Artificial intelligence in marketing

• 1. Introduction

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  Artificial intelligence in marketing began not with chatbots or targeted ads, but with a simple question: can a machine predict what you want before you know you want it? Amazon was asking that question as early as 1998, when the company deployed collaborative filtering to generate personalized product recommendations at scale. That single experiment seeded a field that would, over the following decades, reshape how companies find customers, study their habits, and decide what to say to them. What does it mean when a retailer can guess a woman is pregnant before her own parents know? How did a research field that stalled out in the 1980s come roaring back with such force that a bibliometric study counted 1,580 peer-reviewed papers on the subject between 1982 and 2020? And as spending on AI-driven virtual influencers surpassed USD 4.6 billion in 2024 alone, who is watching the people doing the watching?

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• 2. The Long Road From Expert Systems

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  In the 1980s, AI research centered on expert systems and robotics, and early enthusiasm about applying that work to marketing faded quickly. Adoption remained limited, and for a stretch of roughly two decades, the field went quiet. What revived it was a convergence of three forces: a significant jump in computational power, the rise of big data, and advances in technology that made processing large datasets practical for the first time. Scholarly attention followed the same curve. A large-scale bibliometric study found that peer-reviewed output on AI in marketing surged after 2017, with Expert Systems with Applications emerging as the most prolific journal in the field. The study covered work published all the way back to 1982, and the contrast between the slow early decades and the post-2017 acceleration is striking. Bibliometric mappings of the field have also identified five emergent clusters of inquiry, ranging from trust-based buyer-supplier relationships to social-media sentiment mining, with citation networks still fragmented enough to suggest this is a young field moving fast.

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• 3. Neural Networks at Work

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  Spiegel Inc., a direct-mail company, turned to neural networks to solve a problem that sounds simple but is commercially vital: telling the difference between a one-time buyer and a repeat customer. Using software developed by NeuralWare Inc., Spiegel mapped the demographics of both groups, let the network find the distinguishing patterns, and then used those patterns to focus marketing resources on the customers most likely to come back. The result was a measurable reduction in costs. Neural networks work by passing data through interconnected processing units that mimic, at a structural level, the neurons of the human brain. What makes them useful in marketing specifically is their tolerance for messy, incomplete data; they can detect signal in noise that would trip up a conventional statistical model. A second application came from the airline industry. An application called the Airline Marketing Assistant/Tactician, developed by BehabHeuristics, used neural networks to forecast passenger demand and allocate seats accordingly. The system was deployed by National Air Canada and USAir. A third example, the Target Marketing System built by Churchull Systems for Veratex Corporation, scanned a market database to identify dormant customers so that management could decide which ones were worth re-engaging.

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• 4. Targeting, Personalization, and Prediction

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  Target, the retail company, built AI models capable of inferring from a customer's purchase pattern whether that customer was pregnant, and could estimate the due date with enough precision to time outreach accordingly. A woman who began buying unscented lotion and zinc vitamins, for instance, was flagged by the algorithm as a likely expectant mother. Target estimated it made billions of dollars by targeting pregnant women specifically. This is predictive analytics in its most commercially potent form: using historical data and machine learning to anticipate behavior before the customer announces it. Personalization engines follow a similar logic but apply it more broadly. User data is gathered, machine learning surfaces shared characteristics among users, and those users are grouped into segments. Content and advertisements are then adjusted to match each segment's preferences rather than being broadcast uniformly. Field evidence from consumer-goods and electronics companies indicates that AI-driven personalization can raise conversion rates and marketing return on investment, though data-governance gaps and skills shortages remain real obstacles to wider adoption. Behavioral targeting, the most advanced form of which is called algorithmic marketing, extends this further by acting on implicit signals left by a customer's past behavior rather than waiting for the customer to state a preference.

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• 5. Search, Social, and the Automation Layer

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  Google introduced RankBrain in 2015, a machine learning component built into its search algorithm to interpret the intent behind user queries rather than simply matching keywords. RankBrain was followed by BERT in 2019, which improved the system's ability to handle conversational queries, and then by the Multitask Unified Model, known as MUM, in 2021, which processes information across 75 languages and can handle multiple types of input at once. Taken together, these updates pulled search engine optimization away from keyword density and toward satisfying what a user actually wants to know. On the social side, a company called InsightPool developed a tool that searches through more than 600 million influencer profiles to match brands with creators who fit their target audience. Twizoo deployed AI to aggregate restaurant reviews from social platforms and then expanded by offering a widget that hospitality websites could embed to display those reviews directly to visitors. In the warehouse, Amazon acquired Kiva Systems, makers of fulfillment robots, for $775 million in 2012. Before the acquisition, employees walked the entire warehouse floor to track and retrieve inventory by hand. The Kiva robots took over order fulfillment, product replenishment, and heavy lifting.

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• 6. Virtual Influencers and Generative AI

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  By 2024, global spending on AI-driven virtual influencers crossed USD 4.6 billion, with projections pointing past USD 8 billion by 2025. Virtual influencers are fully computer-generated characters that post content, respond to followers, and promote brands. Because they are not human, brands retain complete creative control and carry no risk of the reputational problems that come with real celebrities. Research shows that members of Generation Z respond to these artificial personas with similar levels of trust, credibility, and emotional connection as they would to human influencers. The degree of engagement correlates with how realistic the character seems and how well its story is told. Creators face a design tension, however. Studies warn about the uncanny valley effect: virtual influencers that look too close to real humans can make viewers uncomfortable. Many creators resolve this by blending realistic features with cartoonish ones. The ethical dimension here is material. Some audiences do not realize they are engaging with a computer-generated persona, and acceptance varies significantly by region. East Asian audiences tend to be more comfortable with AI-generated characters, while Western audiences more frequently expect explicit disclosure. A framework called the Virtual Influencer Trust and Engagement Model, or VITEM, addresses exactly these variables. In a broader sense, generative AI became embedded in marketing workflows from 2023 onward. A 2023 McKinsey report estimated generative AI could produce between 2.6 trillion and 4.4 trillion US dollars in annual economic value globally, with marketing and sales among the four sectors accounting for roughly three-quarters of that figure.

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• 7. Privacy, Bias, and the Ethics Debate

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  In March 2024, the SEC charged Delphia (USA) Inc. and Global Predictions Inc. with making false claims about their AI capabilities in marketing materials. The case gave a concrete example to a concern that had been building in the field: that companies exaggerating their AI sophistication, a practice sometimes called AI washing, can erode consumer trust and damage brand credibility. Privacy sits alongside misrepresentation as a central ethical pressure point. Questions the field has raised include how long consumer data is retained, to whom it is resold, and whether data collected on one person inadvertently captures information about others who never agreed to be tracked. Algorithmic bias introduces a second category of concern. AI systems can encode and amplify the biases of the people who designed them, or fail to detect bias at all because of how their own calculations are structured. Some researchers argue the opposite, that business decisions driven by human judgment carry their own biases, and that AI at scale does not necessarily make things worse. The "collect, reason, act" cycle that underlies most AI marketing systems draws directly from the perception-reasoning-action framework in cognitive science. In an unsupervised model, the system can take action on its own once it has processed incoming data, without a human approving each decision. That autonomy is precisely what makes the ethics debate hard to resolve.

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