AI for ABM – How to Use AI to Run ABM Campaigns on LinkedIn?

How Zena AI Can Help You with Analysing your ABM Campaigns on LinkedIn?

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:

Use AI to define (and refine) your ICP

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:

1. Feed AI your “truth”

   • A sample of closed-won deals

   • A sample of closed-lost or churned accounts

   • Notes from customer interviews and win-loss analysis

2. Ask it to find patterns
   For example:

   • “What do our top 30 customers have in common in terms of industry, size, tech stack, triggers?”

   • “How are our worst-fit customers different from our best-fit customers?”

   • “Which firmographic patterns correlate with high ACV / short sales cycles?”

3. Translate patterns into LinkedIn filters

   • “Based on this ICP description, suggest LinkedIn targeting filters and exclusions.”

   • “Given these 50 logos, what would be a good Boolean search for job titles to target?”

4. Close the loop with engagement data
   Once you run campaigns, use Zena to ask:

   • “Which industries have the highest engagement and pipeline?”

   • “Which company sizes are most likely to reach ‘Considering’ stage?”

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.

Use AI to focus on the right ICP Persona and Target the Right Audience for your ABM campaigns

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:

Use AI to build Account Lists

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.

Use AI to identify top-performing ABM campaigns

Once your ABM campaigns on LinkedIn are running, you want to know:

• What’s actually working?

• For whom?

• At which stage of the funnel?

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:

• “Which LinkedIn campaigns performed best by CTR this month?”

• “Rank campaigns by those that drove the most ‘Interested’-stage companies.”

• “Which ABM campaigns influenced the most revenue this quarter?”

• “How did my ABM campaign performance change month over month?”

• “Which campaigns underperformed based on spend vs engagement?”

• “Which campaign drove the lowest cost per engaged company?”
  

Under the hood, Zena is joining:

• LinkedIn campaign & ad performance

• CRM deals & pipeline

• ZenABM’s ABM stages & account scores

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.

Identify which LinkedIn & ABM Campaigns are performing best for driving engagement, pipeline and revenue

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:

Revenue Attribution – identify which campaigns had most impact on pipeline & revenue with Zena

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.

 

Analyze Top-Performing Ad Formats to Optimize Your ABM Strategy with AI 

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.

Use AI to optimise ABM ad spend and budget allocation

One of my favourite parts of combining AI + ABM data is budget optimisation.

First, you get granular visibility through ZenABM:

• Spend by campaign, campaign group, and account

• Cost per engaged company

• Pipeline per dollar spent

Then you use Zena to answer questions like:

• “Where did we spend the most budget this month?”

• “Which companies cost us the most to engage, and how much pipeline did they generate?”

• “Show spend by campaign + ROI.”

• “Which campaigns wasted budget (high spend, low engagement)?”

• “What’s my cost per engaged company for this quarter?”

At a more strategic level, I’ll also use generic AI (ChatGPT/OpenAI) to do scenario planning, e.g.:

• “We want $1M in pipeline from ABM with a $120k LinkedIn budget. Our ACV is $50k, close rate is 25%, demo-to-opportunity rate is 75%. How many accounts do we need to target and what benchmarks at each stage do we need to hit?”

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.

How to use AI to analyze Ad Spend and Optimize ABM Campaign Budget?

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.

Use AI for speedy ABM Campaign Reporting

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.