Artificial intelligence marketing

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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.

Contents

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]

Historical Development of AIM

Artificial Intelligence has been having an impact on marketing for years, and will continuously grow. The impact of AI has become more clear, and noticeable during 2017. More people have become more aware of AI’s presence. However, AI has a long history, which goes all the way back to the 1980s. The study of AI started with studies relating to robotics, and systems. Despite the initial research, and the studies that were carried out, AI wasn’t exactly becoming widespread. Research on it came to a stop for a while, until research was revived 2 decades later. Different factors such as the advancement in technology, rise of Big Data, and the significant increase in computational power, all opened the door. Eventually Ai became very popular in the marketing world, and caught the eyes of many researchers as well as professionals. [5]

Prior to the application of artificial Intelligence in marketing, there was something called "collaborative filtering". This was used as early as 1998 by Amazon, and one of the first ways companies predicted consumer behavior, which enabled millions of recommendations to different customers. today, when you open Spotify and you see recommended music, or recommended tv shows on Netflix, this is done through AI clustering our behaviors. Based on the data our profile provides, they can make these recommendations. A big milestone in AI marketing happened in 2014, when programmatic ad buying gained much greater popularity. Marketing consists of numerous manual tasks such as researching target markets, insertion orders, and managing high budgets as well as prices. In order to cut costs, and remove the need for these tedious tasks, many companies started to automate the marketing process with AI. In 2015, Google released its most recent algorithm known as RankBrain, which opened new ways to analyzing search inquiries. It's used to accurately determine the reasoning and intent behind users searches. [6]

Tools and Usage

Predictive Analytics

Predictive analytics is a form of analytics involving the use of historical data and artificial intelligence algorithms to predict future trends and outcomes. [7] It serves as a tool for anticipating and understanding user behavior based on patterns found in data. Predictive analytics uses artificial intelligence machine learning algorithms to recognize and predict patterns within data. [8] Machine learning algorithms analyze the data, recognize patterns, and make predictions through continuous learning and adaptation.

Predictive analytics is widely used across businesses and industries as a way to identify opportunities, avoid risks, and anticipate customer needs based on information derived from the analysis of user data. By analyzing historical customer data, artificial intelligence algorithms can deliver relevant and targeted marketing content. [8]

Personalization Engines

Personalization engines use artificial intelligence and machine learning to provide content or advertisements that are relevant to the user. User data is gathered, which then gets processed with machine learning, and patterns and trends among the users are identified. Users with shared characteristics or behaviors are then segmented into groups, and the personalization engine adjusts content and advertisements to match each segment’s preferences. [9] By processing a large amount of data, personalization engines are able to match users to advertisements and recommendations that align with their interests or preferences. [10]

Behavioral Targeting

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. [11] 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

Ethics

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. [12] Currently there are two topics of ethical concern for AIM. Those are of privacy, and algorithmic biases.

Ethics and Privacy

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. [12]

In addition, the purpose of data collection is to enhance consumer experience. [13] 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. [14]

Ethics and Algorithmic Biases

Algorithmic biases are errors in computer programs that have the potential to give unfair advantage to some and disadvantage others. [15] Concerns for AIM is the possibility that AI algorithms can be affected by existing biases from the programmers that designed the AI algorithms. [13] Or the inability of an AI to detect biases because of its own calculations. [12]

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

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. [16]

Collect

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

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

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.

AI marketing and User Personalization

AI's integration across many sectors is transforming innovation, improving efficiency and adaptability. AI's ability to analyze data, and patterns enables it to produce hyper-personalized advertisements. [17] AI marketing will be an important tool for all businesses to thrive in contemporary times. For example, retail companies are doing everything they can to learn about us and our shopping habits. Target is one of the companies that has been smart about predictive analytics. Target AI models were able to predict if a woman was pregnant or not through their shopping habits. For instance, a woman suddenly starts buying unscented lotion and zinc vitamins which are signals that a woman is pregnant. Even if parents don't know that their daughter is pregnant, Target's algorithm can predict when she is due. Target alone estimates that they have made billion dollars by targeting pregnant women. [18] AI allows companies to understand customers buying habits and make personalized ads based on consumers interests. AI's ability to predict and understand customer choices in realtime helps companies tailor their content according to customers needs. This allows companies to reach the right consumers at the right time. With precise targeting businesses can make more profits, increase customer retention rate and address individual needs in real-time. [19]

Integration of Artificial Intelligence in Digital Assistants

Digital Assistants like Alexa, Siri, and Google Assistant have transformed the way customers interact with businesses. Users can ask queries to which the digital assistant’s respond as well as assist the user, providing a personalized experience and increasing customer satisfaction. [20] They also increase customer engagement as the voice integrated platforms are able to drive conversations and proactively suggest suitable services with the use of their natural language processing as well as machine learning models. [21]

Chatbots are also leveraging AI, commonly being used by businesses to help provide customer support. AI driven chatbots are able to use natural language processing to enhance communication with customers. This allows chatbots to anticipate the needs of the customer and take the appropriate actions, improving customer satisfaction. Chatbots enable businesses to have enhanced marketing communication with customers, as well as tailor the support experience depending on the needs of the customer. [22]

Artificial Intelligence in Digital Marketing

Artificial intelligence has transformed the digital marketing landscape by allowing businesses to capture large amounts of consumer data, leading to data-driven marketing strategies. Businesses like Amazon can utilize user’s purchase, search, and viewing history on their platforms, to create customized user experiences. For example, relevant products can be advertised to the user to guide their purchasing behavior. AI algorithms are used to analyze all the available user data and ultimately create user personalized recommendations. [23]

See also

Related Research Articles

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A chatbot is a software application or web interface that is designed to mimic human conversation through text or voice interactions. Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner. Such chatbots often use deep learning and natural language processing, but simpler chatbots have existed for decades.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

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<span class="mw-page-title-main">Analytics</span> Discovery, interpretation, and communication of meaningful patterns in data

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<span class="mw-page-title-main">Customer service</span> Provision of service to customers

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Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.

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<span class="mw-page-title-main">Algorithmic bias</span> Technological phenomenon with social implications

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<span class="mw-page-title-main">Merative</span> U.S. healthcare company

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References

  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.
  5. ↑ Vlačić, Božidar; Corbo, Leonardo; Costa e Silva, Susana; Dabić, Marina (May 2021). "The evolving role of artificial intelligence in marketing: A review and research agenda". Journal of Business Research. 128: 187–203. doi:10.1016/j.jbusres.2021.01.055. hdl: 10400.14/33196 . ISSN   0148-2963.
  6. ↑ Goldberg, Lori (2018-04-20). "A brief history of artificial intelligence in advertising". Econsultancy. Retrieved 2024-04-28.
  7. ↑ "What is Predictive Analytics? | IBM". www.ibm.com. 2024-04-08. Retrieved 2024-04-28.
  8. 1 2 Yadav, Anusuya; Pandita, Deepika (2024-01-28). "A Decision Model for Revolutionizing Digital Marketing Campaigns Powered by AI and Predictive Analytics". 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). IEEE. pp. 791–795. doi:10.1109/ICETSIS61505.2024.10459608. ISBN   979-8-3503-7222-9.
  9. ↑ "What is a Personalization Engine? - Definition by Dynamic Yield". Dynamic Yield. Retrieved 2024-04-28.
  10. ↑ Barbosa, Belém; Saura, José Ramón; Zekan, Senka Borovac; Ribeiro-Soriano, Domingo (2023-03-12). "Defining content marketing and its influence on online user behavior: a data-driven prescriptive analytics method". Annals of Operations Research. doi:10.1007/s10479-023-05261-1. ISSN   1572-9338.
  11. ↑ "Opinion 2/2010 on online behavioural advertising" (PDF). Article 29 Data Protection Working Party.
  12. 1 2 3 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.
  13. 1 2 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.
  14. ↑ 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.
  15. ↑ Friis, Simon; Riley, James (2023-09-29). "Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI". Harvard Business Review. ISSN   0017-8012 . Retrieved 2024-04-28.
  16. ↑ Sharma, Animesh Kumar; Sharma, Rahul (2023). "Considerations in artificial intelligence-based marketing: An ethical perspective". Applied Marketing Analytics. 9 (2): 162–172.
  17. ↑ Akilkhanov, Alan. "Council Post: AI And Personalization In Marketing". Forbes. Retrieved 2024-04-29.
  18. ↑ Duhigg, Charles (2013-12-31), "24. How Companies Learn Your Secrets", The Best Business Writing 2013, Columbia University Press, pp. 421–444, doi:10.7312/star16075-025, ISBN   978-0-231-16075-9 , retrieved 2024-04-29
  19. ↑ Team, Nextdoor Editorial. "Maximizing business growth with targeted marketing". business.nextdoor.com. Retrieved 2024-04-29.
  20. ↑ Brill, Thomas M.; Munoz, Laura; Miller, Richard J. (2019-10-13). "Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications". Journal of Marketing Management. 35 (15–16): 1401–1436. doi:10.1080/0267257X.2019.1687571. ISSN   0267-257X.
  21. ↑ Gupta, Yuvika; Khan, Farheen Mujeeb (2024-04-05). "Role of artificial intelligence in customer engagement: a systematic review and future research directions". Journal of Modelling in Management. ahead-of-print (ahead-of-print). doi:10.1108/JM2-01-2023-0016. ISSN   1746-5664.
  22. ↑ Durmus Senyapar, Hafize Nurgul (2024-03-08). "Artificial Intelligence in Marketing Communication: A Comprehensive Exploration of the Integration and Impact of AI". Technium Social Sciences Journal. 55: 64–81. doi: 10.47577/tssj.v55i1.10651 . ISSN   2668-7798.
  23. ↑ YeğİN, TuğBa (2020-01-01). "Pazarlama Stratejilerinde Yapay Zekanin". Ekev Akademi Dergisi (81): 489–506. doi:10.17753/Ekev1340. ISSN   2148-0710.

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