Introduction & Context:
ANYNA Paris is a premium cosmetics brand specializing in skincare products for women aged 25-45. The brand prides itself on high-quality formulations, targeting beauty-conscious consumers looking for luxury at an affordable price.
Despite a strong product lineup, ANYNA Paris struggled with display advertising inefficiencies. The brand faced high acquisition costs, declining engagement, and poor conversion rates. Their existing display ad campaigns lacked precise targeting, leading to wasted ad spend and minimal returns.
The Challenge (Pain Point & Problem Statement):
- High Cost Per Acquisition (CPA): Display ad CPA averaged $39, while the average order value (AOV) was only $44, making profitability unsustainable.
- Declining Click-Through Rate (CTR): Dropped from 2.1% to 0.9%, signaling poor ad engagement.
- Wasted Ad Spend: Broad audience targeting led to irrelevant impressions and low conversion rates.
Category
Media Buying Display/ InApp/ Programmatic
Clients
ANYNA Paris
Location
Paris, France
Led & Executed by:
Mohamed Chourouki
The Strategy & Execution (Step-by-Step Breakdown of Actions Taken):
We developed a multi-phase, data-driven strategy to optimize display ad performance and improve return on ad spend (ROAS).
Phase 1: Audience Refinement & Segmentation
- Shifted from broad demographic targeting to intent-based audience segmentation, including:
- Retargeting past visitors (30-day window)
- Lookalike audiences based on high-LTV customers
- Engagement-based audiences (users who interacted with video ads or visited multiple pages)
- Implemented Dynamic Retargeting Ads, customizing ad creatives based on users’ browsing behavior.
Phase 2: Ad Creative Optimization
- Developed AI-generated ad variations to test different headlines, product images, and CTA placements.
- Introduced interactive HTML5 ads with motion elements, increasing engagement by 26%.
- Personalized ad creatives per audience segment (e.g., anti-aging products for 40+ users, hydration-focused products for younger demographics).
Phase 3: Performance-Based Bidding & Placement Optimization
- Switched from flat CPC bidding to ROAS-targeted automated bidding, allowing the algorithm to prioritize high-converting users.
- Focused on premium publisher placements, reducing irrelevant traffic.
- Adjusted ad frequency to prevent banner blindness, reducing ad fatigue.
The Challenges & Roadblocks:
- Ad Fatigue & Banner Blindness:
- Solved by rotating creatives every 10 days and introducing interactive elements.
- Misalignment in Attribution:
- Implemented UTM tracking and multi-touch attribution modeling to better understand customer journeys.
- Initial Underperformance of Lookalike Audiences:
- Improved by refining seed audience data, leading to a 36% performance boost.
The Results & Impact (Before vs. After Data Comparison):
Metric | Before Optimization | After Optimization (90 Days) |
Cost Per Acquisition (CPA) | $39 | $17 |
Click-Through Rate (CTR) | 0.9% | 3.8% |
Return on Ad Spend (ROAS) | 1.3x | 4.5x |
Ad Engagement Increase | – | +65% |
Bounce Rate Reduction | – | -28% |
Key Takeaways & Lessons Learned:
- Segmentation is Key: Hyper-targeted audiences drive better engagement and conversion rates.
- Creative Matters: Interactive HTML5 ads significantly outperform static display ads.
- Bidding Strategy Can Make or Break Performance: Smart bidding automation ensures budget efficiency.
- Frequent Creative Testing Prevents Ad Fatigue: Regular ad rotation sustains high engagement levels.
- Attribution Models Should Be Continuously Optimized: Proper tracking leads to better budget allocation.
Conclusion & Final Thoughts:
Through advanced audience segmentation, AI-powered creative testing, and smart bidding strategies, we turned ANYNA Paris’ display ad campaigns from unprofitable to a scalable revenue driver.
By continuously refining data insights and optimizing placements, the brand built a sustainable acquisition strategy, ensuring long-term growth and profitability.
EN 
FR 
RU