Niche
Online store of hemp products (eCommerce, Ukraine). Channels: Google Shopping / Performance Max / Search.
Goal
* Increase the number of purchases and revenue from advertising
* Maintain/improve ROAS
* Segment traffic by categories and eliminate ineffective spending
What has been done
1) Audit and preparation
* Checking account structure, conversions, attribution, correctness of goal imports
* Analyzing product categories and budget allocation logic
2) Campaign structure
* Launch/optimization of Performance Max and Standard Shopping (classic shopping)
* Separate Search campaigns for categories (e.g., pillows/socks/blankets, etc.)
* Setting strategies: Maximize conversion value (tROAS) / Maximize conversions (tCPA) — depending on the type of campaign and task
3) Optimization and scaling
* Regular work with spending by categories: where the budget is "washed out," where it provides maximum volume/value
* Reallocation of budget in favor of the most effective campaigns/categories
* Improving traffic quality through negative keywords/adjusting structure (for Search)
* Controlling traffic share between PMax and Shopping to avoid competition between campaigns
Results (for the reporting period from the screenshot)
* Total spending: ≈ 61,227 UAH
* Conversions: ≈ 537
* Average conversion price: ≈ 114 UAH
* Total conversion value (Revenue/Conv. value): ≈ 638,197 UAH
* ROAS (Conv. value / cost): ≈ 10.4
Separately by campaigns:
* PMax: ROAS ~10, CPA ~152 UAH (scalable volume)
* Standard Shopping: ROAS ~9.3, CPA ~109 UAH (stable performance)
* Search (category): ROAS up to ~14.9, CPA ~80–151 UAH (precise targeting of hot demand)
Conclusion
A system has been built where Shopping/PMax provide scale, and Search campaigns for categories yield the most "hot" purchases. Through control of structure, strategies, and budgets, it has been possible to achieve a stable sales result with high ROAS.
Online store of hemp products (eCommerce, Ukraine). Channels: Google Shopping / Performance Max / Search.
Goal
* Increase the number of purchases and revenue from advertising
* Maintain/improve ROAS
* Segment traffic by categories and eliminate ineffective spending
What has been done
1) Audit and preparation
* Checking account structure, conversions, attribution, correctness of goal imports
* Analyzing product categories and budget allocation logic
2) Campaign structure
* Launch/optimization of Performance Max and Standard Shopping (classic shopping)
* Separate Search campaigns for categories (e.g., pillows/socks/blankets, etc.)
* Setting strategies: Maximize conversion value (tROAS) / Maximize conversions (tCPA) — depending on the type of campaign and task
3) Optimization and scaling
* Regular work with spending by categories: where the budget is "washed out," where it provides maximum volume/value
* Reallocation of budget in favor of the most effective campaigns/categories
* Improving traffic quality through negative keywords/adjusting structure (for Search)
* Controlling traffic share between PMax and Shopping to avoid competition between campaigns
Results (for the reporting period from the screenshot)
* Total spending: ≈ 61,227 UAH
* Conversions: ≈ 537
* Average conversion price: ≈ 114 UAH
* Total conversion value (Revenue/Conv. value): ≈ 638,197 UAH
* ROAS (Conv. value / cost): ≈ 10.4
Separately by campaigns:
* PMax: ROAS ~10, CPA ~152 UAH (scalable volume)
* Standard Shopping: ROAS ~9.3, CPA ~109 UAH (stable performance)
* Search (category): ROAS up to ~14.9, CPA ~80–151 UAH (precise targeting of hot demand)
Conclusion
A system has been built where Shopping/PMax provide scale, and Search campaigns for categories yield the most "hot" purchases. Through control of structure, strategies, and budgets, it has been possible to achieve a stable sales result with high ROAS.