Artificial intelligence marketing

Last updated April 28, 2024

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.

Historical Development

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.^[3]

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.^[4] 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.^[5] 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.^[5]

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.^[6] 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.^[7]

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.^[8] 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.^[9] 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.^[9]

In addition, the purpose of data collection is to enhance consumer experience.^[10] 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.^[11]

Ethics and Algorithmic Biases

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.^[10] Or the inability of an AI to detect biases because of its own calculations.^[9]

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.^[12]

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.

Related Research Articles

Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software which enable machines to perceive their environment and uses learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.

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.

Personalized marketing, also known as one-to-one marketing or individual marketing, is a marketing strategy by which companies leverage data analysis and digital technology to deliver individualized messages and product offerings to current or prospective customers. Advancements in data collection methods, analytics, digital electronics, and digital economics, have enabled marketers to deploy more effective real-time and prolonged customer experience personalization tactics.

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Personalization consists of tailoring a service or product to accommodate specific individuals. It is sometimes tied to groups or segments of individuals. Personalization involves collecting data on individuals, including web browsing history, web cookies, and location. Various organizations use personalization to improve customer satisfaction, digital sales conversion, marketing results, branding, and improved website metrics as well as for advertising. Personalization acts as a key element in social media and recommender systems. Personalization influences every sector of society— be it work, leisure, or citizenship.

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.

Artificial intelligence (AI) has been used in applications throughout industry and academia. Similar to electricity or computers, AI serves as a general-purpose technology that has numerous applications. Its applications span language translation, image recognition, decision-making, credit scoring, e-commerce and various other domains. AI which accommodates such technologies as machines being equipped perceive, understand, act and learning a scientific discipline.

Targeted advertising is a form of advertising, including online advertising, that is directed towards an audience with certain traits, based on the product or person the advertiser is promoting.

In information science, profiling refers to the process of construction and application of user profiles generated by computerized data analysis.

Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects.

In electronic commerce, conversion marketing is marketing with the intention of increasing conversions—that is, site visitors who are paying customers.

The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.

Marketing automation refers to software platforms and technologies designed for marketing departments and organizations automate repetitive tasks and consolidate multi-channel interactions, tracking and web analytics, lead scoring, campaign management and reporting into one system. It often integrates with customer relationship management (CRM) and customer data platform (CDP) software.

Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to arrive at approximate conclusions based solely on input data.

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

Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.

A data management platform (DMP) is a software platform used for collecting and managing data. DMPs allow businesses to identify audience segments, which can be used to target specific users and contexts in online advertising campaigns. They may use big data and artificial intelligence algorithms to process and analyze large data sets about users from various sources. Advantages of using DMPs include data organization, increased insight on audiences and markets, and more effective advertisement budgeting. On the other hand, DMPs often have to deal with privacy concerns due to the integration of third-party software with private data. This technology is continuously being developed by global entities such as Nielsen and Oracle.

Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences.

Merative L.P., formerly IBM Watson Health, is an American medical technology company that provides products and services that help clients facilitate medical research, clinical research, real world evidence, and healthcare services, through the use of artificial intelligence, data analytics, cloud computing, and other advanced information technology. Merative is owned by Francisco Partners, an American private equity firm headquartered in San Francisco, California. In 2022, IBM divested and spun-off their Watson Health division into Merative. As of 2023, it remains a standalone company headquartered in Ann Arbor with innovation centers in Hyderabad, Bengaluru, and Chennai.

Artificial intelligence (AI) in hiring involves the use of technology to automate aspects of the hiring process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs. Despite the potential benefits, the ethical implications of AI in hiring remain a subject of debate, with concerns about algorithmic transparency, accountability, and the need for ongoing oversight to ensure fair and unbiased decision-making throughout the recruitment process.

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.

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