Googe Ads for Retail

Project Description

To resolve algorithmic neglect, I deployed a hyper-granular architecture restricting campaigns to 3 ad groups for budget parity. By pairing Broad Match with tROAS and Customer Match lists, I refined 1st-party signal precision. Synergized Search, PMax, and App campaigns utilized strict exclusions to eliminate cannibalization. Result: 13,133.09% ROAS and 100% inventory visibility.

The Goal

The primary objective was to transform a complex retail account with over 1,000 unique SKUs into a high-efficiency revenue engine. The client needed a structure that could maintain profitability while scaling spend across a massive inventory of diverse product categories

The Challenge

Managing 1,000+ products often leads to "algorithmic neglect," where Google's AI focuses only on a few top-sellers, leaving the rest of the inventory with zero visibility. Additionally, we had to solve for:
Product Cannibalization: Preventing different ad groups from bidding against each other for the same search intent.
Structure Overload: Creating a manageable way to house an ad group for every single distinct product category without losing control of the budget.
Product Feed Maintenance & Management: Keeping track of the ever-changing product feed health, performance-based labeling for the large rotation of SKUs, and tracking of product title performance.

The Result

ROAS: sustained a profitable 13,133.09% ROAS.
Efficiency: successfully manage an account with a large budget, maintaining a granular view of every product group's performance.
Visibility: total inventory coverage with no "neglected" SKUs.