You’re paying $25,000 or more per year for intent data. Every week, you get a list that says something like: “Acme Corp is showing high intent for CRM software.” “Globex is surging on marketing automation.” “Initech is researching data enrichment tools.”
Great. Now what?
Your SDR opens LinkedIn, searches for Acme Corp, and sees 2,400 employees. Who at Acme Corp is interested? Is it the VP of Sales who can actually sign a deal? The marketing coordinator doing research for their boss? The IT admin evaluating security requirements? The intern writing a competitive analysis for a class project?
You don’t know. Your intent data provider told you the company is interested, not the person. So your SDR does what they always do: they pick 5-10 contacts from the org chart who seem like they might be the right people, and they send a sequence. The response rate is somewhere between 1% and 3%, because most of those contacts have no idea what you’re talking about. They weren’t the ones doing the research.
This is the fundamental limitation of company-level intent data. It identifies demand at the organizational level but gives you no way to act on it at the individual level. And in B2B sales, you don’t sell to organizations. You sell to people.
There’s a better approach. Person-level intent data resolves the actual humans behind the research activity - not just the company, but the specific person who’s in-market. Here’s how it works and why it changes everything.
The Company-Level Intent Problem
Company-level intent data providers like Bombora, 6sense, and Demandbase have built an entire category on a single promise: we’ll tell you which companies are in-market before they fill out a form. Forrester’s B2B Intent Data Providers Wave evaluates the major players, but even the highest-rated providers deliver account-level signals only. And they deliver on that promise. Sort of.
The data model works like this: these providers monitor content consumption across thousands of publisher websites. When employees at a specific company consume significantly more content about a topic than their baseline, the company gets flagged as “surging” on that topic.
The signal is real. If 12 people at Acme Corp read 47 articles about “CRM migration” in the past two weeks, that’s probably not random. Something is happening at Acme Corp.
But the signal is incomplete. You know Acme Corp is interested. You don’t know:
- Who at Acme Corp is interested
- What role the interested person plays in the buying decision
- Which specific content they consumed
- Whether the interest is from a buyer or a researcher
- Which department is driving the evaluation
This incompleteness creates a massive gap between the signal (company is interested) and the action (reach out to the right person with the right message). Your intent data vendor filled the gap with confidence - “Acme Corp is surging!” - but your sales team has to fill it with guesswork.
How Company-Level Intent Actually Works
To understand why company-level providers can’t give you person-level data, you need to understand how the data is collected.
The Bombora model (representative of most providers):
- Bombora operates a data cooperative of 5,000+ publisher websites
- When a visitor reads content on one of these publisher sites, Bombora captures the company associated with that visitor (via IP-to-company mapping or cookie data)
- Bombora aggregates this consumption data by company and topic
- When a company’s consumption of a topic exceeds its historical baseline, Bombora flags it as a “surge”
The critical limitation:
IP-to-company mapping tells you the company but not the person. If someone at Acme Corp reads an article about CRM migration, the IP address resolves to “Acme Corp” - but the specific person behind the visit isn’t identified. This is a fundamental architectural constraint, not a feature gap that will be fixed in the next release.
Some providers try to enhance the signal with additional data points - the department is likely “Sales” based on the content topics, or the seniority is likely “Manager+” based on reading patterns. But these are probabilistic guesses, not deterministic matches. The difference matters when your SDR is deciding who to email.
What you actually receive from a company-level provider:
Company: Acme Corp
Topic: CRM Software
Surge Score: 87/100
Trend: Increasing over 3 weeks
Department (estimated): Sales
Seniority (estimated): Manager+That’s it. No name. No email. No LinkedIn profile. No specific role. Your SDR has to go from “Sales, Manager+” at a 2,400-person company to finding the actual person who’s evaluating CRM software. Good luck.
The Spray-and-Pray Workflow
Here’s what the workflow actually looks like when you act on company-level intent data:
Step 1: Your intent data shows Acme Corp is surging on “marketing automation.”
Step 2: Your SDR searches LinkedIn for marketing leaders at Acme Corp.
Step 3: They find 15 people with relevant titles: VP of Marketing, Director of Demand Gen, Marketing Operations Manager, Digital Marketing Manager, etc.
Step 4: They can’t tell which of these 15 people is actually driving the evaluation, so they pick the 5-8 most senior contacts.
Step 5: They write a semi-personalized sequence mentioning marketing automation and send it to all 5-8 people.
Step 6: 7 of the 8 don’t respond because they have no idea what the SDR is talking about - they weren’t the ones researching marketing automation.
Step 7: Maybe 1 responds. Maybe 0 respond. Either way, the SDR spent 45 minutes researching and personalizing outreach to 8 people based on a guess about who at the company is interested.
The economics of spray-and-pray intent outreach:
- Time per surging account: 30-60 minutes (research + personalization)
- Contacts attempted per account: 5-10
- Response rate: 1-3%
- Meetings booked per 100 surging accounts: 3-8
Those are better numbers than pure cold outreach, which is why companies keep paying for intent data. Gartner research shows that B2B buyers complete the majority of their purchase research before engaging with a sales rep, making the gap between company-level signals and actionable person-level data even more costly. But they’re a fraction of what’s possible when you know the actual person.
What Person-Level Intent Changes
Person-level intent does exactly what it sounds like: it identifies the specific individual who’s researching a topic, not just their company.
Instead of “Acme Corp is surging on marketing automation,” you get:
Person: Sarah Chen
Title: VP of Marketing
Company: Acme Corp (250 employees, SaaS)
Email: sarah.chen@acme.com
LinkedIn: linkedin.com/in/sarahchen
Intent Topics:
- Marketing automation tools (score: 85)
- Email deliverability best practices (score: 72)
- HubSpot vs Marketo comparison (score: 68)
Research Activity:
- 14 articles read in the past 7 days
- Visited 3 vendor websites
- Engaged with 2 comparison guidesNow your SDR doesn’t have to guess. They know Sarah Chen is the person doing the research. They know she’s comparing HubSpot and Marketo. They know she’s concerned about email deliverability. They have her email and LinkedIn.
The outreach writes itself: connect with Sarah on LinkedIn, reference the HubSpot vs. Marketo evaluation (without mentioning the intent data), and offer a perspective on how your solution compares.
No guessing. No spraying 8 contacts. No wasted time researching the wrong people.
Side by Side: Company vs. Person-Level Workflows
Let’s compare the two workflows directly:
Company-Level Intent Workflow
Intent signal: "Acme Corp surging on marketing automation"
↓
SDR searches LinkedIn for marketing titles at Acme Corp
↓
SDR picks 8 contacts who might be the researcher
↓
SDR writes semi-personalized outreach to all 8
↓
7 don't respond (wrong people)
↓
1 maybe responds (maybe the right person)
↓
Time spent: 45-60 minutes
Result: 0-1 meetingsPerson-Level Intent Workflow
Intent signal: "Sarah Chen, VP Marketing at Acme Corp,
researching marketing automation tools"
↓
SDR reviews Sarah's intent profile and research topics
↓
SDR writes one highly personalized message
↓
Sarah responds because the message is relevant to her
active research
↓
Time spent: 10-15 minutes
Result: 1 meeting (15-25% response rate)The differences that matter:
| Metric | Company-Level | Person-Level |
|---|---|---|
| Contacts researched per account | 5-10 | 1-3 |
| Time per account | 45-60 min | 10-15 min |
| Message relevance | Generic/semi-personal | Highly personal |
| Response rate | 1-3% | 15-25% |
| SDR capacity (accounts/day) | 8-12 | 30-50 |
| Meetings per 100 accounts | 3-8 | 20-35 |
The efficiency gain is 3-5x. Your SDR works fewer contacts per account but books more meetings, because every message goes to the right person with the right context.
The Numbers: Response Rates Compared
Let’s make the math explicit with a concrete scenario.
Scenario: You have 200 accounts showing intent signals this month.
Company-Level Approach
- 200 accounts x 8 contacts each = 1,600 emails sent
- 1-3% response rate = 16-48 responses
- 30% of responses become meetings = 5-14 meetings
- SDR time: 200 accounts x 45 min = 150 hours
- Cost of intent data: ~$3,000/month (Bombora)
- Total cost per meeting: $500-1,500
Person-Level Approach
- 200 accounts x 2 contacts each = 400 emails sent
- 15-25% response rate = 60-100 responses
- 40% of responses become meetings = 24-40 meetings
- SDR time: 200 accounts x 12 min = 40 hours
- Cost of intent data: $147/month (Orbit Starter)
- Total cost per meeting: $15-40
The person-level approach generates 3-5x more meetings at 10-30x lower cost per meeting. And your SDR gets 110 hours back per month - enough to work significantly more accounts.
The response rate difference (1-3% vs. 15-25%) is the core driver. When you email the actual person who’s researching your category with a message that’s relevant to their specific research, they respond at dramatically higher rates than when you guess.
How Orbit Delivers Person-Level Intent
Orbit is Leadpipe’s person-level intent data product. It works differently from company-level providers at a fundamental level.
How Orbit identifies individuals:
Instead of mapping IP addresses to companies, Orbit uses a cross-site pixel network that monitors research behavior and resolves it to individual people using deterministic matching. The result is a person - name, email, title, company - not a company domain.
Topic coverage:
Orbit tracks intent signals across 20,000+ topics, covering virtually every B2B category. You can monitor broad categories (“marketing automation”) or specific products (“HubSpot alternatives”).
What you get per intent signal:
- Full name, email, phone, LinkedIn profile
- Job title and seniority
- Company name, size, and industry
- Intent topics with relevance scores
- Research intensity (articles consumed, sites visited)
- Trend direction (increasing, stable, decreasing)
Delivery:
Orbit delivers intent signals daily. You can receive them via the Leadpipe dashboard, API, webhook, or direct CRM integration. Most teams configure a webhook that pushes high-intent contacts directly into their SDR’s workflow.
Pricing:
Orbit starts at $147/month. Compare that to Bombora ($2,000-5,000/month for company-level data) or 6sense ($25,000-100,000/year for a platform that still only shows companies). The pricing difference reflects the difference in data architecture - Orbit doesn’t need the massive publisher cooperative that Bombora maintains, because it identifies individuals directly.
Combining Intent Data with Visitor Identification
Intent data and visitor identification are complementary signals. Intent tells you who’s researching your category across the web. Visitor identification tells you who’s researching your product on your website. Together, they create a complete picture of buying behavior.
The combined workflow:
Orbit detects: Sarah Chen researching "visitor identification tools"
↓
SDR reaches out with relevant content
↓
Sarah visits your website (driven by the outreach or organic search)
↓
Leadpipe identifies Sarah on your pricing page
↓
AE engages with full context:
- Cross-web research topics
- Specific pages viewed on your site
- Time spent evaluating your productScoring with both signals:
The strongest buying signal is intent + visit. Someone who’s researching your category AND visiting your website is deep in an active evaluation. Your lead scoring model should weight this combination heavily.
| Signal | Alone | Combined |
|---|---|---|
| Orbit intent > 80 | Strong | - |
| Website visit to pricing | Strong | - |
| Orbit intent > 80 + pricing visit | - | Very strong - immediate outreach |
Leadpipe + Orbit gives you both person-level intent data and website visitor identification. Start with 500 free identified leads, no credit card required.
When Company-Level Still Makes Sense
I’m not going to tell you company-level intent data is useless. It’s not. There are legitimate use cases where company-level is sufficient:
1. Ad targeting at scale. If you’re running LinkedIn or display ads and want to target companies showing intent, company-level data is fine. You’re not reaching out individually - you’re putting your brand in front of the right organizations.
2. Territory prioritization. If your sales team organizes by territory and needs to know which accounts in their territory are active, company-level intent helps them prioritize their call list. It doesn’t tell them who to call, but it tells them which companies to focus on.
3. Large enterprise accounts with clear buying committees. If you sell into Fortune 500 companies where the buying committee is well-defined and your AE already has relationships with 10+ contacts, company-level intent is actionable because the AE knows who to reach out to. They just needed the “when” signal.
Where company-level falls short:
- Mid-market accounts where you don’t already know the contacts
- High-volume outreach where SDR time per account needs to be efficient
- Any scenario where you don’t have pre-existing relationships at the target company
- Budget-constrained teams that can’t afford to waste cycles on wrong contacts
For most B2B companies outside the Fortune 500, person-level intent data is the more efficient, more effective option.
FAQ
Is person-level intent data actually accurate?
Orbit uses deterministic matching, which means the identification is based on verified identity signals, not probabilistic guessing. The intent topics are based on observed content consumption - we know the person consumed content about the topic because we observed it directly, not because we inferred it from IP addresses.
How is this different from just buying a contact list?
A contact list gives you names and emails. Intent data gives you names and emails plus timing. The difference is the “right now” signal - Sarah Chen isn’t just a VP of Marketing at Acme Corp (that’s a list). She’s a VP of Marketing at Acme Corp who is actively researching marketing automation tools this week (that’s intent). The timing transforms a cold contact into a warm one.
Can I use Orbit alongside my existing Bombora or 6sense subscription?
Yes. You can use company-level intent for broad targeting (ads, territory prioritization) and person-level intent for direct outreach. They serve different purposes. Many teams start by testing Orbit alongside their existing provider and gradually shift outreach budgets toward person-level signals as they see the response rate difference.
What topics does Orbit cover?
Over 20,000 topics across virtually every B2B category. You can monitor broad categories like “marketing automation” or specific signals like “HubSpot alternatives” or “Salesforce migration.” If the topic is something B2B buyers research online, Orbit likely covers it. See the full details in our intent API guide.
How quickly can I start using person-level intent data?
Same day. Orbit is self-serve - sign up, define your target topics and ICP filters, and start receiving intent signals. There’s no 6-month implementation. No professional services engagement. No custom data integration project. You can have actionable intent data in your SDR’s inbox within hours.
Stop Guessing Who at the Company Is Interested
Company-level intent data was a breakthrough when it launched. Knowing that a company is in-market before they fill out a form is genuinely valuable information. But the category has stalled at “company” for years, and the gap between “company is interested” and “here’s who to contact” has been filled with guesswork.
Person-level intent closes that gap. Instead of spraying 8 contacts at a surging account and hoping one responds, you reach out to the 1-2 people who are actually doing the research, with a message that’s relevant to what they’re researching right now.
The math works. The response rates work. The economics work. The only question is how much longer you want to pay for a signal that tells you the “what” but not the “who.”
Orbit delivers person-level intent data across 20,000+ topics, starting at $147/month. No annual contract. No credit card for the free trial.
