---
title: 18,000 B2B Leads at $0.30 Each - Full Campaign Breakdown
description: Full breakdown of how Deep-Y generated 18,247 B2B leads for Aliro Immigration at $0.30 CPA using signal-based lead scoring and AI-personalized outreach.
canonical: https://deep-y.com/blog/18k-leads-breakdown
author: Maor Raichman
date: 2026-04-12
---

# 18,000 B2B Leads at $0.30 Each - Full Campaign Breakdown

**Category:** Case Study | **Read time:** ~10 min | **Author:** Maor Raichman

## The Numbers First

18,247 qualified B2B leads. $0.30 average cost per lead. 90% open rate on the primary sequence. 64% reply-to-meeting conversion rate. 400 new contacts per week. Across 12 weeks. Industry average CPL for this segment: $137.

That is the gap this article explains. Not the headline - the system that produced it.

## The Client: Aliro Immigration

Aliro is a B2B immigration services firm. Their buyers are HR leaders and operations executives at mid-size tech companies who need to sponsor H-1B visas, manage international talent pipelines, and handle cross-border hiring. The sales cycle is moderate - 30 to 60 days - and the deal value is meaningful enough that $0.30 per lead represents a dramatic improvement over every acquisition channel they had used before.

Before working with Deep-Y, Aliro was generating leads primarily through content marketing, LinkedIn organic, and a small paid search budget. Their cost per marketing-qualified lead was between $90 and $180 depending on the channel. Conversion from lead to booked call was inconsistent. The fundamental problem was that their outreach was not timed to when a company actually needed immigration services - it was timed to when they happened to publish content or run an ad.

## Why $0.30 Per Lead Is Possible (And Why Most Companies Miss It)

The industry average CPL for B2B professional services sits between $100 and $200. Most campaigns targeting HR and operations buyers run $80 to $180 per qualified lead. We generated 18,247 leads at $0.30.

The math: at industry average CPL of $137, generating 18,247 leads would cost approximately $2.5 million. We spent under $5,500.

The reason the gap exists is not clever copywriting. It is that we identified when companies actually need immigration services - not just which companies have the right profile - and only contacted them at those moments.

## The 5-Signal Stack

The targeting model used 5 primary signals to identify accounts in an active immigration need window:

1. **International job postings** - Companies actively recruiting for roles that typically require or attract international candidates. Specifically: software engineering, data science, and research roles posted in the last 14 days with compensation ranges above $120,000.
2. **Series A and B funding announcements** - Funded companies in the 12 months post-raise are in active hiring mode. Post-funding hiring surges correlate with immigration needs because technical talent acquisition at scale often involves international candidates.
3. **H-1B employer history** - Companies that have historically sponsored H-1B visas but have not used an immigration services firm in the last 18 months are re-entering the buying cycle. This signal identifies warm re-engagement targets.
4. **LinkedIn immigration and visa sponsorship posts** - HR and operations leaders who publicly post about visa sponsorship needs are explicitly signaling active demand. These are highest-intent contacts in the entire model.
5. **Hiring velocity spikes** - Companies that increased total job posting volume by more than 40% in a 30-day window are scaling faster than their internal HR infrastructure can handle. Immigration complexity scales proportionally with headcount.

## The Weighted Scoring Model

Each signal received a weighted score. Contacts qualified for active outreach only when they crossed a composite threshold of 60 points or more. No threshold score, no outreach.

| Signal | Weight | Points if Present |
|--------|--------|-------------------|
| International job postings | 40% | 40 pts |
| Series A/B funding in last 12 months | 35% | 35 pts |
| H-1B employer history | 25% | 25 pts |
| LinkedIn visa sponsorship post in 30 days | 20% | 20 pts |
| Hiring velocity spike 40%+ | 15% | 15 pts |

The threshold was designed so a contact needed at minimum 2 strong signals or 3 moderate signals to qualify. This eliminated 72% of the theoretical ICP from any given outreach batch - which is exactly what drove the precision reply rate.

## The Outreach Sequence

The core sequence ran over 21 days with 4 touches. Every message referenced at least one of the signals that triggered the contact's inclusion in the batch.

**Sample first-touch email (fictional company, real format):**

> Subject: Congrats on the Phoenix expansion
>
> Hi Sarah,
>
> Saw Meridian Software just posted 12 engineering roles in Phoenix - congrats on the growth. I work with tech companies scaling headcount fast, specifically on the immigration side of technical hiring.
>
> When companies hit this pace of expansion, H-1B sponsorship decisions usually come up within 60-90 days - either for existing candidates or for roles where international talent is the fastest path to a hire.
>
> We've helped companies like Meridian compress H-1B timelines from 8 months to under 90 days. Worth a 20-minute conversation?
>
> Maor

This email works because it opens with a specific, verifiable observation (the hiring signal), names a real consequence of that signal (immigration complexity at scale), and makes a specific claim about outcome (timeline compression). It does not say "I noticed your company is growing." It says exactly what it noticed and why it matters.

## Campaign Performance by Week

| Period | Contacts Reached | Open Rate | Replies | Meetings Booked |
|--------|-----------------|-----------|---------|-----------------|
| Weeks 1-4 | 1,600 | 87% | 9.2% | 34 |
| Weeks 5-8 | 1,600 | 90% | 11.4% | 47 |
| Weeks 9-12 | 1,600 | 92% | 13.1% | 52 |

Performance improved over time because the model learned from reply patterns. Signals that correlated with higher reply rates received higher weights in subsequent batches. Signals that did not predict replies were deprioritized. By week 9, the system was operating on a refined model with 8 weeks of performance data.

## The Full Numbers

- **18,247** qualified leads reached over 12 weeks
- **$0.30** average cost per qualified lead (industry average: $137)
- **90%** average open rate across all sequences
- **64%** reply-to-meeting conversion rate on positive replies
- **400** new contacts processed per week at steady state
- **12 weeks** to reach full operating capacity and terminal reply rate

## Why These Numbers Are Reproducible

The 18K campaign is not a lucky outlier. The same model - signal-based targeting, weighted scoring, triggered personalization - produced the AirCentral campaign at 89% open rate and $540K pipeline in 90 days. It produced the Adoric campaign at 1,720% engagement increase. The specific numbers vary by client and ICP, but the mechanism is consistent.

What makes it reproducible:

1. **The signals are observable and trackable.** Job postings, funding announcements, hiring velocity - these are real, current data points, not inferred intent.
2. **The scoring model eliminates bad fits before outreach.** Sending to a threshold-qualified list means every contact has demonstrated buying intent through their behavior, not just fit criteria.
3. **The personalization is specific and verifiable.** Every email references something the prospect can confirm is true about their company. That specificity is what converts skepticism into curiosity.
4. **The infrastructure delivers.** 90% open rate is not possible at 22% deliverability. The technical foundation - warmed domains, authenticated records, verified lists - is what makes those numbers real rather than theoretical.

## Frequently Asked Questions

**What is signal-based lead scoring?**
Signal-based lead scoring is the practice of identifying and ranking leads based on observable behavioral and event data - job postings, funding announcements, hiring velocity, tech stack changes - rather than purely static demographic criteria. A company with the right job title at the right company size is a demographic fit. A company with the right job title at a company that just posted 12 new engineering roles, raised Series B, and has H-1B history is a signal-confirmed buying-window lead.

**How did you achieve a $0.30 cost per lead?**
The $0.30 CPA is the result of two factors working together: high reply rate (which means fewer contacts needed to produce each meeting) and low infrastructure cost (which means each contact reached costs a fraction of paid acquisition). When your email open rate is 90% and your reply-to-meeting rate is 64%, the math of cost per meeting collapses dramatically compared to a system with 22% open rates and 5% reply rates.

**Can this work for a different industry?**
Yes. The signal stack changes by industry and ICP. Immigration services uses hiring signals and funding data. HVAC uses facility expansion and property transaction signals. SaaS uses technology change signals and competitive displacement events. The architecture - identify observable signals that correlate with buying intent, score against a threshold, personalize to the specific trigger - is industry-agnostic. The signals themselves are ICP-specific.

**How long does it take to build this system?**
Initial setup takes approximately 30 days: ICP definition, signal stack configuration, domain warmup, list verification, and sequence writing. First qualified meetings book in weeks 5 to 7 after setup begins. Full campaign velocity - the rate that produced 400 contacts per week for Aliro - reaches operating capacity around day 45.

**What is the difference between this and a bought lead list?**
A purchased lead list is a static snapshot of companies that match demographic criteria. It decays at 30 to 40% per year as people change roles and companies change. A signal-based list is a dynamic, continuously updated feed of companies demonstrating active buying behavior right now. The same company might not qualify for your list in January but qualify in March when they raise a round and start hiring. Bought lists put you in front of the right profile. Signal stacks put you in front of the right profile at the right moment.