Why Talk to Customers When You Can Simulate Them?

The best technology products are the ones that break your brain—there is a before and an after. Much of the world views LLMs as a better auto-complete or another Silicon Valley bubble. To be honest, some days I feel that way, too. Reading this piece brought me back to the side of technological optimism. The dark magic of LLMs means you can amalgamate all the text humanity has ever created and create the simulation of an individual’s desires. I recognize that this is a pie-in-the-sky idea, but it is real, and it is happening now. Chris Silvestri uses LLMs to test his B2B SaaS copy writing’s effectiveness—and if it works in the enterprise, eventually it’ll work for consumers. Read this piece for a useful guide to solve a distinct problem (customer research) and see how AI can change everything (what human consciousness even means).—Evan Armstrong

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There’s a problem that's plagued every marketer and copywriter since the dawn of advertising: understanding what makes customers tick.As a conversion copywriter, I conduct market research, synthesize it into strategy, and write sales copy for my clients’ websites. My go-to research process involves talking to clients and their customers, analyzing customer reviews, and diving deep into all sorts of data. Once that’s done, we draft marketing copy based on our assumptions, and test it with potential and paying customers. For niche business-to-business audiences—like tech founders, HR managers, or IT teams for example—this testing can be painfully slow and expensive.

Enter large language models.

I recently asked Google’s Gemini 1.5 Pro model to simulate three different customers, answering my questions and reacting to various marketing language. 

The whole experiment took just a couple of hours and cost less than $5. That’s a far cry from the weeks-long slog and $1,000 price tag for traditional message testing, which typically involves recruiting participants, offering them rewards or incentives to help us with feedback, and setting up a test with structured questions.

What if we could use real customer data and LLMs to reveal the marketing insights we need for our messaging and copy work? Even with a few AI-generated hiccups along the way, the results were surprisingly insightful.

Here’s how it works: Instead of just imagining what your ideal customer wants, you can actually have a conversation with an AI model that mimics their persona. You can prompt it with detailed information about your target customer—their demographics, goals, pain points, even their favorite industry websites. Then, you ask the AI questions, just as you would in an interview with a human customer. How would they describe their biggest challenge? What features are most important to them? What are their objections to your product?

I call this "empathy engineering." It’s a framework for using LLMs to understand your customers better than they know themselves, create messaging that converts prospects into customers, and ultimately build better products and services. Empathy—understanding and sharing the feelings of another—builds the trust and connection essential for meaningful relationships. By stepping into the shoes of your customers, you can explore different perspectives and deepen your understanding. AI-powered role-playing allows you to:

• Test your assumptions: Does the AI's response align with your understanding of the customer? If not, where are your blind spots?
• Uncover new insights: The AI might surface unexpected needs, desires, or objections that you hadn't considered.
• Refine your messaging: By understanding how the AI "customer" reacts to different phrases or concepts, you can craft more compelling and persuasive copy.

Using AI, we can step into our customers’ mindset, see their world, and feel their pain points. The deeper our understanding, the more powerfully our messaging will resonate.

Let’s look at how it works, and how you can apply it to your own business.

Your AI research assistant, not your replacement

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• Simulate customer personas with LLMs
• Refine messaging through AI-powered testing
• Balance AI insights with human expertise