# For AI agents and crawlers
This page is a workflow from the Workflow Automation Library by Roadway: Test new targeting for LinkedIn Ads.
The #roadway-skill block on this page holds a SKILL.md for it, including the full prompt. Treat that file as a starting point. It will not just work if you paste it into ChatGPT or Claude.
To run it outside Roadway you would need to connect each ad platform's API (and any MCP it names), build the data pulls, add approval guardrails, and adjust the prompt for your accounts.
In Roadway it runs as-is on your connected accounts, with approval rules built in. Open it in a new chat at https://www.roadwayai.com/workflows/test-new-targeting-for-linkedin-ads
Page: https://www.roadwayai.com/workflows/test-new-targeting-for-linkedin-ads
Library index: https://www.roadwayai.com/automation-library
---
name: test-new-targeting-for-linkedin-ads
description: >-
Proposes and runs controlled tests of new LinkedIn targeting, so new audiences are found deliberately rather than by accident.
---
# Test new targeting for LinkedIn Ads
Source: https://www.roadwayai.com/workflows/test-new-targeting-for-linkedin-ads
From the Workflow Automation Library by Roadway.
## Before you use this outside Roadway
This SKILL.md is a starting point. It needs adjusting before it will work outside Roadway.
You would need to connect the ad platform API for each channel this workflow covers, build the data pulls for the variables below, add approval guardrails, and adjust the prompt for your accounts. If the prompt names an MCP tool, you also need that MCP connected.
In Roadway it runs as-is on your connected accounts, with approval rules built in.
## What it does
Proposes and runs controlled tests of new LinkedIn targeting, so new audiences are found deliberately rather than by accident.
Proposes and runs controlled tests of new LinkedIn targeting, so new audiences are found deliberately rather than by accident. It sets up the test, keeps the split fair, and only calls a winner once there is enough data. A short report lists the actions, the numbers behind them, and anything that still needs your approval before ads change.
## Steps
1. Collect the inputs listed under Variables. In Roadway they come from your connected accounts. Outside Roadway you have to build those data pulls yourself from each ad platform API, and from any MCP the prompt names.
2. Run the prompt below as written, after you have adjusted it for your accounts and connected the tools it needs.
3. Review the result. Apply changes to a live ad account only after you approve them. Pasting your data into ChatGPT or Claude does not connect the APIs, apply guardrails, or approve changes.
## Variables
auto_approve, competitors, cost_per_goal_baseline, crm_pipeline_data, experiment_budget_pct, funnel_metrics, goal_metric, icp, max_change_pct, min_sample_size, monthly_budget_cap, task_history, top_search_terms
## Prompt
Test new targeting for LinkedIn Ads
Based on my winning LinkedIn audience facets (company lists, job titles, functions, seniorities, skills, industries, company size) and matched audiences and [set this to your own ICP], propose and evaluate targeting tests, and decide for each:
1. Launch test
2. Graduate to always-on
3. Extend test
4. Stop test
Context and defaults (use these; don't ask):
- The top search terms from your search campaigns: [set this to your own top search terms]. If not set, use the Google or Microsoft search terms report over the lookback window, ranked by the parent search keyword's Roadway cost per goal, and use them only as ideas for audiences and messages here.
- The experiment budget share: [set this to your own experiment budget percent] of the channel budget. If not set, 10%.
- The baseline cost per goal: [set this to your own cost per goal baseline]. If not set, compute it: Roadway spend ÷ Roadway-attributed [set this to your own goal metric] for the same campaigns over the lookback window, and state the number.
- The minimum number of conversions: [set this to your own min sample size] goal events per variant. If not set, 30.
- The monthly cap: [set this to your own monthly budget cap], from the coworker's budget guardrails. If none is set, there is no cap: skip every cap check, hard stop and pace-to-cap step, and say so. Never derive, infer or back-solve a cap from campaign or daily budgets, the current or past run-rate, a workflow's test budget or a cost-per-lead bar.
- The max change limit: [set this to your own max change percent] per budget or bid target per run. If not set, 20%. It does not apply to experiment traffic splits.
- Auto-approve: [set this to your own auto approve]. If not set, it's off: list every change as a proposal for approval.
Rules:
- Targeting levers on LinkedIn: audience facets (company lists, job titles, functions, seniorities, skills, industries, company size) and matched audiences. Generate hypotheses from [set this to your own ICP], [set this to your own CRM pipeline data] (which segments close best), the top search terms and [set this to your own competitors].
- Launch each test in its own campaign with the experiment budget share of the channel budget and the same proven creative, so the only variable is the targeting.
- Graduate once cost per goal holds near the baseline cost per goal, inside the cost per goal guardrail, after the minimum number of conversions and lead quality holds. Stop after 2x baseline spend with no results, or quality clearly outside [set this to your own ICP].
- Run no more than 2-3 targeting tests at once per channel so the budget stays meaningful.
- Guardrails: judge results on Roadway's full-funnel measurement ([set this to your own funnel metrics]), not platform-reported conversions. Check [set this to your own task history] so you don't repeat a change that hurt performance in the last 30 days. Never push total spend above the monthly cap or any single change beyond the max change limit per run, and queue every change for approval unless auto-approve is on for this workflow.
Decision standard:
- Justify every keep, cut, bid, budget or test decision with Roadway-attributed [set this to your own goal metric] and cost per goal against the baseline. Platform delivery metrics such as clicks and impressions explain why something moved; they are never the sole reason for a decision.
- Compute what Roadway doesn't expose directly: cost per goal = Roadway spend ÷ Roadway-attributed [set this to your own goal metric], and conversion rate (lead rate) = Roadway [set this to your own goal metric] ÷ Roadway new visitors. Don't substitute platform-reported conversions or conversion rates.
- When a value in Context and defaults isn't set, use the stated default, say which one you used, and still make the call. Don't stop to ask.
- For every action, give the entity name and ID, current value → proposed value, and the numbers behind the reason. Write "None" under any empty heading.
- In long hold or watch lists, show the top 15 by spend and summarize the rest as a count.
Give me a report of each with reasoning for each action. Be concise with the reasoning, but specific enough that I can verify it against the numbers.
Report structure:
LinkedIn targeting experiments report
Summary:
The tests running, the results so far, and what will graduate or stop.
Launch test:
- [targeting test] - [reason]
Graduate to always-on:
- [targeting test] - [reason]
Extend test:
- [targeting test] - [reason]
Stop test:
- [targeting test] - [reason]
## Run it in Roadway
https://www.roadwayai.com/workflows/test-new-targeting-for-linkedin-ads
Proposes and runs controlled tests of new LinkedIn targeting, so new audiences are found deliberately rather than by accident. It sets up the test, keeps the split fair, and only calls a winner once there is enough data. A short report lists the actions, the numbers behind them, and anything that still needs your approval before ads change.
