The definition of Artificial intelligence (AI) is unpredictable and has changed over the long run because of rapid signs of progress. Besides, after the development of computers in the 1940s, individuals now understand that artificial intelligence does not limit mathematical calculations. People can utilize it to complete various intellectual tasks typically considered human knowledge (Wang, 2019).
Given the several meanings of Artificial Intelligence―it includes the study, plan, and building of an intelligent specialist that can accomplish objectives that create computer programs to finish tasks that require human knowledge. That is to handle issues, recognize languages, and rational thinking for dynamics. Above all, Artificial intelligence is rapidly developing, wherein people can utilize it in numerous ways, in terms of personal assistants through many gadgets to self-driving vehicles. Also, artificial intelligence can be used in surveillance cameras, banking, smartphones – Apple’s Siri, Amazo n’s Alexa – (Russell &Norvig, 2010).
Singh et al. (2013) indicated that specific programming languages could not manage subjective data and gather data that cannot be estimated but only observed. In this manner, the AI machines are automated to work with their own created programming language to effectively control information which means they are composed to control subjectively than numerical data. There are wide range of AI frameworks, but the researcher describes just a few.
ARTIFICIAL INTELLIGENCE (AI )
As indicated by Aghion et al. (2002); Valenti et al. (2008), general-purpose technology typically portrays certain radical developments that invariably have the considerable potential for persistent utilization and practical application in a broad scope of specific areas in the economy. In this effective way, an AI programming system can perform diverse tasks simply like a human promptly does.
Moreover, in specialized terms, it implies that developers need to correctly assemble an AI software program trained to efficiently perform a non-specific purpose for existing users to profit from (for instance, face recognition and accurate detection). Significantly, the authors added that AI does not allude to one sophisticated technology but an immense set of various practical methods that show intelligent behavior in countless contexts (Valenti et al.,2008).
Additionally, utilizing a virtual channel, snapping pictures, and Face ID for unlocking telephones are two practical applications of a computer-based intelligence that correctly identifies facial highlights like eyes, nose, lips, and face shapes. Moreover, government offices or public spots utilize this specific technique for observation, investigation, and security―for instance, some places use face recognition cameras as ID checks before permitting an individual to enter (Valenti et al.,2008).
CUSTOMER ́S EXPERIENCE IN MARKETING
This specific section typically focuses on the changing nature of buyers’ experience; (Jarek & Mazurek, 2019) claim that it expounds on artificial intelligence to fortify the client experience and position the client’s buying journey e-marketing. Besides, a decent comprehension of potential customers is the initial move toward an outstanding customer experience, and utilizing artificial intelligence (AI) can help speed up this necessary arrangement.
According to Sahid and Li (2019), the technological progress from various cognitive domains has pushed computer-based intelligence forward even in the marketing and digital marketing industry, where AI can deal with information quickly and precisely. Further, managers exploit this possible option to better have full command of the market, trend research, and modify the client experience. Marketers are currently utilizing artificial intelligence when doing various complex tasks like digital marketing, search engine optimization, checking social media activities.
According to Aljafar (2016), Apple Incorporation is a modern information technology organization based in the US, most famous for its development, design processes, and sales. The universe recognizes the successful corporation for selling computer (PC) software programs, services, and electronics through its retail stores, online website, wholesalers, and retailers. Moreover, some Apple computer hardware products typically comprise iPad tablets, iPhone smartphones, digital televisions, portable iPod media players. It includes an iOS operating system and iTunes portable media player, which Apple continues upgrading for both existing and new customers.
Significantly, Steve Jobs, Steve Wozniak, and Ronald Wayne established the corporation as a small-scale PC producer. In 2016 Aljafar stated Wayne sold his stake a year later for 800 US dollars; simultaneously, Mike Markkula invested 250,000 dollars and became part of the corporation that tacitly supported Apple with the operational cost required to develop the company progressively.
Following that, the corporation’s returns began proliferating and multiplying every four months for the initial five years. It presented novel forms of its PC in 1977 and 1980, expecting to strive in the business. However, in late 1980, Apple went public because of its ultimate success, where it willingly offered its capital stocks for twenty-two dollars per share in the stock market (Aljafar, 2016).
The AI is widely considered adequate for e-business application prospects, such as online banking and internet trading. The electronic banking system encourages modern payment methods such as paying electronically in cash and checks through e-gadgets. Conclusively, this technology acts as a quick agent in making the universe more efficient. Notably, the expansion of artificial intelligence in marketing for numerous organizations can produce a superior model for subsequent promotional activities.
Aghion, P., Howitt, P., & Violante†, G. L. (2002). General-purpose technology and wage inequality. Journal of Economic Growth, 7(4), 315–345. https://doi.org/10.1023/a:1020875717066
Aljafar, A. (2016). Apple inc industry analysis business policy and strategy. International Journal of Scientific & Engineering Research, 7(3), 406–441.
Valenti, R., Sebe, N., Gevers, T., & Cohen, I. (2008). Machine learning techniques for face analysis. In Machine Learning Techniques for Multimedia: Vol. (pp. 159–187). Springer. https://doi.org/10.1007/978-3-540-75171-7_7
About the author
Joy Okundia is a Bachelor’s International Business student, who has a passion for researching and writing articles. She is also an Intern at IIDM™
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