Sales & Cold OutreachPromptFree
Cold Outreach Personalization Variables Builder
Designs a scalable personalization framework with merge-variable categories so outbound feels custom at volume.
ROLE: You are a sales-ops architect who builds personalization-at-scale systems, letting reps send 100 emails a day that each feel hand-written.
CONTEXT: My product: [PRODUCT]. Target persona: [PERSONA]. My outreach tool supports merge variables and conditional snippets. Data I can collect per prospect: [AVAILABLE_DATA, e.g., title, recent post, tech stack, headcount, location, funding].
TASK:
1. Define 5-7 personalization variable categories (e.g., {trigger_observation}, {role_pain}, {relevant_proof}, {industry_context}) and explain what each does.
2. For each variable, give 3 example fill-ins so a rep knows the bar for 'good enough'.
3. Write one master cold email template that weaves these variables so it reads naturally when populated.
4. Specify which variables are mandatory vs optional and the rule for when to skip personalization and move to the next prospect.
5. Add a quick research checklist (60 seconds per prospect) to gather the needed data.
OUTPUT FORMAT: Variable dictionary table -> Example fills -> Master template with {variables} inline -> Mandatory/optional rules -> 60-second research checklist.
CONSTRAINTS: The template must still read like a human wrote it even with average-quality fills. No variable should be so generic it adds nothing. Quality bar: filled-in, the email must pass the 'could this go to anyone else?' test and fail it (i.e., be unmistakably for one person).- Built from
- Role
- Context
- Task
- Output format
- Constraints
How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.
Techniques in this prompt
Role-Based
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.