Scaling a Large E-commerce Account from 400K EGP to 2M EGP Monthly Revenue

Overview

This case study highlights how a large-scale Google Ads account was transformed from an automation-heavy setup into a scalable, data-driven growth engine.

The objective was not short-term performance spikes, but building an account structure capable of:

• Sustaining higher budgets

• Preserving ROAS during scale

• Enabling confident, data-backed decision-making

Account Background

• Product Categories: 20+

• Total SKUs: 80,000+

• Average Monthly Ad Spend (Before): 9,000 EGP

• Average Monthly Revenue (Before): 400,000 EGP

Initial Account Structure (Before Audit)

Before any intervention, the account was structured as follows:

• 2 Performance Max campaigns running across all products

• No segmentation between:
  • Branded traffic
  • Non-branded traffic

• GA4 Purchase set as the Primary Conversion Action

• No category-level visibility or control

• Limited insight into:
  • True profitability
  • Scalable categories
  • Budget allocation efficiency
    

This structure delivered acceptable results at low spend levels, but the account was not built to scale.

Audit Findings

After conducting a comprehensive Google Ads audit, several critical issues were identified:

1. Over-Reliance on Automation

• Heavy dependence on Performance Max without proper segmentation

• Algorithms learning from mixed, unstructured data

• Limited control over performance drivers
  

2. Conversion Tracking Misalignment

• GA4 Purchase used as the Primary Conversion

• Inaccurate signals feeding Smart Bidding strategies

• Reporting-focused tracking rather than performance-focused tracking
  

3. Lack of Category-Level Segmentation

• 80,000+ SKUs grouped under the same campaigns

• No differentiation based on:
  • Average Order Value (AOV)
  • Cost per Acquisition (CPA)

• Difficulty identifying scalable versus underperforming categories
  

4. No Brand Protection Strategy

• Branded and Non-Branded traffic blended together

• Inflated performance metrics

• Reduced clarity around true acquisition efficiency

Strategy & Execution

1. Conversion Tracking Rebuild

• Implemented a new, dedicated conversion tracking setup

• Redefined:
  • Primary conversion actions
  • Secondary (supporting) conversions

• Objective:
  • Deliver cleaner, more accurate signals to Google’s bidding algorithms
  • Optimize for real business outcomes, not just analytics reporting

Clean tracking was the most critical prerequisite for sustainable scale.

2. Data-Driven Account Restructuring

All product categories were analyzed and regrouped into separate campaign clusters based on:

• Average Order Value (AOV)

• Cost per Acquisition (CPA)

• Conversion volume

This restructuring enabled:

• Faster learning cycles

• Clearer performance visibility

• Smarter budget allocation decisions

3. Branded vs Non-Branded Separation

The account was restructured to fully separate:

• Branded Search & Shopping campaigns

• Non-Branded Search & STD Shopping & Pmax Feed only campaigns

This allowed for:

• Brand traffic protection

• Accurate ROAS measurement

• Controlled scaling decisions

• More efficient budget distribution

4. Pmax assets only Testing

Rather than relying on a single automation setup:

• Multiple creative angles were tested

• Different asset combinations were evaluated

• Feed-led vs asset-led PMax structures were compared

Testing was intentional, controlled, and data-driven.

Scaling Phase

Once tracking, structure, and signals were stabilized, the account entered the scaling phase:

• Gradual budget increases

• Continuous monitoring of:
  • ROAS stability
  • Conversion lag
  • Category-level performance

No reactive or aggressive budget changes during normal volatility

Key Takeaways

• Scaling Google Ads is not about increasing spend

• Automation requires:
  • Clean tracking
  • Clear structure
  • Intentional segmentation

• Large e-commerce accounts cannot be managed with “one campaign for everything”

Sustainable scale starts before budget increases