Designing Agentic AI
Imagine you’re a marketing manager in charge of a direct mail campaign. Your goal is to target high-income households in Austin, Texas—a diverse city of nearly 1 million residents. You have access to a database of over a million households, each with detailed demographic data: income, age, housing type, and more.
Your challenge? Create a segment that includes at least 1,000 households but no more than 10,000, which also needs to align closely with your Ideal Customer Profile (ICP) to maximize relevance and impact.
This isn’t a simple task. With dozens of demographic filters and numerous possible configurations, the combinations quickly explode into billions. To make matters worse, these filters aren’t independent—certain income groups correlate with specific neighborhoods, and younger families are unlikely to live in retirement areas.
Even if you spent days manually researching and testing filters, you’d constantly question whether you’d found the best audience. What if a better combination exists? This uncertainty is what makes audience segmentation such a time-consuming and challenging problem.
