critical for the company, as this alone can make people aware of the company’s product and in doing so can generate a good possibility of it being sold to the customers. It is inevitable for companies to face changes and one such change is the evolution in the way of doing Advertisement. Advertisement is now done with the help of not so newfound helping hand that is Artificial Intelligence and Machine Learning. The answer to the question as to why the change in the process of Advertising is important lies in the before-after statistical observations of companies using this technology. The results themselves are reasonable motivating factors for companies who are yet to acknowledge the change. The serious challenge to this new version of Advertising is to make sure to not allow the usage of it to such a great extent where ordinary person is concerned about his/her privacy. Implementing Advertisements this way, we are quite sure that making laws, enforcing the laws or even having its own governing body can ensure righteous use of deploying this technology. The future of Advertising is going to be even better than before as Artificial Intelligence and Machine Learning will bring more control of Advertising to companies. Summing up, we feel confident that Advertising with Artificial Intelligence and Machine Learning are here for a noticeable and a significant change.

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References

  1. Bendixen MT (1993) Advertising effects and effectiveness. Eur J Mark 27(10):19–32

    Google Scholar 

  2. Sindhya V (2013) A study on the influence and impact of advertising to consumer purchase motive among student teachers. J Res Methods Educ 2(4):1–5

    Google Scholar 

  3. Malik ME, Ghafoor MM, Iqbal HK, Unzila Ayesha (2014) The impact of advertisement and consumer perception on consumer buying behavior. Int Rev Soc Sci Humanit 6(2):55–64

    Google Scholar 

  4. Domazet I, Đokić I, Milovanov O (2017) The influence of advertising media on brand awareness. Manag J Sustain Bus Manag Solut Emerg Econ 23(1):13–22. https://doi.org/10.7595/management.fon.2017.0022

    Article  Google Scholar 

  5. Pollay RW (1985) The subsiding sizzle: a descriptive history of print advertising, 1900–1980. J Mark 49:24–37

    Google Scholar 

  6. Richards J, Daugherty T, Logan K (2009) Advertising history. In: Sterling CH (ed) Encyclopedia of Journalism. Sage Publications, pp 22–25

  7. Gross BL, Sheth JN (1989) Time-oriented advertising: a content analysis of United States magazine advertising, 1890–1988. J Mark 53(4):76

    Google Scholar 

  8. Shah D, Dixit R, Shah A, Shah P, Shah M (2020) A comprehensive analysis regarding several breakthroughs based on computer intelligence targeting various syndromes. Augment Hum Res 5(1):14

    Google Scholar 

  9. Patel H, Prajapati D, Mahida D, Shah M (2020) Transforming petroleum downstream sector through big data: a holistic review. J Pet Explor Prod Technol 10(6):2601–2611

    Google Scholar 

  10. Ahir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality for enhanced education learning, military training and sports. Augment Hum Res 5:7

    Google Scholar 

  11. Evans DS (2009) The online advertising industry: economics, evolution, and privacy. J Econ Perspect 23(3):37–60

    Google Scholar 

  12. Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif Intell Agric 4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002

    Article  Google Scholar 

  13. Kakkad V, Patel M, Shah M (2019) Biometric authentication and image encryption for image security in cloud framework. Multisc Multidiscip Model Exp Des 2(4):233–248

    Google Scholar 

  14. Hudders L, van Reijmersdal EA, Poels K (2019) Editorial: digital advertising and consumer empowerment. Cyberpsychol J Psychosoc Res Cybersp. https://doi.org/10.5817/CP2019-2-xx

    Article  Google Scholar 

  15. Pannu A (2015) Artificial intelligence and its application in different areas. Int J Eng Innov Technol 4(10):79–84

    Google Scholar 

  16. Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artif Intell Agric 2:1–12

    Google Scholar 

  17. Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: a comprehensive review. Artif Intell Agric 4:81–95. https://doi.org/10.1016/j.aiia.2020.06.001

    Article  Google Scholar 

  18. Pandya R, Nadiadwala S, Shah R, Shah M (2020) Buildout of methodology for meticulous diagnosis of K-complex in EEG for aiding the detection of Alzheimer’s by artificial intelligence. Augment Hum Res 5(1):3

    Google Scholar 

  19. Sukhadia A, Upadhyay K, Gundeti M, Shah S, Shah M (2020) Optimization of smart traffic governance system using artificial intelligence. Augment Hum Res 5(1):13

    Google Scholar 

  20. Patel D, Shah Y, Thakkar N, Shah K, Shah M (2020) Implementation of artificial intelligence techniques for cancer detection. Augment Hum Res 5(1):6. https://doi.org/10.1007/s41133-019-0024-3

    Article  Google Scholar 

  21. Kundalia K, Patel Y, Shah M (2020) Multi-label movie genre detection from a movie poster using knowledge transfer learning. Augment Hum Res 5(1):11. https://doi.org/10.1007/s41133-019-0029-y

    Article  Google Scholar 

  22. Kietzmann J, Paschen J, Treen E (2018) Artificial intelligence in advertising. J Advert Res 58(3):263–267

    Google Scholar 

  23. Perlich C, Dalessandro B, Raeder T, Stitelman O, Provost F (2014) Machine learning for targeted display advertising: transfer learning in action. Mach Learn 95:103–127

    MathSciNet  Google Scholar 

  24. Bottou L, Peters J, Quiñonero-Candela J, Charles DX, Chickering DM, Portugaly E, Ray D, Simard P, Snelson E (2013) Counterfactual reasoning and learning systems: the example of computational advertising. J Mach Learn Res 14(2013):3207–3260

    MathSciNet  MATH  Google Scholar 

  25. Jani K, Chaudhuri M, Patel H, Shah M (2020) Machine learning in films: an approach towards automation in film censoring. J Data Inf Manag 2(1):55–64. https://doi.org/10.1007/s42488-019-00016-9

    Article  Google Scholar 

  26. Parekh V, Shah D, Shah M (2020) Fatigue detection using artificial intelligence framework. Augment Hum Res 5:5

    Google Scholar 

  27. Gandhi M, Kamdar J, Shah M (2020) Preprocessing of non-symmetrical images for edge detection. Augment Hum Res 5:10. https://doi.org/10.1007/s41133-019-0030-5

    Article  Google Scholar 

  28. Yadati K, Katti H, Kankanhalli M (2014) CAVVA: Computational affective video-in-video advertising. IEEE Trans Multimedia 16(1):15–23

    Google Scholar 

  29. Panchiwala S, Shah M (2020) A comprehensive study on critical security issues and challenges of the IoT world. J Data Inf Manag. https://doi.org/10.1007/s42488-020-00030-2

    Article  Google Scholar 

  30. Parekh P, Patel S, Patel N, Shah M (2020) Systematic review and meta-analysis of augmented reality in medicine, retail, and games. Vis Comput Ind Biomed Art 3:21. https://doi.org/10.1186/s42492-020-00057-7

    Article  Google Scholar 

  31. Shah K, Patel H, Sanghvi D, Shah M (2020) A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augment Hum Res 5:12. https://doi.org/10.1007/s41133-020-00032-0

    Article  Google Scholar 

  32. Patel D, Shah D, Shah M (2020) The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports. Ann Data Sci 7:1–16. https://doi.org/10.1007/s40745-019-00239-y

    Article  Google Scholar 

  33. Jin S, Lin W, Yin H, Yang S, Li A, Deng B (2015) Community structure mining in big data social media networks with map reduce. Cluster Comput 18(3):999–1010

    Google Scholar 

  34. Aksu H, Babun L, Conti M, Tolomei G, Uluagac AS (2018) Advertising in the IoT era: vision and challenges. IEEE Commun Mag 56(11):138–144

  35. Yin C, Hu J, Zhang X, Xie X (2015) Advertising system based on cloud computing and audio watermarking. In: 2015 international conference on intelligent information hiding and multimedia signal processing (IIH-MSP). https://doi.org/10.1109/iih-msp.2015.81

  36. Gharibshah J, Papalexakis EE, Faloutsos M (2020) Rest: a thread embedding approach for identifying and classifying user-specified information in security forums. arXiv:2001.02660[cs.CL]

  37. Cannella J (2018) Artificial intelligence in marketing. Honors Thesis for Barrett, The Honors College at Arizona State University, pp 1–132

  38. Murgai A (2018) Transforming digital marketing with artificial intelligence. Int J Latest Technol Eng Manag Appl Sci 7(4):259–262

    Google Scholar 

  39. Davenport T, Guha A, Grewal D, Bressgott T (2019) How artificial intelligence will change the future of marketing. J Acad Mark Sci. https://doi.org/10.1007/s11747-019-00696-0

    Article  Google Scholar 

  40. Milgrom PR, Tadelis S (2018) How artificial intelligence and machine learning can impact market design. Technical Report. National Bureau of Economic Research

  41. Shahid MZ, Li G (2019) Impact of artificial intelligence in marketing: a perspective of marketing professionals of Pakistan. Global J Manag Bus Res E-Mark 19(2):1–8

    Google Scholar 

  42. Mogaji E, Olaleye S, Ukpabi D (2020) Using AI to personalise emotionally appealing advertisement. In: Rana N et al (eds) Digital and social media marketing. Advances in theory and practice of emerging markets. Springer, Cham

    Google Scholar 

  43. Columbus L (2017) Ten-ways-big-data-is-revolutionizing-marketing-and-sales. https://www.forbes.com/sites/louiscolumbus/2016/05/09/ten-ways-big-data-is-revolutionizing-marketing-and-sales/#4dab056621cf

  44. Chandrashekar A, Amat F, Basilico J, Jebara T (2017) Netflix blog. https://netflixtechblog.com/artwork-personalization-c589f074ad76

  45. Malpnai B, Nisha M (2020) Role of artificial intelligence in advertising and marketing. Our Heritage 60(30):1–11

    Google Scholar 

  46. Chen TF, Tan T (2016) Application of artificial intelligence to cross-screen marketing: a case study of AI technology company. Adv Intell Syst Res 133:517–519

    Google Scholar 

  47. Kaličanin K, Čolović M, Njeguš A, Mitić V (2019) Benefits of artificial intelligence and machine learning in marketing. In: Paper presented at Sinteza 2019—international scientific conference on information technology and data related research. https://doi.org/10.15308/sinteza-2019-472-477

  48. Adams R (2004) Intelligent advertising. AI Soc 18(1):68–81. https://doi.org/10.1007/s00146-003-0259-9

    Article  Google Scholar 

  49. Khokhar P, Chitsimran D (2019) Evolution of artificial intelligence in marketing, comparison with traditional marketing (September 30, 2019). Our Heritage 67(5):375–389

    Google Scholar 

  50. Karimova GZ, Shirkhanbeik A (2019) Marketing Artificial Intelligence: creating the AI archetype for evoking the personality trust. Acad Mark Stud J 23:1–13

    Google Scholar 

  51. Tiautrakul J, Jindakul J (2019) The artificial intelligence (AI) with the future of digital marketing (May 22, 2019). Available at SSRN: https://ssrn.com/abstract=3405184 or http://dx.doi.org/10.2139/ssrn.3405184

  52. Kose U, Sert S (2017) Improving content marketing processes with the approaches by artificial intelligence. Ecoforum J 6(1), Accessed from http://arxiv.org/pdf/1704.02114v1

  53. Jarek K, Mazurek G (2019) Marketing and artificial intelligence. Central Eur Bus Rev 8(2):46–55. https://doi.org/10.18267/j.cebr.213

    Article  Google Scholar 

  54. Casillas J, Martínez-López FJ (2010) Studies in fuzziness and soft computing. In: Marketing intelligent systems using soft computing: marketing and artificial intelligence: great opportunities, Reluctant Partners, vol 258, pp 1–8. https://doi.org/10.1007/978-3-642-15606-9_1

  55. Van Bruggen G, Smidts A, Wierenga B (1998) Improving decision making by means of a marketing decision support system. Manage Sci 44(5):644–658

    MATH  Google Scholar 

  56. Effendi MJ, Ali SA (2007) Click through rate prediction for contextual advertisment using linear regression. Cornell University Library, 1701.08744. https://arxiv.org/ftp/arxiv/papers/1701/1701.08744.pdf

  57. Bowersox DJ, Daugherty PJ, Droge CL, Germain RN, Rogers DS (1992) Logisitical excellence. Digital Press, pp 1–235

  58. Tanase GC (2018) Artificial intelligence: optimizing the experience of digital marketing. Roman Distrib Commun Mag 9(1):24–28

    Google Scholar 

  59. Siau KL, Yang Y (2017) Impact of artificial intelligence, robotics, and machine learning on sales and marketing. In: MWAIS 2017 proceedings, vol 48. http://aisel.aisnet.org/mwais2017/48

  60. Schmidt HG (1993) Foundations of problem-based learning: some explanatory notes. Med Educ 27(5):422–432

    Google Scholar 

  61. Rockhart JF, Morton MS (1984) Implications of change in information technology for corporate strategy. Interfaces 14(1):84–95

    Google Scholar 

  62. Amaravadi CS, Samaddar S, Dutta S (1995) Intelligent marketing information systems. Mark Intell Plan 13(2):4–13

    Google Scholar 

  63. Dalessandro B, Chen D, Raeder T, Perlich C, Han Williams M, Provost F (2014) Scalable hands-free transfer learning for online advertising. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining—KDD’14. https://doi.org/10.1145/2623330.2623349

  64. Shanahan JG, Kurra G (2011) Digital advertising: an information scientist’s perspective. In: Melucci M, Baeza-Yates R (eds) Advanced topics in information retrieval. The information retrieval series, vol 33. Springer, Berlin

    Google Scholar 

  65. Saraswathi S, Krishnamurthy V, Prasad DVV, Tarun RK, Abhinav S, Rushitaa D (2019) Machine learning based Ad-click prediction system. Int J Eng Adv Technol 8(6):3646–3648

    Google Scholar 

  66. Sharma A, Kulkarni SV, Kalbande D, Dholay S (2019) Cost optimized hybrid system in digital advertising using machine learning. Int J Innov Technol Explor Eng 8(8):934–939

    Google Scholar 

  67. Avila CP, Vijaya MS (2016) Click through rate prediction for display advertisement. Int J Comput Appl 136(1):18–24

    Google Scholar 

  68. Ren K, Zhang W, Chang K, Rong Y, Yu Y, Wang J (2018) Bidding machine: learning to bid for directly optimizing profits in display advertising. IEEE Trans Knowl Data Eng 30(4):645–659

    Google Scholar 

  69. Chapelle O, Manavoglu E, Rosales R (2014) Simple and scalable response prediction for display advertising. ACM Trans Intell Syst Technol 5(4):1–34

    Google Scholar 

  70. Agarwal A, Chapelle O, Dudík M, Langford J (2011) A reliable effective terascale linear learning system. CoRR, https://arxiv.org/abs/1110.4198 (2011)

  71. Provost F, Dalessandro B, Hook R, Zhang X, Murray A (2011) Audience selection for on-line brand advertising: privacy-friendly social network targeting. SSRN Electron J. https://doi.org/10.2139/ssrn.1852644

    Article  Google Scholar 

  72. Kachamas P, Akkaradamrongrat S, Sinthupinyo S, Chandrachai A (2019) Application of artificial intelligent in the prediction of consumer behavior from facebook posts analysis. Int J Mach Learn Comput 9(1):91–97

    Google Scholar 

  73. Wang X, Ryoo J, Bendle N (2019) Predicting the future: machine learning and marketing. Mater Report, pp 1–48

  74. Mahajan KS, Jamsandekar SS, Gurav AM (2017) Machine learning approach for marketing intelligence: managerial application. Int J Eng Comput Sci 6(2):21929–21936

    Google Scholar 

  75. Mas MD (2017) Digital advertising traffic operation: machine learning for process discovery. CORR, Arxiv https://arxiv.org/abs/1701.00001

  76. Dimitrieska S, Stankovska A, Efremova T (2018) Artificial intelligence and marketing. Entrepreneurship 3(2):298–304

    Google Scholar 

  77. Kadyrov T, Ignatov DI (2019) Attribution of customers’ actions based on machine learning approach. MPRA paper No. 97312, 1–13

  78. Spann M, Molitor D, Daurer S (2016) Tell me where you are and i’ll tell you what you want: using location data to improve marketing decisions. GfK Mark Intell Rev 8(2):30–37

    Google Scholar 

  79. Nengroo AS, Kuppusamy KS (2018) Machine learning based heterogeneous web advertisements detection using a diverse feature set. Fut Gener Comput Syst 89:68–77

    Google Scholar 

  80. Diapouli M, Kapetanakis S, Petridis M, Evans R (2017) Behavioural analytics using process mining in on-line advertising proceedings of the ICCBR 2017 Workshops, pp. 147–156

  81. Brown G (2017) Can machines be creative? How technology is transforming marketing personalization and relevance. IDC #EMEA42878217, pp 1–13

  82. Fan T, Chang C (2010) Sentiment-oriented contextual advertising. Knowl Inf Syst 23:321–344. https://doi.org/10.1007/s10115-009-0222-2

    Article  Google Scholar 

  83. Mbwette K (2013) BMW e-marketing analysis. Report, pp 1–12

  84. Odhiambo CA (2012) Social media as a tool of marketing and creating brand awareness. Business Economics and Tourism. Master report, pp 1–80

  85. Sriram MAM (2013) Dove: using social media for social viral campaign—a case study. Cases Manag 21–32

  86. Fridolf F, Arnautovic A (2011) Social media marketing—a case study of Saab automobile AB. Master report, pp 1–75

  87. Patnaik S, Gallup, Robinson P (2011) Going social: case studies of successful Social Media Marketing. In: Beyond knowledge management: what every leader shoul know. Auerbach Publications, pp 1–15

  88. Ananda AS, Hernández-García A, Lamberti L (2015) Social media marketing in Italian luxury fashion. In: 5th annual international workshop on luxury retail, operations and supply chain management, 25–27 May, Milan, Italy

  89. Trattner C, Kappe F (2013) Social stream marketing on Facebook: a case study. Int J Soc Humanist Comput 2(1/2):86. https://doi.org/10.1504/ijshc.2013.05326

    Article  Google Scholar 

  90. Curran K, Graham S, Temple C (2011) Advertising on facebook. Int J E-Bus Dev 1(1):26–33

    Google Scholar 

  91. Tsimonis G, Dimitriadis S (2014) Brand strategies in social media. Mark Intell Plan 32(30):328–344. https://doi.org/10.1108/MIP-04-2013-0056

    Article  Google Scholar 

  92. Subramaniam TV (2020) Impact of social media on digital marketing: starbucks marketing strategy on Twitter. Case study #02 MBA 5083 MIS, pp 1–7

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Acknowledgements

The authors are grateful to Department of Computer Engineering, Sal Institute of College and Engineering and Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, for the permission to publish this research.

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Authors and Affiliations

  1. Department of Computer Engineering, SAL Institute of Technology and Engineering Research, Ahmedabad, Gujarat, India

    Neil Shah, Sarth Engineer, Nandish Bhagat & Hirwa Chauhan

  2. Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India

    Manan Shah

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  1. Neil Shah
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  2. Sarth Engineer
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  3. Nandish Bhagat
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All the authors make a substantial contribution to this manuscript. NS, SE, NB, HC and MS participated in drafting the manuscript. NS, SE, NB, HC and MS wrote the main manuscript. All the authors discussed the results and implication on the manuscript at all stages.

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Correspondence to Manan Shah.

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Shah, N., Engineer, S., Bhagat, N. et al. Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising. Augment Hum Res 5, 19 (2020). https://doi.org/10.1007/s41133-020-00038-8

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