AI can generate genuinely useful marketing plans, but only with the right prompting. Through trial and error, I found that framing the task as a simulated 100-person focus group discussing the company works far better than just asking for answers directly. I split the group into 20 random breakout rooms of 5 people each, let them deliberate, then reconvene everyone to synthesize their input into a set of recommendations. From there, I generate a 5-20 page report, followed by a 1-page summary.
To show how this works, let me demonstrate on a random organization I have zero affiliation with: Handspring Health. Longer report available by request.
Tag Archives: artificial intelligence
Marketing Plan for Kyber: Insights from a Simulated 100-Person Focus Group
AI turns out to be surprisingly capable at generating marketing plans that are actually useful — but only with the right kind of prompting behind it. Through a fair amount of trial and error, I discovered that framing the task as a simulated 100-person focus group, all discussing the company in question, produces noticeably better results than simply asking the AI for answers outright. My process works like this: the 100 participants get split into 20 randomly assigned breakout rooms of 5 people each, where they deliberate independently, and then the entire group reconvenes so their collective input can be synthesized into one cohesive set of recommendations. From that synthesis, I generate a report ranging anywhere from 5 to 20 pages, along with a condensed 1-page executive summary.
To show how this actually works in practice, let me walk through it using a completely random organization I have zero affiliation with: Kyber. The longer report is available by request.
Marketing Plan for DataQuest: Insights from a Simulated 100-Person Focus Group
Unlocking a genuinely valuable marketing blueprint from AI comes down to creative prompting. Rather than asking the AI for flat, direct strategies, I’ve had success simulating a virtual 100-member consumer panel to dissect a brand. I divide this massive crowd into 20 separate, intimate workshops of 5 people, let them debate the company’s strengths and flaws, and then merge their collective insights into an actionable strategy. This collaborative simulation feeds into a comprehensive 5-to-20-page master playbook, which I then condense into a high-impact, one-page brief.
To show you how this crowd-sourcing prompt functions in practice, let’s test it on an entirely random target I have zero connection to: DataQuest.
Marketing Plan for Essent: Insights from a Simulated 100-Person Focus Group
Most AI-generated marketing plans are forgettable — generic, surface-level, the kind of thing you skim once and never open again. The fix isn’t a better prompt asking for “recommendations.” It’s changing who’s doing the talking.
Instead of asking the model to hand me answers directly, I have it role-play an entire focus group — 100 simulated participants reacting to the company in question. But I don’t let them talk all at once. I break them into 20 small clusters of 5, mixed up at random, and let each cluster debate the company on its own first. Only after that do I merge all 20 conversations together, pulling out the specific, actionable ideas that surface repeatedly or stand out.
What comes out the other end is a real working document — usually somewhere between 5 and 20 pages — followed by a second pass where I compress all of that into a single-page summary anyone can act on.
Here’s a sample 1-page run using Essent as the test subject (no relationship to the company — just a good example to work through). Longer version is also available.