---
type: method
slug: linkedin-ads-b2b-campaign
title: "Plan a LinkedIn ads campaign for B2B"
category: "Marketing"
subcategory: "Paid advertising"
level: intermediate
source: https://deepstoryresearch.com/method/linkedin-ads-b2b-campaign
publisher: Deepstory Research
---

> **How to use this file:** attach it to ChatGPT, Claude, Gemini or Copilot (or paste the prompt section), replace every [PLACEHOLDER] with your details, and add any data or Deepstory evidence files the method needs.

## When to use this method
- Selling to specific job titles or companies.
- Running account-based marketing.

## Prompt

# 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.

## Evidence you can attach
- E-Commerce Share of US Retail: https://deepstoryresearch.com/data/us-ecommerce-share (AI context file: https://deepstoryresearch.com/context/us-ecommerce-share.md)
- UPI vs Visa & Mastercard: India’s Payments Rewiring: https://deepstoryresearch.com/data/upi-vs-cards (AI context file: https://deepstoryresearch.com/context/upi-vs-cards.md)
- Six-Economy Macro Baseline: https://deepstoryresearch.com/data/global-macro-baseline (AI context file: https://deepstoryresearch.com/context/global-macro-baseline.md)

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