Case study
PowerLocus
Consumer electronics · Performance launch
Engagement result
18.75x
Campaign ROAS
$1.49 CPA
Building acquisition from a standing start.
PowerLocus launched during the pandemic with no brand awareness and immediate pressure to prove profitability. There was no room for awareness-first experimentation. The system had to work fast, stay efficient, and scale only once unit economics were validated.
- Focus
- Paid social, Google Ads
- Published
The results
Campaign performance and expansion.
Campaign returns informed the approach to expansion into Romania. The figures below describe advertising performance; business profitability also depends on product and operating costs.
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Romania
Market expansion
Launched on back of Greek performance
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Zero-awareness
Launch context
Pandemic market entry
Project context
My role and the results.
- My contribution
- I researched the Greek market, developed and tested campaign creative, and managed paid acquisition across Facebook, Instagram, and Google. Campaign performance informed budget changes and the approach to expansion into Romania.
- What the figures describe
- The 18.75× return on ad spend (ROAS) and $1.49 cost per acquisition describe campaign performance. ROAS compares attributed revenue with advertising spend; it does not account for product costs, fulfilment, returns, or other business expenses.
The challenge
The challenge was not traffic. It was profitable signal. Launching into an established category with no brand recognition means all spend has to earn its place before you increase it.
I ran tightly scoped campaigns across Facebook, Instagram, and Google during the pandemic launch. The case records a $1.49 cost per acquisition, alongside the campaign return shown above.
Operating realities
- Validate demand without existing brand equity in a crowded category
- Control acquisition costs in volatile pandemic market conditions
- Avoid scaling spend before unit economics were rigorously proven
- Generate results fast enough to justify international expansion
Key architectural decisions
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Decision 01
Test campaign returns before scale
I prioritised conversion-led campaigns to test acquisition costs and attributed revenue before increasing spend.
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Decision 02
Build creative around intent, not reach
Product-led storytelling focused on use cases and objections, not brand narratives.
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Decision 03
Let data dictate expansion
Budgets increased only when ROAS, CPA, and quality signals held consistently.
Implementation
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Research
Market entry research
Mapped competitors, consumer media habits, and purchase triggers across the Greek ecommerce landscape to identify opportunity pockets and positioning angles.
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Creative
Creative development & testing
Built messaging variations grounded in social and search insights, then ran systematic A/B tests across copy, visuals, and offers to isolate winners.
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Scaling
Performance-based scaling
Launched with conservative budgets to validate assumptions, then rapidly increased spend once ROAS, CPA, and quality scores consistently exceeded targets.
Why the system held under pressure
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Data-driven decisions
Every creative, audience, and offer was backed by social and search intelligence.
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Strategic scaling
Budgets increased as campaign return and acquisition-cost signals supported further investment.
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Multi-platform orchestration
Facebook, Instagram, and Google worked together to capture intent at every stage.
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High-intent creative
Conversion-focused messaging kept acquisition costs low.
Client endorsement
“Apostolos launched our online advertising campaign with huge success, and our online sales grew exponentially. Since day one, he worked hard to make every advertising dollar count towards the success of our brand. As a result, we decided to expand to more markets with his help and expertise.”
Alexander Glushkov
Managing Director, PowerLocus
The outcome
PowerLocus demonstrates how disciplined decision-making, not aggressive spend, drives sustainable ecommerce growth. By designing for profitability first, I turned a zero-awareness launch into a repeatable expansion model.
Selected proof
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Performance systems
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