# For AI agents and crawlers
This page is a workflow from the Workflow Automation Library by Roadway: Optimize lookalike audiences for Google Display 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/optimize-lookalike-audiences-for-google-display-ads
Page: https://www.roadwayai.com/workflows/optimize-lookalike-audiences-for-google-display-ads
Library index: https://www.roadwayai.com/automation-library
---
name: optimize-lookalike-audiences-for-google-display-ads
description: >-
Keeps Google Display lookalike-style audiences seeded with your best customers and retires the ones that drift.
---
# Optimize lookalike audiences for Google Display Ads
Source: https://www.roadwayai.com/workflows/optimize-lookalike-audiences-for-google-display-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
Keeps Google Display lookalike-style audiences seeded with your best customers and retires the ones that drift.
Keeps Google Display lookalike-style audiences seeded with your best customers and retires the ones that drift. It reviews your live account on every run and proposes the changes that should help most. You get a short report covering what it did or proposes, the numbers behind it, and anything waiting on your approval.
## 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, cost_per_goal_baseline, customer_audiences, funnel_metrics, goal_metric, max_change_pct, monthly_budget_cap, task_history
## Prompt
Optimize lookalike audiences for Google Display Ads
Review every lookalike or predictive audience in my Google Ads (Display & Demand Gen) account and its seed source, and decide for each:
1. Keep
2. Refresh seed
3. Widen
4. Retire
Context and defaults (use these; don't ask):
- The customer audiences: [set this to your own customer audiences]. If none are synced, say so.
- 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 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:
- Lookalikes on this channel: Demand Gen lookalike segments (narrow, balanced, broad). Seed with your highest-value customers or converters from the customer audiences, ideally value-weighted, rather than all site visitors.
- Keep lookalikes that produce [set this to your own goal metric] at or under the baseline cost per goal. Widen the percentage or reach setting on a winning lookalike that is delivery-constrained. Retire lookalikes whose cost per goal is still over the cost per goal guardrail after 1.5x baseline spend.
- Refresh seeds older than 90 days or with fewer than 1,000 matched people, and always exclude the seed audience and current customers.
- If broad or Advantage-style targeting beats every lookalike for 2+ weeks, recommend consolidating into broad targeting with the seed as a signal.
- 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:
Google Display lookalike optimization report
Summary:
Which seeds produce the best customers, the lookalikes to change, and the expected impact on cost per goal or pipeline.
Keep:
- [audience (seed)] - [reason]
Refresh seed:
- [audience (seed)] - [reason]
Widen:
- [audience (seed)] - [reason]
Retire:
- [audience (seed)] - [reason]
## Run it in Roadway
https://www.roadwayai.com/workflows/optimize-lookalike-audiences-for-google-display-ads
Keeps Google Display lookalike-style audiences seeded with your best customers and retires the ones that drift. It reviews your live account on every run and proposes the changes that should help most. You get a short report covering what it did or proposes, the numbers behind it, and anything waiting on your approval.
