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Case Study - Ling Skincare

307% Increase in ROAS
In 30 days for a Skincare Brand

Increase in Conversions
0 %
Increase in ROAS
0 %
Reduction in CPA
0 %

Company Name

Ling Skincare

Industry

Skincare

Ling Skincare, founded by esteemed celebrity esthetician Ling Chan, epitomizes the pinnacle of anti-aging excellence in the USA, deeply rooted in the Asian traditions of meticulous exfoliation and hydration. Her products, renowned for their ability to balance and rejuvenate, seamlessly blend innovative science with traditional Asian botanicals, offering a comprehensive approach to preventative skincare.

Challenges

Our biggest challenge was capturing the attention of
our target audience in a highly competitive skincare
market, where acquiring new customers is both difficult and expensive. We tested different strategies including Search and Shopping ads, but struggled with a low Return on Ad Spend (ROAS), making it clear that we needed a more effective approach to stand out.

Objectives

Primary Objective: Our main goal was to boost conversions and the return on ad spend (ROAS) from our campaign, while also keeping costs under control.

Secondary Objective: Reduce the Cost per Acquisition(CPA).

Approach

We implemented a Performance Max (pMax) campaign, which uses Machine Learning to optimize ad delivery across all Google platforms. This automated approach allowed us to increase sales efficiently
and align with our goals. To ensure scalability and reach, we developed a variety of creative assets, including text, images, and videos.


In addition, we took advantage of the November holiday season by introducing attractive offers that encouraged higher average order values. This approach not only helped us draw in new customers but
also aimed to retain them for future purchases.

Results

 

 

Testimonial

 

 

Key Takeaways

    1. Machine Learning Delivered Results: The pMax campaign significantly improved performance , boosting conversions and ROAS.
    2. Increased Conversions and ROAS: Cross-channel advertising helped engage more customers, resulting in better conversions and higher returns.
    3. Lower Cost Per Conversion: Optimizing ad delivery reduced costs, making the campaigns more
      efficient and profit.
    4. Creative Customization Worked: Using tailored content across multiple formats enhanced engagement and made the campaign scalable.
    5. Cross-Channel Strategy Paid Off: Running ads on multiple platforms increased visibility and maintained consistent messaging, highlighting the importance of a unified approach.


    This case study demonstrates how combining Machine Learning, creative customization, and holiday
    promotions can successfully navigate a competitive market, delivering strong results and improving
    overall campaign profitability.

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