Why Your ABM Ad Targeting Is Only Half as Smart as It Could Be 

You spent three hours building what felt like the perfect ABM campaign. VP and above prospects at 1,000+ employee companies in your exact target industries. Your platform confirmed these people match your ideal customer profile down to the last filter. The ads launched, impressions rolled in, clicks looked solid, and then… the pipeline barely dribbled in.

Sound familiar? There’s a reason your “precisely targeted” campaigns keep delivering mediocre results, and it has everything to do with the invisible foundation powering your ad targeting. The problem isn’t your campaign setup or creative — it’s that you’re essentially taking educated guesses about who might want to buy, rather than people who are actually showing signs of buying behavior.

The hidden foundation of your ad targeting (and why it’s holding you back)

Every time you run a LinkedIn campaign or launch display ads through your ABM platform, there’s an invisible auction happening behind the scenes. In milliseconds, advertisers bid on the chance to show their ads to specific people. The data that powers these auctions is called “bidstream data.”

Think of bidstream data as a massive, real-time catalog. It contains basic information about millions of people browsing the web — their job titles, company sizes, industries, and which websites they visit. When you set up an ABM campaign targeting “VPs of Sales at companies with 500+ employees,” your platform uses bidstream data to find people who match that description.

Here’s the catch: Bidstream data tells you who someone is, but not what they’re actually doing or thinking. It can identify a VP of Sales at a large company, but it can’t tell you if that person is actively looking for solutions, casually browsing, or completely satisfied with their current setup.

Why most ABM platforms are stuck with this approach

Most ABM platforms heavily rely on bidstream data because it’s readily available and covers a huge number of people. These platforms built their targeting capabilities around this data source, adding layers of intent information on top. But they’re still fundamentally limited by the same core problem: bidstream data is “inflated with noise and false signals.”

The result? You end up targeting people who look like your ideal customers on paper but aren’t necessarily showing any signs of being ready to buy. Your ads get plenty of impressions, but the pipeline impact doesn’t match what you’d expect from such “precise” targeting.

How 6sense takes a different approach

Instead of starting with bidstream data and trying to make it smarter, 6sense built something different. Their Signalverse technology processes over 1 trillion buying signals daily from multiple sources — keyword research, B2B publisher networks, review sites, and web activity. Then it uses AI to identify patterns that indicate real buying behavior, not just demographic fits.

The difference is like comparing a phone book to a real-time conversation. Bidstream data gives you names and addresses. 6sense gives you insight into what people are actually researching, discussing, and planning to buy.

The problem every ABM practitioner faces

Here’s what’s really happening behind those “sophisticated” bidstream-powered campaigns: You’re targeting demographic fits enhanced with basic intent signals, but your platform can’t distinguish between casual research and coordinated buying behavior. Most ABM platforms do layer in intent data — tracking keyword searches and content engagement — but they treat all intent signals the same way.

Your platform might know that someone searched for “revenue forecasting tools,” but it can’t tell you if that was a one-off curiosity search or part of a coordinated evaluation process involving multiple stakeholders. It sees individual data points, not buying patterns.

Hypothetical example: Two Chief Revenue Officers both search for “revenue forecasting accuracy” in the same week. Traditional intent-enhanced targeting treats them identically and shows both your ads. But what if one CRO’s search was just casual curiosity, while the other CRO works at a company where the VP of Sales, Marketing Ops head, and three directors have all been researching revenue platforms over the past month?

Most platforms would target both CROs equally because they both triggered the same keyword intent signal. They can’t recognize that the second scenario represents coordinated buying committee behavior while the first is just individual browsing.

This is why campaigns with only “intent-driven targeting” still deliver mixed results. You’re reaching people who show some interest, but you’re not distinguishing between tire-kickers and genuine prospects whose entire buying team is actively evaluating solutions.

The technical difference that matters

While traditional ABM platforms enhance bidstream data with additional intent signals, 6sense takes a fundamentally different approach. The 6sense Signalverse™ captures over 1 trillion signals a day, including real-time intent signals from keyword research, B2B publisher networks, and review sites. Then it uses AI to identify patterns that indicate genuine buying behavior.

But there’s a more fundamental difference: 6sense doesn’t just collect more data. We apply “noise-canceling” intelligence to distinguish between random research activity and actual buying behavior. The platform identifies coordinated intent across multiple stakeholders, tracks how engagement changes over time, and factors in your specific customer patterns to surface accounts that are both a good fit AND actively in-market.

There’s also a critical difference in keyword targeting precision. Many platforms automatically cluster related keywords together to increase volume — so searching for “sales forecasting” might also trigger ads for anyone researching “revenue planning,” “pipeline management,” or “CRM optimization.” This creates broader reach but at the cost of targeting accuracy.

6sense lets you target individual keywords with surgical precision. You can create keyword clusters if you want broader reach, but you have complete control over that decision. When you’re targeting “sales forecasting accuracy,” you’re reaching people specifically interested in forecasting accuracy — not anyone who’s vaguely interested in sales topics.

This granular control means your ad spend goes to prospects who are researching the exact problems your solution solves, not adjacent topics that might be loosely related.

The impact is huge.

How this changes your day-to-day campaign management

For practitioners, this translates into fundamentally different campaign management. Instead of manually building and refreshing static audience lists based on demographic criteria, you work with dynamic segments that update automatically as accounts move through buying stages.

Your LinkedIn campaigns don’t just target “VP of Sales at 500+ employee companies.” They target VPs of Sales whose companies are actively researching sales productivity solutions, showing coordinated buying committee activity, and matching the behavioral patterns of your successful customers.

How your daily workflow changes

• Audience building: Instead of demographic filters, you get AI-suggested segments based on real buying behavior
• Campaign optimization: Instead of optimizing for clicks and impressions, you optimize for accounts that predictably convert to pipeline
• Performance measurement: Instead of engagement metrics, you track pipeline influence and revenue attribution

When your targeting is based on actual buying signals rather than just demographic fits, your campaigns naturally perform better because you’re reaching people who are genuinely interested in what you’re selling.

Real results: What happens when you target behavior instead of demographics

The business case becomes clear when you look at actual results. Companies like Hyland have achieved a six-figure reduction in advertising budget while improving campaign performance. Marketing leader Kelly Webb said going back to traditional targeting “would be like going back to what I call ’90s marketing,’ that spray-and-pray mentality.”

Five9 used 6sense to reach 3,000 accounts across 17 enterprise brands in just three weeks, generating double-digit leads for their business development team from targeted campaigns. These aren’t vanity metrics — they’re pipeline results that directly tie to revenue.

The executive conversation changes

When you present campaign results to leadership, the conversation shifts from “we had great engagement rates” to “we identified and influenced 47 in-market accounts that generated $2.3M in pipeline.” The difference between demographic targeting and behavioral intelligence shows up directly in your pipeline metrics and revenue attribution.

For instance, when Sage Software switched to 6sense targeting and predictive models, they were able to achieve:

• Over 4,000 newly engaged accounts
• Over 1,000 accounts with increased engagement in a single quarter
• 50% increase in opportunity creation

When Corporate Visions took their 6sense use a step further by implementing audience workflows, they saw an additional $12.8M in pipeline within just 2 weeks — a 268% YoY increase in marketing-sourced pipeline.

The bottom line: Stop targeting smart-looking noise

Traditional ABM targeting gets you part of the way from mass advertising to focused campaigns. But if you’re still primarily relying on bidstream data, you’re essentially targeting smart-looking noise. You’re reaching people who look like your buyers on paper but aren’t necessarily acting like buyers in reality.

The teams seeing real results have moved beyond demographic fit to behavioral intelligence. They’re using platforms that can distinguish between casual research and active buying behavior, identify coordinated buying committee activity, and focus ad spend on accounts that are genuinely in-market.

Your “perfectly targeted” campaigns might be less perfect than you think. The question isn’t whether your prospects fit your ideal customer profile — it’s whether they’re actually showing signs of being ready to buy what you’re selling.