In paid search marketing, change is the only constant. For years, we’ve relied on exact match keywords and mirrored phrase match ad groups to protect brand terms. That approach worked when queries were predictable and competition was light. But search behavior has evolved. With searchers relying on longer conversational queries and search engines increasingly using AI to understand intent, our luxury skincare client needed to adapt.
Why the Old Approach Wasn’t Enough
Initially, our branded campaigns relied on a classic structure. One campaign held exact brand keywords, and a second mirrored it with phrase match. Budgets were fixed, and all brand terms were grouped together. The strategy captured core brand traffic but lacked flexibility, leading to:
- Limited impression share: Competing retailers and marketplaces bidding on our brand pushed our ads down the page. At times, we held less than 65% of impression share.
- Rising costs: As more competitors entered the auction, cost per click (CPC) increased and the old structure prevented us from adjusting quickly. Return on ad spend (ROAS) began to slide.
- Missed opportunities: Longer queries combining brand and product names were being matched to generic phrase match keywords, which did not always trigger the right ad or landing page.
The result was stagnating brand efficiency. We needed to improve performance while preparing for Q4, which meant rethinking the structure and embracing the tools Google currently provides.
The Restructure: Embracing AI for Branded Search
To adapt, we rolled out a significant restructure of our branded campaigns. The goals were clear: increase impression share, capture more revenue, and control cost per lead. Here is what changed:
- We split brand keywords into two focused campaigns.
A core brand campaign targeted only the brand name in exact and phrase match. A brand and product campaign covered combinations of the brand plus product names. - We upgraded to AI-powered campaigns.
We moved the core brand campaign from a standard text campaign to an AI Max for Search campaign. This upgrade unlocked the Brand Inclusion setting, which tells Google’s machine learning to focus exclusively on branded searches. AI Max leverages real-time auction signals — such as search intent, device, and user context — to adjust bids dynamically and assemble ad variations that match long-tail queries. - We implemented tighter CPC guardrails.
To avoid runaway costs and single-click CPC spikes, we reduced maximum CPC bids across all brand ad groups and allowed the algorithms to work within more conservative limits. Budgets were redistributed toward the campaigns and ad groups that delivered better returns. - We refreshed ad copy and landing pages.
We created ad copy tailored to the luxury skincare audience and built separate landing pages for core and product-specific searches. This gave searchers the best user experience and most relevant offers. - We performed ongoing checks.
After the restructure, we tested additional negatives, refined match types, and adjusted budgets weekly based on performance.
Post-Restructure Impact of AI Max
Improved Impression Share and Engagement
Segmenting the campaigns and letting AI Max handle bidding increased our Search impression share on core brand terms from around 65% to over 90%. Our ads now appear in more auctions and hold higher positions, preventing competitors and retail partners from snatching prime real estate. Click-through rate (CTR) held steady even as impression share rose, proof that the AI generated headlines resonated with shoppers.
Revenue & Efficiency Gains
Looking at results after the restructure compared with the earlier period, branded campaigns delivered strongly.
- Revenue up 74%: Conversions and purchase value surged once AI Max took the helm. More qualified shoppers found the right product pages, and the luxury brand saw a noticeable lift in online sales.
- Transactions up 38%: Splitting campaigns captured incremental demand from users searching for specific products. Customers who once landed on generic pages now converted at higher rates.
- Modest spend increase (9%): Even though budgets were slightly higher, cost per conversion dropped by roughly 10%. We spent more efficiently, thanks to dynamic bidding and better alignment between queries and ads.
- ROAS growth: ROAS rose substantially, reflecting more revenue from every dollar invested.
Lessons for a Modern Search Landscape
Like many of my colleagues, I come from an “old school” paid search background. Our team was skeptical of algorithmic bidding and hesitant to relinquish control. But modern search behavior and Google’s evolving auction dynamics demand a new approach. This test showed that:
- AI is not perfect, but it is invaluable. Machines can interpret user intent faster than manual rules. AI Max assembles customized headlines from existing assets and surfaces them alongside helpful content, capturing qualified clicks that old-school campaigns miss.
- Segmentation matters. Separating core brand from product-specific queries gives us the flexibility to adjust budgets, bids, and landing experiences. It also makes it easy to ramp up spending on high-margin products without jeopardizing core brand efficiency.
- Human guidance remains essential. We still review search term reports weekly and set negatives to avoid irrelevant matches. We watch for spikes in CPC and adjust budgets to maintain efficiency. AI needs a good navigator.
Where We Go From Here
The restructure delivered an impressive turnaround for our branded campaigns, but paid search is never “set and forget.” Our next steps include:
- Deepening the use of AI-powered formats: We plan to expand into other PMax asset groups and test video and image assets for brand protection.
- Refining product-specific bids: By monitoring conversion data, we can reallocate spend toward bestselling products and trim underperformers.
- Exploring cross-channel synergy: We have begun sharing data from Search Console and Microsoft Ads to inform our keyword strategy across platforms.
Final Thoughts
The way people search has changed. Simplistic exact match campaigns no longer capture every branded opportunity. By embracing AI and structuring campaigns around user intent, our luxury skincare brand client unlocked significant gains in impression share, revenue, and overall efficiency.
The old rules taught us discipline, and the new rules require adaptability. This case study shows that when you blend thoughtful segmentation with modern machine learning, brand search is not only protected — it thrives.Want to see how we can deliver for you? We’d be happy to dig in and discuss brand strategy that will help you reach your goals! Reach out to us using our contact form, or connect with us on LinkedIn.
