In the mid-20th century, AI was primarily an academic concept, but marketers made early applications in basic data analysis.

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This was Primarily Done Through Two Means:

• Customer segmentation: Marketers used algorithms like k-means clustering to segment customers based on demographics and purchase behaviors, allowing for more targeted—though still rudimentary—campaigns. 

• Strategic optimization: Other techniques, such as linear programming and game theory, were used to optimize marketing-mix and pricing strategies.  (The Evolution of AI in Marketing: A Brief History, 2023).

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During this phase, AI shifted from reacting to customer data to predicting future behavior.

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Advancement In Three Ways:

• Predictive analytics: Marketers leveraged big data to forecast customer trends, sales, and potential churn. AI models enabled precise lead scoring, helping sales teams prioritize the most promising prospects.

• Hyper-personalization: AI enabled marketing efforts to be customized for individual customers. Tools could dynamically adjust emails, website content, and landing pages in real-time based on a user's behavior.

• Social listening: AI-powered sentiment analysis tools allowed marketers to understand public opinion toward their brand in real-time (Russell, 2025).

With the rise of the internet, e-commerce, and Customer Relationship Management (CRM) systems, accessible customer data expanded.

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Two Emerging Themes: ​

• Data mining: Marketers used data mining to identify patterns in customer behavior. The Apriori algorithm, developed in 1994, helped businesses discover associations between products purchased 

• Early personalization: Recommendation engines and email marketing tools emerged. Companies like Amazon and Netflix began using AI-powered algorithms to analyze browsing history and past purchases to suggest new products or content (The Evolution of AI in Marketing: A Brief History, 2023).

Generative AI tools revolutionized marketing by automating creative processes.

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• Content creation at scale: Generative AI tools allow marketers to quickly produce vast amounts of written copy, images, and video assets for emails, blog posts, and social media. This significantly reduces content creation time and cost.

• Next-level personalization: Marketers use generative AI, grounded with first-party data, to create hyper-relevant content variations for different audience micro-segments. 

•  AI agents can dynamically adjust campaign elements like ad copy or timing based on real-time performance data. This enables more agile and data-driven campaigns (Harkness et al., 2023).

Technological advances like increased computational power and machine learning moved AI from a theoretical to, real-time application.

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• Programmatic advertising: AI began automating the purchase and placement of ads in real-time, optimizing targeting and budget allocation based on audience behavior.

• Refined analytics: Machine learning techniques like collaborative filtering became an industry standard for delivering personalized content and ad experiences.

• Customer support: Basic AI-powered chatbots appeared on websites, providing automated, 24/7 customer service  (Nagl & King, 2024). 

Looking forward, AI is evolving toward more autonomous systems and integrated experiences.

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• AI agents: These autonomous or semi-autonomous AI applications can perform complex marketing workflows with minimal human intervention, from managing customer inquiries to executing entire campaigns.

• Multimodal AI: AI systems will process and analyze different data types—text, images, video, and voice—within a single platform to enable more seamless and sophisticated marketing automation.

• Ethical AI: With the rise of advanced AI, addressing ethical concerns around data privacy, bias, and responsible use is becoming a top priority for marketers and consumers alike (Chiara, 2025) .