Rhys Fisher is the COO & Co-Founder of Rally. He previously co-founded a boutique analytics agency called Unvanity, crossed the Pyrenees coast-to-coast via paraglider, and now watches virtual crowds respond to memes. Follow him on Twitter @virtual_rf
Showing 1-9 of 12 articles
What if a movie scene could reshape consumer preferences and you could test it before releasing your campaign? Using AI personas and implanted memories inspired by Sideways, I simulated a media-driven market shift that made virtual shoppers abandon Folgers for premium Ethiopian coffee.
Research with synthetic personas lets anyone make smarter decisions fast, cheaply, and with good-enough accuracy. Instead of interrupting real humans for every idea in your head, you can spin up virtual crowds instantly, ask them questions 24/7, then take the best ideas and test them with real humans.
Feed your AI personas authentic, lived-experiences, and closed-loop sims break open: you can watch sentiment shift in real time, flag churn risk months early, and pressure-test your renewal strategies before you commit.
Polls say most of us believe in “equal justice,” yet the moment a headline names our hero or our villain, our moral math flips. In my latest experiment, I fed Rally personas a stream of fake social-media posts and hard coded them to have a favorite between Kanye and Will Smith. The result? Turns out AI has double standards. So do humans. We witnessed how virtual audience simulations could be used to pressure-test in silicon before PR teams click publish.
When it’s your money on the line, do you really pay double for "Made in USA"? Ramon tested it with real shoppers and walked away empty handed. We tested real-time buyer decisions using role-playing agents, and the results might surprise you.
Prompt engineering got its hype moment when Anthropic flashed a $225 K salary for anyone who could bend words into gold. Rally raises the stakes. It lets you address a stadium of GPT personas at once—but that power demands a new playbook: virtual-audience simulation prompting. Treat the crowd like a poll (“How many of you prefer …?”) and you get lukewarm averages. Challenge them like a panel (“Pick A, B, or C—defend your choice”) and every agent steps up with sharp, contrasting takes that expose blind spots, trade-offs, and hidden opportunities. The gap between those two sentences is the difference between genuine market signal and background static. This article shows you how to close it.
What if you could see tomorrow’s social-media outrage unfold today—predicting thousands of angry comments before anyone even hits “reply”? I simulated future threads using AI personas, anticipated sentiment with near-perfect accuracy (up to 99.9%), and uncovered exactly how tiny shifts in prompts and audience design can make or break predictions. Here’s the play-by-play of that experiment, and why accuracy alone is meaningless without the context that shapes it.
In a battle of AI decision-making, we put straightforward GPT predictions head-to-head with virtual audience simulations. Can basic GPT models, relying on pure heuristics, match up to a synthetic audience that mirrors human nuance? In this experiment, we challenged GPT 4o, o3-mini, and a role-prompted GPT 4o-mini against Rally’s tailored virtual audience.
With synthetic research and persona-driven prompting still in beta, little is known about how different AI models shape responses. In our simulated buying committee experiment, we found that more powerful models can replicate the complex decision-making seen in high-stakes human scenarios. If you want your synthetic research to truly mirror human thought, here's how choosing between Fast and Smart mode can unlock insights that reflect real-world nuance.
