Alberto Torres | Technical Email Marketing Engineer

Technical Stack

The Tools Behind a $4.2M Operation

Managing 23M+ subscribers and 3.6M+ monthly sends requires the right infrastructure. Here's what I use daily to analyze 400+ campaigns per cycle and drive measurable revenue improvements.

Data & Code

SQL Analytics (Looker + Claude/Gemini)
HTML/CSS (Email Debugging & Templates)
JSON (API Payloads & Data Mapping)
Google Analytics & Looker Studio
API/Webhook Integrations
Terminal & Claude Code (Script Execution)

ESP & Automation

Ongage, MessageBird (Revenue Operations)
Klaviyo (4+ years)
Brevo (Email, Drip Campaigns & CRM)
Marketo & HubSpot
Zapier & Make.com (Complex Workflows)
Google Sheets + Apps Script (Data Integration)

Scale & AI Workflow

3.6M+ Monthly Sends
Claude AI (Queries, Troubleshooting, Interpretation)
Gemini (Raw Data Processing)
User Lifecycle Tracking & Funnel Analytics
Multi-Domain Management

My Proprietary System

The Forensic Framework: How I Think About Email Revenue

Most marketers send campaigns and hope. I built a systematic decision engine that removes guesswork—tier classification, fatigue detection, and slot optimization working together.

Tier Classification

Revenue Per Email (RPE) Based

🏆 TOP SHELF RPE > $0.001

Premium slots only (MD). High-value offers with proven performance.

Ex: milestone_tng ($0.0039), destiny_tng ($0.0032)

📊 MEDIUM SHELF $0.0005 - $0.001

Secondary slots (AM, TAR). Rotation candidates.

Ex: mdfoodbreakfast ($0.00089)

⛔ LOW SHELF RPE < $0.0005

Eliminate or pause. Dragging down averages.

Ex: bundled_offers ($0.00000)

Fatigue Detection

Early Warning System

LOW FATIGUE 0-5% penalty

• CTR decline < 10%
• Unsub rate < 0.15%

→ Safe to continue

MEDIUM FATIGUE 10-20% penalty

• CTR decline 10-30%
• Unsub rate 0.15-0.25%

→ Increase rotation gap

HIGH FATIGUE 30%+ penalty

• CTR decline > 30%
• Unsub rate > 0.25%

→ PAUSE IMMEDIATELY

Formula: Optimal_Gap = (Peak_CTR / Current_CTR) × 14 days

Slot Multipliers

Time-Based Optimization

MD (Midday)

11am-1pm window

1.00x

BASELINE

AM (Morning)

7am-9am window

0.53x

TAR (Targeted)

Segment-specific

0.44x

PM (Afternoon)

3pm-5pm window

0.34x

Rule: TOP_SHELF offers → MD slots only. Medium performers rotate through AM/TAR.

📊 Real Data From My Dashboard

Email Performance & Audience Fatigue Chart - June to August 2025

Fatigue Detection in Action: Open Rate, CTR, and Unsub trends tracked over 90-day windows

This framework analyzed 400+ campaigns and drove a 30% revenue reversal.

See It In Action

Proof, Not Promises

Here's Exactly How I Fixed Operations Like Yours

Your email program is underperforming. Revenue's flat or declining. You're drowning in manual work. Sound familiar? I've been there—and here's how I systematically fixed it.

$4.2M Operation 23M+ Subscribers 400+ Campaigns/Cycle SQL & AI

Building a $4.2M Revenue Engine That Runs on Autopilot

📉 The Problem:

$4.2M in revenue but profit margins stuck at 22%. Performance was tanking, offer rotation was a mess, and there wasn't any systematic tracking. Revenue was bleeding out and nobody could figure out why.

🔍 The Diagnosis:

Manual processes + poor segmentation = revenue bleed. No forensic tracking meant we were flying blind on what actually drove performance.

🛠️ The Fix:

Forensic Tracking System

Built custom tracking analyzing 400+ campaigns per reporting cycle across 90-day windows, systematically finding offer fatigue patterns, slot performance sweet spots, and optimal send times

Automated Revenue Optimization

Built Zapier workflows connecting Looker analytics to Ongage, eliminating manual processes and enabling real-time rotation based on AI-predicted fatigue scores

Scale Operations

Managed 5 daily campaigns across 23M+ subscribers with 3.6M+ monthly sends across multiple domains while maintaining deliverability

✅ The Result:

30% Revenue Decline Reversed

12% Q/Q RPE Increase

53% Unsub Rate Drop

400+ Campaigns Analyzed/Cycle

🔍 Forensic Wins (Real Cases):

💰 Scoredit Fix

$15K/mo

Revenue gap identified via placement analysis—identical traffic, 100x revenue difference

📈 KFC Recovery

+155.6%

CTR improvement via strategic rotation—documented methodology for replication

🛡️ CVS Save

-56.2%

CTR decline caught early via fatigue detection—implemented 30-day pause protocol

Demographic Analytics

AI Subscriber Analysis

Looker Master Dashboard

Affiliate Revenue Tracking

Looker Analytics - Real-time

LIVE

Revenue/Email

$0.0183

12% Q/Q

Click Rate

0.60%

Unsub Rate

0.8%

-53%

looker_query.sql

● RUNNING

-- Finding dormant high-value subscribers...

SELECT email, total_ltv, days_inactive

FROM subscribers

WHERE days_inactive > 90

AND total_ltv > 500

AND engagement_score > 0.7

ORDER BY total_ltv DESC

LIMIT 10000;

✓ Query complete: 10,000 high-value targets found

✓ Zapier workflow triggered → Ongage campaigns

✓ Revenue decline reversed: +30%

_

📅 Weekly Revenue Heatmap Slot × Day Performance

	AM	MD	PM	TAR
Mon	$607	$1,204	$390	$430
Tue	$0	$1,726	$645	$640
Wed	$0	$1,588	$265	$796
Thu	$512	$2,103	$478	$689
Fri	$723	$1,891	$312	$445

Green = High RPE | Yellow = Medium | Red = Low | Gray = Skip

decision_algorithm.py

# Forensic Calendar Decision Engine
DECISION_CRITERIA = {
  1: "Historical RPE by slot",
  2: "Days since last send (min 10)",
  3: "Brand conflict check",
  4: "Slot multiplier applied",
  5: "Day-of-week psychology"
}

SLOT_MULTIPLIERS = {
  "MD": 1.00,  # Baseline - premium
  "AM": 0.53,  # Morning secondary
  "TAR": 0.44, # Targeted segments
  "PM": 0.34   # Afternoon lower
}

# Output: $41K+ weekly projection
# Accuracy: 87% vs actual revenue

Revenue by Slot

Testing Framework

Campaign Scheduling 5 Properties $41K+/Week Projections

The Forensic Calendar: Turning Data Into Daily Decisions

📉 The Problem:

Campaign scheduling was guesswork. Offers got burned out, premium slots wasted on low performers, and nobody tracked why certain days outperformed others. We needed a system—not opinions.

🛠️ The System I Built:

5-Criteria Decision Algorithm

Historical RPE → Fatigue check (10-day minimum) → Brand conflicts → Slot multipliers → Day psychology

Revenue Heatmap Analysis

MD slot = 1.00x baseline (premium placement). PM slot = 0.34x. TOP_SHELF offers only go to MD.

Brand Conflict Prevention

No duplicate brands per week. Competitive offers separated by 3+ days minimum.

AI-Assisted Optimization

Claude for SQL query building, Gemini for pattern detection across 5 properties simultaneously

✅ The Output:

Weekly Projections $41K+

Revenue forecasting per calendar

Prediction Accuracy 87%

Projection vs actual revenue

Properties Managed 5

Simultaneous calendar coordination

Decision Speed 2hrs

Full weekly calendar build time

Key Insight: The methodology matters more than individual campaign wins. Once I built this system, calendar planning went from 8+ hours of guesswork to 2 hours of systematic data application.

Looker SQL (AI-assisted) Claude/Gemini Ongage

Campaign-by-Campaign Revenue Reconciliation

UTM Tracking Implementation & Verification

Revenue Forensics Data Reconciliation Financial Accuracy

Uncovering Hidden Revenue Leaks Through Cross-Platform Forensics

📉 The Problem:

Revenue numbers weren't adding up. Our internal tracking showed one figure, the affiliate platform reported another, and nobody knew which was accurate.

Inconsistent revenue reporting between our system and affiliate partners
No systematic process for tracking discrepancies across campaigns
Financial reporting lacked confidence due to attribution gaps

🛠️ The Fix:

Campaign-Level Tracking: Built granular comparison system tracking every campaign's revenue across both platforms
Timeline Analysis: Mapped discrepancies over time to identify systematic patterns vs. one-off errors
Root Cause Investigation: Identified attribution timing differences and tracking pixel issues
Reconciliation Protocol: Established ongoing process to catch and resolve discrepancies proactively

Accuracy Improved

Established reliable reconciliation process

Issues Identified

Caught attribution timing gaps

Financial Trust

Restored confidence in reporting

Prevention Built

Ongoing monitoring protocol

Key Insight: Revenue discrepancies aren't always about wrong numbers—they're often about different measurement windows. Understanding the "why" behind mismatches is more valuable than just forcing numbers to match.

Track Record

How I Got Here

5+ years solving the same problems you're facing right now—from Fortune 500 companies to high-growth startups.

Email & SMS Marketing Specialist

Tibrio | 2021 - Present

Managing $4.2M+ annual email revenue operation with 23M+ subscribers

Built forensic tracking system analyzing 400+ campaigns per reporting cycle across 90-day windows
Reversed 30% revenue decline through data-driven fatigue analysis and strategic rotation
Configured trigger-based transactional flows using JSON data mapping and webhook integrations
Debugged HTML email templates across clients, troubleshooting rendering issues and inline CSS
Improved profit margins from 22% to 40% through strategic automation and AI
Reduced unsubscribes from 1.7% to 0.8% (53% reduction) through systematic fatigue prevention
Built complex Zapier workflows connecting Klaviyo, Looker, and Sinch
Ran 50+ A/B tests per quarter optimizing engagement
Managed 5 daily campaigns with 3.6M+ monthly sends across multiple domains

Marketing Automation Consultant

Various (UPS, RESTO) | 2019 - 2021

Improved UPS international email clicks by 17% through deliverability optimization
Built chat automations tracking $224K+ in conversions for restaurant industry client
Designed websites that increased conversions 14% over industry benchmarks

I Reverse Revenue Declines Using Data Analytics & Automation.

See How I Reversed a 30% Revenue Decline

The Tools Behind a $4.2M Operation

Data & Code

ESP & Automation

Scale & AI Workflow

The Forensic Framework: How I Think About Email Revenue

Tier Classification

Fatigue Detection

Slot Multipliers

Here's Exactly How I Fixed Operations Like Yours

Building a $4.2M Revenue Engine That Runs on Autopilot

📉 The Problem:

🔍 The Diagnosis:

🛠️ The Fix:

Forensic Tracking System

Automated Revenue Optimization

Scale Operations

✅ The Result:

🔍 Forensic Wins (Real Cases):

The Forensic Calendar: Turning Data Into Daily Decisions

📉 The Problem:

🛠️ The System I Built:

5-Criteria Decision Algorithm

Revenue Heatmap Analysis

Brand Conflict Prevention

AI-Assisted Optimization

✅ The Output:

Uncovering Hidden Revenue Leaks Through Cross-Platform Forensics

📉 The Problem:

🛠️ The Fix:

How I Got Here

Email & SMS Marketing Specialist

Marketing Automation Consultant

Your Revenue Decline Won't Reverse Itself