How Campaign Architecture Added $1M Net Profit to a Toy Brand

Case study: How Campaign Architecture Added $1M Net Profit to a Toy Brand

Industry

Toys & Games

Service

Amazon PPC / Amazon Catalog Management

Amazon PPC / Amazon Catalog Management

Delivered In

12 months

Results

Net Profit Added

+$1M

+$1M

YoY Net Profit Growth

+45%

+45%

Overview

A toy brand with 500 ASINs and $2M in annual net profit was spending heavily on advertising. Revenue was growing. Profit was not.

The problem was not the budget. The problem was how the budget was being deployed. High CPCs on irrelevant keywords, campaigns built without logic, and ad spend consistently flowing toward low-margin products while high-margin ones were being underfunded. The business looked healthy from the top. At the unit level, the economics did not hold up.

The goal was not to cut spend. It was to make the same spend work harder. Over 12 months, a full restructure of campaign architecture and product prioritization across the 500-ASIN catalog added $1M in net profit while revenue continued to grow alongside it.

Challenge

  1. High CPCs on irrelevant keywords draining budget with no return

  2. Campaigns bidding against each other across the same keywords

  3. High-margin products underfunded while low-margin ones consumed the budget

  4. 500 ASINs treated as equal investment opportunities with no segmentation

Approach

Campaign Architecture Rebuild

Every existing campaign was audited and rebuilt from scratch around one principle: every campaign must have a clear purpose, a defined product tier, and match type discipline. Campaigns bidding against each other were collapsed. Targeting was tightened. A negative keyword framework was built and applied across the full account.

Product Prioritization and Budget Allocation

The 500-ASIN catalog was segmented by margin contribution and conversion performance. High-margin, high-converting products were funded properly for the first time. Low-margin products that could not justify the cost of advertising were deprioritized. Budget allocation was rebuilt to reflect what the data actually supported.

Ongoing Optimization

With clean architecture in place, performance data became readable and actionable. Bidding decisions were made on search term performance, not habit. Twelve months of disciplined execution compounded the initial restructure into a fundamentally different profitability outcome.

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