Bakground
L’Oréal manages a broad portfolio of brands across different product categories, each with its own audience, positioning, and search demand. With several divisions active in Google Ads, the volume of keywords and daily signals quickly becomes massive.
Challenge
Keeping all of the accounts optimized manually took significant time and made it difficult to maintain consistency across brands. At the same time, budgets needed clearer signals to perform efficiently, and many relevant searches weren’t being captured. The combination of manual workload, uneven budget performance, and gaps in keyword coverage made it increasingly challenging to run search at scale.
“We implemented Amanda AI to run Search across several brands and categories. Instead of relying on manual work, we focused on creating a setup that supported scaling and improved the quality of the signals sent to Google.”
The solution
The solution was built around three core areas:
Improved efficiency: Reducing manual work by automating ongoing adjustments in Google Ads.
Conversions: +136%
Impressions: +154%
Operational workload: -19%
Budget allocation: Helping budgets perform better by strengthening the signals that guide Google’s bidding.
Avg CPC: -50%
Avg CPA: -53%
Ad spend: +12%
Keyword intelligence: Expanding and cleaning up keyword portfolios so more relevant searches were captured, with less overlap between brands and categories.
+154% impressions
+128% clicks
Improved operational efficiency
Managing search activity across several brands required continuous adjustments to bids, keywords, and signals. By automating this daily work, L’Oréal reduced the amount of manual maintenance needed in each account.
This allowed specialists to focus more on category priorities and strategic work, rather than repetitive tasks.
These improvements also reduced the number of hours spent on operational work by 19%.
As the setup scaled, these results became evident: ad spend increased by 12%, conversions by 136%, impressions by 154%, and clicks by 128%.
At the same time, the average CPA decreased by 53%, showing that the increased volume was achieved with significantly better cost efficiency than before.
Automated, intelligent budget distribution
Google’s bidding performs best when it receives clear, consistent signals. In the earlier setup, budget outcomes differed between brands because the underlying keyword signals were uneven — some relevant searches weren’t captured, while others overlapped across categories.
By strengthening the signals sent to Google, budgets could be used more efficiently. A greater portion of the budget was allocated to the relevant queries, and fewer irrelevant auctions were entered. This shift contributed to a significant improvement in cost efficiency, with average CPC decreasing by 50% and the average CPA decreasing by 53% across the activated brands.
Over the same period, overall ad spend increased by only 12%, while conversions grew by 136%, indicating that budgets reached significantly more valuable users without requiring a proportional increase in spend.
Better Keyword Mining with organic search data
L’Oréal expanded its keywords using Search Console data and continuous keyword discovery. As part of this, organic rankings and branded search behavior were factored in to avoid bidding on queries where the brands already had strong organic visibility. At the same time, Amanda AI’s Keyword Fencing was applied to keep brands and categories separate, reducing overlap and making intent mapping more consistent.
This broader and cleaner coverage significantly increased reach, with impressions up 154% and clicks up 128%. As expected, with a wider keyword set, the CTR decreased by 10% and the conversion rate decreased by 35%. Despite this, total conversions grew by 136%, supported by a 50% lower average CPC and a 53% lower average CPA across the activated brands.
The results
The collaboration highlighted the value of combining automated daily adjustments with human oversight. While automation handled the daily workload at scale, specialists focused on brand priorities and expansion. This combination supported stronger relevance, higher efficiency, and faster execution across the activated brands.
A collaboration between wo/man and machine where the numbers speak for themselves.
