Artificial intelligence marketing

Not to be confused with Marketing and artificial intelligence.

Artificial intelligence marketing (AIM) is a form of marketing that uses artificial intelligence concepts and models such as machine learning, Natural process Languages, and Bayesian Networks to achieve marketing goals. The main difference between AIM and traditional forms of marketing resides in the reasoning, which is performed by a computer algorithm rather than a human.

Each form of marketing has a different approach to the core of the marketing theory. Traditional marketing directly focuses on the needs of consumers; meanwhile some believe the shift AI may cause, will lead marketing agencies to manage consumer needs instead.[1]

Artificial Intelligence is used in various digital marketing spaces, such as content marketing, email marketing, online advertisement (in combination with machine learning), social media marketing, affiliate marketing, and beyond.[2][3]

The Potential of Artificial Intelligence is constantly being explored in digital marketing. In real time AI has been used by Marketing professionals because they claim it helps them prioritize customer satisfaction. Marketing Professionals can analyze the performance  of rival companies as well as their campaigns, which can reveal the wants and needs of their customers.[4]

  1. ^ Grandinetti, Roberto (2020-06-10). "How artificial intelligence can change the core of marketing theory". Innovative Marketing. 16 (2): 91–103. doi:10.21511/im.16(2).2020.08. ISSN 1816-6326.
  2. ^ YEĞİN, TUĞBA (2020-01-01). "Pazarlama Stratejilerinde Yapay Zekanin". Ekev Akademi Dergisi (81): 489–506. doi:10.17753/ekev1340. ISSN 2148-0710. S2CID 216545054.
  3. ^ "How AI is already being used for online advertising". www.storeya.com. Retrieved 2022-10-07.
  4. ^ Haleem, Abid; Javaid, Mohd; Asim Qadri, Mohd; Pratap Singh, Ravi; Suman, Rajiv (2022-01-01). "Artificial intelligence (AI) applications for marketing: A literature-based study". International Journal of Intelligent Networks. 3: 119–132. doi:10.1016/j.ijin.2022.08.005. ISSN 2666-6030.