Marketing

Audience research and LinkedIn targeting

AI cross-references website visitor intelligence and CRM data to identify high-intent industries and companies, rank them, and turn that insight into LinkedIn target audiences for ad campaigns.

Why the human is still essential here

The marketer decides which segments matter, interprets surprising audience patterns, and chooses how to translate the data into campaign strategy.

How people use this

Visitor-to-account identification

AI matches anonymous website visits to likely companies and industries so marketers can see which accounts are showing buying intent before filling out a form.

6sense / Clearbit

CRM-informed matched audiences

AI syncs high-fit accounts and lifecycle stages from the CRM into LinkedIn Matched Audiences so paid campaigns focus on the most relevant companies.

HubSpot / LinkedIn Campaign Manager

Intent-based account prioritization

AI ranks target accounts by page visits, firmographic fit, and engagement signals so budget is concentrated on the audiences most likely to convert.

Demandbase / 6sense

Related Prompts (4)

Community stories (1)

LinkedIn

I'm building an AI marketing team of one.

I'm building an AI marketing team of one.

Here's the thing about being a one-person marketing & sales team at an open-source software company (JobRunr): you don't have time for everything.


So I asked Patrick for help.


Patrick is my AI executive assistant, built on OpenClaw. And this week, he built me a LinkedIn Ads review system. From scratch.


Let me walk you through how we got here.


Step 1: We did the research first.


Before writing a single ad, we analyzed our actual data. Not vibes. Data.


We pulled company visitor data from Scarf and cross-referenced it with HubSpot. IP matching, domain lookups. That gave us a ranked list of which industries and companies are actually visiting our Pro and Pricing pages.


Finance. Insurance. Government. Not what I would have guessed.


We built company lists from that intelligence and matched them to LinkedIn audiences.


Step 2: We built a messaging framework.


Patrick analyzed all our existing customer data. Hubspot data, email conversations, intake questionnaires, support questions, ads,... Then we used that data to fill in a creative strategy map and come up with 3 angles per target-audience.



Step 3: The visuals.


I showed Patrick our existing ad visuals. He reverse-engineered the visual language and wrote image generation prompts matching our brand. Generated with Gemini. I could approve, reject, download, tweak in Photoshop, re-upload. All from a review tool he built.


Step 4: The review tool.


Patrick built a web app where I review everything:

→ Ad copy with live LinkedIn preview

→ Image variants (approve, download, replace, regenerate)

→ Comments and feedback

→ One-click approve to push live


No SaaS subscription. Just me and my agent.


The proof? One ad Patrick created by analyzing my best performers and generating a variant is now my highest CTR ad at 2.65%. AI-generated. Outperforming all my manual ones.


The difference between "write me an ad" and this? Real context. Your CRM data. Your analytics. Your brand. Your actual performance numbers.


And most importantly: Your taste. You are the creative director who shows the way.


Full stack: OpenClaw, LinkedIn Marketing API, HubSpot, Scarf, Gemini, and a custom review tool. Fully built using Telegram conversations.


Happy to share more details. Or just roast my AI visuals. Either works.

ND
Nicholas D'hondtHead Of Growth at JobRunr
Mar 10, 2026