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If you’re looking for some inspiration on how to use AI for ABM (Account Based Marketing) – in this post, you will find 3 practical ways to use AI for your ABM on LinkedIn:
I still remember staring at LinkedIn Campaign Manager for hours, trying to work out which accounts were actually moving, who to pass to sales, and which campaigns I should kill. That pain is what made me lean so hard into AI – both “generic” AI like OpenAI’s API for creative work, and purpose-built AI like Zena (our LinkedIn Ads AI analyst inside ZenABM) for analysis.
Account-Based Marketing (ABM) is basically:
When we started ABM at Userpilot, our first “proper” ABM program on LinkedIn generated over $900k in pipeline in five months – with around $8 in pipeline per $1 spent. It was not glamorous (think: spreadsheets, hacked-together reporting, brutal ops), but it was clearly better than generic cold outbound for our ACV and deal size.
The catch: LinkedIn Campaign Manager is not built for ABM. You get campaign-level stats, but not the company-level view you need to run real ABM. That’s exactly where AI + a tool like ZenABM come in.
When people hear “AI for ABM” they often think “write me some headlines”. That’s maybe 10% of the value. Here’s where I actually use AI across an ABM program:
You can absolutely do parts of this with ChatGPT or the OpenAI API directly. Where it gets really powerful is when you combine a generic LLM with your own first-party data – which is exactly what Zena AI does on top of ZenABM 😉
Watch this interactive demo to see how Zena can help you use AI for your ABM campaigns on LinkedIn easier!
Let’s start with the easy and fun part: ads.
When we ran our first big ABM campaign, we needed dozens of variants:
Doing that manually is painful. Here’s how I now use OpenAI (or ChatGPT) to scale this without turning everything into generic AI mush.
I usually paste in:
Then I ask for:
Example style of prompt:
“You’re an experienced B2B SaaS marketer. Write 5 LinkedIn sponsored content ad variants for {persona}, at the {ABM stage} stage.
Focus on {pain} and {outcome}. Use clear, conversational language, 1–2 short paragraphs + a CTA.”
I never paste the output straight into LinkedIn. I:
If you’re running multi-persona ABM campaings, doing this in ChatGPT alone gets messy. With separate projects on chatGPT for different personas and ad formates, you can:
The goal is not to let AI generate a 100% usable ad creative on its own – it’s to get from blank page to a good 80% starting point, fast.
Generic AI can’t help you much if it doesn’t see your data. That’s why we built Zena.
Zena is an AI chatbot that sits on top of:
Instead of exporting CSVs, cleaning them, and then uploading to ChatGPT, you just connect ZenABM once – and then you can literally chat with your LinkedIn ad + CRM data.
For example, you can ask Zena things like:
And get answers like:
That’s AI for ABM in the most literal sense: instead of you doing analysis, you ask the question and Zena does the heavy lifting on top of actual first-party data.
With Zena, you can de-anonymize ad performance on the target account (company) level. It will show you exactly how each company interacted with your ads and how far they moved through your funnel (see: account stages).
You’ll know which companies clicked your ads, viewed your content, or filled out forms – and you’ll see this data tied to your CRM records.
This lets you answer questions like “Which companies engaged with our recent campaign?” and “How much pipeline did we generate from company X last month?” without manual analysis.
Zena can also help you identify high-value targets for sales outreach. For instance, it can quickly show you which companies had the most ad clicks last week, indicating they’re ready for engagement:
Most ICP descriptions are hand-wavy paragraphs in a Notion doc. AI can help you turn that into something structured and data-driven.
Here’s how I do it:
AI doesn’t “know” your ICP better than you do – but it’s very good at summarising noisy inputs and spotting patterns you might miss when you’re tired and staring at dashboards.
AI tools for ABM on LinkedIn like Zena break down engagements by job title (and other demographics) so you see which roles are most interested in your content. For example, it might show that “Marketing Managers” clicked your latest campaign 200 times, whereas “CTOs” clicked only 50 times.
Since Zena tells you exactly who (which job titles) is clicking your ads, with this data, you can adjust your targeting in future campaigns or tailor messaging to the job functions that matter most.
You can also ask Zena which companies had the highest cost per click and didn’t convert, so you can exclude them from your targeting:
Zena AI also gives you instant insights into how the companies are progressing down the funnel:
AI can completely transform how you build target account lists for ABM on LinkedIn using Clay, turning what used to be a manual, error-prone research process into a scalable, data-rich workflow. Instead of starting with a vague ICP description, you can feed Clay + an LLM (ChatGPT/OpenAI) your closed-won deals, your best-fit customer attributes, your technographic requirements, industries, funding stages, hiring signals, competitor users, and even top engaged companies from your broad-targeting LinkedIn ad campaigns – and let AI generate a precise, structured set of accounts, enriched with firmographic and technographic filters.
In Clay, you can then automate the entire process: use AI to classify each domain by ICP fit (“Is this company similar to our top 50 customers?”), enrich companies with BuiltWith or Clearbit data (“Which of these companies use Amplitude + Segment?”), apply intent-like triggers (“Show me companies hiring PMs or UX roles in the last 60 days”), and even score each company using an AI-generated scoring rubric (“Score this company from 1–100 based on ICP match, tech stack relevance, revenue, and recent growth signals”).
Once Clay assembles the enriched dataset, AI can then summarize each company’s narrative – why it’s a good fit, which product pain it likely has, what tools it uses, and which personas you should target on LinkedIn—and create tiered account lists (Tier 1, Tier 2, Tier 3) based on predicted ACV or ease of entry.
Finally, with one click, you can sync these structured, AI-scored ICP-fit accounts directly into HubSpot or Salesforce, where ZenABM can immediately start tracking LinkedIn account-level engagement. The result is a living, AI-curated target account list that updates automatically, reflects real buying triggers, and is 10× more accurate than any static list built by hand.
Once your ABM campaigns on LinkedIn are running, you want to know:
You can get this by uploading CSV files to chatGPT from Campaign manager of course, but With Zena, I can get this without building a single pivot table.
Typical questions I ask:
Under the hood, Zena is joining:
On the surface, you just get straight answers like:
“Campaign group ‘Feature Adoption – PMs’ generated $280k in influenced pipeline on $14k spend (20x pipeline per $), driven mainly by these three single image ads…”
Which is exactly the type of sentence your CEO or CRO cares about.
Zena AI will tell you which of your LinkedIn campaigns and ABM campaigns are performing best – in terms of driving engagement, or pipeline.
Zena can shows detailed engagement data per campaign – clicks, impressions, and more – for each company:
It can also give you insights into your top-performing ABM campaigns (spanning several LinkedIn campaigns, targetting e.g. different audiences) over time:
Zena combines ad engagement data with CRM deals to give you pipeline influenced by LinkedIn ads and show you how your LinkedIn ads affect revenue?. It can break this down by month, so you can track trends over time.
AI helps you compare performance across different ad formats. Whether you’re using text link ads (TLAs), image ads, video ads, or carousel ads, tools like Zena can analyze each format’s effectiveness in terms of driving engagements and pipeline:
You might learn that video ads are driving the highest click-through rates or that carousel ads are attracting a particular industry. With these insights, you can double down on the formats that work and refine your creative strategy.
One of my favourite parts of combining AI + ABM data is budget optimisation.
First, you get granular visibility through ZenABM:
Then you use Zena to answer questions like:
At a more strategic level, I’ll also use generic AI (ChatGPT/OpenAI) to do scenario planning, e.g.:
It’s the same math I described in my ABM LinkedIn guide – but instead of spending an afternoon in Excel, I sanity-check my numbers with AI in minutes.
AI gives you more more visibility into ad spend at both the ABM campaign and target account level:
Tools like Zena AI will report how much of your budget was spent on each campaign:
…and even each individual company you target:
This means you can track ROI precisely – seeing, for instance, that Company X got 30% of your budget but only contributed 5% of pipeline, signaling a need to reallocate spend.
This granular view helps you optimize your ABM campaign budgets for maximum impact.
Tired of spending hours preparing reports? AI can help you generate and share detailed reports in seconds.
You can export your LinkedIn Campaign Manager “Performance” “Engagment” and “Delivery” data for each month into CSV files, upload them into your chatGPT, and ask it to give you performance breakdowns month by month.
Or you can simply ask Zena AI for the report you need (it already has access to your LinkedIn adsAPI data) – for example, “Create a monthly LinkedIn Ad performance report” or “Give me an executive summary of my ABM campaign/ LinkedIn campaign performance in October vs November” – and Zena will pull all relevant data from LinkedIn and your CRM.
You’ll get a ready-to-share report detailing clicks, impressions, top accounts, ROI metrics, and more. This speeds up your workflow and ensures your team can make decisions based on the latest data.
Throughout all these features, Zena’s user-friendly chat interface makes it simple. It transforms complex LinkedIn Ads analytics into plain English insights so you can focus on strategy, not spreadsheets.
If I had to summarise how to use AI for ABM on LinkedIn, it would be:
If you’re already running LinkedIn ads and you’re serious about ABM, start small:
That’s the combo that finally let me stop living in CSV exports – and start running ABM on LinkedIn like a grown-up.
