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MethodMarketingPaid advertisingIntermediate

Plan a LinkedIn ads campaign for B2B

A LinkedIn campaign with account/role targeting, offer, formats, copy and lead-quality controls.

This is a practical, copy-paste process for selling to specific job titles or companies.. Use it when you want a clear structure, then add your own details and the linked evidence.

Published by Deepstory Research. Reviewed Sep 2026.

How to use this method

  1. 1. Copy or downloadCopy the prompt below, or download the method file and attach it to your chat.
  2. 2. Paste into any AIChatGPT, Claude, Gemini or Copilot. Replace each [PLACEHOLDER] with your details, or let the AI ask you.
  3. 3. Add evidenceAttach the evidence files listed on this page so the AI works from sourced numbers, not guesses.

The prompt

Paste it as-is. The AI will ask for anything still in [BRACKETS].

Download .md
# Plan a LinkedIn ads campaign for B2B
Method by Deepstory Research: deepstoryresearch.com/method/linkedin-ads-b2b-campaign

You are a B2B paid-media manager. Follow the method below step by step. Goal: A LinkedIn campaign with account/role targeting, offer, formats, copy and lead-quality controls.

## My inputs
- Offer: [OFFER]  (example: free supply-chain risk assessment)
- Target roles and industries: [TARGET_ROLES_AND_INDUSTRIES]  (example: heads of procurement in manufacturing)
- Budget: [BUDGET]  (example: $5k/month)
- Lead handling: [LEAD_HANDLING]  (example: CRM and SDR follow-up)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Define targeting (company list, titles, seniority, skills) and audience size.
2. Choose an offer that earns a lead at this stage.
3. Pick formats (single image, document, lead gen form, conversation ads).
4. Write 3 ad variants per format.
5. Design lead-form questions that qualify.
6. Set lead-quality metrics and follow-up SLA.

## Deliver the output in this structure
- Targeting spec
- Offer
- Formats
- Ad copy
- Lead form
- Measurement & follow-up

## Quality rules
- Audience size should be large enough to exit learning; say what size you assume.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

What you need to give the AI

InputPlaceholderExample
Offer[OFFER]free supply-chain risk assessment
Target roles and industries[TARGET_ROLES_AND_INDUSTRIES]heads of procurement in manufacturing
Budget[BUDGET]$5k/month
Lead handling[LEAD_HANDLING]CRM and SDR follow-up

Use it when

  • Selling to specific job titles or companies.
  • Running account-based marketing.

What you get

  • Targeting spec
  • Offer
  • Formats
  • Ad copy
  • Lead form
  • Measurement & follow-up

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

  1. 1

    Define targeting (company list, titles, seniority, skills) and audience size.

  2. 2

    Choose an offer that earns a lead at this stage.

  3. 3

    Pick formats (single image, document, lead gen form, conversation ads).

  4. 4

    Write 3 ad variants per format.

  5. 5

    Design lead-form questions that qualify.

  6. 6

    Set lead-quality metrics and follow-up SLA.

Quality checks built into the prompt

  • • Audience size should be large enough to exit learning; say what size you assume.
  • • Every number labelled FACT, ESTIMATE or ASSUMPTION; claims outside attached evidence tagged [UNVERIFIED].

Evidence to use with this method

Methods tell the AI how to think. Evidence gives it sourced numbers to think with. Download a context file and attach it with the prompt.

Run next

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Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment decisions need a qualified professional.