Case Study

Scaling to 500K Emails/

Month with EmailBison

By the Marketing Boutique team · Last updated: March 2026

Outbound infrastructure upgrade for a cybersecurity awareness training platform that scaled email capacity, meetings, and pipeline while reducing acquisition costs.

+316% Email Volume

+271% Qualified Meetings

–58% Cost per Meeting

Client

Cybersecurity Awareness Training Platform

Industry

Cybersecurity SaaS

Stage

Growth Stage

ACV

$18K – $45K

Case Snapshot

bg image

Email Volume 120K → 500K / month

Change: +316%

Pipeline Generated $420K → $1.6M / month

Change: +280%

Cost per Qualified Meeting $680 → $280

Change: –58%

Qualified Meetings 14 → 52 / month

Change: +271%

Key Results

At a Glance

Services Used: GTM Engineering, High Volume Outbound Timeline: 6 months ACV $18K $45K/year Stack: Email Bison · Clay · Make.com · HubSpot · Apollo · LinkedIn Sales Navigator

The Context

The Challenge

The outbound model required massive scale targeting 500K emails/month across 15,000+ contacts

Existing setup (80 domains, 160 mailboxes) capped at 120K emails/month, limiting growth

Shared sending infrastructure caused unstable deliverability, fluctuating week to week

Scaling required hundreds of domains and mailboxes, making operations complex and expensive

Man Using Laptop

High volume outbound required scale the existing system couldn’t support.

Man Using Laptop

Scaling volume required rethinking infrastructure, not just increasing output.

Constraint

The Core Problem

Scaling outbound was limited by shared infrastructure and unstable deliverability

The system couldn’t support high volume sending without operational complexity

Growth required a new infrastructure model, not incremental optimization

System Architecture

Our Approach

The solution was migrating to Email Bison moving from shared IP pools to single tenant dedicated IPs to stabilize deliverability, and using their API for bulk provisioning.

DRAG TO EXPLORE

Proven Outcomes

Results

After 6 Months

The AI pipeline transformed outbound performance while allowing the existing SDR team to operate at significantly higher capacity.

METRICBEFOREAFTERCHANGE
Monthly email volume
120,000500,000
+4.2x
Inbox placement rate
87% (±5% variance)92% (±1% variance) ↗
+5.7x
Reply rate (blended)
2.1%2.4%
+14%
Qualified meetings/month
14%52%
+3.7x
Pipeline generated/month
~$420K~$1.6M
+3.8x%
Sending cost/month
~$4,200~$2,100
-50%
$0KMonthly Email VolumeFrom 120K to 500K emails sent
0%Inbox Placement RateFrom 87% to 92% (+ improved stability)
0xQualified Meetings / MonthFrom 14 to 52 meetings
$0MPipeline / MonthFrom ~$420K to ~$1.6M

Performance Breakdown

Reply rates by segment

Hot AI personalized : 3.8%

Security breach trigger news signal : 6.2%

Competitor displacement KnowBe4/Proofpoint : 4.1%

Standard Template : 1.6%

Cost Efficiency

Cost per qualified meeting

$280 per qualified meeting

Previous cost : $680 per meeting

Total engagement investment $87K over 6 months including infrastructure, automation, and AI operations

Lessons Learned

What Didn’t Work

and What We Changed

Building a multi agent pipeline required several iterations. Here are the key issues we encountered and how we fixed them.

Infrastructure Issue

Reply handling broke under custom Make.com setup

Replacing Smartlead’s native Unibox introduced complexity in handling reply data. Early-stage parsing issues caused duplication and inconsistent threading, impacting CRM accuracy and response tracking.

Problem

Custom reply handling caused duplicate records (~8%) due to improper parsing and multi-part reply issues.

Fix

Added deduplication using email + timestamp and a delay to allow proper consolidation before processing.

Deliverability Constraint

IP warmup lag limited inbox placement at scale

While domains were ready, IP reputation lagged behind, reducing inbox placement for high-sensitivity targets. This exposed IP warmup—not domain setup—as the real bottleneck at scale.

Problem

Low inbox placement (84%) due to underdeveloped IP reputation despite domain readiness.

Fix

Throttled traffic and extended IP warmup, prioritizing reputation before scaling volume.

Personalization Gap

Generic sequences triggered spam complaints

High-volume outreach without sufficient variation made emails feel automated. Lack of contextual personalization increased spam signals and reduced deliverability.

Problem

Standard sequences triggered a 0.4% spam complaint rate due to low personalization.

Fix

Introduced dynamic variables and industry-specific proof points, reducing complaints to 0.15%.

FAQ

Frequently

Asked Questions

Have questions? Our FAQ section has you covered with
quick answers to the most common inquiries.

What is the difference between shared IP and dedicated IP email sending?

How many sending domains do you need for high-volume outbound?

Is high-volume outbound just spam?

Get Started

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