What Comes After a Persona Document Like Make My Persona
A team that has already built a persona document is not asking "what is our customer like." It is asking a sharper question: which message, price, or feature framing will that customer actually choose. A one-page document cannot answer that. Neither can a chat window that lets you type questions at a simulated customer.
What a persona document is built to do
HubSpot's free persona generator walks a user through a guided form, then styles the answers into a single page carrying a name, demographics, goals, challenges, and a stock photo. It ships in about 15 minutes at no cost, and marketing teams have used it to produce a large volume of first-draft personas (HubSpot's Free AI Persona Generator).
It is a reference document for sprint planning, onboarding, or aligning a team on who they are building for. It was never built to test a decision, and it cannot say whether price A or price B wins, or why one message beats another.
Three answers to "who is our customer," and what each one leaves open
| Approach | What it produces | What it cannot tell you |
|---|---|---|
| Static persona document | A one-page reference: goals, demographics, a photo | Which specific message, price, or feature wins, or why |
| Conversational persona interface | A plausible-sounding answer to any question typed in | Whether that answer reflects how real buyers would choose, since there is no controlled comparison or confidence interval behind it |
| Controlled discrete choice experiment | A measured causal effect on an actual decision, with a confidence interval | Open-ended, exploratory ideation before a team knows what it wants to test |
The middle row is where most teams land once they outgrow a static document, and it is also where the risk hides. A transcript from a simulated persona can sound specific and confident with no way to check whether it reflects real buyer behavior. Building a launch, price, or pitch on that transcript carries the same risk as building it on a guess.
What a controlled experiment adds
Subconscious runs controlled discrete choice experiments (McFadden DCE, Mixed Logit, ICLV) against a defined set of choices, then reports which option moves the outcome and by how much, with a confidence interval attached: a measured comparison, not one plausible response.
/research documents the experiment design, and the leaderboard is the running record of how simulated results compare against real human studies, including where they miss. Subconscious reports 93% replication accuracy against real human outcomes across a corpus of published studies, defined as how often a simulated study reproduces the direction and outcome of the original human study (go.subconscious.ai/paper).
When a decision is big enough to justify it, a team can move from a simulated experiment to a real-human validation study without changing the underlying causal question: the same experiment design carries forward, so a team does not have to treat "fast and simulated" and "slow and real" as two unrelated projects.
Where this does not fit
A defined choice set and a decision worth testing are prerequisites. A team still doing open-ended, exploratory persona work, before anyone has settled on the message or price to test, has nothing yet to compare. That is still the job for early ideation and a lightweight, shareable persona document.
This is not an open-ended interview with a single simulated persona. The output is a measured effect across a defined set of options, not a conversation transcript.
Deciding which one to use
- Need a shareable reference document for internal alignment: a free persona generator is the right, fast tool.
- Need to explore a customer segment before knowing what to test: stay in open-ended research.
- Have a specific message, price, or feature decision and need to know which option wins, and why, before committing budget: that calls for a controlled discrete choice experiment.
Teams in the third case can see how the process runs or book a walkthrough.
