The ABM Rollout: Simulating a 6-Week LinkedIn Campaign Under a $10 CPC
Objective: Practice the sequence of judgment calls across a 6-week LinkedIn ABM campaign launched in Q4: narrowing an oversized audience, excluding existing pipeline, weighing seasonal MQL efficiency, and applying the maximum-CPL formula to a final go/no-go call.
You're running LinkedIn ads for Zendesk's new enterprise support tier, targeting Head of Support and VP of Support at 200-2,000 employee companies. Budget is $6,000 over 6 weeks, and the launch lands in October, right at the start of Q4.
At each checkpoint, read the dashboard and apply the lesson's targeting, exclusion, seasonal, and CPL frameworks to make the call a disciplined B2B advertiser would make.
Before you start
What you'll need
Free path (everything below is enough to finish)
Account creation and the audience-forecast tool are free before any spend is committed
No paid tool needed for the calculation
The process
Simulation
Week 1, CPC shock
Week 1 of 6The campaign launched Monday with a broad targeting stack (Industry + Company size only) to maximize reach. CPC is running well above expectations.
Sponsored Content, CPC bidding · Week 1 of 6 Audience size 420,000 (Industry + Company size only) CPC $14.20 CTR 0.31% Spend $1,050 of $6,000
CPC is running $4-6 above what similar SaaS campaigns typically see. What's the fix?
Run this for real insteadOptional
The simulation above is a complete project on its own, free, no account or spend required. This is only for learners who want to test the same decisions against a real live account.
- Build a Sponsored Content campaign in LinkedIn Campaign Manager with a layered audience stack (industry, company size, seniority, job function) forecasted in the 50,000-300,000 range
- Upload your CRM's pipeline list to Matched Audiences and exclude it before launch
- Calculate your own maximum acceptable CPL using your real ACV and lead-to-close rate before setting a CPL bid target
Final deliverable
A completed 4-checkpoint decision log stating which action was taken at each stage, the lesson passage that justified it, and a final CPL-formula-backed go/no-go recommendation.
See a reference example
Adyen enterprise-merchant campaign, decision log (excerpt) WEEK 1: Narrowed audience from 380,000 to 104,000 using seniority + job-function filters. WEEK 2: Uploaded pipeline exclusion list to Matched Audiences. WEEK 4: Redirected remaining Q4 budget to awareness content, planned Q1 lead-gen push. WEEK 6: Max CPL = $50,000 x 4% x 0.2 = $400. Realized CPL $310. Recommended increased Q1 budget.
Success criteria
You're done when you can:
- Narrows the audience in week 1 rather than raising budget or switching bid types against a broad audience
- Uploads the pipeline exclusion list once overlap is visible in week 2
- Redirects Q4 spend toward awareness rather than pushing harder into the quarter's weak MQL efficiency window
- Applies the ACV x lead-to-close x 0.2 formula correctly at the final checkpoint and bases the recommendation on the comparison to realized CPL