AI Won't Replace Marketers, But It Will Redefine What Makes Them Great | AdExchanger
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AI Won't Replace Marketers, But It Will Redefine What Makes Them Great | AdExchanger
"Every platform, publisher and technology partner now promises "AI-powered" solutions that will make campaigns smarter, faster and cheaper. But as the noise grows louder, one truth remains: AI is not a silver bullet; it's a set of tools that, when built on quality data and guided by human expertise, can elevate every part of the marketing process, from planning and activation to optimization and measurement."
"Audience discovery has always been both an art and a science, and AI has expanded the scientific side dramatically. Advanced audience modeling can now combine deterministic and probabilistic data to identify high-fidelity lookalikes based on mobile usage, app engagement and geospatial movement patterns. For marketers, this means the ability to reach intent-based personas (think travelers, gamers, shoppers and other high-value cohorts) at scale."
"Predictive behavioral models extend this further by anticipating when a user is most likely to engage or convert. By analyzing device activity, time-of-day patterns and past purchase behavior, AI helps brands understand not only who their audiences are but when and how to reach them. When done responsibly, this turns passive behavioral data into actionable intent signals. Still, AI's precision has limits. Biases in training data can reinforce inaccurate assumptions."
Artificial intelligence can elevate marketing when built on quality data and guided by human expertise. AI automates insights, detects patterns invisible to humans and generates reports rapidly, while requiring people to define questions, interpret outputs and align automation with brand goals. AI improves audience discovery by combining deterministic and probabilistic data to build high-fidelity lookalikes from mobile usage, app engagement and geospatial patterns, and predictive behavioral models anticipate when users are likely to engage. Responsible use converts passive behavioral data into actionable intent signals. Limitations include bias in training data and overfitting, which can reduce model generalizability.
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