There is a version of demand gen that feels like throwing darts blindfolded. You build an audience in LinkedIn based on job titles and company sizes, run ads to thousands of people, and hope that some percentage of them happen to be in-market right now. Your CPL is $250. Your close rate from those leads is 2%. And when the board asks about efficiency, you talk about “brand awareness” and “top of funnel.”
Then there is the version where you know exactly who is in-market. You know which companies are actively researching your category. You know which specific people visited your website, what pages they viewed, and how many times they came back. You build ad audiences from that data, and your CPL drops to $40 because you are not guessing anymore.
This guide is about the second version. It covers how demand gen teams use visitor identification and intent data to build campaigns that target buyers who are already in motion - not cold audiences who might care someday.
The Core Problem: Most Ad Spend Targets Cold Audiences
The fundamental inefficiency in B2B demand gen is targeting. You are buying impressions and clicks from audiences where:
- 95-98% of the people are not in-market for your category
- Your targeting criteria (job title + company size + industry) correlate weakly with actual purchase intent
- You have no way to know which individuals in your audience are actively evaluating solutions
The result is predictable: high CPMs, high CPLs, low conversion rates, and long payback periods. Your LinkedIn campaign might reach 50,000 people, but only 500 of them are actively looking for what you sell. You are paying to show ads to 49,500 people who will scroll right past.
What if you could build an audience that started with the 500?
Two Data Sources That Change the Math
Source 1: Identified Website Visitors
Visitor identification gives you person-level data on who visits your website. Not company-level. Person-level. You get names, emails, job titles, companies, and the exact pages they viewed.
For demand gen, this data has three immediate applications:
- Retargeting audiences built from identified individuals (not anonymous cookies)
- Lookalike audiences modeled on your highest-intent visitors
- Suppression lists to stop spending on people already in your pipeline
Source 2: Orbit Intent Audiences
Orbit provides person-level intent data - not just “Acme Corp is researching visitor identification” but “Sarah Chen at Acme Corp is showing purchase intent for visitor identification tools.” This intent data is derived from content consumption patterns, search behavior, and engagement signals across 20,000+ topics.
For demand gen, Orbit enables:
- In-market audiences - people actively researching your category right now
- Competitor audiences - people researching your competitors by name
- Category surge audiences - accounts where research activity spiked in the last 7-14 days
The combination of visitor identification (who came to your site) and intent data (who is researching your category across the web) gives you a targeting layer that no amount of firmographic filtering can replicate.
Play 1: Retargeting Identified Visitors on LinkedIn
Traditional retargeting relies on cookies. You install a retargeting pixel, build an audience of website visitors, and show them ads. The problem: cookie-based audiences are shrinking (privacy regulations, browser changes), anonymous (you cannot filter by ICP), and imprecise (you are retargeting sessions, not people).
Visitor identification fixes this. Instead of retargeting cookie pools, you retarget identified individuals.
How to Set It Up
- Export identified visitors from Leadpipe - Filter by ICP criteria (company size, industry, job title) and intent signals (visited pricing page, case study, or comparison pages).
- Create a matched audience in LinkedIn - Upload the email list as a “Matched Audience.” LinkedIn matches the emails to profiles and lets you target those specific people with ads.
- Build the creative around their stage - Pricing page visitors get bottom-of-funnel ads (demo offer, free trial). Case study readers get mid-funnel ads (customer story, ROI calculator). Blog readers get top-of-funnel ads (educational content, webinar invite).
Expected Results
| Metric | Cookie Retargeting | Identified Visitor Retargeting |
|---|---|---|
| Audience quality (ICP fit) | Unknown | Filtered to ICP |
| Match rate to LinkedIn | 30-50% | 50-70% |
| CTR | 0.3-0.5% | 0.8-1.5% |
| CPL | $150-300 | $40-80 |
| Pipeline conversion rate | 1-3% | 5-12% |
The CPL difference alone justifies the approach. But the real advantage is quality: every person in your audience is someone who visited your website, matched your ICP, and showed behavioral intent. That is a qualitatively different audience than “anyone with VP of Marketing in their LinkedIn title.”
Play 2: Intent-Based Ad Audiences
Retargeting captures people who already found your website. Intent audiences capture people who are researching your category but haven’t found you yet. This is the expansion play.
Building Intent Audiences with Orbit
Orbit lets you build audiences based on intent topics. The most effective approach for demand gen:
Audience 1: Category Intent
- Topic: “visitor identification,” “website deanonymization,” “B2B lead generation tools”
- Filter: ICP-fit companies (100-5,000 employees, B2B SaaS, North America)
- Intent threshold: Surging (above baseline activity in last 14 days)
- Expected audience size: 2,000-10,000 people
Audience 2: Competitor Intent
- Topic: Competitor brand names (e.g., “Clearbit,” “6sense,” “Demandbase”)
- Filter: Same ICP criteria
- Intent threshold: Any recent activity
- Expected audience size: 500-3,000 people
Audience 3: Problem Intent
- Topic: “anonymous website traffic,” “lead conversion optimization,” “sales pipeline growth”
- Filter: Same ICP criteria
- Intent threshold: Surging
- Expected audience size: 5,000-20,000 people
Matching Intent Audiences to LinkedIn
Upload the email lists from each audience as LinkedIn Matched Audiences. Run separate campaigns for each with tailored creative:
- Category intent ads: Position your product as the category leader. “The #1 rated visitor identification tool for B2B teams.”
- Competitor intent ads: Address the switching trigger. “Why 200+ teams switched from [Competitor] to Leadpipe.” Link to your comparison post.
- Problem intent ads: Lead with the pain point. “97% of your website visitors leave anonymous. Here is how to identify them.”
Play 3: Suppression Lists That Actually Work
One of the most overlooked demand gen tactics is suppression - not showing ads to people who should not see them. Visitor identification makes this precise.
Who to Suppress
- Existing customers - Stop spending on people who already pay you.
- Active pipeline - If someone is in an active deal, ads are wasted. The sales rep owns the relationship.
- Disqualified leads - If you already talked to them and they are not a fit, stop retargeting them.
- Employees of your own company - This seems obvious, but it is missed surprisingly often.
- Competitors - If competitor employees are visiting your site (and visitor identification will show you they are), suppress them from your ad spend.
The Budget Impact
Most B2B companies waste 15-25% of their retargeting budget on people who should be suppressed. On a $20,000/month retargeting spend, that is $3,000-5,000 per month in wasted budget. Proper suppression lists - built from visitor identification data - recover that spend and redirect it toward net-new prospects.
Play 4: Content Personalization Based on Visit Behavior
Visitor identification data tells you what someone looked at before they engaged with your campaigns. Use that to personalize the content journey.
Dynamic Landing Pages
When an identified visitor clicks an ad, send them to a landing page that reflects what they have already seen on your site:
- If they read a case study: Landing page leads with the ROI data from that case study and offers a deeper conversation.
- If they visited the pricing page: Landing page leads with a pricing comparison and offers a personalized quote.
- If they read competitor content: Landing page leads with your differentiation points.
This requires some technical setup (dynamic landing pages based on CRM/visitor data) but the conversion lift is significant: 20-40% higher landing page conversion rates compared to generic pages.
Email Nurture Sequences
For identified visitors who are not yet ready for a sales conversation (blog readers, first-time visitors), build nurture sequences that reference their browsing behavior:
Sequence for pricing page visitors:
- Day 1: “The ROI of visitor identification - real numbers from real companies” (link to cost of anonymous traffic post)
- Day 3: Customer case study relevant to their industry
- Day 7: Direct CTA for demo or free trial
Sequence for blog readers:
- Day 1: Related educational content (deeper post on the topic they read)
- Day 5: Data-driven post relevant to their role
- Day 10: Soft CTA - “Want to see how this works for [their industry]?”
Play 5: Campaign Attribution with Visitor-Level Data
This is the play that earns you credibility with the CMO and CFO. Visitor identification gives you something most demand gen managers never have: person-level proof that your campaigns influenced pipeline.
How It Works
- You run a LinkedIn campaign targeting intent-based audiences.
- Some of those people visit your website (organic search, direct, or through the ad).
- Visitor identification captures who they are and what pages they view.
- Some of those visitors enter the pipeline (via SDR outreach or form fill).
- You can now trace the full journey: saw LinkedIn ad -> visited website -> identified as [person] -> viewed [pages] -> became opportunity -> closed.
The Report
Build a monthly report with:
| Metric | Value |
|---|---|
| Ad-influenced website visitors (identified) | Count of identified visitors who also saw your ads |
| Ad-influenced pipeline | Pipeline where the contact saw an ad before entering the funnel |
| Ad-influenced revenue | Closed-won revenue from contacts who engaged with ads |
| True cost per pipeline dollar | Total ad spend / ad-influenced pipeline |
This attribution model is more honest than last-touch (which ignores the ad) or first-touch (which ignores the website visit). It shows the full multi-touch journey with person-level evidence.
Budget Allocation: A Framework
Here is how to think about reallocating budget once you have visitor identification and intent data in place:
Before Visitor ID + Intent
| Channel | Budget | CPL | Pipeline |
|---|---|---|---|
| LinkedIn Ads (firmographic targeting) | $15,000 | $250 | $180,000 |
| Google Ads (keyword targeting) | $10,000 | $180 | $150,000 |
| Content syndication | $5,000 | $60 | $45,000 |
| Retargeting (cookie-based) | $5,000 | $120 | $40,000 |
| Total | $35,000 | $175 avg | $415,000 |
After Visitor ID + Intent
| Channel | Budget | CPL | Pipeline |
|---|---|---|---|
| LinkedIn Ads (intent-based targeting) | $10,000 | $60 | $300,000 |
| Google Ads (keyword targeting) | $10,000 | $180 | $150,000 |
| Visitor ID retargeting (identified) | $5,000 | $40 | $150,000 |
| Visitor identification tool | $3,000 | $3 (per qualified) | $450,000 |
| Content syndication | $2,000 | $60 | $18,000 |
| Total | $30,000 | $28 avg | $1,068,000 |
The total spend went down by $5,000 and the pipeline went up by 2.5x. The driver is not magic - it is targeting precision. When you only show ads to people who are in-market, every dollar works harder.
Getting Started: The Demand Gen Quick Wins
You do not need to rebuild your entire program overnight. Start with the highest-impact plays:
Week 1: Deploy visitor identification. Start collecting identified visitor data. Export your first ICP-filtered visitor list.
Week 2: Upload your first retargeting audience to LinkedIn. Build one campaign targeting pricing page visitors with a bottom-of-funnel offer.
Week 3: Build your first intent audience using Orbit. Target category-intent surging accounts with awareness-level content.
Week 4: Set up suppression lists. Remove existing customers, active pipeline, and disqualified leads from all campaigns.
Month 2: Analyze results. Compare CPL and conversion rates from intent-based campaigns vs. your legacy firmographic campaigns. The data will make the case for reallocation.
Within 60 days, you will have enough data to demonstrate the efficiency improvement and build the case for shifting more budget toward intent-based demand gen.
