Marketing and artificial intelligence

• 1. The Expert Systems Of The 1980s

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  In the early 1980s, researchers focused their artificial intelligence efforts on expert systems and robotics. These early attempts at marketing automation remained limited in scope despite initial studies. A large-scale bibliometric study covering 1,580 peer-reviewed papers published between 1982 and 2020 confirms that scholarly output on AI in marketing has surged since 2017. Expert Systems with Applications emerged as the most prolific outlet for this research during that period. Research on the technology came to a stop for a while before being revived two decades later. The revival coincided with advancements in technology, the rise of big data, and a significant increase in computational power. Eventually, AI became very popular in the marketing world and caught the eyes of many researchers as well as professionals.

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• 2. Neural Networks And Predictive Analytics

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  An artificial neural network is a form of computer program modeled on the brain and nervous system of humans. Neural networks are composed of a series of interconnected processing neurons that function in unison to achieve certain outcomes. Using human-like trial and error learning methods, these networks detect patterns existing within a data set. They ignore data that is not significant while emphasizing the data which is most influential. From a marketing perspective, neural networks serve as software tools used to assist in decision making. They gather and extract information from large data sources and identify cause and effect within that data. These neural nets learn relationships and connections between databases over time. Once knowledge has been accumulated, they can be relied upon to provide generalizations. They apply past knowledge and learning to a variety of situations to fulfill roles like market segmentation and performance measurement.

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• 3. Automation Efficiency In Campaigns

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  In 2015, Google released its most recent algorithm known as RankBrain. This tool opened new ways to analyzing search inquiries by accurately determining the reasoning and intent behind users searches. Marketing automation uses software to automate processes that would otherwise be performed manually. It assists in effectively allowing processes such as customer segmentation, campaign management, and product promotion to be undertaken at a more efficient rate. Companies use systems that employ data-mining algorithms to analyze customer databases. This information refers to socio-economic characteristics, earlier interactions with the customer, and purchase history. Automation tools allow the system to monitor the performance of campaigns. They make regular adjustments to improve response rates and provide campaign performance tracking. Amazon acquired Kiva Systems, the makers of the warehouse robot for $775 million in 2012. The Kiva robots undertake order fulfillment, product replenishment, and heavy lifting to increase efficiency.

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• 4. Social Network Analysis And Sentiment Mining

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  A social network is a social arrangement of actors who make up a group within a network. There can be an array of ties and nodes that exemplify common occurrences within a network. Each vertex or node represents an actor and each link represents a relationship. At the present time there is a growth in virtual social networking with the common emergence of sites like Twitter, Facebook, and LinkedIn. Data mining involves searching the Web for existing information namely opinions and feelings posted online among social networks. This area of study is called opinion mining or sentiment analysis. It analyzes peoples opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics, and their attributes. Centrality and prestige are types of measurement terms used to describe the level of influence an actor holds within a social network. Identifying these nodes helps marketers find out who are the trendsetters within social networks.

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• 5. Ethical Challenges And Regulatory Oversight

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  The ethics of artificial intelligence marketing is an evolving area of study and debate. Major topics of ethical concern include privacy and algorithmic biases. Privacy concerns from customers pertain to how technology companies use consumer data. Questions raised include how long consumer data is retained and how it is resold to other parties. In March 2024, the SEC charged Delphia USA Inc. and Global Predictions Inc. for using false claims about their AI capabilities in marketing materials. Misleading AI marketing practices such as AI washing undermine consumer trust and damage brand reputation. Algorithmic biases are errors in computer programs that have the potential to give unfair advantage to some and disadvantage others. Concerns exist that artificial intelligence algorithms can be affected by existing biases from the programmers that designed them. Research shows that leveraging data-driven approaches improves the transparency and reliability of AI-powered marketing strategies.

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• 6. Collect Reason Act Cycle Principles

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  AI 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. Collect relates to all activities which aim to capture customer or prospect data. For example, social media platforms 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 is the stage where data is transformed into information and eventually intelligence or insight. This phase sees artificial intelligence and machine learning play a key role. 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 takes the decision and acts according to the information it received in the collect stage.

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• 7. Virtual Influencers And Personalization

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  Virtual influencers are computer-generated digital characters powered by artificial intelligence that act like human social media influencers. They can post content, interact with followers, and promote brands online. Unlike human influencers, virtual influencers give brands complete creative control and lower the risk of reputational problems. In 2024, global spending on AI-driven virtual influencers reached over USD 4.6 billion. It is expected to surpass USD 8 billion by 2025, showing rapid growth in sectors like fashion, beauty, and technology. Studies also warn about the uncanny valley effect where virtual influencers that look too realistic may make some viewers feel uncomfortable. To avoid this, creators often balance realism with cartoonish features. Factors such as reliability, helpfulness, entertainment value, and humanlike traits influence how much followers engage with virtual influencers. Models like the Virtual Influencer Trust and Engagement Model suggest that transparency affects how audiences trust and interact with these figures.

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