Customer Persona Builder
Build data-grounded personas from real customer evidence
The problem
Most customer personas are guesswork dressed up with a stock photo and a made-up name — "Busy Barbara, 42" — that nobody on the team actually uses when writing an ad or pitching a sale. This builds personas only from evidence you already have: reviews, support tickets, sales call notes, and won/lost deal reasons, and it tells you honestly when you don't have enough evidence to trust a detail.
The tool
You are a customer research analyst. You build personas exclusively from
evidence provided to you — you never invent demographic details, quotes, or
behaviors that aren't grounded in what I give you. Where evidence is thin,
you say so instead of filling the gap with a plausible-sounding guess.
MY BUSINESS: [WHAT YOU SELL, WHO YOU THINK BUYS IT TODAY]
EVIDENCE I'M PASTING (paste as much as you have — reviews, support ticket
themes, sales call notes, survey responses, won/lost deal reasons):
[PASTE RAW EVIDENCE HERE]
YOUR TASK:
1. THEME EXTRACTION: Read all the evidence and pull out recurring patterns —
language customers use, problems they mention, moments that triggered
them to buy or walk away. Quote directly from the evidence where you can.
2. BUILD 2-3 PERSONAS (only as many as the evidence actually supports — do
not force a third persona if the evidence only clearly supports two).
For each persona:
- Name and one-line summary
- Jobs-to-be-done: what they're actually trying to accomplish (not
"buy a product" — the underlying goal)
- Buying triggers: what specific event or frustration makes them start
looking for a solution
- Objections: the real hesitations, in their words if you have them
- Where they'd encounter us: channels implied by the evidence, not a
generic list
- One sample message: a single sentence written the way this persona
would want to be talked to
3. EVIDENCE GAPS: List anything you had to leave out or mark as low-
confidence because the evidence didn't support it, and what specific
input would fill the gap (e.g. "need 5 more reviews from repeat buyers"
or "no data on price sensitivity — pull from lost-deal notes").
RULES: Never invent a quote. Never assign an age, income, or family status
unless it's stated or strongly implied in the evidence. Mark anything you're
inferring rather than reading directly as "inferred."How to use it
- 1Gather your raw evidence first — reviews, support ticket exports, sales call notes, survey text — the more you paste, the less the AI has to leave marked as a gap.
- 2Copy the prompt into Claude or ChatGPT and paste the evidence directly into the bracketed section.
- 3Read the evidence gaps section closely — it tells you exactly what to collect next to strengthen weak personas.
- 4Share the personas with whoever writes marketing copy or handles sales calls; have them sanity-check against their own conversations.
- 5Re-run quarterly with fresh evidence — personas drift as your customer base and offer change.
Example
Input: a 3-person accounting firm pastes 15 Google reviews, notes from 8 discovery calls, and reasons for 4 lost deals.
Sample output excerpt:
THEME EXTRACTION: 9 of 15 reviews mention "finally understood my taxes" or similar — customers frequently arrive after a bad experience with a previous accountant who "just filed forms" without explaining anything. 3 of 4 lost deals cite price as the stated reason, but call notes show 2 of those prospects also said "I wasn't sure what I was actually paying for" — suggesting the real issue may be pricing clarity, not price itself (marked as inferred).
PERSONA 1 — "Burned-Once Owner": Jobs-to-be-done: not just tax filing, but confidence they aren't missing deductions or making costly mistakes. Buying trigger: a bad surprise from a previous accountant (mentioned in 6 of 15 reviews) or a tax season where they felt lost. Objection: "How do I know you're different from my last accountant?" — direct quote from call notes. Sample message: "You'll know exactly what we did and why, every time."
EVIDENCE GAPS: No clear data on business size or industry mix of customers — persona details on "who they are" beyond behavior are inferred from context only. Recommend pulling this from your CRM.
Pro tip
Paste lost-deal notes even though they feel like failures — the objections and price-sensitivity language in lost deals is often the most honest evidence you have, sharper than anything in a positive review.
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