Most personas are demographic posters — “Marketing Mary, 32, urban, loves coffee” — that nobody opens twice. They don’t drive a single decision because they don’t say what the buyer does today, what they wish they could do, what would disqualify them, or what they Google at 11pm when they’re stuck. These prompts force behavioral specificity, real-quote anchoring, JTBD links, and explicit anti-personas so the result actually shows up in product, marketing, and pricing decisions. Pair with jobs-to-be-done prompts for the upstream framing.
Best for
- Pre-MVP product discovery
- Marketing-positioning work
- Designing onboarding flows
- Sales-enablement
- Sharpening go-to-market
1. Behavior-led persona
Build a persona for {product}. Inputs: {paste interview notes / survey data}. Output: behavioral name (not "Marketing Mary"), 1-line context, what they do today, what they wish they could do, their decision criteria, what disqualifies them as a buyer.2. Persona from sales calls
Below are 5 sales-call transcripts. Extract the underlying persona: shared problem framing, shared vocabulary, shared decision triggers. Avoid demographic mush.
{paste calls}3. Anti-persona generator
For {product}, define 3 anti-personas: who will look like a fit but will churn or never buy. For each: why they look like a fit, why they actually are not, the early signal to disqualify them.4. JTBD-linked persona
Build a JTBD-linked persona for {product}. Output: the job they hire {product} for, the situation that triggers the hire, the alternatives they considered, the trade-off they accept.5. Buying-committee persona (B2B)
For my B2B product, build the buying-committee personas: economic buyer, technical evaluator, end user, blocker. For each: their concern, what wins them over, what loses them.6. Persona shift over time
My early customers were {persona A}. New customers feel different. Inputs: {paste new-customer notes}. Identify the shifting persona and what about our product attracted the new group.7. Persona-quote bank
Below are 20 customer interview quotes. Cluster into 3 personas. For each persona, give 5 verbatim quotes that capture their voice. These quotes go into landing-page copy.
{paste quotes}8. Persona-to-positioning bridge
For persona {paste persona}, write the positioning statement: "For {persona}, who {problem}, {product} is a {category} that {benefit}, unlike {alternative}, we {differentiator}." Then 3 variants.9. Persona × channel map
For my 3 personas {paste}, map: where they hang out, what they Google, what content they consume, what triggers them to seek a solution. Use this to plan channels.10. Persona-pricing fit
For each of my 3 personas, evaluate fit at each pricing tier. Output: which tier matches, the willingness-to-pay signal, the objection at this tier, what would justify them moving up.11. Persona-feature prioritization filter
For persona {paste}, filter my backlog: {paste 20 features}. For each: high-priority for this persona / nice-to-have / waste-of-time. Justify each call from their JTBD.12. Persona update from new evidence
Below: my current persona doc. Below: 10 new customer interviews. Update the persona — what to add, what to remove, what changes confidence level. Mark what is now contradicted.
{paste both}13. Persona pressure-test
Below is my draft persona. Pressure-test: (a) is it behaviorally specific, (b) is it grounded in real data, (c) is it actionable, (d) does it have a meaningful "anti-" version. Score 1-5 each and propose fixes.
{paste}Common mistakes
- Demographic-only personas (“32, urban, marketing”) — no decision can be made from this
- Personas not grounded in real interviews / data — vibes-based personas drive vibes-based features
- No anti-persona — every user looks like a fit, churn surprises everyone
- Persona doc that no PM, marketer, or designer actually opens after kickoff
- Same persona used unchanged for 2+ years while the market shifted around you
- Cute persona names (“Marketing Mary”, “Founder Frank”) instead of behavioral names that mean something