AI-powered ad targeting: How to & use cases

Let’s get right to it: AI-powered ad targeting is genuinely useful, a little overhyped, and most teams are barely scratching the surface of what it can do.

The core promise is real: smarter audiences, in-market buyers surfaced faster, less budget spent on people who were never going to convert. But there's a catch that doesn't make it into most of the marketing. It's only as good as the data underneath it. Give it anonymous clicks and vague signals, and it'll confidently optimize toward the wrong people. Give it contact-level data, AKA real, named individuals with actual buying intent, and things get genuinely interesting.

Let's talk about how to do it well.

What actually changes with AI ad targeting

Most ad targeting starts with demographics: job title, company size, industry. Reasonable starting point. Not where campaigns win or lose.

The meaningful shift is from static to signal-driven – from "who fits this profile?" to "who's showing buying intent right now?" That's what separates targeting that builds pipeline from targeting that builds impressions.

We surveyed 80 B2B marketers about their confidence in reaching the right person at the right time. The average score was 5.7 out of 10. Not a disaster, buuut not a number you'd build a pipeline forecast around, either. The consensus: strong on the basics, light on the timing signals that actually unlock precision.

Better inputs fix that gap. Here's how to build them.

How to set up smarter AI ad targeting

Step 1: Get contact-level data under the hood

Anonymous traffic is a weak foundation. If every signal maps to "unknown visitor," you're optimizing for unknowns and the model learns exactly what you teach it.

The fix is de-anonymizing your site traffic, or connecting real contact identities to the people already engaging with your brand. Once you know who is visiting, not just that someone is, AI has meaningful patterns to build from instead of guessing at intent from thin air.

Step 2: Use signals that actually correlate with buying behavior

There's no shortage of data. The challenge is pointing your stack at the right stuff. Signals worth prioritizing include:

• Website visits and page depth (pricing pages, comparison pages especially)
• Off-site research (review sites, competitor content, category comparisons)
• Ad clicks that didn't lead to a conversion (these are actually more useful than they get credit for)
• CRM history and engagement patterns

The more your inputs reflect real buying behavior, the more accurately the model can identify who's worth spending on.

Step 3: Filter to your ICP before you push anywhere

This step is easy to skip when you're excited to launch. Aaaand it's a mistake every time 🤦

A larger audience isn't a better audience. Before syncing anything to an ad platform, cut your contact list down to the people who actually fit: role, seniority, company type, funnel stage. Knowing who to exclude is just as important as knowing who to keep and your AI-powered targeting will perform better with tighter, higher-quality signal than with volume.

Step 4: Build audiences that update themselves

Buyers move through stages. Intent shifts. Someone lukewarm two weeks ago might be actively evaluating today and a static list won't catch that.

Signal-driven audiences update in real time: contacts get added as they show intent, removed when they go cold, adjusted as your ICP evolves. AI handles the ongoing maintenance; you set the logic once and it stays current without manual exports or quarterly refreshes.

Step 5: Sync across channels (and keep it consistent)

Push your audiences to wherever your buyers spend time: LinkedIn, Google, Meta, Reddit. The goal is for one qualified contact to get a coherent experience across platforms. Not three separate campaigns that don't know about each other.

When your stack works together, messaging builds on itself rather than starting over with every impression.

Step 6: Close the loop on who actually clicked

When someone clicks your ad and bounces without converting, most setups treat it as a lost impression. It's actually a warm signal: someone’s curious enough to engage, but not quite not ready to raise their hand just yet.

Knowing who those people are opens the door for smarter follow-up: a timely outbound touch, a nurture sequence, a more relevant retargeting ad. It also feeds better data back into your targeting, so the next campaign starts from a smarter place than the last one did. Attribution gets cleaner too, which – if you've ever tried to explain pipeline contribution to a CFO – is not a small thing.

Use cases worth actually building

Retargeting based on real behavior

Cookie-based retargeting reaches everyone who visited your site. That's a wide net (and a lot of noise). Behavioral retargeting lets you narrow to people whose actions suggest genuine interest: multiple pricing page visits, comparison content, repeat sessions in the same week.

The difference is that you're following up with people you have real reason to follow up with.

💡The result: Retargeting that feels relevant to the person receiving it. Which, let’s face it, is rarer than it should be in B2B.

Catching buyers during active research

Some of your best prospects are evaluating options right now – say,  reading comparison guides, browsing G2, looking at competitors – and they haven't visited your site yet.

Getting your brand in front of them during the research window, based on off-site intent signals, means you enter the conversation earlier. Being early tends to go better than showing up after a decision has essentially been made.

💡The result: You're in the mix before the shortlist gets finalized.

Funnel-stage targeting that matches where people actually are

The same message doesn't land equally well at every stage of the buying journey. That's obvious in theory and surprisingly rare in practice.

With proper segmentation, each stage gets something that fits:

• Early stage: Perspective and education over product pitch
• Mid-funnel: Proof points, customer stories, comparisons
• Late stage: Demo asks, direct offers, free trials

Less shouting into the void.

💡The result: Ads that feel timely rather than generic, which people can actually tell.

Lookalike audiences that start from the right place

Lookalike audiences perform in proportion to the quality of the seed list. Start with broad, unfiltered site traffic and you get a lookalike that reflects all of it: accidental visits, wrong-fit personas, and everything in between.

Start with verified contacts who matched your ICP and converted, and the model builds outward from an accurate picture of what a real buyer looks like. Better inputs, better expansion.

💡The result: Lookalikes that stay closer to ICP rather than quietly drifting toward volume.

Suppression that's actually set up properly

Suppression doesn't get nearly enough attention. Excluding existing customers, low-fit personas, and contacts with no budget authority is one of the simpler ways to make sure spend is going toward net-new pipeline.

CRM sync makes this automatic  so you're not manually pulling lists or realizing months later that you've been retargeting accounts that closed last quarter.

💡The result: Cleaner campaigns, less waste, and better signal for everything downstream.

Cross-channel targeting that actually coordinates

LinkedIn on Monday. Google on Wednesday. Display on Thursday. If those touchpoints come from three disconnected audiences, the experience on the receiving end is fragmented: same brand, different messages, and no continuity.

Synced contact-level audiences fix that. The same buyer gets a coherent sequence across channels, and each impression builds on the last rather than resetting.

💡The result: Campaigns that compound over time, which is where efficiency actually lives.

Put contact-level intelligence behind your AI ad targeting with Vector

AI-powered ad targeting is only as powerful as the data feeding it. Most tools are working from anonymous signals and demographic guesses. Vector gives your campaigns something better: real buyer identities, live intent signals, and contact-level audiences that update automatically as your market moves.

With Vector, you can:

• De-anonymize site visitors and know exactly who's engaging with your brand
• Build signal-driven audiences from verified contacts — not just job title filters
• Sync fresh audiences to LinkedIn, Google, Meta, and Reddit automatically
• See who clicked your ads, even when they didn't convert
• Suppress the wrong people before they eat your budget

Want to learn more? Vector has two plans for B2B marketers: Reveal shows you who’s ads-ready, Target lets you put your ads directly in front of them.