Artificial intelligence marketing

Artificial intelligence marketing (AIM) is a form of marketing that leverages artificial intelligence concepts and models such as machine learning 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.

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.^[1]^[2]

Behavioral targeting[edit]

Behavioral targeting refers to the act of reaching out to a prospect or customer with communication based on implicit or explicit behavior shown by the customer's past.^[3] Understanding of behaviors is facilitated by marketing technology platforms such as web analytics, mobile analytics, social media analytics, and trigger-based marketing platforms. Artificial Intelligence Marketing provides a set of tools and techniques that enable behavioral targeting.

Machine learning is used to improve the efficiency of behavioral targeting. Additionally, to prevent human bias in behavioral targeting at scale, artificial intelligence technologies are used. The most advanced form of behavioral targeting aided by artificial intelligence is called algorithmic marketing.

Impact[edit]

Ethics[edit]

Ethics of Artificial Intelligence Marketing (AIM) is an evolving area of study and debate. AI ethics has overlapping idea, encompasses many industries, fields of study, and social impacts ^[4]. Currently there are two topics of ethical concern for AIM. Those are of privacy, and algorithmic biases.

Ethics and Privacy[edit]

Currently privacy concerns from customers pertain to how technology companies like AIM and big data companies use consumer data. some questions that have been risen are how long consumer data is retained, how and to whom data is resold to (marketing, AI, data, private companies etc.), weather the data collected from one individual also contains data of other persons that did not wish for their data to be shared ^[4].

In addition, the purpose of data collection is to enhance consumer experience ^[5]. By using consumer data and combining that data with AI and marketing techniques, firms will have better understandings of what their customers want, and make customized products and services for their customers ^[6].

Ethics and Algorithmic Biases[edit]

Algorithmic biases are errors in computer programs that have the potential to give unfair advantage to some and disadvantage others. Concerns for AIM is the possibility that AI algorithms can be affected by existing biases from the programmers that designed the AI algorithms ^[7]. Or the inability of an AI to detect biases because of its own calculations ^[4].

On the other hand, there is the belief that AI bias in business is an inflated argument as business and marketing decisions are based on human-biases and decision-makings. In part to further the shareholders goals for their business and from decisions for what they indent to sell to attract specific consumers .

Collect, reason, act[edit]

Artificial intelligence marketing principles are based on the perception-reasoning-action cycle found in cognitive science. In the context of marketing, this cycle is adapted to form the collect, reason and act cycle.^[8]

Collect[edit]

This term relates to all activities which aim to capture customer or prospect data; for example on social media platforms, where the platform will measure the duration of time a post was viewed. Whether taken online or offline, this data is then saved into customer or prospect databases.

Reason[edit]

This is the stage where data is transformed into information and, eventually, intelligence or insight. This is the phase where artificial intelligence and machine learning in particular play a key role.

Act[edit]

With the intelligence gathered in the reason stage, one can then act. In the context of marketing, an act would be an attempt to influence a prospect or customer purchase decision using an incentive driven message.

In an unsupervised model, the machine in question would take the decision and act according to the information it received in the collect stage.

References[edit]

^ YEĞİN, TUĞBA (2020-01-01). "Pazarlama Stratejilerinde Yapay Zekanin". Ekev Akademi Dergisi (81): 489–506. doi:10.17753/ekev1340. ISSN 2148-0710. S2CID 216545054.
^ "How AI is already being used for online advertising". www.storeya.com. Retrieved 2022-10-07.
^ "Opinion 2/2010 on online behavioural advertising" (PDF). Article 29 Data Protection Working Party.
^ ^a ^b ^c Davenport, Thomas; Guha, Abhijit; Grewal, Dhruv; Bressgott, Timna (January 2020). "How artificial intelligence will change the future of marketing". Journal of the Academy of Marketing Science. 48 (1): 24–42. doi:10.1007/s11747-019-00696-0. ISSN 0092-0703 – via ResearchGate.
^ Bharti, Preeti; Park, Byungjoo (2023-05-31). "The Ethics of AI in Online Marketing: Examining the Impacts on Consumer privacyand Decision-making". International Journal of Internet, Broadcasting and Communication. 15 (2): 227–239. doi:10.7236/IJIBC.2023.15.2.227 – via ResearchGate.
^ Hermann, Erik (August 2022). "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective". Journal of Business Ethics. 179 (1): 43–61. doi:10.1007/s10551-021-04843-y. ISSN 0167-4544. PMC 8150633. PMID 34054170.
^ Bharti, Preeti; Park, Byungjoo (2023-05-31). "The Ethics of AI in Online Marketing: Examining the Impacts on Consumer privacyand Decision-making". International Journal of Internet, Broadcasting and Communication. 15 (2): 227–239. doi:10.7236/IJIBC.2023.15.2.227 – via ResearchGate.
^ Sharma, Animesh Kumar; Sharma, Rahul (2023). "Considerations in artificial intelligence-based marketing: An ethical perspective". Applied Marketing Analytics. 9 (2): 162–172.