Every B2B marketer has spent money on lookalike audiences and been told the magic algorithm will find their next customer. Sometimes it works. Usually it finds people who look similar to existing customers on demographic attributes and have no particular reason to care about your product this quarter.
I am George, founder of Leadpipe. We run Orbit audiences and lookalike audiences on the same campaigns in the same accounts to keep our own benchmarks honest. This post is the actual comparison: how each source defines “audience,” where they overlap, and which one converts better in practice.
The short answer
Lookalike audiences are similarity models. Orbit audiences are behavioral signals. Lookalikes ask “who looks like my existing customers on attributes the platform observes?” Orbit asks “who is actively researching my category across 5 million websites right now?” Lookalikes are good for reach and scale when you have a strong seed list. Orbit is better when you need the audience to actually be in-market this quarter. For performance-focused B2B campaigns, Orbit audiences converted two to five times better than lookalike audiences in the tests we have run. For top-of-funnel brand campaigns with a decent seed, lookalikes still pull their weight. The two are not perfectly substitutable; they solve different problems.
Now the details.
The two ways to define an audience
Here is the philosophical split, which matters because it determines what each audience is actually useful for.
| Approach | Question it answers | How it picks people |
|---|---|---|
| Lookalike | Who resembles my existing customers? | Platform-observed similarity on attributes (LinkedIn title, company, engagement; Meta demographics, interests, graph features) |
| Orbit (intent) | Who is actively researching my category? | Cross-site behavioral signals classified against 20,810 topics, matched to an identity graph |
Similarity and intent are correlated but not the same. Two people can be identical in firmographics and vastly different in buying posture. The VP of Sales at a 200-person SaaS who is shopping for a CRM this month and the VP of Sales at a 200-person SaaS who renewed their CRM last quarter look identical to LinkedIn. They look very different to Orbit.
This is the same distinction we write about in intent data vs visitor identification and in-market buyer intent.
What LinkedIn lookalikes actually are
LinkedIn’s “audience expansion” and matched lookalike features are useful tools built on data LinkedIn has on its platform.
Strengths:
- Access to LinkedIn’s member graph is industry-leading for professional attributes (title, seniority, company, skills).
- Native placement inside LinkedIn ads, Sales Navigator surfacing, and InMail targeting.
- Very large active reach (hundreds of millions of members).
- Useful for B2B brand-level reach when your ICP is “VP+ in SaaS companies with 200-1000 employees.”
Limits:
- No behavioral intent layer. LinkedIn does not see what people are researching across the open web.
- Similarity is attribute-based, not behavior-based. Two similar titles at similar companies can have wildly different buying postures.
- Audience freshness is tied to LinkedIn session activity, not category research.
LinkedIn is strong at firmographic reach. It is not strong at telling you who in that firmographic slice is in-market right now.
What Meta lookalikes actually are
Meta’s lookalike audiences are a decade-old feature built on Meta’s graph and interest signals.
Strengths:
- Very large scale (billions of users) and mature modeling.
- Useful for B2C and for B2B products with consumer-like buying patterns (freelancer tools, SMB SaaS, anything targeting individual buyers).
- Cheap CPMs relative to LinkedIn.
Limits:
- Professional attributes (title, seniority, company) are self-declared and often stale.
- No B2B intent layer. Meta does not track category research behavior.
- Post-iOS 14 and with continuing signal loss from platform changes, similarity models have narrower inputs than they did five years ago.
Meta lookalikes can work for B2B if the buyer is also consumer-like in how they research (high individual agency, low committee involvement). For enterprise or committee buying, the signal-to-noise ratio is tough.
What Orbit audiences are
Orbit is behavioral. The full anatomy is in what’s inside an Orbit intent topic, but the short version:
- Cross-site pixel network on roughly 5 million websites.
- Roughly 60 billion signals per day.
- 20,810 intent topics.
- Identity graph resolution to person level.
- 24-hour refresh.
The audience is a query: “people who are researching topic X at intent score 70+, filtered by seniority, industry, company size, and other ICP fields.” The output is the specific people matching that query today, with full contact data attached.
Head to head: what converts better
Here is a compressed view of the comparison dimensions that matter for a marketer or ad buyer.
| Dimension | LinkedIn lookalike | Meta lookalike | Orbit audience |
|---|---|---|---|
| Core selector | Similarity | Similarity | Behavior |
| Ideal seed size | 300+ | 1,000+ | N/A (seed not required) |
| B2B firmographic precision | High | Medium | High (346 industries, seniority, size, revenue) |
| In-market signal | None | None | Daily intent score per person per topic |
| Refresh cadence | Platform-dependent | Platform-dependent | Daily |
| Person-level contact data | Only via Lead Gen forms | Only via Lead Gen forms | Name, email, phone, LinkedIn in payload |
| Cross-channel portability | Locked in LinkedIn | Locked in Meta | CSV, API, any platform via Matched Audiences / Customer Match |
| Best for | B2B reach, brand, upper funnel | B2C, SMB, high-volume reach | In-market intercept, outbound, ABM |
| Limits | No behavior layer, platform-locked | Weak B2B attributes, signal loss | Smaller raw reach than ad platforms |
Where lookalikes win
Three scenarios where lookalike audiences are the right tool, and I say this as someone selling the alternative.
- You need reach at scale with a weak bottom-of-funnel signal. Early-stage brand awareness, category education, first-touch outreach. Lookalikes get you to 500K people fast.
- You have a high-performing seed list (top customers, closed-won opps). LinkedIn and Meta both build decent similarity models on high-quality seeds.
- Your audience is consumer-like. Solo founders, freelance buyers, individual-decision SMB SaaS. Meta lookalikes are cheap and effective here.
Where Orbit wins
Three scenarios where Orbit is structurally better.
- In-market intercept. You want to reach the people researching your category this week, not people who resemble past customers generally. Orbit is designed for this; lookalikes cannot answer this question.
- Outbound and ABM. You need person-level contact data (email, phone, LinkedIn), not just an audience ID inside an ad platform. Orbit ships that data in the CSV; lookalikes do not.
- Competitive intercept. You want the people looking at “Competitor X alternatives” this week. Lookalikes cannot surface a competitive-research audience because they have no behavior layer.
What the conversion math tends to look like
We do not publish a single universal number because campaign-level CTRs and conversion rates vary wildly by category, creative, and stage. Patterns we see repeatedly:
- Orbit audiences uploaded to LinkedIn Matched Audiences convert at roughly 2 to 5 times the rate of LinkedIn lookalikes on the same offer. The reason is that Orbit has done the qualifying work before LinkedIn ever shows the ad.
- Orbit + LinkedIn is cheaper per SQL than LinkedIn lookalikes alone on most B2B campaigns, even though the CPM on a smaller custom audience is higher. Fewer impressions against a pre-qualified audience beats more impressions against a similarity cohort.
- Meta lookalikes for B2B tend to be high volume, low conversion on enterprise offers. SMB offers perform closer to parity.
The right mental model: lookalikes are how you find people who might be interested; intent audiences are how you find people who already are. Fewer false positives at the top of funnel means fewer wasted impressions at the bottom.
For a deeper framing of why intent beats lookalike for B2B, see orbit person-level intent audiences and orbit LinkedIn ads audiences.
The hybrid that actually works
The best-performing B2B campaign structure is almost always a hybrid, not a pure one or the other.
- Top of funnel: LinkedIn lookalikes. Reach, brand, category education. You are building awareness at scale.
- Mid funnel: Orbit audiences pushed to LinkedIn Matched Audiences or Google Customer Match. Intercept the in-market slice. These are your highest-converting ad dollars.
- Bottom of funnel: Orbit audience exported to SDR queue or AI SDR. Direct outbound to the specific people the ad touched and the specific people still in-market but never clicked.
This stack uses each tool for what it is actually good at. Lookalikes reach. Orbit converts. Together they are stronger than either alone.
We walk through the LinkedIn side in orbit LinkedIn ads audiences and the Google Ads optimization side in leadpipe google ads optimization.
What Meta can actually do for B2B
If Meta is part of your stack, one workflow works well: upload Orbit audiences as Custom Audiences, then let Meta build lookalikes on top of that. The lookalike seed is behavioral, not firmographic, which usually improves the downstream audience quality relative to a seed of “all customers” or “all leads.”
This is a cheap experiment. Build a 500-person Orbit audience on a narrow topic. Upload it to Meta. Create a 1% lookalike. Run a small test. Compare to your existing Meta lookalike baseline. We have seen marketers move their entire top-of-funnel spend to this pattern.
Why most B2B lookalike campaigns underperform
Three recurring failure modes.
- The seed is too broad. “All customers” usually produces a lookalike of “all employed professionals.” Narrow your seed to best customers, high-LTV, high-velocity, and the similarity model has real information to work with.
- There is no behavior layer. Lookalikes are stuck at Phase 0 of the buying cycle. They cannot tell you who moved into Phase 2 this month. That is the intent gap.
- Ad platforms inflate reach. “Audience size: 2.3M” looks impressive. It is a lot of impressions against people who are mostly not in-market. Small, dense, intent-qualified audiences produce more pipeline per dollar than big, thin lookalike cohorts.
Orbit does not fix lookalikes. It complements them. The two are different tools for different jobs.
Related reading
- Orbit LinkedIn ads audiences
- Orbit person-level intent audiences
- Orbit competitive intelligence
- Intent data vs visitor identification
- Person-level intent data, how it works
- In-market buyer intent
- Leadpipe LinkedIn ads integration
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