PPC Case Study: 609,000+ UAH in Revenue in 60 Days with an Average ROAS of 9.0 Using Google Performance Max
eCommerce in the furniture niche
– Business scaling through paid search advertising
December, 2025 - present day
Ukraine
Challenges of the Project
The client came to our company with a request to build a systematic sales channel through Google Performance Max in the furniture niche.
The main goal was to reach a predictable ROAS and scale sales across key categories: shelving units, wardrobes, and related products. The focus was on making advertising work as a profit-scaling tool rather than a cost item without clear payback. Although campaigns were already running before the start of our cooperation, their performance was low and had plateaued.
Overall dynamics of ad spend and conversion value over a 60-day period:
Project Analytics
At the start of the collaboration, the project already had active advertising campaigns, but ROAS remained at around 3 with no further growth. The campaigns were running, but the system had effectively hit a ceiling:
- the algorithm was not scaling sales volume;
- decisions were being made based on short time periods;
- the budget was not being redistributed according to category performance.
At the level of individual days, the picture looked unstable: on some days ROAS increased sharply, while on others it dropped to minimum levels; the average order value varied significantly by category, and demand was uneven. This created a sense of instability and made it impossible to systematically move beyond ROAS = 3.
From the very first stage of the project, however, we made decisions based on weekly dynamics rather than daily fluctuations. This allowed us to optimize profitable campaigns even during short-term downturns.
Over 60 days, we achieved the following ROAS results:
– growth from around 3 to a stable 9.0;
– the main operating range was 8–10;
– the average performance across the full period was 10–11;
– individual peaks reached 15–20+ during weeks with lower ad spend.
Weeks with abnormally high ROAS occurred when the budget was small and there were only 1–2 orders with a high average order value, so overall performance was evaluated based on the average metric across the full spend volume.
As a result, the system demonstrates stable efficiency over time, without dependence on short-term fluctuations.
Team actions
While working on the project, our team applied a structured approach. The key step we took before scaling was restructuring the advertising campaigns. We separated them by category, which made it possible to:
- avoid mixing products with different profit margins;
- keep CPA within an acceptable range;
- identify growth opportunities separately for each segment.
Over 60 days, we achieved the following results:
| Category | Approximate ROAS |
| Full product range | 14.8 |
| Tables | 9.6 |
| Shelves | 9.2 |
| Wardrobes (our optimization focus) | 5.2 |
A structured approach makes it possible to scale strong categories while working precisely on weaker ones without compromising overall performance.
In the updated structure, we took the following into account:
1. Bid strategy optimization. We ran comparative tests of different strategies (Maximize Conversions / Maximize Conversion Value / tCPA) and selected the model that delivered a stable sales volume with a controlled acquisition cost.
2. Geographic targeting adjustments. We narrowed geo-targeting to regions with higher conversion rates, which reduced the share of ineffective traffic and increased the average ROAS.
3. Target CPA optimization. We gradually adjusted the Target CPA to increase the volume of conversions while staying within an acceptable acquisition cost.
All of this was done to find the right balance between scale and efficiency—and we achieved it.
In addition, we conducted an analysis of the product matrix by evaluating product performance in the feed:
- identified items with the highest ROAS;
- paused products with no sales or low profitability;
- reallocated the budget to SKUs generating the majority of revenue.
Results
Key performance metrics after two months of work on the project:
- ad spend – $1,565.48 (67,620 UAH);
- revenue – $14,736.13 (644,308 UAH);
- average ROAS – 9.0 (the metric remains stable);
- weekly sales volume – 30–45 orders;
- conversions – 250+;
- average CPA – approximately $6.37 (275 UAH).
At the same time, the algorithm maintains stability during weeks with lower demand and scales effectively during periods of increased demand.
Conclusion
The key insight from this case is simple: to achieve a significant improvement in results, you do not always need major changes to the website or the offer. Sometimes it is enough to identify the key lever that helps modern algorithms bring in a target audience genuinely interested in the product.
As a result, without changing the offer or product range, we were able to increase ROAS from 3 to a stable 9 through hands-on experience with Google Ads algorithms and foundational performance analysis. It is also worth noting that weekly analytics made it possible to manage scale without emotional decision-making.
