Overview
Our client, a USA-based male athleisure brand, partnered with us to accelerate growth while maintaining profitability. Rather than relying on guesswork, we implemented a structured performance marketing strategy focused on creative experimentation, audience expansion, Meta's Advantage+ Shopping Campaigns, and disciplined scaling.
Within just 2.5 months, these efforts resulted in $130,000 in sales while maintaining a healthy 3X Return on Ad Spend (ROAS).
Objective
The primary objective was simple but ambitious:
- Drive substantial revenue growth.
- Maintain a healthy Return on Ad Spend (ROAS).
- Scale campaigns without significantly increasing acquisition costs.
- Build a repeatable system for sustainable growth.
The Challenge
The client had strong products and an established presence in the fitness apparel market, but profitable scaling required a more structured advertising approach.
The biggest challenge was identifying the creative combinations that truly resonated with the target audience while expanding reach to new customers without compromising campaign efficiency. As ad spend increased, maintaining a healthy Cost Per Acquisition (CPA) and ensuring consistent performance became equally important.
To achieve sustainable growth, we needed a strategy that combined creative testing, audience discovery, Meta's automation capabilities, and disciplined scaling.
Our Strategy
Instead of depending on a single campaign or creative, we built a complete growth framework consisting of four core pillars.
1. Creative Testing
Every successful advertising account starts with finding winning creatives. We launched a dedicated campaign focused entirely on testing different creative angles across every important variable, including headlines, descriptions, primary text, ad creatives, and call-to-action variations.
Our testing included:
- UGC videos
- Meme creatives
- Influencer videos
- UGC images
- Before & After comparisons
- Testimonial videos
- Lifestyle creatives
- Multiple messaging angles
Rather than assuming what would perform best, every creative was validated through data. This continuous testing process allowed us to identify high-performing combinations that generated stronger engagement, lower acquisition costs, and higher conversion rates.
2. Audience Prospecting
Once winning creatives were identified, the next objective was finding new customers at scale. We developed multiple audience segments to expand reach while maintaining efficiency.
These included:
- Interest-based audiences
- Lookalike Audiences (LLAs)
- Broad targeting
- Custom Audiences
Broad targeting was tested both with and without exclusions to understand how Meta's algorithm responded under different conditions. At the same time, multiple Lookalike Audiences were created using valuable customer data to uncover new high-quality prospects. This systematic audience testing helped us continuously expand reach while maintaining campaign performance.
3. Advantage+ Shopping Campaign (ASC)
To leverage Meta's machine learning capabilities, we implemented an Advantage+ Shopping Campaign (ASC) using our highest-performing creatives. This allowed Meta's optimization engine to automatically identify the best opportunities for conversions while keeping the Cost Per Acquisition (CPA) under control.
One of the most successful experiments involved combining Advantage+ Shopping Campaigns with Catalog Ads. This was a strategy we had not previously implemented in other accounts. Initially, the Cost Per Purchase (CPP) remained relatively high. However, rather than making early decisions, we allowed the campaign sufficient time to optimize.
After approximately 10 days, performance began improving significantly. From the 9th day onward, the CPP started decreasing consistently, turning the campaign into one of the strongest performers in the account. Based on this experience, we highly recommend allowing Meta's optimization process enough time before evaluating campaign performance.
4. Manual Scaling
After identifying winning creatives and audiences, the next phase focused on profitable scaling. Instead of aggressively increasing budgets, we followed a structured scaling methodology.
Winning ad sets were gradually scaled by increasing budgets based on 10x the average CPA from the previous seven days. Alongside budget scaling, we implemented Bid Cap Scaling, creating multiple ad sets with different Cost Per Purchase (CPP) targets ranging from 50% below to 50% above the average CPP.
To further maximize profitability, we also tested Fixed ROAS bidding, experimenting with ROAS targets ranging from 2.5X to 4X. This disciplined scaling approach allowed campaigns to grow while maintaining efficiency and protecting profitability.
Conclusion
Through a comprehensive approach combining dynamic creative testing, audience prospecting, Advantage+ Shopping Campaigns, and strategic manual scaling, we successfully helped this USA-based male fitness apparel brand achieve $130,000 in sales with a 3X ROAS in just 2.5 months.
Rather than relying on a single tactic, this success was driven by continuous experimentation, data-backed decision-making, and disciplined optimization at every stage of the campaign.
Note: The results mentioned in this case study are based on actual client data. Specific client details have been anonymized to protect privacy.